Adaptive Forwarding Rate Control for Network Coding ...

The 2010 Military Communications Conference - Unclassified Program - Networking Protocols and Performance Track

ADAPTIVE FORWARDING RATE CONTROL FOR NETWORK CODING IN TACTICAL MANETS Soon Y. Oh

Eun-Kyu Lee, and Mario Gerla

UtopiaCompression 11150 Olympic Blvd. Suite 820 Los Angeles, CA 90064 [email protected]

Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 {eklee, gerla}@cs.ucla.edu

Abstract—In this paper, we propose a novel packet forwarding scheme based on network coding that is resilient to jamming attack in a tactical area. Wireless communication is necessary in a battlefield, but it is fragile to jamming attacks from an adversary because of the wireless shared medium. Jamming attack is easily achieved by emitting continuous radio signals and it can interfere with other radio communications. Channel switching over multiple channels or route detouring have been proposed to restore communication from jamming attacks, but they require a special radio system or knowledge of the network topology. Our new scheme exploits packet redundancy of network coding. It dynamically changes the level of redundancy adapting to local jamming conditions and thus injects redundant encoded packets when and where a jamming attack occurs. In absence of jamming, it decreases forwarding rate to save resources so that our protocol efficiently manages the network resources. We provide performance evaluations of resiliency and efficiency of the new scheme via simulation study.

I. I NTRODUCTION Wireless mobile ad hoc networks (MANETs) are selforganizing wireless networks composed of a set of mobile participants without any infrastructure support. They are promising solutions to today’s network centric warfare. However, radio communications in the tactical MANET face several formidable security and reliability challenges due to the shared medium. One challenge is jamming. A jamming attack is easily delivered by emitting continuous signal or injecting dummy packets into the shared medium causing interference with existing communications or in some cases abusing the MAC layer of other nodes within a range. Consequently, jamming attacks can seriously impede wireless communications. For example, it is known that severe disruption can occur to all Wi-Fi traffic within 100 meter range if a standard PDA with 802.11 [1] is turned on to transmit. In such a jamming situation, conventional links, networks, and transport protocols fail to operate properly. Previous jamming attack solutions exploit spatial or spectrum diversity [2]–[8]. If nodes detect jamming, they switch the communication channel [3], [4], [6]–[8] or send packets on a detour [2]. However, channel switching or detouring around the jamming area requires a special radio system or the knowledge of the network topology, respectively. Moreover, these methods cannot handle multicast communications even though they are critical in a tactical field where nodes move as groups and must communicate to accomplish their missions. Thus, spatial and spectrum diversity are, for different reasons, not practical solutions to protect from jamming attacks in the tactical MANET. In this paper, we propose a novel MANET protocol that

protects existing uni and multicast communications from jamming. The new protocol exploits temporal diversity using network coding: each intermediate node dynamically adjusts the encoding and forwarding rate based on local channel conditions. Say, if the channel conditions become worse due to jamming, a node generates and forwards more packets after encoding; otherwise, it tries to reduce the number of relayed packets. The main contributions of this paper are as follows. First, we develop a novel and simple scheme, Adaptive Forwarding scheme for network coding that can cope with jamming attacks. Our scheme does not require a special radio system or the knowledge of a whole network topology. Second, we produce the first protocol that protects multicast from jamming attacks. Third, the protocol enables nodes to respond independently to jamming without requiring information exchanging or synchronization with other nodes. Next, the protocol provides localized protection in which only nodes in the jammed area exercise dynamic and redundant forwarding so that resources in other areas can be saved. Lastly, extensive simulation-based experiments enable us to accurately evaluate robustness and efficiency of performance. The remainder of this paper is organized as follows: Section II introduces various jamming attack strategies in MANET; Section III describes the proposed Adaptive Forwarding scheme in details; Section IV presents simulation results; Section V illustrates related work, and the paper is concluded in section VI. II. JAMMING ATTACK M ODELS There are various jamming attack strategies. They interfere with other wireless communications by generating a continuous high power noise (or dummy regular messages) across the entire spectrum in a given area. We call a node generating such interference a “jammer” and proceed to classify jamming attack strategies based on jammer’s behavior. We introduce a few representative classes below.

