Energy Efficient Broadcasting Using Network Coding Aware Protocol in Wireless Ad hoc Network

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1 Energy Efficient Broadcasting Using Network Coding Aware Protocol in Wireless Ad hoc Network 1 Shuai Wang 2 Athanasios Vasilakos 1 Hongbo Jiang 1 Xiaoqiang Ma 1 Wenyu Liu 1 Kai Peng 1 Bo Liu 1 Yan Dong 1 Huazhong University of Science and Technology, Wuhan , China 2 University of Western Macedonia, Greece {wangshuai87410, hongbojiang2004, mxqhust}@gmail.com, vasilako@ath.forthnet.gr, {liuwy, pkhust, liubo, dongyan}@hust.edu.cn Abstract Energy efficient broadcasting is of paramount importance for many broadcast applications in wireless ad hoc networks. With respects network coding, it has been proved that the energy gain is upper bounded by 3. However, the coding opportunity is often highly dependent on the established routing paths, resulting in that a lot of coding opportunities could be lost in practice. By combining network coding with the Connected Dominating Set (CDS)-based broadcasting, we take full use of network coding. The intuition behind our algorithm is to intersect information flows at nodes in CDS to increase the coding opportunities. We design a scheme, named NCDS, that uses Network Coding over Connected Dominating Set, to reduce energy consumption. Our experimental results show that NCDS provides up to 161% gains compared to blind flooding, and 37% gains compared to CDSbased broadcasting without network coding. I. INTRODUCTION Wireless ad hoc networks have been widely deployed as a means of data communication because of their flexible structures. Usually, they are utilized in dangerous scenarios like battle fields where it is costly or infeasible to replace the batteries of nodes. Meanwhile, wireless devices in wireless ad hoc networks have become smaller than ever before, which limits the size of nodes batteries. Therefore, energy efficiency has become a crucial factor in wireless ad hoc networks. Broadcasting is one of the most frequent events in wireless ad hoc networks for the dissemination of data and control messages in many applications. There has been an increasing focus on designing energy efficient broadcast algorithm to prolong the life span of wireless ad hoc networks [6], [7], [8]. Network coding, originally proposed by R. Ahlswede et al. in [1], is a great breakthrough in the area of information theory, and increasingly popular as a mechanism to increase the utilization of both wired and wireless networks. In wireless networks, COPE [2], one of the most practical network coding scheme, is first proposed by S. Katti et al. It exploits the physical-layer broadcast property of wireless channels and finds coding opportunities. By doing so, multiple packets can be coded together and then forwarded within a single transmission. As a result, it decreases the number of transmissions of wireless ad hoc networks. By assuming that each transmission carries the same size packet and the energy consumption is proportional to the number of transmissions, Liu et al. [3] proved that the energy gain is upper bounded by 3. N 1 P1 N 3 (a) P1 N 1 N 3 N 1 N N 1 3 N3 P1 N 5 N 5 N 6 N 6 (c) Fig. 1. (a) Chain Topology, (b) cannot form Chain Topology, (c) X Topology, and (d) cannot form X Topology The idea how network coding can decrease transmission times is illustrated in Figure 1. Figure 1(a) shows a basic coding topology, called Chain Topology [2]. Suppose that node N 1 has a packet for node N 3, and node N 3 has a packet for node N 1. Both of the two packets must be relayed by node. Without network coding, four transmissions are needed to achieve the message exchange. With network coding, however, three transmissions are sufficient to do this. After N 1 transmits and N 3 transmits, the intermediate node broadcasts a packet obtained by XOR-ing and. Then, node N 1 can decode packet by performing an XOR of packets and the XOR-ed packet it receives. Node N 3 can obtain packet using the same decoding mechanism. Here, no opportunistic listening [2] is required. Another typical coding scenario is called X topology [2], as shown in Figure 1(c). Node N 5, are within the transmission ranges of N 1, respectively. By opportunistic listening, node can overhear the transmission of ; node N 5 can overhear the transmission of. When receiving the XOR-ed packet P2, can decode packet by performing (P1 P2 ) and so does N 5. It only requires three transmissions compared with that of four transmissions without network coding. One problem here is that the coding opportunity is highly dependent on the established routes paths [5]. In practice, it is more severe when the established routes may not form the available coding topology including Chain Topology and X Topology (see Figure 1(b) and Figure 1(d)). In these cases, many coding opportunities are lost. In this paper, we strive to achieve the energy gain by network coding and provide an energy efficient broadcasting (b) (d)

