Saturday 22 February 2014
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Friday 21 February 2014
Wednesday 19 February 2014
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A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks
A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks:
Table-Driven Routing Protocols
Table-driven routing protocols attempt to maintain consistent, up-to-date routing information from each node to every other node in the network. These protocols require each node to maintain one or more tables to store routing information, and they respond to changes in network topology by propagating updates throughout the network in order to maintain a consistent network view. The areas in which they differ are the number of necessary routing-related tables and the methods
by which changes in network structure are broadcast. The following sections discuss some of the existing table-driven ad hoc routing protocols.
Destination-Sequenced Distance-Vector Routing — The Destination-Sequenced Distance-Vector Routing protocol (DSDV) described in [2] is a table-driven algorithm based on the classical Bellman-Ford routing mechanism [3]. The improvements made to the Bellman-Ford algorithm include freedom from loops in routing tables. Every mobile node in the network maintains a routing
table in which all of the possible destinations within the network and the number of hops to each destination are recorded. Each entry is marked with a sequence number assigned by the destination node. The sequence numbers enable the mobile nodes to distinguish stale routes from new ones, thereby avoiding the formation of routing loops. Routing table updates are periodically transmitted throughout the network in order to maintain table consistency. To help alleviate the potentially
large amount of network traffic that such updates can generate, route updates can employ two possible types of packets. The first is known as a full dump. This type of packet carries all available routing information and can require multiple network protocol data units (NPDUs). During periods of
occasional movement, these packets are transmitted infrequently.
Smaller incremental packets are used to relay only that information which has changed since the last full dump. Each of these broadcasts should fit into a standard-size NPDU, thereby decreasing the amount of traffic generated. The mobile nodes maintain an additional table where they store the data sent in the incremental routing information packets.
New route broadcasts contain the address of the destination, the number of hops to reach the destination, the sequence number of the information received regarding the destination,
as well as a new sequence number unique to the broadcast [2]. The route labeled with the most recent
sequence number is always used. In the event that two updates have the same sequence number, the route with the smaller metric is used in order to optimize (shorten) the path.
Mobiles also keep track of the settling time of routes, or the weighted average time that routes to a destination will fluctuate before the route with the best metric is received (see [2]). By delaying the broadcast of a routing update by the length of the settling time, mobiles can reduce network traffic
and optimize routes by eliminating those broadcasts that would occur if a better route was discovered in the very near future.
Clusterhead Gateway Switch Routing — The Clusterhead Gateway Switch Routing (CGSR) protocol differs from the previous protocol in the type of addressing and network organization scheme employed. Instead of a “flat” network, CGSR is a clustered multihop mobile wireless network with
several heuristic routing schemes [4]. The authors state that by having a cluster head controlling a group of ad hoc nodes, a framework for code separation (among clusters), channel access, routing, and bandwidth allocation can be achieved. A cluster head selection algorithm is utilized to elect a node as the cluster head using a distributed algorithm within the cluster. The disadvantage of having a cluster head scheme is that frequent cluster head changes can adversely affect routing protocol performance since nodes are busy in cluster head selection rather than packet relaying. Hence, instead of invoking cluster head reselection every time the cluster membership changes, a Least Cluster
Change (LCC) clustering algorithm is introduced. Using LCC, cluster heads only change when two cluster heads come into contact, or when a node moves out of contact of all other cluster heads.
CGSR uses DSDV as the underlying routing scheme, and hence has much of the same overhead as DSDV. However, it modifies DSDV by using a hierarchical cluster-head-to-gateway routing approach to route traffic from source to destination. Gateway nodes are nodes that are within communication
range of two or more cluster heads. A packet sent by a node is first routed to its cluster head, and then the packet is routed from the cluster head to a gateway to another cluster head, and so on until the cluster head of the destination node is reached. The packet is then transmitted to the destination.
Figure 2 illustrates an example of this routing scheme. Using this method, each node must keep a “cluster member table” where it stores the destination cluster head for each mobile node in the network. These cluster member tables are broadcast by each node periodically using the DSDV algorithm. Nodes update their cluster member tables on reception of such a table from a neighbor.
In addition to the cluster member table, each node must also maintain a routing table which is used to determine the next hop in order to reach the destination. On receiving a packet, a node will consult its cluster member table and routing table to determine the nearest cluster head along the route to the destination. Next, the node will check its routing table to determine the next hop used to reach the selected cluster head. It then transmits the packet to this node.
Dynamic Source Routing — The Dynamic Source Routing (DSR) protocol presented in [8] is an on-demand routing protocol that is based on the concept of source routing. Mobile nodes are required to maintain route caches that contain the source routes of which the mobile is aware. Entries in the route cache are continually updated as new routes are learned. The protocol consists of two major phases: route discovery and route maintenance. When a mobile node has a packet to send to some destination, it first consults its route cache to determine whether it already has a route to the destination. If it has an unexpired route to the destination, it will use this route to send the packet. On the other hand, if the node does not have such a route, it initiates route discovery by broadcasting a route request packet. This route request contains the address of the destination, along with the source node’s address and a unique identification number. Each node receiving the packet checks whether it knows of a route to the destination. If it does not, it adds its own address to the route record of the packet and then forwards the packet along its outgoing links. To limit the number of route requests propagated on the outgoing links of a node, a mobile only forwards the route request if the request has not yet been seen by the mobile and if the mobile’s address does not already appear in the
route record.
A route reply is generated when the route request reaches either the destination itself, or an intermediate node which contains in its route cache an unexpired route to the destination [9]. By the time the packet reaches either the destination or such an intermediate node, it contains a route record
yielding the sequence of hops taken. Figure 4a illustrates the formation of the route record as the route request propagates through the network. If the node generating the route reply is the destination, it places the route record contained in the route request into the route reply. If the responding node is
an intermediate node, it will append its cached route to the route record and then generate the route reply. To return the route reply, the responding node must have a route to the initiator. If it has a route to the initiator in its route cache, it may use that route. Otherwise, if symmetric links are supported,
the node may reverse the route in the route record. If symmetric links are not supported, the node may initiate its own route discovery and piggyback the route reply on the new route request. Figure 4b shows the transmission of the route reply with its associated route record back to the source node.
As discussed earlier, the table-driven ad hoc routing approach is similar to the connectionless approach of forwarding packets, with no regard to when and how frequently such routes are
desired. It relies on an underlying routing table update mechanism that involves the constant propagation of routing information.
This is not the case, however, for on-demand routing protocols. When a node using an on-demand protocol desires a route to a new destination, it will have to wait until such a route can be discovered. On the other hand, because routing information is constantly propagated and maintained in table-driven routing protocols, a route to every other node in the ad hoc network is always available, regardless of whether or not it is needed. This feature, although useful for datagram traffic, incurs substantial signaling traffic and power consumption. Since both bandwidth and battery power are
scarce resources in mobile computers, this becomes a serious limitation.
Table 3 lists some of the basic differences between the two classes of algorithms. Another consideration is whether a flat or hierarchical addressing scheme should be used. All of the protocols considered here, except for CGSR, use a flat addressing scheme. In [20] a discussion of the two addressing schemes is presented. While flat addressing may be less complicated and easier to use, there are doubts as to its scalability. Applications and Challenges Akin to packet radio networks, ad hoc wireless networks have an important role to play in military applications. Soldiers equipped with multimode mobile communicators can now communicate in an ad hoc manner without the need for fixed wireless base stations. In addition, small vehicular devices equipped with audio sensors and cameras can be deployed at targeted regions to collect important location and environmental information which will be communicated back to a processing node via ad hoc mobile communications. Ship-to ship ad hoc mobile communication is also desirable since it provides
alternate communication paths without reliance on ground or space-based communication infrastructures. Commercial scenarios for ad hoc wireless networks include:
• Conferences/meetings/lectures
• Emergency services
• Law enforcement
People today attend meetings and conferences with their laptops, palmtops, and notebooks. It is therefore attractive to have instant network formation, in addition to file and information sharing without the presence of fixed base stations and systems administrators. A presenter can multicast slides and audio to intended recipients. Attendees can ask questions and interact on a commonly shared whiteboard. Ad hoc mobile communication is particularly useful in relaying information (status, situation awareness, etc.) via data, video, and/or voice from one rescue team member to another over a small handheld or wearable wireless device.
Again, this applies to law enforcement personnel as well.
Current challenges for ad hoc wireless networks include:
•Multicast [21]
•QoS support
•Power-aware routing [22]
•Location-aided routing [23]
As mentioned above, multicast is desirable to support multiparty wireless communications.
Since the multicast tree is no longer static (i.e., its topology is subject to change over time), the multicast routing protocol must be able to cope with mobility, including multicast membership
dynamics (e.g., leave and join). In terms of QoS, it is inadequate to consider QoS merely at the network level without considering the underlying media access control layer [24]. Again, given the problems associated with the dynamics of nodes, hidden terminals, and fluctuating link characteristics, supporting end-to-end QoS is a nontrivial issue that requires in-depth investigation. Currently, there is a trend toward an adaptive QoS approach instead of the “plain” resource reservation method with hard QoS guarantees. Another important factor is the limited power supply in handheld devices, which can seriously prohibit packet forwarding in an ad hoc mobile environment. Hence, routing traffic based on nodes’ power metrics is one way to distinguish
routes that are more long-lived than others. Finally, instead of using beaconing or broadcast search, location aided routing uses positioning information to define associated regions so that the routing is spatially oriented and limited.
This is analogous to associativity-oriented and restricted broadcast in ABR.
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Table-Driven Routing Protocols
Table-driven routing protocols attempt to maintain consistent, up-to-date routing information from each node to every other node in the network. These protocols require each node to maintain one or more tables to store routing information, and they respond to changes in network topology by propagating updates throughout the network in order to maintain a consistent network view. The areas in which they differ are the number of necessary routing-related tables and the methods
by which changes in network structure are broadcast. The following sections discuss some of the existing table-driven ad hoc routing protocols.
Destination-Sequenced Distance-Vector Routing — The Destination-Sequenced Distance-Vector Routing protocol (DSDV) described in [2] is a table-driven algorithm based on the classical Bellman-Ford routing mechanism [3]. The improvements made to the Bellman-Ford algorithm include freedom from loops in routing tables. Every mobile node in the network maintains a routing
table in which all of the possible destinations within the network and the number of hops to each destination are recorded. Each entry is marked with a sequence number assigned by the destination node. The sequence numbers enable the mobile nodes to distinguish stale routes from new ones, thereby avoiding the formation of routing loops. Routing table updates are periodically transmitted throughout the network in order to maintain table consistency. To help alleviate the potentially
large amount of network traffic that such updates can generate, route updates can employ two possible types of packets. The first is known as a full dump. This type of packet carries all available routing information and can require multiple network protocol data units (NPDUs). During periods of
occasional movement, these packets are transmitted infrequently.
Smaller incremental packets are used to relay only that information which has changed since the last full dump. Each of these broadcasts should fit into a standard-size NPDU, thereby decreasing the amount of traffic generated. The mobile nodes maintain an additional table where they store the data sent in the incremental routing information packets.
New route broadcasts contain the address of the destination, the number of hops to reach the destination, the sequence number of the information received regarding the destination,
as well as a new sequence number unique to the broadcast [2]. The route labeled with the most recent
sequence number is always used. In the event that two updates have the same sequence number, the route with the smaller metric is used in order to optimize (shorten) the path.
Mobiles also keep track of the settling time of routes, or the weighted average time that routes to a destination will fluctuate before the route with the best metric is received (see [2]). By delaying the broadcast of a routing update by the length of the settling time, mobiles can reduce network traffic
and optimize routes by eliminating those broadcasts that would occur if a better route was discovered in the very near future.
Clusterhead Gateway Switch Routing — The Clusterhead Gateway Switch Routing (CGSR) protocol differs from the previous protocol in the type of addressing and network organization scheme employed. Instead of a “flat” network, CGSR is a clustered multihop mobile wireless network with
several heuristic routing schemes [4]. The authors state that by having a cluster head controlling a group of ad hoc nodes, a framework for code separation (among clusters), channel access, routing, and bandwidth allocation can be achieved. A cluster head selection algorithm is utilized to elect a node as the cluster head using a distributed algorithm within the cluster. The disadvantage of having a cluster head scheme is that frequent cluster head changes can adversely affect routing protocol performance since nodes are busy in cluster head selection rather than packet relaying. Hence, instead of invoking cluster head reselection every time the cluster membership changes, a Least Cluster
Change (LCC) clustering algorithm is introduced. Using LCC, cluster heads only change when two cluster heads come into contact, or when a node moves out of contact of all other cluster heads.