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Continuous jamming: A jammer continually emits radio signal once it starts jamming. The jammer sends out random bits or regular blank messages. It continues to emit radio signal as long as its battery permits even though there are no wireless communications to interfere with. Thus, power usage is not efficient even though the effectiveness of the jamming attack (when victims are present in the spectrum) is high. The jammer can be easily detected by channel monitoring, making him an easy artillery target. The continuous jamming can be

turned into intermittent jamming (in a single channel) by simply switching communication channels. • Periodic jamming: Instead of continuously emitting jamming signal, a periodic jammer switches between sleeping and jamming mode periodically. It tries to fill one channel or multiple channels by round-robin method with random bits or regular packets. Further, it sends out signal to one area or multiple areas in round-robin mode as well. Periodic jamming is harder to detect than continuous jamming, but it can still be predicted and detected via channel monitoring. • Random jamming: Like the periodic jammer, a random jammer alternates sleeping; but jamming intervals are random. The jammer may move around or (if it uses a directional antenna) it may change the direction of an antenna to change the jammed area. Since random jamming occurs at unexpected time points and lasts for unpredictable duration, it is more difficult to detect than the previous two methods. However, jamming effectiveness is degraded. • Intelligent jamming: An intelligent jammer achieves high jamming effectiveness with very low energy requirements and low probability of detection by others. For example, it can specifically target interfering critical control messages, e.g., CTS or ACK packets in 802.11. If the sender (the victim) misses a CTS (or ACK), it keeps retransmitting a RTS (or data) and finally, it gives up all packet transmissions. Thus, the intelligent jammer can easily accomplish its goal of session disruption without being distinguished from normal interferers in a congested situation. However, intelligent jamming can only be applied to particularly vulnerable protocols such as 802.11 unicast. We can expect jamming attacks at any time during our daily life. For example, the jammer can disrupt the wireless network of a business competitor. However, the most common MANET jamming attacks occur in tactical scenarios. Tactical area jamming can have significant effects on combat outcome. The jammer wants to disrupt communications without being detected. Continuous jamming is not suitable. Intelligent jamming is not effective neither since it cannot handle multicast; it uses UDP and broadcast mode (no RTC, CTS, and ACK). Thus, random jamming with signal monitoring will be used in this study as it is more difficult to detect than periodic jamming. In our simulation model, the jammer injects radio signal for a random duration after a random time interval. III. A DAPTIVE F ORWARDING S CHEME In this section, we introduce the proposed Adaptive Forwarding scheme. A. overview Network coding is known to be robust to channel error. However, its performance rapidly degrades in the presence of high packet error rates caused by jamming or congestion [9]. Namely, a destination node cannot collect enough encoded packets. Adaptive Forwarding counteracts jamming attacks by

