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Efficient expanding ring search for MANETs.

1. Introduction

Wireless connectivity was a dream but with the evolution of mobile devices such as notebooks and PDAs, wireless networks appeared and this duly become a reality. Wireless networks might be infrastructure-oriented, such as the access point dependent networks [16] or infrastructure-less such as Mobile Ad hoc NETworks (MANETs) [8], [16], [22]. Some of the dominant initial motivations for MANET technology came from military applications in environments lacking infrastructure. However, while such applications remain important, MANET research has diversified into areas such as disaster relief, sensors networks, and personal area networks [22].

The design of an efficient and reliable routing strategy is a very challenging problem due to the limited resources in MANETs [16]. Many multi-hop routing protocols have been proposed and investigated in the literature as in [2], [12], [14], [19], [20], [23]. MANETs routing protocols is divided into three categories [1], [19]: proactive, reactive, and hybrid. In proactive routing protocols (table-driven), the routes to all destinations (or parts of the network) are determined statically at the start up then maintained using a periodic route update process, An example of this class is the Optimised Link State Routing Protocol (OLSR) [2]. However, in reactive routing protocols (on-demand), routes are determined dynamically when they are required by the source using a route discovery process. Its routing overhead is lower than the proactive routing protocols if the network size is relatively small [9]. In on-demand routing, when a source node needs to send messages to an unseen destination; it initiates a broadcast-based route discovery process to look for one or more possible paths to the destination. Examples of this class are the Dynamic Source Routing (DSR) [14] and Ad Hoc On Demand Distance Vector (AODV) [20]. Finally, hybrid routing protocols combine the basic properties of the first two classes of protocols; so they are both reactive and proactive in nature as in Zone Routing Protocol (ZRP) [12].

On-demand routing algorithms search for the desired route only when needed. Since the nodes have no periodical tasks that covers the network, on-demand routing protocols are known to use low bandwidth and consume less power which makes them appealing for MANETs scenarios [1]. When a source node needs to send packets to an unseen destination, it initiates a route discovery process looking for a route, or several routes, to that destination using broadcasting techniques. After discovering the needed route(s), the source will start transmitting data packets using the route(s) discovered.

DSR and AODV algorithms both use broadcasting for route discovery process. These protocols may depend on a simple flooding as a form of broadcasting, where each node may receive multiple copies of a unique route request packet and retransmit it exactly once. In simple flooding, when a source node needs a route to a particular destination it first searches its routing table where any seen or overheard route might have been stored for future use; if not found, a route discovery process is started using any form of broadcasting. In the simple flooding, route requests keep propagating until the time to live (TTL) field reaches zero or the whole connected network is covered. Unfortunately, the simple flooding leads to redundancy that will highly congest the network and increases the chances of collision: these combined are known as the broadcast storm problem [24]. Moreover, flooding consumes a lot of network resources such as bandwidth and power which can be reduced by stopping the route request as soon as the needed route discovered as a way of controlling the flooding.

The route discovery process often floods the network with route request packets looking for routes throughout the network. Unfortunately, the route request will keep spreading even after a route has been found which will congest the network and waste its resources. The route discovery process can be improved by minimising such overhead and reducing or better stopping the unnecessary propagation of route request packets after discovering the route.

In this paper, a new route discovery algorithm, Blocking-ERS-Plus, based on Blocking-ERS [17] is proposed. It works by broadcasting a control packet called chase packets by the source node after receiving a route reply. Chase packet is a control packet that is broadcasted after finding the desired route to stop the fulfilled route request from further propagation. The chase packet is allowed to cover as much as it needs. Propagation of the route requests is deliberately delayed to provide the chasing mechanism with an opportunity to stop the propagation of the fulfilled route request to minimise network congestion.

The rest of this report is organised as follows: Section 2 presents the related work and section 3 shades some light on Blocking-ERS. Section 4 presents our newly proposed algorithm, Blocking-ERS-Plus, based on Blocking-ERS, evaluates the performance, conducts a comparative study with Blocking-ERS and AODV [20], and describes the simulation environment and observation. Finally, Section 5 concludes this study.