CGSR uses DSDV as the underlying routing scheme, and hence has much of the same overhead as DSDV. However, it modifies DSDV by using a hierarchical cluster-head-to-gateway routing approach to route traffic from source to destination. Gateway nodes are nodes that are within communication
range of two or more cluster heads. A packet sent by a node is first routed to its cluster head, and then the packet is routed from the cluster head to a gateway to another cluster head, and so on until the cluster head of the destination node is reached. The packet is then transmitted to the destination.
Figure 2 illustrates an example of this routing scheme. Using this method, each node must keep a “cluster member table” where it stores the destination cluster head for each mobile node in the network. These cluster member tables are broadcast by each node periodically using the DSDV algorithm. Nodes update their cluster member tables on reception of such a table from a neighbor.
In addition to the cluster member table, each node must also maintain a routing table which is used to determine the next hop in order to reach the destination. On receiving a packet, a node will consult its cluster member table and routing table to determine the nearest cluster head along the route to the destination. Next, the node will check its routing table to determine the next hop used to reach the selected cluster head. It then transmits the packet to this node.
Dynamic Source Routing — The Dynamic Source Routing (DSR) protocol presented in [8] is an on-demand routing protocol that is based on the concept of source routing. Mobile nodes are required to maintain route caches that contain the source routes of which the mobile is aware. Entries in the route cache are continually updated as new routes are learned. The protocol consists of two major phases: route discovery and route maintenance. When a mobile node has a packet to send to some destination, it first consults its route cache to determine whether it already has a route to the destination. If it has an unexpired route to the destination, it will use this route to send the packet. On the other hand, if the node does not have such a route, it initiates route discovery by broadcasting a route request packet. This route request contains the address of the destination, along with the source node’s address and a unique identification number. Each node receiving the packet checks whether it knows of a route to the destination. If it does not, it adds its own address to the route record of the packet and then forwards the packet along its outgoing links. To limit the number of route requests propagated on the outgoing links of a node, a mobile only forwards the route request if the request has not yet been seen by the mobile and if the mobile’s address does not already appear in the
route record.
A route reply is generated when the route request reaches either the destination itself, or an intermediate node which contains in its route cache an unexpired route to the destination [9]. By the time the packet reaches either the destination or such an intermediate node, it contains a route record
yielding the sequence of hops taken. Figure 4a illustrates the formation of the route record as the route request propagates through the network. If the node generating the route reply is the destination, it places the route record contained in the route request into the route reply. If the responding node is
an intermediate node, it will append its cached route to the route record and then generate the route reply. To return the route reply, the responding node must have a route to the initiator. If it has a route to the initiator in its route cache, it may use that route. Otherwise, if symmetric links are supported,
the node may reverse the route in the route record. If symmetric links are not supported, the node may initiate its own route discovery and piggyback the route reply on the new route request. Figure 4b shows the transmission of the route reply with its associated route record back to the source node.
As discussed earlier, the table-driven ad hoc routing approach is similar to the connectionless approach of forwarding packets, with no regard to when and how frequently such routes are
desired. It relies on an underlying routing table update mechanism that involves the constant propagation of routing information.
This is not the case, however, for on-demand routing protocols. When a node using an on-demand protocol desires a route to a new destination, it will have to wait until such a route can be discovered. On the other hand, because routing information is constantly propagated and maintained in table-driven routing protocols, a route to every other node in the ad hoc network is always available, regardless of whether or not it is needed. This feature, although useful for datagram traffic, incurs substantial signaling traffic and power consumption. Since both bandwidth and battery power are
scarce resources in mobile computers, this becomes a serious limitation.
Table 3 lists some of the basic differences between the two classes of algorithms. Another consideration is whether a flat or hierarchical addressing scheme should be used. All of the protocols considered here, except for CGSR, use a flat addressing scheme. In [20] a discussion of the two addressing schemes is presented. While flat addressing may be less complicated and easier to use, there are doubts as to its scalability. Applications and Challenges Akin to packet radio networks, ad hoc wireless networks have an important role to play in military applications. Soldiers equipped with multimode mobile communicators can now communicate in an ad hoc manner without the need for fixed wireless base stations. In addition, small vehicular devices equipped with audio sensors and cameras can be deployed at targeted regions to collect important location and environmental information which will be communicated back to a processing node via ad hoc mobile communications. Ship-to ship ad hoc mobile communication is also desirable since it provides
alternate communication paths without reliance on ground or space-based communication infrastructures. Commercial scenarios for ad hoc wireless networks include:
• Conferences/meetings/lectures
• Emergency services
• Law enforcement
People today attend meetings and conferences with their laptops, palmtops, and notebooks. It is therefore attractive to have instant network formation, in addition to file and information sharing without the presence of fixed base stations and systems administrators. A presenter can multicast slides and audio to intended recipients. Attendees can ask questions and interact on a commonly shared whiteboard. Ad hoc mobile communication is particularly useful in relaying information (status, situation awareness, etc.) via data, video, and/or voice from one rescue team member to another over a small handheld or wearable wireless device.
Again, this applies to law enforcement personnel as well.
Current challenges for ad hoc wireless networks include:
•Multicast [21]
•QoS support
•Power-aware routing [22]
•Location-aided routing [23]
As mentioned above, multicast is desirable to support multiparty wireless communications.
Since the multicast tree is no longer static (i.e., its topology is subject to change over time), the multicast routing protocol must be able to cope with mobility, including multicast membership
dynamics (e.g., leave and join). In terms of QoS, it is inadequate to consider QoS merely at the network level without considering the underlying media access control layer [24]. Again, given the problems associated with the dynamics of nodes, hidden terminals, and fluctuating link characteristics, supporting end-to-end QoS is a nontrivial issue that requires in-depth investigation. Currently, there is a trend toward an adaptive QoS approach instead of the “plain” resource reservation method with hard QoS guarantees. Another important factor is the limited power supply in handheld devices, which can seriously prohibit packet forwarding in an ad hoc mobile environment. Hence, routing traffic based on nodes’ power metrics is one way to distinguish
routes that are more long-lived than others. Finally, instead of using beaconing or broadcast search, location aided routing uses positioning information to define associated regions so that the routing is spatially oriented and limited.
This is analogous to associativity-oriented and restricted broadcast in ABR.
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A reinforcement learning ticket-based probing path discovery scheme for MANETs
A reinforcement learning ticket-based probing path discovery scheme for MANETs:
Abstract:
In this paper, a path discovery scheme which supports QoS routing in mobile ad hoc networks (MANETs) in the presence of imprecise information is investigated. The aim is to increase the probability of success in finding feasible paths and reduce average path cost of a previously proposed ticket based probing (TBP) path discovery scheme. The proposed scheme integrates the original TBP scheme with a reinforcement learning method called the on-policy first visit Monte Carlo (ONMC) method. We investigate the performance of the ONMC method in the presence of imprecise information. Our numerical study shows that, in respect to a flooding based algorithm, message overhead reduction can be achieved with marginal difference in the path search ability and additional computational and storage requirements. When the average message overhead of the ONMC method is reduced to the same order of magnitude of the original TBP, the ONMC method gains an improvement of 28% in success ratio and 7% reduction in the average path cost over the original TBP
1. Introduction
A mobile ad hoc network (MANET) consists of a set of mobile nodes (hosts) which are equipped
with transmitters and receivers that allow them to communicate without the need of wire-based
infrastructures. Most of the existing routing protocols in MANETs have been focused on only best-effort data traffic. Routing schemes which can support connections with QoS requirements have only recently begun to receive attention. There are two keys to support QoS routing, namely, feasible route search and resource reservation [2–4,12,15]. Feasible route search can be done by distributed routing or source routing. In distributed routing, other nodes apart from the source node are involved in the feasible path(s) search and computation by identifying their neighboring nodes as the next hop
router. On the other hand, in source routing, a feasible path(s) is computed solely at the source
node. An alternative method is to perform flooding but have the source node calculate some
measure of control over the amount of flooding. This is a mixed feature of distributed and source routing which is the underlying concept of the Ticket-Based Probing (TBP) scheme [2,3] where
the amount of flooding here can be controlled by issuing a limited number of logical tickets at the
source node. The work reported in this paper builds on the earlier work of [2,3] in that it integrates the reinforcement learning (RL) framework into the TBP scheme. Despite several attractive features––as will be presented later on, the TBP scheme has some outstanding challenges. One of such issues relates to the restricted flooding method: the computation of a suitable number of logical tickets issued at the source node. More specifically, the original TBP scheme relies on an heuristic rule of ticket computation.
We enhance the original TBP scheme by using a RL technique. The RL-based TBP scheme learns a good rule for issuing tickets by interacting directly with the environment or by simulation.
Note that there are other works which apply the RL framework to MANET routing: [16] proposed
a multicast routing approach based on Q-learning concept, [13] suggested a possible application of
their multiagent routing scheme in MANETs. However, these works do not deal explicitly with
QoS requirements or message overhead. More recently in [8] a power-aware routing algorithm is
presented. In [8] the authors extend their work on cognitive packet networks (CPN) routing protocol
which provides intelligent QoS driven routing for peer-to-peer connections (see also [7,9]). The CPN
protocol uses smart packets to discover and maintain lists of neighbor nodes, and to dynamically
set up source-to-destination routes. Smart packets select routes using a RL algorithm rather
than flooding. In this paper, we gather further experimental evidence on the advantages and
limitations of RL techniques when employed to solve the underlying problem of QoS routing in
MANETs using flooding based strategies [17]. The underlying aim of the scheme presented in this
paper is to maximize the probability of success in finding feasible routes in dynamic topology networks in the presence of inaccurate information.
2. A POMDP model for MANETs Partial observability can occur when the topology of the MANET is highly dynamic. In such network, each mobile node acts as a router since there is no fixed infrastructure for routing support. Every mobile node is free to move and can enter or leave the network at any instant. In order to maintain up-to-date routing information at other mobile nodes, message exchanges between mobile nodes are required. These information exchanges are done periodically or when a topology change is detected. But even so, imprecise information can still arise due to delayed-arrival or lost update messages and restricted transmission of updating messages.
Furthermore, within MANETs which support quality-of-service (QoS) routing, residual resource
information in the network is critical. In particular, each mobile node maintains a state of the
network, i.e., the delay and bandwidth information to all other destinations in the network. Note
that such information depends on the route and consequently, on the current topology of the network.
The information is propagated through the MANET according to some updating protocol.
Since an accurate view of such information is difficult to obtain, each mobile node is faced with
only an ‘‘observation’’ of its environment which is most likely incomplete and inaccurate. Based on
its current network observation, each mobile node acts as an agent which must make certain decisions, e.g., how many control messages are needed to find a feasible path for some new connection arrival, when and how to perform path maintenance if an existing path is about to break, etc.
In this paper, we study a delay-constrained least cost routing problem. For this constrained routing
problem, there are two tasks. Firstly, we need to determine the suitable number of tickets ðM
Þ.In [2,3], M0(¼) Y0 G0 where Y 0 and G are the number of yellow and green tickets, respectively. These two types of tickets have different purposes. The yellow tickets are for maximizing the chances of finding feasible paths while the green tickets are 0 for maximizing the chances low cost paths.
6. Conclusion
In this paper, the TBP scheme based on the ONMC method studied in [17], is applied to support
QoS routing in a MANET environment. The reinforcement learning (RL)-based ONMC method,
relies on a look-up table representation which stores a value function for every observation and
action pair. The simulation study shows that the TBP schemes based on the ONMC method can achieve good ticket-issuing policies, in terms of the accumulated reward per episode, higher success ratio and lower average path cost, when compared to the original heuristic TBP scheme and a floodingbased TBP scheme. The RL-based TBP path discovery scheme (based on the ONMC method) here proposed, is flexible enough to foster various other objectives and costs functions proposed in the recent literature. In the present version of the RLbased TBP algorithm, decisions are made only once at the source node as new call requests are offered to the network.
Preliminary numerical results reported here suggest that the ONMC scheme can control the amount of flooding in the network. More specifically, it achieves 22.1–58.4% reduction in the average number of search messages compared to the flooding-based TBP scheme with marginal reduction 0.5–1.7% in success ratio. In addition, the ONMC scheme can attain 13–24.3% higher success ratio than the original heuristic TBP scheme at the expense of higher average message overhead. However, as the maximum number of allowable tickets is reduced to a level in which the average message overhead of the ONMC and the original TBP schemes are of the same magnitude, the ONMC scheme still gains 28% higher success ratio and 7% lower average path cost over the original heuristic TBP scheme.
In terms of implementation, the savings in the amount of generated search messages obtained by
the RL-based TBP schemes is at the expense of reasonable storage and computational requirements
of on-line decision parameters. The storage requirements grow linearly with the number of nodes in the network. The ONMC method requires OðjOjjAjÞ iterations where jOj and jAj are the sizes of the observation and action spaces. Note that jOj depends on the granulity of the quantized delay intervals whereas jAj depends on the number of tickets.