exploiting redundant packet generation in network coding. Unlike end-to-end source coding, network coding allows each intermediate node along the path to participate in the encoding process. Thus, each node can generate an unlimited number of encoded packets individually from packets received from upstream nodes. If a node detects jamming attacks at downstream nodes, it generates/forwards more encoded packets. When channel conditions become stable again, the node decreases the number of forwarded packets. The main purpose of Adaptive Forwarding in network coding is the efficient use of resources namely wireless channel bandwidth and processing power. A node keeps low forwarding rate in stable channel conditions and boosts the rate only during jamming attacks. Adaptive Forwarding is a local phenomenon. It is possible that only a small fraction of nodes increase the forwarding rate in the area exposed to jamming. This is in contrast to end-to-end coding, say Fountain Coding or Raptor Coding that in case of attack, must increase the redundancy along the entire path, with loss of efficiency. A critical challenge in jamming control is the ability to discriminate between jamming and congestion. Both events cause high packet collision and delays. We propose the following “probing” strategy. Each intermediate node periodically probes the channel by marginally increasing the forwarding rate. If it detects improved (shorter) delay and packet delivery, it assumes that the channel is jammed. If instead the rate increase causes degradation in delay and packet delivery, it concludes that the channel is congested. A detailed discussion and evaluation of this discrimination strategy is beyond the scope of this paper. In our simulation, for simplicity we only assume exposure to jamming. B. Network Coding Unlike conventional store and forwarding, network coding allows intermediate nodes to encode packets and to send/forward the encoded packet instead of the original packet. Since the seminal work by Ahlswede et al. [10], network coding has been extensively studied to improve the performance of wireless networks [11], [12]. To implement network coding, we use the “Random Linear Network Coding” scheme [13]. A source node divides a data stream into equally sized packets p1 , p2 , p3 , . . ., where subscripts represent consecutive and unique sequence numbers. Note that we use lowercase boldface letters to denote vectors or packets and uppercase boldface letters to denote matrices. Those packets are grouped into k packets called generation, e.g., k = 8 in our simulation. The generations do not overlap, and only packets in the same generation are encoded together. For random linear coding, a coefficient for encoding is randomly drawn from a finite field, e.g., GF(28 ) in our simulations. A set of coefficients, called global encoding vector, e = [e1 . . . ek ], is recorded in the packet header and sent along with the encoded packet for the purpose of later decoding at the receivers. A coded packet cj is a linear combination of packets in the same generation, and the

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subscript j is generation id. That is, cj =

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Upon receiving an encoded packet, intermediate nodes store it into their local buffer1 if its encoding vector is innovative that is linearly independent to other buffered encoding vectors in the same generation. Intermediate nodes re-encode and forward packets when they receive k innovative packets in the same generation or a certain period has passed since the first packet in that generation arrived. Re-encoding is through the same process that the data source has undergone to generate a coded packet. Note that the packets in the buffer are coded at least once and thus the re-encoded packet ´cj is generated by, k−1 ∑ ´cj = eˆi ci . (2) i=0

Moreover, the global encoding vector is attached at the header of re-encoded packet after linearly combined. That is, ´ej =

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eˆi ei

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If a receiver collects enough encoded packets, k innovative packets in the generation, original packets are recovered by Gaussian elimination calculation with a global encoding vector. Now cj is the received coded packets, ej is the global encoding vector, and pj is the original packet. Let E = [e1 . . . ek ], C = [c1 . . . ck ], and P = [p1 . . . pk ]. Then, the receiver can obtain original data P using, P = E−1 C

(4)

C. Multicast Routes Conventional MANET multicast routing protocols first create a multicast tree or a mesh before they start packet transmissions. Most of them aim to build a Steiner tree which is known to provide the optimal multicast route and to minimize the total link cost. However, generating the Steiner tree is an NP-Complete problem. In practice most protocols establish a rooted tree or a mesh (i.e., a redundant multiple path structure). Properly speaking, tree structure protocols such as MAODV [14] and E-ODMRP [15], are not well suited to run network coding since they do not provide path redundancy, an important complement of network coding [9], [16]. Mesh structure multicast protocols, e.g., ODMRP [17], are more appropriate for network coding as well as Adaptive Forwarding, and will be used in our experiments. We note that Adaptive Forwarding does not have a route establishing mechanism of its own - rather, it builds on an existing routing algorithm. Besides multicast, Adaptive Forwarding can also work with pure broadcast (i.e., flooding). In this case, Adaptive 1 We simply assume that buffer on each node is large enough to store all the data for a limited amount of time