2. Related Work

Limited Broadcasting [11] aims at improving the route discovery process without the need for historical or location information. It achieves this by employing chase packets approach. The algorithm broadcasts a route request using only V of the channel time while the rest of the channel time is dedicated to transmit the route reply and broadcast the chase packet after finding the route. The main purpose of broadcasting a chase packet is basically to stop the route request packet from further propagation after finding the needed route. The main deficiency of this algorithm is that it always favours the chase over the route requests. For instance, if there is a route request ready to be broadcasted by any node it will be given a V of the time to be sent. Doing so would delay all route requests for the source node in hand as well as other source nodes that might be trying to send route request yet getting low priority due to time division between route requests, chase, and reply packets.

In the Limited Broadcasting algorithm, the sender is responsible for initiating the chase packet which may then experience an extra delay in catching up with the route request. This shortcoming has been addressed in Limited-hop Broadcast Algorithm (LHBA) [25]; it solves this problem by initiating the chase packets by any node that discovers a route. However, this algorithm may congest the network with traffic causing a storm problem of chase packets also it is unsuitable for multi-path discovery. It uses chase packets to optimise the route request by reducing the redundancy of the route request in an effort to alleviate the broadcast storm problem. In this algorithm the chase packet is broadcasted to K hop neighbours to free this part of the network from the fulfilled route request.

Traffic locality oriented route discovery algorithm with chase packets (TLRDA-C) [3], [4] improves route discovery process in on-demand routing protocols by defining a local neighbourhood that includes the most likely destinations for each given source node. It broadcasts any route request travelling within their source node's neighbourhood according to the routing algorithm used. Outside such neighbourhood, propagation of the route request is deliberately delayed to provide the associated chase packet with an opportunity to stop the fulfilled route request and minimise network congestion.

The broadcast of the route request can be controlled using the TTL field in the route request packet. Expanding Ring Search (ERS) [15], [21] is one of the route request improvements techniques that lower the overhead cost when succeeded. It is presented first for DSR then proposed for AODV. In ERS the source node searches for the target in multi ring scheme instead of one-to-all scheme. This is achieved by increasing the TTL value from an initial value to a predefined threshold to expand the radius of the search linearly. Blocking-ERS [17], [18] has later been introduced to reduce the energy consumption in ERS. B-ERS uses chase packets to optimise the route request process; more explanation in the following subsection.

3. Blocking-ERS

Blocking-ERS (B-ERS for short) [17], [18] improves energy consumption by stopping the fulfilled route requests. It uses chase packets to stop the propagation of route requests after discovering the needed route. It is an improvement of ERS where each new ring starts from the previous ring instead of starting from the source node as in ERS. Blocking-ERS works by introducing a delay equal to 2hop-count*NTT at each ring where rings are increased sequentially and Node Traversal Time (NTT) follows the on-demand routing algorithm used. After this delay the intermediate node may receive a chase packet called "stop_instruction" from the source node. Stopinstruction is broadcasted to cover the current ring only. In case of receiving the chase packet, the intermediate node will discard both the route request and the chase packet. If no chase packet is received within 2hop-count*NTT time, it will rebroadcast the route request to cover a larger ring. Chase packet is broadcasted up to the ring where the finder of the route resides at maximum to cover only that ring. The source node needs to know how many hops away does the finder of the route reside, thus route reply packet should extended by one byte to carry the value of the hop count.

In the presence of mobility, B-ERS suffers from performance degradation due to the immature discard of chase packets where most of the times the fulfilled route request flee with the help of mobility from the associated chase packet.

In this report, we are proposing a new algorithm called Blocking-ERS-Plus (B-ERS+ for short) to overcome this deficiency in B-ERS. It works by continuing to broadcast chase packets until the catching is insured to maximise the success rate of the catching mechanism.

4. Blocking-ERS-Plus

Blocking-ERS-Plus (B-ERS+ for short) is an improvement of B-ERS to increase the success rate of the catching process which improves network performance in terms of latency and overhead for MANETs. Steps of B-ERS+ is shown in Figure 1.

These two algorithms differ only in the processing of the chase packets. In B-ERS, the chase packet is allowed to be broadcasted only in the ring where the finder of the route resides. However, in B-ERS+ it is allowed to be broadcasted beyond that in an effort to catch the fulfilled route request even after the intermediate node dealing with that route request moves away from its place when the route request received. BERS+ uses the chase module, as in Figure 2, for processing chase packets.