2 in wireless ad hoc networks. We design a scheme, named NCDS, which uses network coding over connected dominating set, to reduce energy consumption. We consider CDS due to two reasons. On one hand, CDS is constructed for efficient broadcasting to avoid the broadcast storm problem [4] caused by simple blind flooding. With CDS-based broadcasting, only the nodes in CDS forward data to the entire network which decrease the transmissions for broadcasting dramatically. On the other hand, the infrastructure formed by CDS provides a common path for information flows and improves the opportunities of intersections of information flows suitably. This significantly increase the coding opportunities. We implement our algorithm with NS-2 and our experimental results show that NCDS provides up to 161% gains compared to blind flooding and 37% gains compared to CDS-based broadcasting. The rest of our paper is organized as follows. In Section II, we present the background on CDS-based broadcasting and our motivation for a network coding based protocol for broadcasting. Our scheme NCDS is presented in Section III. In Section IV, Experiment results and discussion are presented. Finally, Section V concludes the paper. II. BACKGROUND AND MOTIVATION In this section, we first describe the key idea of CDSbased broadcasting algorithm and the energy gain brought by CDS-based broadcasting compared with blind flooding. Then, we introduce the most practical network coding scheme - COPE and its coding opportunity lost problem. We highlight, through an example, the coding opportunity that can be increased greatly by applying network coding, to the CDS-based broadcasting. Very interestingly, CDS solves the coding opportunity lost problem by improving more coding opportunities because more information flows are intersected at nodes in CDS. Meanwhile, network coding makes the CDSbased broadcasting more efficient. It adds an extra network coding energy gain to the energy gain brought by CDS-based broadcasting compared with blind flooding. A. CDS-based Broadcasting For broadcasting, Minimum Connected Dominating Set (MCDS) [10] is considered to minimize broadcast transmissions. Let us consider a 16 node network shown in Figure 2. The minimum connected dominating set is {J, K, I, F, C}. Assume that node J is the broadcast source node. When node J transmits a packet, the packet reaches all neighbor nodes, O, M, H, D, E and K. Note that this transmission requires only one packet transmission. Node K, which belongs to MCDS, re-broadcasts the received packet to its neighbor nodes. This packet reaches nodes I and N. Node I re-broadcasts the packet since I is in MCDS. Then, node F forwards the packet and this packet reaches node P, I and C. Nodes P and I ignore the packet as they have already received the same packet from nodes I and K respectively. Finally, node C transmits the packet to its neighbors with one packet transmission. Because node J, K, I, F and C broadcast the packet to complete the broadcast task, the cost of MCDS-based broadcasting is only five transmissions. Finding a minimum connected dominating set (MCDS) in a network is an NP-complete problem. Some CDS approximation broadcast algorithms [6], [7], [8] are proposed. The key idea of these algorithms is similar as the example mentioned above - using only dominating nodes to forward the broadcast packet. Nodes that are not dominating nodes only receive the broadcast packet without forwarding it. Therefore, the number of redundant transmissions is reduced. H M D J O Fig. 2. E K P F A N I C G 16-node random topology B. Network Coding and Coding Opportunity Lost Problem While considering network coding algorithm, we should make sure that all next-hop nodes can decode their corresponding original packets. Consider k packets p 1, p 2,..., p k at a node that have distinct next-hop nodes n 1, n 2,..., n k respectively. This node can XOR the k packets together to form the coded packet p = (p 1,p 2,...,p k ) only if the nexthop node n i for each packet p i already has all k 1 packets p j for j i. Usually, we judge whether node n i owns all other packets by using these two rules: (1) Node n i is the previous-hop node of packet p j,or (2) Node n i overheard packet p j (opportunistic listening). However, network coding opportunity may not always occur. In the existing designed routing, it is possible that two flows which meet the coding rules don t intersect with each other, see Figure 1(b) and Figure 1(d). Our study is motivated by the observation of CDS-based broadcasting energy gain. Meanwhile, the backbone constructed by CDS provides a common path for information flows. Since many information flows of opposite direction often intersect at such backbone nodes, the coding opportunities will increase dramatically. As shown in Figure 2, we illustrate how network coding improves the CDS-based broadcasting. In this example, there are two broadcast sources, B and D. We construct a wireless backbone where the nodes J, K, I, F, C are selected as CDS, the paths of information flow for broadcasting are D J K I F C and B C F I K J. The CDS-based broadcast flows overlap at the common L B