Forwarding is applied to broadcast mode without route establishment. Broadcast is quite effective in extreme mobility situations where a multicast mesh is hardly and inefficiently maintained. Adaptive Forwarding is helpful in that it reduces the uncontrolled packet relaying induced by flooding. D. Channel Monitoring and Data Forwarding Adaptive Forwarding monitors channel condition via the promiscuous listening mode. Nodes detect jamming attacks by monitoring the successful forwarding by down-stream neighbors. Say, if the channel is heavily occupied and packet delivery at down-stream neighbors starts decreasing, jamming is assumed. To monitor successful packet delivery, each node stamps the rank, r, of the generation as well as the encoding vector in the packet header. The rank indicates the number of received innovative packets in the generation. If r is less than generation size k, the node needs more innovative packet in the generation; otherwise, the node has received enough encoded packets. A node can overhear transmissions of down-stream neighbors because of the wireless shared medium. If a down-stream neighbor has not completed the generation, e.g., r < k, the node in question forwards more encoded packets to that neighbor to help it complete the generation. The number of packet retransmissions is larger than 1 and less than k − r. If the node fails to hear down-stream node transmissions, it does not retransmit since the link may be broken due to mobility (or the down-stream node is a leaf node). If the packet delivery at the down-stream node does not improve, a node suspects congestion. It thus starts decreasing the forwarding rate. Intermediate nodes must relay received packets, but a receiver does not if it is a leaf node. Thus, the receiver cannot “implicitly” solicit packet retransmissions by up-stream nodes. To solve this problem, we use the timer function. Once a certain interval has passed since the first packet in that generation arrived, receiver nodes (leaf nodes) send out dummy packets recording the rank r in the header. Once the generation is completed, a receiver skips dummy packet transmission. E. Adaptive Forwarding Rate Control Forwarding rate, c, is the “fraction of the packets in a generation” transmitted by an intermediate node defined by, m c= (5) k where m is the number of packet forwarded and k is the generation size. Nodes adjust c after reading down-stream node’s r value. The higher the fraction c, the higher the redundancy. In fact, informally, the product of the fraction and the “min cut” in the pathway to the receiver(s) determines the redundancy. If a node overhears that the down-stream neighbor has failed to complete the generation, r < k, it increases the forwarding rate c; otherwise, it decreases c. More precisely, the value is linearly increased/decreased as follows, { cold + αk if r < k, cnew = (6) cold − αk otherwise

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where α is a constant value, e.g., α = k1 in our simulation. We assume the jammer cannot completely fill the channels so that nodes in the jammed area still receive some packets from up-stream nodes and manage to transmit some of their own packets to downstream. Thus up-stream nodes overhear packet transmission from nodes in the jammed area and read r < k. They linearly increase the forwarding rate squeezing more packets through until the nodes in the jammed area collect a full generation. When the jamming attack is terminated, nodes linearly decrease the rate since redundant packet transmission is no longer necessary. Adaptive Forwarding employs the maximum and the minimum forwarding rate thresholds to prevent unlimited increasing and decreasing and thus c value is bound cmin ≤ c ≤ cmax . In our simulation, we use cmax = 2k and cmin = k2 . S

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IV. S IMULATION In this section, we validate our Adaptive Forwarding scheme using QualNet [18], a packet level network simulator. We use IEEE 802.11 MAC and PHY layer with two-way ground path-loss propagation model and 2Mbps channel bandwidth. A source node transmits 1Kbps constant bit rate traffic. Each simulation run lasts 500 seconds. All results are averaged over 100 simulation runs. Two topologies are designed: a grid topology and a random topology. The simulation settings above are applied to both topologies unless otherwise specified. The analytic model is applied only to the grid topology. As shown in Figure 1, the grid topology has a single source and multiple receivers. Every node except the first hop nodes has n multiple upstream nodes. That is, a node is able to receive n packets from n parent nodes. In the grid topology, n is a fixed value, e.g., 3 in our simulations. The number of hops from a source to receivers is defined as h (h=5 in Figure 1). In the random topology, 50 nodes including a single source and 10 multicast receivers are randomly distributed in a square field. The Adaptive Forwarding scheme works with any MANET routing protocol, uni or multicast. However, in these simulation experiments, we use broadcast without any network layer routing protocol to isolate the performance of the Adaptive Forwarding scheme excluding the impact of other protocols.