From the results of our experimental work gathered so far, it can be said that RL techniques
can play an important role in controlling search messages overhead in environments in which the
outcome of a decision is only partially observable. It is important to note that parameters of the
algorithm and the granularity of, for example, the action set A is important and further investigation
is being carried out at present on these issues. We are currently investigating methods to further reduce the average number of search messages and the integration of TBP schemes with other
POMDP RL approaches which will be reported in a forthcoming paper.
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Abstract:
In this paper, a path discovery scheme which supports QoS routing in mobile ad hoc networks (MANETs) in the presence of imprecise information is investigated. The aim is to increase the probability of success in finding feasible paths and reduce average path cost of a previously proposed ticket based probing (TBP) path discovery scheme. The proposed scheme integrates the original TBP scheme with a reinforcement learning method called the on-policy first visit Monte Carlo (ONMC) method. We investigate the performance of the ONMC method in the presence of imprecise information. Our numerical study shows that, in respect to a flooding based algorithm, message overhead reduction can be achieved with marginal difference in the path search ability and additional computational and storage requirements. When the average message overhead of the ONMC method is reduced to the same order of magnitude of the original TBP, the ONMC method gains an improvement of 28% in success ratio and 7% reduction in the average path cost over the original TBP
1. Introduction
A mobile ad hoc network (MANET) consists of a set of mobile nodes (hosts) which are equipped
with transmitters and receivers that allow them to communicate without the need of wire-based
infrastructures. Most of the existing routing protocols in MANETs have been focused on only best-effort data traffic. Routing schemes which can support connections with QoS requirements have only recently begun to receive attention. There are two keys to support QoS routing, namely, feasible route search and resource reservation [2–4,12,15]. Feasible route search can be done by distributed routing or source routing. In distributed routing, other nodes apart from the source node are involved in the feasible path(s) search and computation by identifying their neighboring nodes as the next hop
router. On the other hand, in source routing, a feasible path(s) is computed solely at the source
node. An alternative method is to perform flooding but have the source node calculate some
measure of control over the amount of flooding. This is a mixed feature of distributed and source routing which is the underlying concept of the Ticket-Based Probing (TBP) scheme [2,3] where
the amount of flooding here can be controlled by issuing a limited number of logical tickets at the
source node. The work reported in this paper builds on the earlier work of [2,3] in that it integrates the reinforcement learning (RL) framework into the TBP scheme. Despite several attractive features––as will be presented later on, the TBP scheme has some outstanding challenges. One of such issues relates to the restricted flooding method: the computation of a suitable number of logical tickets issued at the source node. More specifically, the original TBP scheme relies on an heuristic rule of ticket computation.
We enhance the original TBP scheme by using a RL technique. The RL-based TBP scheme learns a good rule for issuing tickets by interacting directly with the environment or by simulation.
Note that there are other works which apply the RL framework to MANET routing: [16] proposed
a multicast routing approach based on Q-learning concept, [13] suggested a possible application of
their multiagent routing scheme in MANETs. However, these works do not deal explicitly with
QoS requirements or message overhead. More recently in [8] a power-aware routing algorithm is
presented. In [8] the authors extend their work on cognitive packet networks (CPN) routing protocol
which provides intelligent QoS driven routing for peer-to-peer connections (see also [7,9]). The CPN
protocol uses smart packets to discover and maintain lists of neighbor nodes, and to dynamically
set up source-to-destination routes. Smart packets select routes using a RL algorithm rather
than flooding. In this paper, we gather further experimental evidence on the advantages and
limitations of RL techniques when employed to solve the underlying problem of QoS routing in
MANETs using flooding based strategies [17]. The underlying aim of the scheme presented in this
paper is to maximize the probability of success in finding feasible routes in dynamic topology networks in the presence of inaccurate information.
2. A POMDP model for MANETs Partial observability can occur when the topology of the MANET is highly dynamic. In such network, each mobile node acts as a router since there is no fixed infrastructure for routing support. Every mobile node is free to move and can enter or leave the network at any instant. In order to maintain up-to-date routing information at other mobile nodes, message exchanges between mobile nodes are required. These information exchanges are done periodically or when a topology change is detected. But even so, imprecise information can still arise due to delayed-arrival or lost update messages and restricted transmission of updating messages.
Furthermore, within MANETs which support quality-of-service (QoS) routing, residual resource
information in the network is critical. In particular, each mobile node maintains a state of the
network, i.e., the delay and bandwidth information to all other destinations in the network. Note
that such information depends on the route and consequently, on the current topology of the network.
The information is propagated through the MANET according to some updating protocol.
Since an accurate view of such information is difficult to obtain, each mobile node is faced with
only an ‘‘observation’’ of its environment which is most likely incomplete and inaccurate. Based on
its current network observation, each mobile node acts as an agent which must make certain decisions, e.g., how many control messages are needed to find a feasible path for some new connection arrival, when and how to perform path maintenance if an existing path is about to break, etc.
In this paper, we study a delay-constrained least cost routing problem. For this constrained routing
problem, there are two tasks. Firstly, we need to determine the suitable number of tickets ðM
Þ.In [2,3], M0(¼) Y0 G0 where Y 0 and G are the number of yellow and green tickets, respectively. These two types of tickets have different purposes. The yellow tickets are for maximizing the chances of finding feasible paths while the green tickets are 0 for maximizing the chances low cost paths.
6. Conclusion
In this paper, the TBP scheme based on the ONMC method studied in [17], is applied to support
QoS routing in a MANET environment. The reinforcement learning (RL)-based ONMC method,
relies on a look-up table representation which stores a value function for every observation and
action pair. The simulation study shows that the TBP schemes based on the ONMC method can achieve good ticket-issuing policies, in terms of the accumulated reward per episode, higher success ratio and lower average path cost, when compared to the original heuristic TBP scheme and a floodingbased TBP scheme. The RL-based TBP path discovery scheme (based on the ONMC method) here proposed, is flexible enough to foster various other objectives and costs functions proposed in the recent literature. In the present version of the RLbased TBP algorithm, decisions are made only once at the source node as new call requests are offered to the network.
Preliminary numerical results reported here suggest that the ONMC scheme can control the amount of flooding in the network. More specifically, it achieves 22.1–58.4% reduction in the average number of search messages compared to the flooding-based TBP scheme with marginal reduction 0.5–1.7% in success ratio. In addition, the ONMC scheme can attain 13–24.3% higher success ratio than the original heuristic TBP scheme at the expense of higher average message overhead. However, as the maximum number of allowable tickets is reduced to a level in which the average message overhead of the ONMC and the original TBP schemes are of the same magnitude, the ONMC scheme still gains 28% higher success ratio and 7% lower average path cost over the original heuristic TBP scheme.
In terms of implementation, the savings in the amount of generated search messages obtained by
the RL-based TBP schemes is at the expense of reasonable storage and computational requirements
of on-line decision parameters. The storage requirements grow linearly with the number of nodes in the network. The ONMC method requires OðjOjjAjÞ iterations where jOj and jAj are the sizes of the observation and action spaces. Note that jOj depends on the granulity of the quantized delay intervals whereas jAj depends on the number of tickets.
From the results of our experimental work gathered so far, it can be said that RL techniques
can play an important role in controlling search messages overhead in environments in which the
outcome of a decision is only partially observable. It is important to note that parameters of the
algorithm and the granularity of, for example, the action set A is important and further investigation
is being carried out at present on these issues. We are currently investigating methods to further reduce the average number of search messages and the integration of TBP schemes with other
POMDP RL approaches which will be reported in a forthcoming paper.
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A quadratic optimization method for connectivity and coverage control in backbone-based wireless networks
A quadratic optimization method for connectivity and coverage control in backbone-based wireless networks:
abstract:
The use of directional wireless communications to form flexible mesh backbone networks,
which provide broadband connectivity to capacity-limited wireless networks or hosts, promises to circumvent the scalability limitations of traditional homogeneous wireless networks. The main challenge in the design of directional wireless backbone (DWB) networks is to assure backbone network requirements such as coverage and connectivity in a dynamic wireless environment. This paper considers the use of mobility control, as the dynamic reposition of backbone nodes, to provide assured coverage-connectivity in dynamic environments. This paper presents a novel approach to the joint coverage-connectivity optimization problem by formulating it as a quadratic minimization
problem. Quadratic cost functions for network coverage and backbone connectivity are defined in terms of the square distance between neighbor nodes, which are related to the actual energy usage of the network system. Our formulation allows the design of self-organized network systems which autonomously achieve energy minimizing configurations driven by local forces exerted on network nodes. The net force on a backbone node is defined as the negative energy gradient at the location of the backbone node. A completely distributed algorithm is presented that allows backbone nodes to adjust their positions based on information about neighbors’ position only. We present initial simulation results that show the effectiveness of our force-based mobility control algorithm to
provide network configurations that optimize both network coverage and backbone connectivity
in different scenarios. Our algorithm is shown to be adaptive, scalable and self organized.
1. Motivation
In a flat network architecture, all wireless nodes are homogeneous in terms of their communication capabilities. It is well known that the throughput capacity of a flat wireless network architecture without an infrastructure support is not scalable as the number of nodes becomes large [1]. Consequently, in order to meet the increasing communication demands of wireless users, it is necessary to supplement the wireless network with a higher layer of communication mode. This new communication mode enables a small group of nodes to communicate with each other over longer distances and at higher bit rates than the ordinary wireless nodes. We refer to these nodes as base
station or backbone nodes. Such nodes are equipped with a hybrid communication mode that enables them to communicate with both the terminal wireless nodes and the other base station nodes. Base station nodes form a backbone for the network, through the use of point-to-point communication technologies such as free space optical communication, fiber optic communication, directional
antennas, etc.
In a backbone-based wireless network architecture, communication between two terminal wireless nodes takes place by a multi-hop transmission scheme over the wireless nodes until the traffic of the source reaches one of the backbone nodes, then it travels over the backbone network until it reaches a backbone node which is close enough to the intended destination, and finally it travels over a few wireless nodes until it reaches its destination. From a networking point of view, some of the most important design aspects of this architecture are: the required number of backbone nodes for a certain target performance, and the efficient placement of the backbone nodes in the network.
Moreover, DWB networks must provide robust end-to end broadband connectivity in a dynamic wireless environment, where the state of wireless links is constantly changing due to node mobility and atmospheric obscuration [3]. The DWB topology, as defined by the location of the backbone nodes and the connections between them, needs to adjust to the changing environment. Thus, it is
important to provide the DWB with the capability of autonomous reconfiguration. The location of the backbone nodes needs to be updated in order to dynamically meet the wireless users demands.
We propose to solve the joint coverage-connectivity optimization problem by formulating it as an energy minimization problem. Convex energy functions for both coverage and connectivity are defined in terms of the distance between each terminal and its assigned backbone node (for network coverage) and between neighbor backbone nodes (for backbone connectivity). We then compute
forces on every node as the negative energy gradient and show how backbone nodes achieve the optimal topology configuration when reacting to local forces exerted by neighbor nodes.
Similar problems have been considered in the past which can be related to the coverage-connectivity optimization problem for DWB networks. In facility location problems (FLPs), a set of client locations are to be serviced by facilities. If a client at location j is assigned to a facility at location i, a cost of c is incurred that is proportional to the distance between i and j. The objective is to find the optimal placement of facilities so as to minimize the total assignment costs. In DWB-based networks, terminal nodes are considered as clients and backbone nodes as facilities. The main difference between facility location problems and our problem is that in FLPs the connectivity between facilities (backbone nodes) is not considered, which is of critical importance in DWBbased networks, as terminal nodes cannot communicate with each other if the backbone is not connected. Also, in
facility location problems, the location of the facilities take discrete integer values. FLPs are thus usually formulated as integer programming problems and are shown to be NPComplete. Approximation algorithms have been developed that give constant approximation guarantees [4].
4. Simulation results
In order to gain more insight on the problem and verify the performance of our optimization algorithm, we present results from initial simulation studies with different design parameters.
In all simulations, N terminal nodes are distributed in a 100 100 two dimensional plane. Terminal nodes are organized in clusters. The first terminal node is placed in the plane according to a uniform random distribution. Each other terminal is placed within clusterRange of the previous terminal node with probability p, and uniformly in the 100 100 plane with probability 1 p cluster
. Next, M backbone nodes are placed at random in the plane and an initial ring topology is built connecting all backbone nodes. Then, our mobility control algorithm is executed to make backbone
nodes adjust their position until convergence to the optimal backbone configuration.