[FIGURE 1 OMITTED]

Upon receiving the chase packet, the steps in Figure 2 are performed at each node. If the chase packet is a duplicate, it is discarded (line 2). Otherwise, the needed information is stored (line 4) where each node keeps track of all received route requests and chase packets by storing the needed information i.e. their broadcast ID and originator IP address. If the route request and the chase packet information are stored in the same table, a bit flag is needed to distinguish between route requests and chase packet records. If the matching route request is broadcasted already then the chase packet is broadcasted as well (line 7) but if the route request is waiting to be broadcasted then both the route request and its matching chase packet are discarded (line 9). If the route request is not received yet, the chase packet is discarded (line 12) after storing the needed information (line 4).

The chase packet format includes the route request ID and the source IP address to uniquely identify a particular route request that is associated with the chase packet. It also includes a broadcast ID that is used with the source IP address to identify a redundant chase packet. Unlike B-ERS, B-ERS+ does not need to extend the format of the route reply packet because the source node broadcasts the chase packet without restricting it to the ring where the finder of the route resides.

The chase packet format includes the route request ID and the source IP address to uniquely identify a particular route request that is associated with the chase packet. It also includes a broadcast ID that is used with the source IP address to identify a redundant chase packet.
Figure 2: Steps performed by each node to process the chase
packets in B ERS+.

Steps performed by each node upon receiving the chase packet in B-ERS+

1: If the chase packet is a duplicate then
2:   Discard it.
3: Else
4:   Store chase information
5:   If the route request received then
6:     If the route request broadcasted then
7:       Broadcast the chase packet.
8:     Else
9:       Discard both packets.
10:    End if
11: Else
12:    Discard the chase packet.
13: End if
14: End if


5. Simulation and Performance Analysis

Simulations have been conducted to evaluate B-ERS+ and compare it with TLRDA-C, B-ERS, and simple flooding used in AODV algorithms using ns2 simulator version 2.29 [10]. BERS+ was implemented as a modification to the existing AODV implementation. The same case is true for TLRDA-C and B-ERS..

The comparison metrics include:

Network coverage is the number of receiving nodes per route request where the node is counted as one if it receives one or more copies of the same route request. This metric provides an indication of the success rate of the chasing mechanism where each algorithm is compared to AODV because it uses simple flooding which gives complete coverage.

Route request latency is the average delay per hop among all route requests in a single run.

End-to-end delay is the total delay for the actual transmitted data plus the route discovery time which is the round trip time from sending a route request until receiving the route reply.

Packet loss is the number of dropped packets in a single run.

Routing overhead is measured by the number of received route requests plus number of received chase packets in the whole network.

Modelling movements is not obvious in MANETs. In order to simulate a new protocol, it is necessary to use a mobility model that reasonably represents the movements of a typical node [7]. With the lack of real traces, an accurate synthetic mobility models should be chosen carefully to determine whether the proposed protocol would be useful when implemented in practice. In MANETs, the entity mobility models typically represent nodes whose movements are completely independent of each other in un-cooperative movements, e.g. the Random Way Point (RWP) model [15]. On the other hand, a group mobility model may be used to simulate a cooperative characteristic such as working together to accomplish a common goal. Such a model reflects the behaviour of nodes in a group as the group moves together, e.g. Reference Point Group Mobility (RPGM) model [6], [13].

5.1 Simulation Environment

Each run was simulated for 900 seconds of simulation time, ignoring the first 30 seconds as a start-up period for the whole network until it becomes stable. For each topology, 30 runs were performed. The results of these runs were averaged to produce the graphs shown below, Figures 2-16, and a 95% confidence interval is produced which is shown as standard error bars in the relevant figures. Table 1 provides a summary of the chosen simulation parameter values.

The simulation area is kept constant in all scenarios to study the algorithms performance in small to moderate size environments, since we are interested in knowing the behaviour of the algorithms in both environments. A traffic generator was used to simulate constant bit rate (CBR) with payload of 512 bytes. Moreover, data sessions between different source and destination pairs in groups of ten nodes. Data packets are transmitted at a rate of four packets per second, assuming nodes are identical so the transmission range is fixed to 100m in all nodes. This approximately simulates networks with a minimum hop count of 10 hops between two border nodes one on opposite sides of the other in a connected network. Links are bidirectional, and mobile nodes operate in a flat arena.