We use two metrics: Packet Delivery Ratio (PDR) the fraction of recovered packets averaged over all receivers; and Normalized Packet Overhead(OH) the total number of packet transmissions by the network divided by the total number of data packets actually recovered. We evaluate and compare Adaptive Forwarding performance with fixed rate network coding in which fixed forwarding rate c = 1. A. Grid Topology We inject a random jamming attack in the grid topology. At random time, jamming attack starts, which affects the second hop nodes, node 5, 6, and 7 in Figure 1. In our simulation, they lose 50∼90% of packets that previous hop nodes have transmitted. Jamming attack lasts a few seconds to several tens of seconds. Figure 2 shows the PDR of Adaptive Forwarding and fixed forwarding rate network coding in the grid topology with random jamming attack. The Adaptive Forwarding scheme with network coding delivers 100% packets under serious jamming. Fixed forwarding rate network coding maintains over 99% packet delivery ratio up to 60% of jamming loss at the second hop nodes, but performance rapidly degrades above 60% jamming loss. Finally, PDR drops 50% with 90% jamming loss while Adaptive Forwarding keeps 100% packet delivery. Figure 3 presents the OH of adaptive and fixed forwarding rate network coding. In normal situation (no or low jamming), Adaptive Forwarding features lower overhead than fixed forwarding. Adaptive Forwarding OH increases as a function of jamming loss since up-stream nodes must increase their forwarding rate to compensate the loss. At 90% jamming loss, the Adaptive Forwarding OH is higher than fixed forwarding rate network coding. While the jamming loss increases, the number of received packets at destinations decreases so that fixed rate network coding OH increases, too. Figure 4 describes the forwarding rate change in the grid topology. Y axis is the number of forwarded packets at one node, and X-axis is simulation time. Grey area represents the jamming attack period. In Figure 4, the black line represents the number of forwarded packets m in the generation k (where k = 8 in our case) at node 3 which is the up-stream node of the second hop where jamming occurs. The dotted line is the number of forwarded packets in the generation at node 9 which is the down-stream node of the second hop nodes (see Figure 1). We expect that node 3 will do most of the work to combat jamming, by increasing the forwarding rate; while node 9 is far enough from jamming to be less affected. Both nodes forward the same number of packets at the beginning, but within the second jamming period (the first period is too short), the two lines in Figure 4 become separated. Node 3 increases forwarding rate up to the maximum value =16 i.e., cmax = 2k k during the jamming period, and it reduces the rate once jamming ends. During the jamming period, initially node 9 fails to overhear the complete generation (from its downstream nodes) since up-stream nodes cannot send enough packets due to jamming. So, node 9’s forwarding rate increases. However, once the first hop nodes, i.e., node 2,

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3 and 4, react to jamming and transmit enough redundant packets, this problem is resolved and node 9’s forwarding rate decreases. This confirms our conjecture that Adaptive Forwarding tends to keep the retransmissions localized in the jamming area. In Figure 4, we can clearly see how Adaptive Forwarding dynamically adjusts the forwarding rate against jamming. B. Random Network Topology In a network topology, 50 nodes are randomly distributed in a 1500m by 1500m field, and one source node broadcasts packets to 10 multicast receivers. Nodes move around based on Random Waypoint mobility model in which the maximum and the minimum node speeds are 20m/s and 1m/s, respectively, with no pause time. Figure 5 shows an example of topology. Grey area is the jamming area where the jammer applies random jamming scheme at random times and with random intervals. Figure 6 and 7 show the PDR and the OH of both forwarding schemes in the random network topology. Like the grid topology case, Adaptive Forwarding outperforms fixed rate network coding in terms of packet delivery ratio. Curves in Figure 6 show the same patterns to those in Figure 2, but PDRs are degraded in Figure 6 due to random distribution and mobility. Nodes in the grid topology are completely connected all the time; yielding 100% PDR with Adaptive Forwarding. On the other hand, nodes may be temporarily separated or partitioned due to randomness and mobility in the random topology, thus 100% delivery cannot be guaranteed. In Figure 6, degradation of PDR in fixed forwarding rate scheme is not as severe as one of Figure 2 since the jamming attack affects only a portion in the field. Thus, there are few receivers in the jamming area which can also escape from the area due to high mobility. We can observe only one receiver in the jamming area in the snapshot in Figure 5. The OHs of two schemes cross over each other at 70% of packet loss and Adaptive Forwarding overhead keeps growing as the forwarding rate increases in the attempt to maintain a high packet delivery ratio. In the fixed forwarding rate case, both the numbers of forwarded packets and received packets decrease in terms of channel/link error, and thus overhead change is not significant.