Before we report quantitative results, we first present several figures to visually illustrate the effectiveness of the mobility control algorithm. Fig. 2 shows results for a network with 100 terminal nodes and 10 backbone nodes. The values used for the variables clusterRange and p are 10 and 0.2, respectively. The backbone nodes are labeled and the links between them are shown in thick dotted
lines. Links from each terminal node to its closest backbone node are shown in thinner dotted lines. Fig. 2a shows the initial non-optimal network configuration. Fig. 2b shows the resulting network configuration after running the mobility control algorithm with a balancing factor g ¼ 1. In this case, the low value of g allows backbone nodes to move close to the terminal nodes to optimize network coverage at the expense of low backbone connectivity. Fig. 2c and d show the resulting network configurations with g ¼ 5 and g ¼ 10, respectively. The higher value of g increases the weight on the backbone connectivity objective thus making the backbone nodes move closer to each other. Finally Fig. 2e and f show the resulting network configurations for g ¼ 20 and g ¼ 30, where stronger
backbone networks are produced at the expense of lower network coverage. In Fig. 3 we show a network with 20 backbone nodes. The number of terminals remains 100
and they are randomly placed according to the procedure explained above. As seen in Fig. 3, the increased number of backbone nodes allows more flexibility to optimize network coverage with less penalization on backbone connectivity, and vice versa.
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abstract:
The use of directional wireless communications to form flexible mesh backbone networks,
which provide broadband connectivity to capacity-limited wireless networks or hosts, promises to circumvent the scalability limitations of traditional homogeneous wireless networks. The main challenge in the design of directional wireless backbone (DWB) networks is to assure backbone network requirements such as coverage and connectivity in a dynamic wireless environment. This paper considers the use of mobility control, as the dynamic reposition of backbone nodes, to provide assured coverage-connectivity in dynamic environments. This paper presents a novel approach to the joint coverage-connectivity optimization problem by formulating it as a quadratic minimization
problem. Quadratic cost functions for network coverage and backbone connectivity are defined in terms of the square distance between neighbor nodes, which are related to the actual energy usage of the network system. Our formulation allows the design of self-organized network systems which autonomously achieve energy minimizing configurations driven by local forces exerted on network nodes. The net force on a backbone node is defined as the negative energy gradient at the location of the backbone node. A completely distributed algorithm is presented that allows backbone nodes to adjust their positions based on information about neighbors’ position only. We present initial simulation results that show the effectiveness of our force-based mobility control algorithm to
provide network configurations that optimize both network coverage and backbone connectivity
in different scenarios. Our algorithm is shown to be adaptive, scalable and self organized.
1. Motivation
In a flat network architecture, all wireless nodes are homogeneous in terms of their communication capabilities. It is well known that the throughput capacity of a flat wireless network architecture without an infrastructure support is not scalable as the number of nodes becomes large [1]. Consequently, in order to meet the increasing communication demands of wireless users, it is necessary to supplement the wireless network with a higher layer of communication mode. This new communication mode enables a small group of nodes to communicate with each other over longer distances and at higher bit rates than the ordinary wireless nodes. We refer to these nodes as base
station or backbone nodes. Such nodes are equipped with a hybrid communication mode that enables them to communicate with both the terminal wireless nodes and the other base station nodes. Base station nodes form a backbone for the network, through the use of point-to-point communication technologies such as free space optical communication, fiber optic communication, directional
antennas, etc.
In a backbone-based wireless network architecture, communication between two terminal wireless nodes takes place by a multi-hop transmission scheme over the wireless nodes until the traffic of the source reaches one of the backbone nodes, then it travels over the backbone network until it reaches a backbone node which is close enough to the intended destination, and finally it travels over a few wireless nodes until it reaches its destination. From a networking point of view, some of the most important design aspects of this architecture are: the required number of backbone nodes for a certain target performance, and the efficient placement of the backbone nodes in the network.
Moreover, DWB networks must provide robust end-to end broadband connectivity in a dynamic wireless environment, where the state of wireless links is constantly changing due to node mobility and atmospheric obscuration [3]. The DWB topology, as defined by the location of the backbone nodes and the connections between them, needs to adjust to the changing environment. Thus, it is
important to provide the DWB with the capability of autonomous reconfiguration. The location of the backbone nodes needs to be updated in order to dynamically meet the wireless users demands.
We propose to solve the joint coverage-connectivity optimization problem by formulating it as an energy minimization problem. Convex energy functions for both coverage and connectivity are defined in terms of the distance between each terminal and its assigned backbone node (for network coverage) and between neighbor backbone nodes (for backbone connectivity). We then compute
forces on every node as the negative energy gradient and show how backbone nodes achieve the optimal topology configuration when reacting to local forces exerted by neighbor nodes.
Similar problems have been considered in the past which can be related to the coverage-connectivity optimization problem for DWB networks. In facility location problems (FLPs), a set of client locations are to be serviced by facilities. If a client at location j is assigned to a facility at location i, a cost of c is incurred that is proportional to the distance between i and j. The objective is to find the optimal placement of facilities so as to minimize the total assignment costs. In DWB-based networks, terminal nodes are considered as clients and backbone nodes as facilities. The main difference between facility location problems and our problem is that in FLPs the connectivity between facilities (backbone nodes) is not considered, which is of critical importance in DWBbased networks, as terminal nodes cannot communicate with each other if the backbone is not connected. Also, in
facility location problems, the location of the facilities take discrete integer values. FLPs are thus usually formulated as integer programming problems and are shown to be NPComplete. Approximation algorithms have been developed that give constant approximation guarantees [4].
4. Simulation results
In order to gain more insight on the problem and verify the performance of our optimization algorithm, we present results from initial simulation studies with different design parameters.
In all simulations, N terminal nodes are distributed in a 100 100 two dimensional plane. Terminal nodes are organized in clusters. The first terminal node is placed in the plane according to a uniform random distribution. Each other terminal is placed within clusterRange of the previous terminal node with probability p, and uniformly in the 100 100 plane with probability 1 p cluster
. Next, M backbone nodes are placed at random in the plane and an initial ring topology is built connecting all backbone nodes. Then, our mobility control algorithm is executed to make backbone
nodes adjust their position until convergence to the optimal backbone configuration.
Before we report quantitative results, we first present several figures to visually illustrate the effectiveness of the mobility control algorithm. Fig. 2 shows results for a network with 100 terminal nodes and 10 backbone nodes. The values used for the variables clusterRange and p are 10 and 0.2, respectively. The backbone nodes are labeled and the links between them are shown in thick dotted
lines. Links from each terminal node to its closest backbone node are shown in thinner dotted lines. Fig. 2a shows the initial non-optimal network configuration. Fig. 2b shows the resulting network configuration after running the mobility control algorithm with a balancing factor g ¼ 1. In this case, the low value of g allows backbone nodes to move close to the terminal nodes to optimize network coverage at the expense of low backbone connectivity. Fig. 2c and d show the resulting network configurations with g ¼ 5 and g ¼ 10, respectively. The higher value of g increases the weight on the backbone connectivity objective thus making the backbone nodes move closer to each other. Finally Fig. 2e and f show the resulting network configurations for g ¼ 20 and g ¼ 30, where stronger
backbone networks are produced at the expense of lower network coverage. In Fig. 3 we show a network with 20 backbone nodes. The number of terminals remains 100
and they are randomly placed according to the procedure explained above. As seen in Fig. 3, the increased number of backbone nodes allows more flexibility to optimize network coverage with less penalization on backbone connectivity, and vice versa.
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A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols
A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols:
Abstract:
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to the limited transmission range of wireless network interfaces, multiple network "hops" may be needed for one node to exchange data with another across the network. In recent years, a variety of new routing protocols targeted specifically at this environment have been developed, but little performance information on each protocol and no realistic performance comparison between them is available. This paper presents the results of a detailed packet-level simulation comparing four multi-hop wireless ad hoc network routing protocols that cover a range of design choices: DSDV, TORA, DSR, and AODV. We have extended the ns-2 network simulator to accurately model the MAC and physical-layer behavior of the IEEE 802.11 wireless LAN standard, including a realistic
wireless transmission channel model, and present the results of simulations of networks of 50 mobile nodes.
1 Introduction
In areas in which there is little or no communication infrastructure or the existing infrastructure is expensive or inconvenient to use, wireless mobile users may still be able to communicate through the
formation of an ad hoc network. In such a network, each mobile node operates not only as a host but also as a router, forwarding packets for other mobile nodes in the network that may not be within direct wireless transmission range of each other. Each node participates in an ad hoc routing protocol that allows it to discover “multi-hop” paths through the network to any other node. The idea of ad hoc networking is sometimes also called infrastructureless networking [13], since the mobile nodes in the network dynamically establish routing among themselves to form their own network “on the fly.” Some examples of the possible uses of ad hoc networking include students using laptop computers to participate in an interactive lecture, business associates sharing information during a meeting, soldiers relaying information for situational awareness on the battlefield [12, 21], and emergency
disaster relief personnel coordinating efforts after a hurricane or earthquake.
Many different protocols have been proposed to solve the multihop routing problem in ad hoc networks, each based on different assumptions and intuitions. However, little is known about the actual performance of these protocols, and no attempt has previously been made to directly compare them in a realistic manner. This paper is the first to provide a realistic, quantitative analysis
comparing the performance of a variety of multi-hop wireless ad hoc network routing protocols. We present results of detailed simulations showing the relative performance of four recently proposed ad hoc routing protocols: DSDV [18], TORA [14, 15], DSR [9, 10, 2], and ODV [17]. To enable these simulations, we extended the ns-2 network simulator [6] to include:
Node mobility.
A realistic physical layer including a radio propagation model supporting propagation delay, capture effects, and carrier sense [20].
Radio network interfaces with properties such as transmission power, antenna gain, and receiver sensitivity.
The IEEE 802.11 Medium Access Control (MAC) protocol using the Distributed Coordination Function (DCF) [8].
Our results in this paper are based on simulations of an ad hoc network 50 wireless mobile nodes moving about and communicating with each other. We analyze the performance of each protocol and explain the design choices that account for their performance.
2 Simulation Environment
ns is a discrete event simulator developed by the University of California at Berkeley and the VINT project [6]. While it provides substantial support for simulating TCP and other protocols over conventional networks, it provides no support for accurately simulating the physical aspects of multi-hop wireless networks or the MAC protocols needed in such environments. Berkeley has recently released ns code that provides some support for modeling wireless LANs, but this code cannot be used for studying multi-hop ad hoc networks as it does not support the notion of node position; there is no spatial diversity (all nodes are in the same collision domain), and it can only
model directly connected nodes.
In this section, we describe some of the modifications we made to ns to allow accurate simulation of mobile wireless networks.
2.1 Physical and Data Link Layer Model
To accurately model the attenuation of radio waves between antennas close to the ground, radio engineers typically use a model that attenuates the power of a signal as 1 r 2 at short distances (r is the
distance between the antennas), and as 1 r 4 at longer distances.
The crossover point is called the reference distance, and is typically around 100 meters for outdoor low-gain antennas 1.5m above the ground plane operating in the 1–2GHz band [20]. Following this
practice, our signal propagation model combines both a free space propagation model and a two-ray ground reflection model. When a transmitter is within the reference distance of the receiver,
3.2.2 Implementation Decisions
IMEP must queue objects for some period of time to allow possible aggregation with other objects, but the IMEP specification [5] does not define this time period, and we discovered that the overall
performance of the protocol was very sensitive to the choice of this value. After significant experimentation, we chose as the best balance between packet overhead and routing protocol convergence, to aggregate H
ELLO and ACK packets for a time uniformly chosen between 150 ms and 250 ms, and to not delay TORA routing messages for aggregation. The reason for not delaying these messages is that the TORA link reversal process creates short-lived routing loops that exist from the time that the link-reversal starts until the time that all nodes that need to be aware of the reversal receive the corresponding U
PDATE (Section 5.2). Delaying the transmission of TORA routing messages for aggregation, coupled with any queuing delay at the network interface, allows these routing loops to last long enough that
significant numbers of data packets are dropped.
The TORA and IMEP specifications [15, 5] do not define the precise semantics of reliable object delivery required by TORA, but experimentation showed that very strong semantics must be provided
in order to prevent the creation of long-lived routing loops. In particular, all TORA objects must be delivered reliably and in order, without any duplication. Additionally, all neighboring nodes
in the ad hoc network must have a consistent picture of the network with regard to each destination. This implies that anytime a node A decides its link to a neighbor B has gone down, B must also decide that the link to A has gone down.
4.1 Movement Model
Nodes in the simulation move according to a model that we call the “random waypoint” model [10]. The movement scenario files we used for each simulation are characterized by a pause time. Each node begins the simulation by remaining stationary for pause time seconds. It then selects a random destination in the 1500m, 300m space and moves to that destination at a speed distributed uniformly between 0 and some maximum speed. Upon reaching the destination, the node pauses again for pause time seconds, selects another destination, and proceeds there as previously described, repeating this behavior for the duration of the simulation. Each simulation ran for 900 seconds of simulated time.
We ran our simulations with movement patterns generated for 7 different pause times: 0, 30, 60, 120, 300, 600, and 900 seconds. A pause time of 0 seconds corresponds to continuous motion, and a
pause time of 900 (the length of the simulation) corresponds to no motion.