3 nodes J, K, I, F and C, and then network coding can occur at these nodes. If the source nodes D (or E, H, M, O) and B (or A) keep broadcasting packets utilizing CDS-based broadcasting, network coding can be done continuously at the common nodes J, K, I, F and C. Note that the coding opportunity is increased dramatically at this condition and the benefits of network coding are fully exploited. Meanwhile, network coding makes the routing path constructed by CDS high efficient. Since the execution of network coding algorithm is independent of CDS-based broadcasting approach, we can get an extra network coding energy gain based on CDS-based broadcasting energy gain. Note that network coding can occur very often in broadcast session, especially in multiple source/multiple message broadcast application. Due to packets of any broadcast session will continuously pass through the backbone constructed by CDS, network coding can be done frequently at these nodes. C. Inner infrastructure network coding energy gain independent of MAC layer, we adopt an underneath MAC protocol through our algorithm. A. An Approximation algorithm to Minimum Connected Dominating Set Construction It is a well-known NP-complete problem to find a Minimum Connected Dominating Set. Thus, we utilize an approximation algorithm to determine the forward nodes for broadcasting. In earlier work [9], we proposed a backbone extraction scheme for large scale wireless network. Here, we construct the backbone of wireless ad hoc network by utilizing an Approximation algorithm to Minimum Connected Dominating Set (A-MCDS) - the nodes which have the maximum degree are chose as the dominating set. The description of our algorithm: Initially, all nodes are in state WAIT and their states will change to DOMINATEE, DOMINATOR, CONNECTOR when they receive the related message. See Figure 4. DOMINATOR P 3 A P 3 P 4 C P 4 B WAIT ACTIVE CONNECTOR DOMINATEE P 3 P 4 P 3 P 4 F G Fig. 4. Transformation diagram of node state Fig. 3. Inner Infrastructure Network Coding Energy Gain We can also get an inner infrastructure network coding energy gain for CDS-based broadcasting. Let us consider the example shown in Figure 3. It is a part of the 16-node network topology in Figure 2 - the dominator C and its dominatees A, B, F and G. In this example, dominator C broadcasts four packets to its four dominatees A, B, F and G. Assume that some of these packets have been received by its dominatees as shown in Figure 3 - i.e., dominatee A receives p 1, p 2 and p 3, dominatee B receives p 1, p 2 and p 4, dominatee F receives p 1, p 3 and p 4, and dominatee G receives p 2, p 3 and p 4. Note that each of the four packets is lost by some dominatee. Without network coding, the dominator has to transmit all the four lost packets. However, with network coding, it is sufficient to transmit one XORed packets. The dominator only needs to send p 1 p 2 p 3 p 4. Despite the fact that they lost different packets, all four dominatees can retrieve the four packets they needed by XORing the coded packet with the packets they have received. III. NCDS SCHEME DESCRIPTION In this section, we introduce our NCDS scheme, which consists of two phases. The first is to construct a Minimum Connected Dominating Set (MCDS) for broadcasting. As the construction of MCDS is NP-complete, an approximation algorithm to MCDS is given. The second is applying network coding to CDS-based broadcasting. Noting that NCDS is Step1: Learn one-hop neighbor information At first, each node in the network sends the HELLO message to its neighbors. Compared with the HELLO message in AODV [11], we add the degree information of the node. After collecting the messages from its neighbors, every node creates its neighbor list, containing the degree information of its neighbors. Step2: Find the DOMINATOR All nodes at this step are set in state WAIT initially. Select one node randomly and mark the node as DOMINATOR. This node sends DOMINATOR MESSAGE to its neighbor and its neighbors change their states to state DOMINATEE. Each node in state DOMINATEE sends DOMINATEE MESSAGE to its neighbors. If its neighbors are in state WAIT, then this node is marked state ACTIVE. Our algorithm then changes the node in state ACTIVE, whichever has the maximum degree, to state DOMINATOR, with their neighbors (in state WAIT) marked state DOMINATEE. Step3: Find the CONNECTOR We select the node in state DOMINATEE, in which neighbors have the maximum number of DOMINATOR, as the CONNECTOR. To make CDS more efficient for transmission, we add CONNECTOR for every two hop distance DOMINATOR after the former process. After all of the nodes are marked state DOMINATOR, DOMINATEE or CONNECTOR, the algorithm terminates. All the nodes in DOMINATOR or CONNECTOR form a connected dominating set.