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Fig. 4. The number of forwarded packets change as a function of simulation time in the grid topology.

V. R ELATED W ORK Navda et al. [4] explored a channel hopping technique with 802.11 MAC and PHY layer to achieve resiliency to jamming attacks. Each communication, in the proposed system, proactively hops channels according to a pseudo-random sequence regardless of existence of a jammer. Xu et al. [6] proposed reactive channel hopping where each communication jumps to other available channels only when communicating nodes have detected jamming in the current channel. The authors investigate jamming attack detection strategies both in MAC and PHY layers. In the MAC layer, a sensing-time threshold mechanism detects abnormal failures due to attacks. A sending node monitors the duration of carrier-sensing time, and if it is above the threshold, the node declares occurrence of jamming attack. The PHY layer detects jamming attack by monitoring the level of ambient noise and comparing it with own statistical model which has been built from noise levels gathered prior to the jamming attack. Jiang and Xue [2] showed global and local restoration methods that reroute flows and/or re-assign the flows to new channels in response to jamming attacks. In the global restoration, all the flows in the network are rerouted in the way of maximizing the minimum network throughput. The local restoration, on the other hand, reroutes and/or assigns new channels to flows on the intermediate paths affected by jamming attacks. Liu et al. [3] proposed cylinder architecture, a layered networking stack that implements multiple protocols for each layer. For example, the MAC layer may implement ALOHA, CSMA/CA, and TDMA. Then, each pair of the sender and the receiver selects one combination of layered protocols, namely mechanism, for their communication. By hopping between multiple mechanisms dynamically, the pair can avoid jamming. Noubir et al. [5] addressed feasibility of a low-power jamming attack where corrupting a small number of data bits leads to the loss of the entire packet and proposed a combination of an error control code and interleavers. The control code encodes the data bits, and the encoded packet bits are interleaved in a secret way. As a result, the proposed mechanism forces jammers to do more effort and to consume more energy to corrupt the same amount of bits in the communication channel.

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Fig. 6. PDRs in random network topology. One source and 10 receivers. Maximum node speed is 20m/s and minimum node speed is 1m/s.

Channel hopping is good ammunition for jamming attack, but it requires re-design of MAC and PHY layer that enable channel scanning and hopping. Global and local path restoration also requires a centralized architecture and algorithm that knows the whole network topology and channel assignments. Thus it is not a practical solution for the ad hoc network. Moreover, for the cylinder architecture, we must implement all protocol for each layer in the interface. Noubir et al. proposed temporal redundancy, but it only handles low-power jamming attack. VI. C ONCLUSION Jamming attack in the MANET can cause widespread disruption and have significant effects on existing communications. Previous research has introduced several remedies, but most proposed schemes are not practical in tactical scenarios. In particular, they do not mention solutions that work with multicast. In this paper, we propose a novel scheme, Adaptive Forwarding, based on network coding that dynamically adjusts forwarding rate locally reacting to channel condition so that nodes inject redundant encoded packets only when and where jamming occurs. In normal situation, Adaptive Forwarding decreases the forwarding rate to save resources. We report significant performance gains (with respect to fixed forwarding) through the simulation study. In future work, we plan to study the performance of the proposed algorithm in congested scenarios, to test the ability to distinguish jamming attack from normal congestion. In addition, we plan to implement and test Adaptive Forwarding in our MANET testbed. ACKNOWLEDGEMENT The work presented in this paper was sponsored in part by the US Navy under a Small Business Technology Transfer (STTR) Phase II program (contract number N00039-09-C0041). This program is managed by the Joint Program Executive Office Joint Tactical Radio System (JPEO JTRS); and some of the Network Coding work was done through participation in the International Technology Alliance sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001. R EFERENCES

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Fig. 7. OHs in random network topology. One source and 10 receivers. Maximum node speed is 20m/s and minimum node speed is 1m/s.