Because the performance of the protocols is very sensitive to movement pattern, we generated scenario files with 70 different movement patterns, 10 for each value of pause time. All four routing protocols were run on the same 70 movement patterns.
We experimented with two different maximum speeds of node movement. We primarily report in this paper data from simulations using a maximum node speed of 20 meters per second (average speed 10 meters per second), but also compare this to simulations using a maximum speed of 1 meter per second.
8 Conclusions
The area of ad hoc networking has been receiving increasing attention among researchers in recent years, as the available wireless networking and mobile computing hardware bases are now capable of supporting the promise of this technology. Over the past few years, a variety of new routing protocols targeted specifically at the ad hoc networking environment have been proposed, but little performance information on each protocol and no detailed performance comparison between the protocols has previously been available.
This paper makes contributions in two areas. First, we describe our modifications to the ns network simulator to provide an accurate simulation of the MAC and physical-layer behavior of the IEEE 802.11 wireless LAN standard, including a realistic wireless transmission channel model. This new simulation environment provides a powerful tool for evaluating ad hoc networking protocols and other wireless protocols and applications. Second, using this simulation environment, we present the results of a detailed packet-level simulation comparing four recent multi-hop wireless ad hoc network routing protocols. These protocols, DSDV, TORA, DSR, and AODV, cover a range of design choices, including periodic advertisements vs. ondemand route discovery, use of feedback from the MAC layer to indicate a failure to forward a packet to the next hop, and hop by hop routing vs. source routing. We simulated each protocol in ad hoc networks of 50 mobile nodes moving about and communicating with each other, and presented the results for a range of node mobility rates and movement speeds.
Each of the protocols studied performs well in some cases yet has certain drawbacks in others. DSDV performs quite predictably, delivering virtually all data packets when node mobility rate and movement speed are low, and failing to converge as node mobility increases. TORA, although the worst performer in our experiments in terms of routing packet overhead, still delivered over 90%of the packets in scenarios with 10 or 20 sources. At 30 sources, the network was unable to handle all of the traffic generated by the routing protocol and a significant fraction of data packets were dropped. The performance of DSR was very good at all mobility rates and movement speeds, although its use of source routing increases the number of routing overhead bytes required by the protocol. Finally, AODV performs almost as well as DSR at all mobility rates and movement speeds and accomplishes its goal of eliminating source routing overhead, but it still requires the transmission of many routing overhead packets and at high rates of node mobility is actually more expensive than DSR.
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Abstract:
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to the limited transmission range of wireless network interfaces, multiple network "hops" may be needed for one node to exchange data with another across the network. In recent years, a variety of new routing protocols targeted specifically at this environment have been developed, but little performance information on each protocol and no realistic performance comparison between them is available. This paper presents the results of a detailed packet-level simulation comparing four multi-hop wireless ad hoc network routing protocols that cover a range of design choices: DSDV, TORA, DSR, and AODV. We have extended the ns-2 network simulator to accurately model the MAC and physical-layer behavior of the IEEE 802.11 wireless LAN standard, including a realistic
wireless transmission channel model, and present the results of simulations of networks of 50 mobile nodes.
1 Introduction
In areas in which there is little or no communication infrastructure or the existing infrastructure is expensive or inconvenient to use, wireless mobile users may still be able to communicate through the
formation of an ad hoc network. In such a network, each mobile node operates not only as a host but also as a router, forwarding packets for other mobile nodes in the network that may not be within direct wireless transmission range of each other. Each node participates in an ad hoc routing protocol that allows it to discover “multi-hop” paths through the network to any other node. The idea of ad hoc networking is sometimes also called infrastructureless networking [13], since the mobile nodes in the network dynamically establish routing among themselves to form their own network “on the fly.” Some examples of the possible uses of ad hoc networking include students using laptop computers to participate in an interactive lecture, business associates sharing information during a meeting, soldiers relaying information for situational awareness on the battlefield [12, 21], and emergency
disaster relief personnel coordinating efforts after a hurricane or earthquake.
Many different protocols have been proposed to solve the multihop routing problem in ad hoc networks, each based on different assumptions and intuitions. However, little is known about the actual performance of these protocols, and no attempt has previously been made to directly compare them in a realistic manner. This paper is the first to provide a realistic, quantitative analysis
comparing the performance of a variety of multi-hop wireless ad hoc network routing protocols. We present results of detailed simulations showing the relative performance of four recently proposed ad hoc routing protocols: DSDV [18], TORA [14, 15], DSR [9, 10, 2], and ODV [17]. To enable these simulations, we extended the ns-2 network simulator [6] to include:
Node mobility.
A realistic physical layer including a radio propagation model supporting propagation delay, capture effects, and carrier sense [20].
Radio network interfaces with properties such as transmission power, antenna gain, and receiver sensitivity.
The IEEE 802.11 Medium Access Control (MAC) protocol using the Distributed Coordination Function (DCF) [8].
Our results in this paper are based on simulations of an ad hoc network 50 wireless mobile nodes moving about and communicating with each other. We analyze the performance of each protocol and explain the design choices that account for their performance.
2 Simulation Environment
ns is a discrete event simulator developed by the University of California at Berkeley and the VINT project [6]. While it provides substantial support for simulating TCP and other protocols over conventional networks, it provides no support for accurately simulating the physical aspects of multi-hop wireless networks or the MAC protocols needed in such environments. Berkeley has recently released ns code that provides some support for modeling wireless LANs, but this code cannot be used for studying multi-hop ad hoc networks as it does not support the notion of node position; there is no spatial diversity (all nodes are in the same collision domain), and it can only
model directly connected nodes.
In this section, we describe some of the modifications we made to ns to allow accurate simulation of mobile wireless networks.
2.1 Physical and Data Link Layer Model
To accurately model the attenuation of radio waves between antennas close to the ground, radio engineers typically use a model that attenuates the power of a signal as 1 r 2 at short distances (r is the
distance between the antennas), and as 1 r 4 at longer distances.
The crossover point is called the reference distance, and is typically around 100 meters for outdoor low-gain antennas 1.5m above the ground plane operating in the 1–2GHz band [20]. Following this
practice, our signal propagation model combines both a free space propagation model and a two-ray ground reflection model. When a transmitter is within the reference distance of the receiver,
3.2.2 Implementation Decisions
IMEP must queue objects for some period of time to allow possible aggregation with other objects, but the IMEP specification [5] does not define this time period, and we discovered that the overall
performance of the protocol was very sensitive to the choice of this value. After significant experimentation, we chose as the best balance between packet overhead and routing protocol convergence, to aggregate H
ELLO and ACK packets for a time uniformly chosen between 150 ms and 250 ms, and to not delay TORA routing messages for aggregation. The reason for not delaying these messages is that the TORA link reversal process creates short-lived routing loops that exist from the time that the link-reversal starts until the time that all nodes that need to be aware of the reversal receive the corresponding U
PDATE (Section 5.2). Delaying the transmission of TORA routing messages for aggregation, coupled with any queuing delay at the network interface, allows these routing loops to last long enough that
significant numbers of data packets are dropped.
The TORA and IMEP specifications [15, 5] do not define the precise semantics of reliable object delivery required by TORA, but experimentation showed that very strong semantics must be provided
in order to prevent the creation of long-lived routing loops. In particular, all TORA objects must be delivered reliably and in order, without any duplication. Additionally, all neighboring nodes
in the ad hoc network must have a consistent picture of the network with regard to each destination. This implies that anytime a node A decides its link to a neighbor B has gone down, B must also decide that the link to A has gone down.
4.1 Movement Model
Nodes in the simulation move according to a model that we call the “random waypoint” model [10]. The movement scenario files we used for each simulation are characterized by a pause time. Each node begins the simulation by remaining stationary for pause time seconds. It then selects a random destination in the 1500m, 300m space and moves to that destination at a speed distributed uniformly between 0 and some maximum speed. Upon reaching the destination, the node pauses again for pause time seconds, selects another destination, and proceeds there as previously described, repeating this behavior for the duration of the simulation. Each simulation ran for 900 seconds of simulated time.
We ran our simulations with movement patterns generated for 7 different pause times: 0, 30, 60, 120, 300, 600, and 900 seconds. A pause time of 0 seconds corresponds to continuous motion, and a
pause time of 900 (the length of the simulation) corresponds to no motion.
Because the performance of the protocols is very sensitive to movement pattern, we generated scenario files with 70 different movement patterns, 10 for each value of pause time. All four routing protocols were run on the same 70 movement patterns.
We experimented with two different maximum speeds of node movement. We primarily report in this paper data from simulations using a maximum node speed of 20 meters per second (average speed 10 meters per second), but also compare this to simulations using a maximum speed of 1 meter per second.
8 Conclusions
The area of ad hoc networking has been receiving increasing attention among researchers in recent years, as the available wireless networking and mobile computing hardware bases are now capable of supporting the promise of this technology. Over the past few years, a variety of new routing protocols targeted specifically at the ad hoc networking environment have been proposed, but little performance information on each protocol and no detailed performance comparison between the protocols has previously been available.
This paper makes contributions in two areas. First, we describe our modifications to the ns network simulator to provide an accurate simulation of the MAC and physical-layer behavior of the IEEE 802.11 wireless LAN standard, including a realistic wireless transmission channel model. This new simulation environment provides a powerful tool for evaluating ad hoc networking protocols and other wireless protocols and applications. Second, using this simulation environment, we present the results of a detailed packet-level simulation comparing four recent multi-hop wireless ad hoc network routing protocols. These protocols, DSDV, TORA, DSR, and AODV, cover a range of design choices, including periodic advertisements vs. ondemand route discovery, use of feedback from the MAC layer to indicate a failure to forward a packet to the next hop, and hop by hop routing vs. source routing. We simulated each protocol in ad hoc networks of 50 mobile nodes moving about and communicating with each other, and presented the results for a range of node mobility rates and movement speeds.
Each of the protocols studied performs well in some cases yet has certain drawbacks in others. DSDV performs quite predictably, delivering virtually all data packets when node mobility rate and movement speed are low, and failing to converge as node mobility increases. TORA, although the worst performer in our experiments in terms of routing packet overhead, still delivered over 90%of the packets in scenarios with 10 or 20 sources. At 30 sources, the network was unable to handle all of the traffic generated by the routing protocol and a significant fraction of data packets were dropped. The performance of DSR was very good at all mobility rates and movement speeds, although its use of source routing increases the number of routing overhead bytes required by the protocol. Finally, AODV performs almost as well as DSR at all mobility rates and movement speeds and accomplishes its goal of eliminating source routing overhead, but it still requires the transmission of many routing overhead packets and at high rates of node mobility is actually more expensive than DSR.
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A routing protocol based on node density for ad hoc networks
A routing protocol based on node density for ad hoc networks:
Abstract:
Ad hoc networks are a type of mobile network that functions without any fixed infrastructure. One of the weaknesses of ad hoc networks is that a route used between a source and a destination is likely to break during communication. To solve this problem, one approach consists of selecting routes whose nodes have the most stable behavior. Another strategy aims at improving the route repair procedure. This paper proposes a method for improving the success rate of local route repairs in mobile ad hoc networks. This method is based on the density of the nodes in the neighborhood of a route and on the availability of nodes in this neighborhood. Theoretical computation and simulation results show that the data packet loss rate decreased significantly compared to other methods which are well documented in the literature. In addition, the time required to complete a local route repair following a failure was significantly reduced.
1. Introduction
In recent years, we have witnessed considerable accomplishments in the design, development, and
deployment of wireless communication networks. Personal and mobile communications are made
possible by the convergence of several different technologies, specifically computer networking
protocols, wireless/mobile communication systems, distributed computing and Internet [6,14,25].
The mixed wired and wireless network topologies that are becoming so common, including fixed and
ad-hoc connection types, create the need to rationally exploit dynamically variable routing as a
function of network conditions [10]. At the same time, a phenomenal growth in data traffic and a wide range of new requirements of emerging applications call for new mechanisms for the control and management of communication networks [20]. The emergence of real-time applications and the widespread use of wireless and mobile devices have generated the need to provide quality of service (QoS) support in wireless and mobile networking environments [24].
A mobile ad hoc network (MANETs) is a mobile wireless network composed of several mobile
nodes, likely to communicate among themselves without the intervention of any centralized management or existing infrastructure. Hence, these mobile nodes must necessarily be able to cooperate to allow communication between themselves. Their main asset resides in the fact that they are not tributary to fixed installations.
2. Background and related work
A routing protocol is the mechanism by which user traffic is directed and transported through the
network from a source node to a destination node. The objectives include maximizing network performance from an application point of view, while minimizing the cost imposed on the network in
terms of capacity. QoS routing is an essential part of a QoS architecture. It is a routing mechanism
under which paths for flows are determined on the basis of some knowledge of the resources available in the network as well as on the QoS requirements of the flows or connections [24].