The RPGM mobility generator was used [5] to generate mobility scenarios for all of our simulation runs since it models the random motion of groups of nodes and of individual nodes within the group. Group movements are based upon the movement of the group reference point following its direction and speed with speeds between 1 and 15m/s. Speed Deviation Ratio (SDR) and the Angle Deviation Ratio (ADR) are used to control the deviation of the velocity (speed and direction) of group members from their leader's velocity. Moreover, nodes move randomly within their group where SDR =ADR= 0.5. Each group contains 10 nodes. The minimum speed is 1 with 50s as pause time.

5.2 Simulation Analysis

The simulation analysis considers all the three analysis: network size, traffic load, and mobility analyses.

5.2.1 Effect of Network Size

Figures 3 to 7 display the results of running our algorithms, TLRDA-C and B-ERS+ against both B-ERS and AODV for 900 seconds using networks with different number of nodes, from 20 to 100 in an area of 1000m x 1000m with a minimum speed of 1m/s and a maximum speed of 15m/s. The number of communication sessions is ten.

Figure 3 shows that the success rate of the catching process has been improved dramatically for B-ERS+ and TLRDA-C compared to B-ERS and AODV. The rate of success in the catching process is determined by the amount of coverage. The optimal success rate is when the coverage equals the number of hops between source node and the finder of the route but this is impossible to obtain without the use of external resources. When the network is covered completely by a route request while the algorithm uses chasing technique, the rate of the success in the chasing process is zero so less coverage means higher success rate. In AODV where simple flooding is used, there are no chase packets so the network is almost covered by default where the coverage is 100% most of the time. In BERS, the coverage is nearly equal to AODV because the discard of the chase packet before catching the associated route request makes the fulfilled route requests cover the whole network most of the time. However, B-ERS coverage is less than AODV by 6% and 1% in small and moderate size network respectively.

This little improvement might be due to a rare catching or pocket loss especially when contention is high as in moderate network. On the other hand, the coverage in B-ERS+ is improved by 76% to 80% compared to AODV. B-ERS+ improvement in terms of success rate over the original B-ERS is 74% to 78%.

[FIGURE 3 OMITTED]

Figure 4 explores the end-to-end delay for B-ERS+, TLRDA-C, B-ERS, and AODV. B-ERS+ reduces the average end-to-end delay more than B-ERS, and AODV because the network in B-ERS+ is less congested. TLRDA-C achieves lower end-to-end delay than B-ERS+ due to the faster propagation of the route request within its neighbourhood region remembering that TLRDA-C broadcasts with less contention as in TLRDA-D. The reason behind the end-to-end delay increment in both B-ERS and B-ERS+ is delaying route requests from start and before discovering the required route. The average end-to-end delay improvement in TLRDA-C is better than B-ERS+ by 59% to 67% while B-ERS+ improves the end-to-end delay by up to 25% over B-ERS and by up to

[FIGURE 4 OMITTED]

Figure 5 shows the superiority of TLRDA-C by minimising the average of route request latency. The average route request latency of B-ERS+ is reduced more than B-ERS which means that the catching process was more successful in B-ERS+. TLRDA-C improves the average of route request latency by 31% to 62% over B-ERS+ while B-ERS+ improves it by 44% to 60% over B-ERS.

[FIGURE 5 OMITTED]

The overhead increases with increment of network density regardless of the algorithm used as shown in Figure 6 because the average number of route request received may increase more when the route request spreads deeper in the network. This figure depicts the routing overhead for all four algorithms. B-ERS+ reduces the number of received route request but increases the number of chase packets received compared to BERS. Nevertheless, the routing overhead in B-ERS+ is improved by 45% to 55% over B-ERS and by 33% to 40% over AODV. This improvement increases with the increment of network size leading to an improvement in power consumption and bandwidth utilisation as well. TLRDA-C reduces routing overhead more than B-ERS+ in moderate size networks by 28% while B-ERS+ reduces it more in small size environment by 24%.

Figure 7 shows that B-ERS+ reduces the packet loss of B-ERS by 22% to 67% and by 23% to 58% over AODV. B ERS+ improvement increases with the increment of network size. TLRDA-C reduces packet loss more than B-ERS+ in low moderate (70 nodes) to moderate size networks by up to 13% while B-ERS+ reduces it more in small to low moderate (60

[FIGURE 6 OMITTED]

Therefore, the network performance is improved for BERS+ compared to B-ERS by reducing latency and overhead due to the higher success rate of the catching process in BERS+. This improvement increases with moderate networks.