[2] S. Jiang and Y. Xue, “Optimal wireless network restoration under jamming attack,” in Proceedings of the 18th International Conference on Computer Communications and Networks (ICCCN), San Francisco, CA USA, 2009. [3] X. Liu, G. Noubir, R. Sundaram, and S. Tan, “Spread: Foiling smart jammers using multi-layer agility,” in INFOCOM, 2007, pp. 2536–2540. [4] V. Navda, A. Bohra, S. Ganguly, and D. Rubenstein, “Using channel hopping to increase 802.11 resilience to jamming attacks,” in IEEE Infocom Minisymposium, Anchorage, AK, May 2007. [5] G. Noubir and G. Lin, “Low-power dos attacks in data wireless lans and countermeasures,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 7, no. 3, pp. 29–30, 2003. [6] W. Xu, T. Wood, W. Trappe, and Y. Zhang, “Channel surfing and spatial retreats: defenses against wireless denial of service,” in WiSe ’04: Proceedings of the 3rd ACM workshop on Wireless security. New York, NY, USA: ACM, 2004, pp. 80–89. [7] M. Strasser, C. Popper, S. Capkun, and M. Cagalj, “Jamming-resistant key establishment using uncoordinated frequency hopping,” in IEEE Symposium on Security and Privacy, 2008. [8] M. Strasser, C. Popper, and S. Capkun, “Efficient uncoordinated fhss anti-jamming communication,” in ACM MobiHoc, 2009. [9] A. Fujimura, S. Y. Oh, and M. Gerla, “Network coding vs. erasure coding: Reliable multicat in ad hoc networks,” in IEEE Military Communications Conference (MILCOM ’08), San Diego, CA, USA, Nov 2008. [10] R. Ahlswede, N. Cai, S. yen Robert Li, and R. W. Yeung, “Network information flow,” IEEE Transactions on Information Theory, vol. 46, pp. 1204–1216, 2000. [11] S. Katti, D. Katabi, W. Hu, H. Rahul, and M. M´edard, “The importance of being opportunistic: Practical network coding for wireless environments,” in 43rd Allerton Conference on Communication, Control, and Computing, Monticello, IL, Sep 2005. [12] S. Katti, H. Rahul, W. Hu, D. Katabi, M. M´edard, and J. Crowcroft, “Xors in the air: practical wireless network coding,” IEEE/ACM Trans. Netw., vol. 16, no. 3, pp. 497–510, 2008. [13] P. A. Chou, Y. Wu, and K. Jain, “Practical network coding,” in 41rd Allerton Conference on Communication, Control, and Computing, Monticello, IL, Oct 2003. [14] E. M. Royer and C. E. Perkins, “Multicast operation of the ad-hoc ondemand distance vector routing protocol,” in The 5th annual ACM/IEEE international conference on Mobile computing and networking (MobiCom ’99), Seattle, WA, USA, 1999. [15] S. Y. Oh, J. sang Park, and M. Gerla, “E-odmrp: Enhanced odmrp with motion adaptive refresh,” Journal of Parallel and Distributed System, vol. 64, no. 8, pp. 1044–1053, 2008. [16] S. Y. Oh, M. Gerla, and A. Tiwari, “Robust manet routing using adaptive path redundancy and coding,” in THE FIRST International Conference on COMmunication Systems and NETworkS (COMSNETS), Bangalore, India, Jan 2009. [17] S. J. Lee, W. Su, and M. Gerla, “On-demand multicast routing protocol in multihop wireless mobile networks,” Mobile Networks and Applications, vol. 7, no. 6, pp. 441–453, 2002. [18] QualNet, Scalable Networs Inc., http://www.scalble-networks.com.

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