Resource reservation is necessary for providing guaranteed end-to-end performance for multimedia
applications. However, resource reservation is supported neither in the present Internet nor in mobile ad hoc networks. Also, the data packets sent by these applications could follow different paths and reach the destination out of order, which is not desirable. The current routing protocols used in IP networks are transparent to any particular QoS that different flows could require. As a result, routing decisions are made without referring to the QoS requirements of the flow. This means that flows are often routed over paths that are unable to support their requirements while alternate paths with sufficient resources exist. This will increase the call blocking probability. Hence, the goal of QoS routing algorithms is to find a path in the network that satisfies the given requirements [13,19,20].
.
.
.
.
When a mobile station A initiates communication with a mobile station B, A can easily reach B
through the information included in its routing table. In order to update a node’s routing table in
a context of dynamic topological changes, each node in the network periodically sends an update
message with routing information to its neighbors. Updates depend on two criteria: periodicity of
updates and events occurrence, such as the arrival of a new node in the neighborhood. Its goal is to
make it possible for a mobile host to locate any other unit in the network at any time. These updates
can be carried out either in a complete or an incremental way. The main weakness of this protocol
is the enormous bandwidth consumed by routing traffic control. Furthermore, DSDV is
slow; a mobile unit must wait to receive routing information to update the corresponding entry in
its routing table.
.
.
.
.
Global route repair: If a link failure were detected during the transmission of a packet from
source A to destination B, the node that detected the failure would return an error message to the
source. Then, the source initiates a new route discovery phase to find a new path between A and
B. This phase requires much time to complete and overloads the network with many routing messages. This results in bandwidth waste that is detrimental to the overall performance of the
network.
Local route repair: When a node detects a link failure, it does not systematically send an error
message to the source. First, it attempts to repair the route itself. It only sends an error message to
the source if this first attempt fails. Consider the case presented in Fig. 1. Step (a) represents the
route before failure detection. In step (b), Node D detects a link failure between D and G. According
to the local route repair procedure of step (c), D diffuses a route request (RREQ) packet which is
propagated across the network. When it receives this packet, J returns a route reply (RREP) packet
to D. This packet is forwarded by I, G and F. When D finally receives it, the route is repaired and communication can carry on.
5. Conclusion
The routing method presented in this paper aims to improve QoS management in MANETs by
taking into account the density of a node, defied as the number of mobile units available in the radio
range of the node. Our approach was based on a thorough analysis of the available mechanisms and tools that take into account quality of service in ad hoc networks. Then, we introduced the concept of
density and described how the network could exploit this information to improve the QoS offered.
The method can be subdivided into two parts: a selection mechanism that chooses the most robust
route available, and a mechanism to forecast the failure of a local route repair before it occurs.
The route selection mechanism described aims at selecting among several routes, the one whose
maintenance is the easiest to realize. The simulation results confirmed our hypothesis: the duration
of a route repair after a failure is improved. In addition, our mechanism drastically reduced the
data packet loss rate.
We also highlighted a situation where an attempt to repair a route locally could be useless and even harmful for the network: if the density around the node which initiated the local route repair is too low, the route repair procedure is bound to fail. In this situation, it is preferable to turn over directly to the source after having detected the failure of the link and to proceed to a global route repair. To validate our work, we implemented our improvements of the AODV protocol in Opnet Modeler. Then, we tested our protocol for a given configuration. The results obtained are encouraging: the data packet loss rate is strongly reduced compared to the initial version. In addition, the time required to complete a local route repair following a failure decreased significantly.
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Abstract:
Ad hoc networks are a type of mobile network that functions without any fixed infrastructure. One of the weaknesses of ad hoc networks is that a route used between a source and a destination is likely to break during communication. To solve this problem, one approach consists of selecting routes whose nodes have the most stable behavior. Another strategy aims at improving the route repair procedure. This paper proposes a method for improving the success rate of local route repairs in mobile ad hoc networks. This method is based on the density of the nodes in the neighborhood of a route and on the availability of nodes in this neighborhood. Theoretical computation and simulation results show that the data packet loss rate decreased significantly compared to other methods which are well documented in the literature. In addition, the time required to complete a local route repair following a failure was significantly reduced.
1. Introduction
In recent years, we have witnessed considerable accomplishments in the design, development, and
deployment of wireless communication networks. Personal and mobile communications are made
possible by the convergence of several different technologies, specifically computer networking
protocols, wireless/mobile communication systems, distributed computing and Internet [6,14,25].
The mixed wired and wireless network topologies that are becoming so common, including fixed and
ad-hoc connection types, create the need to rationally exploit dynamically variable routing as a
function of network conditions [10]. At the same time, a phenomenal growth in data traffic and a wide range of new requirements of emerging applications call for new mechanisms for the control and management of communication networks [20]. The emergence of real-time applications and the widespread use of wireless and mobile devices have generated the need to provide quality of service (QoS) support in wireless and mobile networking environments [24].
A mobile ad hoc network (MANETs) is a mobile wireless network composed of several mobile
nodes, likely to communicate among themselves without the intervention of any centralized management or existing infrastructure. Hence, these mobile nodes must necessarily be able to cooperate to allow communication between themselves. Their main asset resides in the fact that they are not tributary to fixed installations.
2. Background and related work
A routing protocol is the mechanism by which user traffic is directed and transported through the
network from a source node to a destination node. The objectives include maximizing network performance from an application point of view, while minimizing the cost imposed on the network in
terms of capacity. QoS routing is an essential part of a QoS architecture. It is a routing mechanism
under which paths for flows are determined on the basis of some knowledge of the resources available in the network as well as on the QoS requirements of the flows or connections [24].
Resource reservation is necessary for providing guaranteed end-to-end performance for multimedia
applications. However, resource reservation is supported neither in the present Internet nor in mobile ad hoc networks. Also, the data packets sent by these applications could follow different paths and reach the destination out of order, which is not desirable. The current routing protocols used in IP networks are transparent to any particular QoS that different flows could require. As a result, routing decisions are made without referring to the QoS requirements of the flow. This means that flows are often routed over paths that are unable to support their requirements while alternate paths with sufficient resources exist. This will increase the call blocking probability. Hence, the goal of QoS routing algorithms is to find a path in the network that satisfies the given requirements [13,19,20].
.
.
.
.
When a mobile station A initiates communication with a mobile station B, A can easily reach B
through the information included in its routing table. In order to update a node’s routing table in
a context of dynamic topological changes, each node in the network periodically sends an update
message with routing information to its neighbors. Updates depend on two criteria: periodicity of
updates and events occurrence, such as the arrival of a new node in the neighborhood. Its goal is to
make it possible for a mobile host to locate any other unit in the network at any time. These updates
can be carried out either in a complete or an incremental way. The main weakness of this protocol
is the enormous bandwidth consumed by routing traffic control. Furthermore, DSDV is
slow; a mobile unit must wait to receive routing information to update the corresponding entry in
its routing table.
.
.
.
.
Global route repair: If a link failure were detected during the transmission of a packet from
source A to destination B, the node that detected the failure would return an error message to the
source. Then, the source initiates a new route discovery phase to find a new path between A and
B. This phase requires much time to complete and overloads the network with many routing messages. This results in bandwidth waste that is detrimental to the overall performance of the
network.
Local route repair: When a node detects a link failure, it does not systematically send an error
message to the source. First, it attempts to repair the route itself. It only sends an error message to
the source if this first attempt fails. Consider the case presented in Fig. 1. Step (a) represents the
route before failure detection. In step (b), Node D detects a link failure between D and G. According
to the local route repair procedure of step (c), D diffuses a route request (RREQ) packet which is
propagated across the network. When it receives this packet, J returns a route reply (RREP) packet
to D. This packet is forwarded by I, G and F. When D finally receives it, the route is repaired and communication can carry on.
5. Conclusion
The routing method presented in this paper aims to improve QoS management in MANETs by
taking into account the density of a node, defied as the number of mobile units available in the radio
range of the node. Our approach was based on a thorough analysis of the available mechanisms and tools that take into account quality of service in ad hoc networks. Then, we introduced the concept of
density and described how the network could exploit this information to improve the QoS offered.
The method can be subdivided into two parts: a selection mechanism that chooses the most robust
route available, and a mechanism to forecast the failure of a local route repair before it occurs.
The route selection mechanism described aims at selecting among several routes, the one whose
maintenance is the easiest to realize. The simulation results confirmed our hypothesis: the duration
of a route repair after a failure is improved. In addition, our mechanism drastically reduced the
data packet loss rate.
We also highlighted a situation where an attempt to repair a route locally could be useless and even harmful for the network: if the density around the node which initiated the local route repair is too low, the route repair procedure is bound to fail. In this situation, it is preferable to turn over directly to the source after having detected the failure of the link and to proceed to a global route repair. To validate our work, we implemented our improvements of the AODV protocol in Opnet Modeler. Then, we tested our protocol for a given configuration. The results obtained are encouraging: the data packet loss rate is strongly reduced compared to the initial version. In addition, the time required to complete a local route repair following a failure decreased significantly.
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A novel location estimation based on pattern matching algorithm in underwater environments
A novel location estimation based on pattern matching algorithm in underwater environments:
abstract:
In this paper, we present a novel approach based on pattern recognition to treat the underwater localization. The goal is to achieve underwater localization by the pattern matching algorithm. It should be noted that the reflected signals in underwater environments do not affect our location estimation. Therefore, the underwater localization in this study is straightforward and efficient by using the pattern matching algorithm. We exploit the maximum likelihood (ML) to perform our study. Initially, the underwater signals are collected by the sound receiver at some sampling locations. These signals are suitably processed by the ML models and are stored in database. The test location in real-time is estimated through the database. Experimental results show that good accuracy of positioning can be obtained by proposed schemes. The proposed localization schemes can be applied to many other applications in underwater environments.
1. Introduction
Source localization plays an important role in radar, sonar, geological exploration, and wireless communication. In general, it can involve the joint estimation of frequencies, Doppler shifts, directions of arrival (DOA) and time/time difference of arrival. The geometric approaches such as joint estimation of DOA and time delay are based on transmitting a known signal and estimating DOA from time delay of the reflected signal [1–5]. Although the above approaches show good performance, it is usually difficult to deal with the reflected signals and is then affected by multi-path fading effect.
This motivates us to develop an alternative approach for underwater localization.
In this paper, we present a novel approach for underwater localization based on WLAN (wireless local area network) location fingerprinting [6–8]. The fingerprinting based localization behaves
like the process of pattern matching, which is divided into two stages, i.e., the offline and online stages. During the offline stage, signals collected by the sound receiver at the sampling locations
are stored to constitute the database. During the online stage, the signal in real-time is measured and the location can be estimated based on the database built in the offline stage.
The aim of our study is to achieve underwater localization by the pattern matching algorithm. Note that the reflected signals do not affect our location estimation. The underwater localization in this study is straightforward and efficient because the pattern matching algorithm does not require pure direct-radiation signals.
Experimental results show that the good accuracy of positioning is obtained by proposed schemes. Therefore, the innovation of underwater localization can be accomplished based on our approach.
In Section 2, the theoretical formulations are given. Experimental setup and results are given in Section 3. Finally, the conclusion is given in Section 4
2. Formulations
2.1. Preparation for signal collection
For transmitting and receiving signals in the underwater environment, in this study a fish finder is adopted as the sound projector to transmit the AM (amplitude modulation) signals and a hydrophone is adopted as the sound receiver. During the offline stage, the AM signals in the underwater environment are collected by the sound receiver at M reference locations.We then convert these AM signals from time-domain to frequency-domain through Fast Fourier Transform (FFT) and the magnitudes of spectrum at the specific frequency from each sound projector are stored to constitute the database. During the online stage, the actual location coordinate of the sound receiver can then be estimated based on the database built in the offline stage by pattern matching algorithms.
3. Results
3.1. Experimental setup
In this section, we begin with a description of our experimental setup and experimental results. We perform our experiment in the towing tank of the Systems and Naval Mechatronic Engineering
Department of National Cheng Kung University, Tainan, Taiwan, as shown in Fig. 1. The dimension of the towing tank is 175 m(L) 8 m(W) 4 m(D) meters. The experimental setup in the towing tank is shown in Fig. 2. We adopt two GARMIN-340C fish finders as the physical sound projectors and one B&K 8106 hydrophone as the sound receiver. Both of the two fish finders transmit AM signals. The center frequencies of the two transmitters are 50 kHz and 200 kHz, respectively. The procedures of signal collection stage are shown in Fig. 3. An NI-6251 DAQ (data acquisition) card is utilized to gather the AM signal from each sound projector. In Fig. 3, the notebook PC is installed with the LabVIEW-8.2 software for data acquisition and instrument control. The received signals are converted from time-domain to frequency-domain through FFT. Note that the above signal acquisition and processing are implemented by the commercial software LabVIEW-8.2.