[FIGURE 7 OMITTED]

5.2.2 Effect of traffic load

Figures 8 to 12 display the results of running TLRDA-C and B-ERS+ against AODV and B-ERS for 900 seconds using networks of size 70 nodes in an area of 1000m x 1000m with a random speed ranging between 1m/s and 15m/s. The amount of traffic ranges from 5 to 35 communication sessions incremented by five.

Figure 8 demonstrates the network coverage for AODV, BERS, TLRDA-C and B-ERS+. AODV covers the network completely as expected from simple flooding but when the network is injected with heavy traffic, as in 35 data sessions, the number of receiving nodes is almost double the network size which means that some of the route requests are reinitiated more than once by the source node due to the high congestion and contention. At 30 data sessions, B-ERS succeeded in some of the chasing process but still its success rate is lower than B-ERS+. B-ERS+ improves the success rate over B-ERS dramatically by 85% to 87%. B-ERS+ catches more route requests compared to B-ERS because it broadcasts chase packets to cover a larger area enabling each chase packet to reach the associated route request. B-ERS+ improves the success rate over AODV by 84% to 95%. B-ERS+ catches more route requests than TLRDA-C by up to 32% because it imposes larger amount of delays to route request which enables the chase packet to reach the associated route request earlier.

[FIGURE 8 OMITTED]

Figure 9 shows that B-ERS+ improves the end-to-end delay over B-ERS because B-ERS+ frees the network from unneeded route requests which reduces the network congestion. This improvement ranges from 39% to 44% over B-ERS and 26% to 54% over AODV. B-ERS+ still suffers from high end-to-end delay due to imposing delay to route request propagation before discovering the route. TLRDA-C achieves end-to-end delay better than B-ERS+ by 31% to 40%.

[FIGURE 9 OMITTED]

Figure 10 reveals the superiority of TLRDA-C among all four algorithms in terms of the average of route request latency because of the higher success rate in the catching process. The route request latency increases with traffic load due to the increment in the number of packets in the network which adds more contention and may result in more collision. TLRDA-C improves the average of route request latency by 42% to 55% over B-ERS+ while B-ERS+ improves route request latency over B-ERS by 30% to 42% and 17% to 24% over AODV.

B-ERS+ incurs less packet loss in the whole network compared to B-ERS as shown in Figure 12 because the network in B-ERS+ is less congested. The packet loss is increased with the increment of traffic load for all four algorithms. B-ERS+ improves packet loss by 29% to 71% over B-ERS, by up to 20% over TLRDA-C, and by 38% to 70% over AODV.

[FIGURE 10 OMITTED]

Figure 11 depicts the routing overhead for all four algorithms. B-ERS+ incurs lower routing overhead than B-ERS due to the higher success rate of the catching process. B-ERS+ improvement increases with traffic load reaching 62% and 82% in heavy load over B-ERS and AODV respectively. TLRDA-C and B-ERS+ have almost the same routing overhead.

[FIGURE 11 OMITTED]

5.2.3 Effect of mobility

Figures 13 to 17 were extracted from simulating the four algorithms for 900 seconds using networks of size 70 nodes in an area of 1000m x 1000m using six maximum speeds. The maximum speed takes one of the following values: 2, 5, 7, 10, 13, and 15m/s. The traffic load was fixed at 10 communication sessions.

Furthermore, TLRDA-C improves end-to-end delay over BERS+, B-ERS, and AODV as shown in Figure 14. This improvement is due to the quick broadcasting within the neighbourhood region. TLRDA-C's improvement is from 23% to 40% over B-ERS+ while B-ERS+ improves end-to-end delay by 41% to 52% over B-ERS and by 31% to 42% over AODV.

[FIGURE 12 OMITTED]

Figure 13 demonstrates network coverage as an indicator of the success rate of the catching process like the previous analyses. B-ERS+ improves the success rate of B-ERS regardless of speed by 80% to 86%. The success rates of TLRDA-C and B-ERS+ are very close to each other with a difference ranges from -9% to 15%.