In that software, the length of FFT is 262144, the type of time window is Hanning, and the sampling frequency is chosen as 500 kHz. We collect 100 samples of spectrum magnitudes at 84 (M = 84) reference locations separated by 1 m, as shown in Fig. 2.
Then we adopt the same procedure [13] to partition the data set into 2 independent groups, 10 samples for testing and 90 samples for training. In the parametric training, we estimate the means and
standard deviations of the likelihood functions from the training data and these statistical parameters are stored in the database.
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abstract:
In this paper, we present a novel approach based on pattern recognition to treat the underwater localization. The goal is to achieve underwater localization by the pattern matching algorithm. It should be noted that the reflected signals in underwater environments do not affect our location estimation. Therefore, the underwater localization in this study is straightforward and efficient by using the pattern matching algorithm. We exploit the maximum likelihood (ML) to perform our study. Initially, the underwater signals are collected by the sound receiver at some sampling locations. These signals are suitably processed by the ML models and are stored in database. The test location in real-time is estimated through the database. Experimental results show that good accuracy of positioning can be obtained by proposed schemes. The proposed localization schemes can be applied to many other applications in underwater environments.
1. Introduction
Source localization plays an important role in radar, sonar, geological exploration, and wireless communication. In general, it can involve the joint estimation of frequencies, Doppler shifts, directions of arrival (DOA) and time/time difference of arrival. The geometric approaches such as joint estimation of DOA and time delay are based on transmitting a known signal and estimating DOA from time delay of the reflected signal [1–5]. Although the above approaches show good performance, it is usually difficult to deal with the reflected signals and is then affected by multi-path fading effect.
This motivates us to develop an alternative approach for underwater localization.
In this paper, we present a novel approach for underwater localization based on WLAN (wireless local area network) location fingerprinting [6–8]. The fingerprinting based localization behaves
like the process of pattern matching, which is divided into two stages, i.e., the offline and online stages. During the offline stage, signals collected by the sound receiver at the sampling locations
are stored to constitute the database. During the online stage, the signal in real-time is measured and the location can be estimated based on the database built in the offline stage.
The aim of our study is to achieve underwater localization by the pattern matching algorithm. Note that the reflected signals do not affect our location estimation. The underwater localization in this study is straightforward and efficient because the pattern matching algorithm does not require pure direct-radiation signals.
Experimental results show that the good accuracy of positioning is obtained by proposed schemes. Therefore, the innovation of underwater localization can be accomplished based on our approach.
In Section 2, the theoretical formulations are given. Experimental setup and results are given in Section 3. Finally, the conclusion is given in Section 4
2. Formulations
2.1. Preparation for signal collection
For transmitting and receiving signals in the underwater environment, in this study a fish finder is adopted as the sound projector to transmit the AM (amplitude modulation) signals and a hydrophone is adopted as the sound receiver. During the offline stage, the AM signals in the underwater environment are collected by the sound receiver at M reference locations.We then convert these AM signals from time-domain to frequency-domain through Fast Fourier Transform (FFT) and the magnitudes of spectrum at the specific frequency from each sound projector are stored to constitute the database. During the online stage, the actual location coordinate of the sound receiver can then be estimated based on the database built in the offline stage by pattern matching algorithms.
3. Results
3.1. Experimental setup
In this section, we begin with a description of our experimental setup and experimental results. We perform our experiment in the towing tank of the Systems and Naval Mechatronic Engineering
Department of National Cheng Kung University, Tainan, Taiwan, as shown in Fig. 1. The dimension of the towing tank is 175 m(L) 8 m(W) 4 m(D) meters. The experimental setup in the towing tank is shown in Fig. 2. We adopt two GARMIN-340C fish finders as the physical sound projectors and one B&K 8106 hydrophone as the sound receiver. Both of the two fish finders transmit AM signals. The center frequencies of the two transmitters are 50 kHz and 200 kHz, respectively. The procedures of signal collection stage are shown in Fig. 3. An NI-6251 DAQ (data acquisition) card is utilized to gather the AM signal from each sound projector. In Fig. 3, the notebook PC is installed with the LabVIEW-8.2 software for data acquisition and instrument control. The received signals are converted from time-domain to frequency-domain through FFT. Note that the above signal acquisition and processing are implemented by the commercial software LabVIEW-8.2.
In that software, the length of FFT is 262144, the type of time window is Hanning, and the sampling frequency is chosen as 500 kHz. We collect 100 samples of spectrum magnitudes at 84 (M = 84) reference locations separated by 1 m, as shown in Fig. 2.
Then we adopt the same procedure [13] to partition the data set into 2 independent groups, 10 samples for testing and 90 samples for training. In the parametric training, we estimate the means and
standard deviations of the likelihood functions from the training data and these statistical parameters are stored in the database.
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A Mobile Host Protocol Supporting Route Optimization and Authentication
A Mobile Host Protocol Supporting Route Optimization and Authentication:
Abstract
Host mobility is becoming an important issue due to the recent proliferation of notebook and
palmtop computers, the development of wireless network interfaces, and the growth in global
internetworking. This paper describes the design and implementation of a mobile host protocol,
called the Internet Mobile Host Protocol (IMHP), that is compatible with the TCP/IP protocol
suite, and allows a mobile host to move around the Internet without changing its identity. In
particular, IMHP provides host mobility over both the local and wide area, while remaining
transparent to the user and to other hosts communicating with the mobile host.
IMHP features route optimization and integrated authentication of all management packets.
Route optimization allows a node to cache the location of a mobile host and to send future
packets directly to that mobile host. By authenticating all management packets, IMHP guards
against possible attacks on packet routing to mobile hosts, including the interception or redirection
of arbitrary packets within the network. A simple new authentication mechanism is
introduced that preserves the level of security found in the Internet today, while accommodating
the transition to stronger authentication based on public key cryptography or shared keys that
may either be manually administered or provided by a future Internet key management protocol.
1. Introduction
In recent years, computers have become increasingly compact and very powerful. At the same time,
connectivity to the global network is becoming widespread, and with the recent introductions of commercial wireless network interfaces, users nowhave a real opportunity for continuous network connectivity wherever they may happen to be working.
Unfortunately, existing internetwork protocols do not easily accommodate mobile hosts. Host movement today, even if only between local subnets, involves slow, manual, error prone host and network reconfiguration procedures that a typical user does not have the skills or desire to carry out. Moreover, even when this movement process is successfully performed, the mobile host loses its former identity in terms of its host network address, and any network applications on the mobile host and on other hosts communicating with it must usually be restarted.
A number of mobile host protocol proposals that are compatible with the TCP/IP protocol suite have
been proposed [1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 20]. These proposals each retain the home IP address of a mobile host for use in identifying it at the network level, but also in some way associate a second IP address with the mobile host to indicate the mobile host’s current location. Each of the proposals has a number of advantages and disadvantages, some of which are discussed in [10, 11]. Proposals have also been made for supporting host mobility in an OSI environment [12].
Many of these mobile host protocols provide some form of route optimization that allows other nodes
(hosts or routers) to learn the current location of a mobile host, either from management packets or from an IP option attached to data packets. Nodes learning the location of a mobile host in this way can cache this location and then send later packets for the mobile host directly to that location. However, none of these proposals (except [6, 7]) provide a mechanism for the node learning a mobile host’s current location to authenticate it. The result is that a malicious host anywhere in the Internet could easily send forged management or data packets in order to intercept or redirect packets destined to a mobile host. In today’s Internet, only hosts connected to the normal packet routing path can cause similar disruption [18].
The only previous mobile host protocol proposal that provides for extensions to guard against this type of attack [6, 7] compromises its wide area operation to maintain its authentication mechanisms. The protocol does not support route optimization in the wide area, but rather normally forces all packets addressed to a mobile host connected away from its home network to pass through the mobile host’s home network before being forwarded to the mobile host at its current location. This non-optimal routing results in reduced
performance transparency to the user as a result of increased network overhead for packets sent to the
mobile host.
This paper describes the design and implementation of a new mobile host protocol, called the Internet
Mobile Host Protocol (IMHP), that features both route optimization and integrated authentication of all management packets. IMHP operates equally well in both the local and the wide area, and provides
performance transparency and operational transparency to the user. IMHP introduces an optional new
mechanism for providing simple authentication of management packets that preserves the level of security found in today’s Internet [18], and is designed to accommodate stronger authentication based on public key cryptography or on shared keys that may either be manually administered or provided by a future Internet key management protocol. IMHP thus provides effective authentication today, while providing a good migration path to stronger authentication when available. A more detailed specification of IMHP is found in [15].
Section 2 of this paper describes the necessary IMHP infrastructure. Section 3 discusses the IMHP
authentication procedures and their operation in the protocol. In Section 4, the rules used by each node when forwarding packets are presented, and in Section 5, the means by which nodes learn and cache the location of mobile hosts are described. Section 6 discusses additional features of the protocol, and Section 7 details a number of examples of the operation of the protocol. Section 8 describes an implementation of IMHP, and Section 9 presents conclusions.
2. IMHP Infrastructure
The IMHP architecture includes four functional entities: mobile hosts, local agents, cache agents, and home agents. This section defines each entity and describes its basic operation. Although defined separately, the functionality of several of these entities may be combined within a single node.
Mobile Host
A mobile host is a normal host with additional software that allows it to move through the network in a manner transparent to the user and to software above the network routing layer within the host. A mobile host is assigned a constant, unique home address that belongs to a home network in the same way as any other host. Correspondent hosts (either mobile or stationary) use the home address of a mobile host in sending packets to the mobile host regardless of the mobile host’s current location.
Local Agent When a mobile host connects to the network, it must be able to determine that it has moved to a new network and must identify a local agent connected to the new local network with which to register. The first function may be performed with network data link layer support, if available, or may use an advertisement and solicitation protocol that is defined by IMHP. Registration is performed using a registration protocol that is defined by IMHP.
Each local agent maintains a visitor list identifying all mobile hosts currently registered with this local agent. A local agent uses the visitor list to forward packets it receives addressed to these mobile hosts to the local network to which the mobile host is connected. A local agent times out the visitor list entry for a mobile host after a lifetime period negotiated with the mobile host during the registration process; once a visitor list entry times out, it is deleted by the local agent. In order to maintain uninterrupted service from its current local agent, a mobile host must re-register with its local agent within this lifetime period.
A local agent, during the registration process, provides the mobile host with a care-of address, which
is generally the local agent’s own address, that defines the location of the mobile host. The combination of a mobile host’s home address and care-of address is known as a binding. If the care-of address and home address elements of a binding are the same, then the mobile host is assumed to be connected to its home network.
Whenever a mobile host registers with a local agent, the mobile host must arrange to reliably notify any previous local agents that might still have a visitor list entry for it that this mobile host has moved. Each previous local agent uses this notification to delete any visitor list entry held for the mobile host, ensuring that the local agent does not continue to forward packets to a local network when the mobile host has moved elsewhere in the network. The notification to a previous local agent must be periodically retransmitted (with a back-off mechanism) either until it is acknowledged or until the previous local agent would have timed out the visitor list entry it held for the mobile host.
A mobile host will typically use the local agent with which it is currently registered as a default router.
However, if the mobile host is connected to a local network, such as its home network, for which it is able to obtain better routing information, it may use any local router.
.
.
.
.
.
Mobile Host to Home Agent Authentication
When a mobile host in a home agent’s home list attempts to register, the home agent must be able to
authenticate the binding it receives for the mobile host. Registration with the home agent is a particularly important transaction, because the home agent in the IMHP architecture must always know the current binding of each mobile hosts in its home list. Similarly, a mobile host must be able to authenticate a reply or other management packet it receives from its home agent. This authentication is achieved in IMHP by including an authenticator based on a shared secret in all management protocol messages between a mobile host and its home agent (Figure 2 (a)).
This shared secret allows a strong degree of authentication between the mobile host and its home
agent. The base level of authentication defined by IMHP in this case involves performing a checksum
of the important fields in the registration packet (or reply) and the shared secret, using the MD5 one way cryptographic hash function [17]. The resulting checksum is sent as the authenticator in registration
messages between the mobile host and its home agent. The possibility of replay attacks is minimized by
including a monotonically increasing sequence number in registration and reply packets.
Administration of a shared secret between a mobile host and its home agent does not require any
network key management infrastructure, since the mobile host and its home agent are both generally owned by the same organization (they are both assigned home addresses within the same IP network owned by that organization). The shared secret may be set manually, for example, when the mobile host is at home, at the same time as other configuration of the mobile host and home agent is being performed.