[FIGURE 13 OMITTED]

Route requests latency for TLRDA-C is lower than B-ERS+, B-ERS, and AODV regardless of speed as shown in Figure 15. As mentioned previously, this improvement is due to the higher success rate of TLRDA-C in the catching process and the quick broadcasting within the neighbourhood region. TLRDA-C improves the average of route request latency by 54% to 69% over B-ERS+.

[FIGURE 14 OMITTED]

B-ERS+ improves route request latency by 26% to 42% over B-ERS because when the route request propagate further in the network the hop count increases which increase the amount of delay imposed. Moreover, B-ERS+ improves route request latency by up to 22% over AODV.

[FIGURE 15 OMITTED]

TLRDA-C and B-ERS+ incur low routing overhead; lower than B-ERS and AODV as shown in Figure 16. Routing overhead increases more with fast networks regardless of the algorithm used. The improvement of the routing overhead in B-ERS+ ranges from 56% to 72% over B-ERS and by 44% to 60% over AODV. TLRDA-C and B-ERS+ routing overheads are relatively close.

[FIGURE 16 OMITTED]

Moreover, TLRDA-C and B-ERS+ lose fewer packets compared to AODV and B-ERS as shown below in Figure 17 because the networks are less congested in the case of TLRDA-C and B-ERS+. The packet loss is increased with the increment of maximum speed for all four algorithms. B-ERS+ improves packet loss by 68% to 76% over B-ERS and is by 65% to 86% compared to AODV while the difference between B-ERS+ and TLRDA-C ranges from -20% to 10%.

Finally, B-ERS+ outperforms both AODV and B-ERS regardless of network size, traffic load, or maximum speed in all metrics used.

Simulation experiments and analyses were conducted to study the performance of B-ERS+ while concentrating on the three-performance cases: network size, traffic load, or mobility. Almost the same behaviours were demonstrated by those simulation experiments and analyses for the following metrics: network coverage, end-to-end delay, route request latency, and routing overhead as well as packet loss depicted in

[FIGURE 17 OMITTED]

B-ERS+ outperforms B-ERS algorithm by reducing end-toend delay due to the reduction in network congestion. It also improves route request latency, routing overhead, packet loss due to the higher rate of success in the catching process compared to B-ERS. Moreover, TLRDA-C outperform BERS+ in terms of route request latency and end-to-end delay while incurring almost the same overhead.

6. Conclusions

B-ERS achieves low success rate due to the early discard of chase packets which hinder the chasing process in the presence of mobility. B-ERS+ is a modification of B-ERS where the chase packets are allowed to travel in the network until the caching is insured. The simulation analyses show that B-ERS+ outperforms B-ERS by reducing the latency in terms of end-to-end delay and route request latency due to the success in freeing the network from unneeded route requests which reduces network congestion. B-ERS+ incurs lower overhead compared to B-ERS by reducing routing overhead and packet loss due to the higher rate of success in the catching process. TLRDA-C outperform B-ERS+ in terms of route request latency and end-to-end delay while incurring almost the same overhead.

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Mznah Al-Rodhaan1, Lewis Mackenzie2, Mohamed Ould-Khaoua3

(1) Information Technology Department, College of Computer and Information Sciences, King Saud University, Saudi Arabia

(2) Department of Computing Science, University of Glasgow, Glasgow, United Kingdom.

(3) Department of Electrical & Computer Engineering, Sultan Qaboos University, Al-Khodh, Muscat, Oman.
Table 1: Simulation Parameters

Parameter              Value

Transmission range     100m
Topology size          1000x1000m
Simulation time        900s
Packet size            512bytes
Packet rate            4pkt/s
Data sessions          5,10, ..., 35
Traffic type           CBR(UDP)
Routing protocol       AODV
Number of Nodes        20,30, .., 100
Number of runs/point   30
Antenna type           Omni Antenna
MAC protocol           IEEE 802.11
Maximum speed          2,5,7,10,13,15m/s
Minimum speed          1m/s
Mobility Model         RPGM model
SDR, ADR               0.5
Propagation model      Two-Ray Ground model
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Title Annotation:Mobile Ad hoc NETworks
Author:Rodhaan, Mznah Al-; Mackenzie, Lewis; Ould-Khaoua, Mohamed
Publication:International Journal of Communication Networks and Information Security (IJCNIS)
Geographic Code:1USA
Date:Dec 1, 2010
Words:5578
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