Authenticating a Visitor List Entry
A cache agent establishes a location cache entry for a mobile host only when it obtains an authenticated binding for the host. Thus, a location cache entry for a mobile host may always be used to forward packets to that host. In contrast, a local agent may create a visitor list entry for a mobile host as soon as the mobile host registers with it. However, a visitor list entry may not be used to forward packets unless they were tunneled to the local agent, until after the local agent has authenticated the identity of the registered mobile host. This restriction reduces the possibility of packets being delivered incorrectly by a local agent to a malicious host. An unauthenticated visitor list entry may be used to forward packets tunneled to the local agent, as the source of the tunnel may be assumed to have an authenticated binding for the mobile host, since otherwise the source would not have tunneled the packet. This mechanism ensures that existing connections may continue, still using a close to optimal route, as soon as a mobile host registers with a new local agent and notifies its previous local agents.
A local agent authenticates a visitor list entry by confirming that the mobile host’s home agent has a
binding indicating that the mobile host is registered with this local agent. This authentication can be done using the mechanisms previously described. When the lifetime indicated with this binding expires, the visitor list entry must be re-authenticated. The visitor list entry may also be authenticated as part of the registration process by combining the same type of authentication mechanism into the registration request and reply messages as is used in the general procedure for obtaining an authenticated binding.
The assumption that a local agent may use an unauthenticated visitor list entry to forward a tunneled
packet introduces a minor security risk. Suppose a cache agent has a location cache entry that indicates that a mobile host is registered with a particular local agent but, in fact, the mobile host is located elsewhere or has disappeared from the network entirely. Also, assume that a malicious host pretends to be the mobile host and registers with the local agent. Any packets the cache agent tunnels to the local agent will be forwarded to the malicious host. Fortunately, this risk is limited in duration, in the worst case, by the lifetime of the location cache entry in the cache agent.
9. Conclusion
This paper has described the main features, operations, and implementation of a new mobile host protocol, called the Internet Mobile Host Protocol (IMHP), which achieves transparent mobility featuring route
optimization and integrated authentication of all management packets. IMHP is designed to take advantage of strong authentication mechanisms when the necessary infrastructure becomes available in the Internet and yet provides a simple authentication mechanism that can be used today that preserves the current level of security in the Internet.
In the short term, IMHP can provide a valuable service to the majority of users on the Internet who either do not have the need for strong authentication mechanisms beyond that provided by the Internet today or are unwilling to pay the price of less than optimal routing in the meantime in the absence of a key distribution infrastructure. In the long term, as key management systems become available, IMHP can provide route optimization for all packets from any correspondent host to any mobile host, regardless of a mobile host’s location, in a manner that will satisfy future authentication requirements.
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Abstract
Host mobility is becoming an important issue due to the recent proliferation of notebook and
palmtop computers, the development of wireless network interfaces, and the growth in global
internetworking. This paper describes the design and implementation of a mobile host protocol,
called the Internet Mobile Host Protocol (IMHP), that is compatible with the TCP/IP protocol
suite, and allows a mobile host to move around the Internet without changing its identity. In
particular, IMHP provides host mobility over both the local and wide area, while remaining
transparent to the user and to other hosts communicating with the mobile host.
IMHP features route optimization and integrated authentication of all management packets.
Route optimization allows a node to cache the location of a mobile host and to send future
packets directly to that mobile host. By authenticating all management packets, IMHP guards
against possible attacks on packet routing to mobile hosts, including the interception or redirection
of arbitrary packets within the network. A simple new authentication mechanism is
introduced that preserves the level of security found in the Internet today, while accommodating
the transition to stronger authentication based on public key cryptography or shared keys that
may either be manually administered or provided by a future Internet key management protocol.
1. Introduction
In recent years, computers have become increasingly compact and very powerful. At the same time,
connectivity to the global network is becoming widespread, and with the recent introductions of commercial wireless network interfaces, users nowhave a real opportunity for continuous network connectivity wherever they may happen to be working.
Unfortunately, existing internetwork protocols do not easily accommodate mobile hosts. Host movement today, even if only between local subnets, involves slow, manual, error prone host and network reconfiguration procedures that a typical user does not have the skills or desire to carry out. Moreover, even when this movement process is successfully performed, the mobile host loses its former identity in terms of its host network address, and any network applications on the mobile host and on other hosts communicating with it must usually be restarted.
A number of mobile host protocol proposals that are compatible with the TCP/IP protocol suite have
been proposed [1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 20]. These proposals each retain the home IP address of a mobile host for use in identifying it at the network level, but also in some way associate a second IP address with the mobile host to indicate the mobile host’s current location. Each of the proposals has a number of advantages and disadvantages, some of which are discussed in [10, 11]. Proposals have also been made for supporting host mobility in an OSI environment [12].
Many of these mobile host protocols provide some form of route optimization that allows other nodes
(hosts or routers) to learn the current location of a mobile host, either from management packets or from an IP option attached to data packets. Nodes learning the location of a mobile host in this way can cache this location and then send later packets for the mobile host directly to that location. However, none of these proposals (except [6, 7]) provide a mechanism for the node learning a mobile host’s current location to authenticate it. The result is that a malicious host anywhere in the Internet could easily send forged management or data packets in order to intercept or redirect packets destined to a mobile host. In today’s Internet, only hosts connected to the normal packet routing path can cause similar disruption [18].
The only previous mobile host protocol proposal that provides for extensions to guard against this type of attack [6, 7] compromises its wide area operation to maintain its authentication mechanisms. The protocol does not support route optimization in the wide area, but rather normally forces all packets addressed to a mobile host connected away from its home network to pass through the mobile host’s home network before being forwarded to the mobile host at its current location. This non-optimal routing results in reduced
performance transparency to the user as a result of increased network overhead for packets sent to the
mobile host.
This paper describes the design and implementation of a new mobile host protocol, called the Internet
Mobile Host Protocol (IMHP), that features both route optimization and integrated authentication of all management packets. IMHP operates equally well in both the local and the wide area, and provides
performance transparency and operational transparency to the user. IMHP introduces an optional new
mechanism for providing simple authentication of management packets that preserves the level of security found in today’s Internet [18], and is designed to accommodate stronger authentication based on public key cryptography or on shared keys that may either be manually administered or provided by a future Internet key management protocol. IMHP thus provides effective authentication today, while providing a good migration path to stronger authentication when available. A more detailed specification of IMHP is found in [15].
Section 2 of this paper describes the necessary IMHP infrastructure. Section 3 discusses the IMHP
authentication procedures and their operation in the protocol. In Section 4, the rules used by each node when forwarding packets are presented, and in Section 5, the means by which nodes learn and cache the location of mobile hosts are described. Section 6 discusses additional features of the protocol, and Section 7 details a number of examples of the operation of the protocol. Section 8 describes an implementation of IMHP, and Section 9 presents conclusions.
2. IMHP Infrastructure
The IMHP architecture includes four functional entities: mobile hosts, local agents, cache agents, and home agents. This section defines each entity and describes its basic operation. Although defined separately, the functionality of several of these entities may be combined within a single node.
Mobile Host
A mobile host is a normal host with additional software that allows it to move through the network in a manner transparent to the user and to software above the network routing layer within the host. A mobile host is assigned a constant, unique home address that belongs to a home network in the same way as any other host. Correspondent hosts (either mobile or stationary) use the home address of a mobile host in sending packets to the mobile host regardless of the mobile host’s current location.
Local Agent When a mobile host connects to the network, it must be able to determine that it has moved to a new network and must identify a local agent connected to the new local network with which to register. The first function may be performed with network data link layer support, if available, or may use an advertisement and solicitation protocol that is defined by IMHP. Registration is performed using a registration protocol that is defined by IMHP.
Each local agent maintains a visitor list identifying all mobile hosts currently registered with this local agent. A local agent uses the visitor list to forward packets it receives addressed to these mobile hosts to the local network to which the mobile host is connected. A local agent times out the visitor list entry for a mobile host after a lifetime period negotiated with the mobile host during the registration process; once a visitor list entry times out, it is deleted by the local agent. In order to maintain uninterrupted service from its current local agent, a mobile host must re-register with its local agent within this lifetime period.
A local agent, during the registration process, provides the mobile host with a care-of address, which
is generally the local agent’s own address, that defines the location of the mobile host. The combination of a mobile host’s home address and care-of address is known as a binding. If the care-of address and home address elements of a binding are the same, then the mobile host is assumed to be connected to its home network.
Whenever a mobile host registers with a local agent, the mobile host must arrange to reliably notify any previous local agents that might still have a visitor list entry for it that this mobile host has moved. Each previous local agent uses this notification to delete any visitor list entry held for the mobile host, ensuring that the local agent does not continue to forward packets to a local network when the mobile host has moved elsewhere in the network. The notification to a previous local agent must be periodically retransmitted (with a back-off mechanism) either until it is acknowledged or until the previous local agent would have timed out the visitor list entry it held for the mobile host.
A mobile host will typically use the local agent with which it is currently registered as a default router.
However, if the mobile host is connected to a local network, such as its home network, for which it is able to obtain better routing information, it may use any local router.
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Mobile Host to Home Agent Authentication
When a mobile host in a home agent’s home list attempts to register, the home agent must be able to
authenticate the binding it receives for the mobile host. Registration with the home agent is a particularly important transaction, because the home agent in the IMHP architecture must always know the current binding of each mobile hosts in its home list. Similarly, a mobile host must be able to authenticate a reply or other management packet it receives from its home agent. This authentication is achieved in IMHP by including an authenticator based on a shared secret in all management protocol messages between a mobile host and its home agent (Figure 2 (a)).
This shared secret allows a strong degree of authentication between the mobile host and its home
agent. The base level of authentication defined by IMHP in this case involves performing a checksum
of the important fields in the registration packet (or reply) and the shared secret, using the MD5 one way cryptographic hash function [17]. The resulting checksum is sent as the authenticator in registration
messages between the mobile host and its home agent. The possibility of replay attacks is minimized by
including a monotonically increasing sequence number in registration and reply packets.
Administration of a shared secret between a mobile host and its home agent does not require any
network key management infrastructure, since the mobile host and its home agent are both generally owned by the same organization (they are both assigned home addresses within the same IP network owned by that organization). The shared secret may be set manually, for example, when the mobile host is at home, at the same time as other configuration of the mobile host and home agent is being performed.
Authenticating a Visitor List Entry
A cache agent establishes a location cache entry for a mobile host only when it obtains an authenticated binding for the host. Thus, a location cache entry for a mobile host may always be used to forward packets to that host. In contrast, a local agent may create a visitor list entry for a mobile host as soon as the mobile host registers with it. However, a visitor list entry may not be used to forward packets unless they were tunneled to the local agent, until after the local agent has authenticated the identity of the registered mobile host. This restriction reduces the possibility of packets being delivered incorrectly by a local agent to a malicious host. An unauthenticated visitor list entry may be used to forward packets tunneled to the local agent, as the source of the tunnel may be assumed to have an authenticated binding for the mobile host, since otherwise the source would not have tunneled the packet. This mechanism ensures that existing connections may continue, still using a close to optimal route, as soon as a mobile host registers with a new local agent and notifies its previous local agents.
A local agent authenticates a visitor list entry by confirming that the mobile host’s home agent has a
binding indicating that the mobile host is registered with this local agent. This authentication can be done using the mechanisms previously described. When the lifetime indicated with this binding expires, the visitor list entry must be re-authenticated. The visitor list entry may also be authenticated as part of the registration process by combining the same type of authentication mechanism into the registration request and reply messages as is used in the general procedure for obtaining an authenticated binding.
The assumption that a local agent may use an unauthenticated visitor list entry to forward a tunneled
packet introduces a minor security risk. Suppose a cache agent has a location cache entry that indicates that a mobile host is registered with a particular local agent but, in fact, the mobile host is located elsewhere or has disappeared from the network entirely. Also, assume that a malicious host pretends to be the mobile host and registers with the local agent. Any packets the cache agent tunnels to the local agent will be forwarded to the malicious host. Fortunately, this risk is limited in duration, in the worst case, by the lifetime of the location cache entry in the cache agent.
9. Conclusion
This paper has described the main features, operations, and implementation of a new mobile host protocol, called the Internet Mobile Host Protocol (IMHP), which achieves transparent mobility featuring route
optimization and integrated authentication of all management packets. IMHP is designed to take advantage of strong authentication mechanisms when the necessary infrastructure becomes available in the Internet and yet provides a simple authentication mechanism that can be used today that preserves the current level of security in the Internet.
In the short term, IMHP can provide a valuable service to the majority of users on the Internet who either do not have the need for strong authentication mechanisms beyond that provided by the Internet today or are unwilling to pay the price of less than optimal routing in the meantime in the absence of a key distribution infrastructure. In the long term, as key management systems become available, IMHP can provide route optimization for all packets from any correspondent host to any mobile host, regardless of a mobile host’s location, in a manner that will satisfy future authentication requirements.
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