4 Lemma 1: The resulting graph of DOMINATOR and CONNECTOR from A-MCDS algorithm is a CDS. (Proof is omitted) Lemma 2: The time complexity of A-MCDS algorithm is O(n), and the message complexity is also O(n), where n is the number of nodes. (Proof is omitted) B. Applying Network Coding to CDS-based Broadcasting In this section, we describe how to use network coding in CDS-based broadcast. Similar to COPE [2], our scheme inserts a coding layer between the IP and MAC layers which detects coding opportunities and performs encoding/decoding. Coding opportunity is often illustrated with several typical network topologies, such as the chain topology and X topology. The authors of COPE also provided the cross topology and wheel topology, where higher coding gains can be achieved. The later two scenarios do not occur as often as the the first two. With opportunistic listening or not, all these coding scenarios can be divided into two categories. Here, we mainly consider our NCDS scheme with opportunistic listening. When opportunistic listening is used, nodes in the network can overhear many packets and thus coding opportunity is improved. The corresponding packet processing algorithm is shown in Algorithm 1. Based on the neighborhood information, node u checks whether all its neighbors N(u) have already received the packet (line 1). If so, it does not forward the packet (line 2). Otherwise, node u checks whether packet p is a native one. In our algorithm, only the nodes in CDS forward packets or take the encoding procedure (line 5). For each node in CDS, it tries to see whether it can get any coding opportunities to encode the packet with the remaining packets in the output queue that it needs to forward (line 8). If yes, it will encode these packets together and send the coded packet in one transmission (line 10). If not, the node will buffer the packet for a time period T threshold (lines 12-13) and process it later. Note that it may create more coding opportunities. If the buffer time for packet p exceeds T threshold, node u sends the packet immediately (lines 15-16). Finally, if the received packet p is a coded one, we decode it before processing the packet (lines 21-22). When a forwarder (nodes in CDS) sends a coded packet to its neighbors, we should make sure that all its neighbors have overheard enough packets to decode the coded packet. We specify the Broadcast Coding Rule as follows: Definition 1: (Broadcast Coding Rule) Consider node u s neighbor N(u) and an encoded packet p = (p 1,p 2,...,p K ). All the neighbors have overheard K 1 packets among p i, i =1, 2,...,K; and thus they can decode the coded packet p. We desire to encode as many packets as possible. For simplicity, we use a greedy encoding algorithm shown in Algorithm 2: whenever having an opportunity to transmit, the node picks the first packet p in the output queue, checks whether the remaining packet q satisfies the broadcast coding Algorithm 1 ProcessPkt(p) 1: if All N(u) RecvP kt(p) then 2: return; 3: end if 4: if NativePkt(p) then 5: if {u} V CDS == then 6: return; 7: else 8: K = EncodeP kt(); 9: if K! =1then 10: SendCodedP kt(); 11: else 12: while BufferTime(p) <T threshold do 13: BufferPkt(p, T threshold ); 14: end while 15: if BufferTime(p) T threshold then 16: SendN ativep kt(p); 17: end if 18: end if 19: end if 20: else 21: for all q = DecodeP kt(p) do 22: P rocessp kt(q) 23: end for 24: end if rule and encodes as many packets as possible with the packet p (line 3-7). Normally the number of packets that can be encoded into a single packet is small (bounded by the node degree), the computational overhead is insignificant. Algorithm 2 EncodePkt() 1: Pick packet p at the head of the output queue 2: K =1 3: for all remaining packets q in the output queue do 4: if q satisf ies the Broadcast Coding Rule then 5: p = p q 6: K = K +1 7: continue 8: end if 9: end for 10: return (p, K) IV. PERFORMANCE EVALUATION In this section, we present the performance evaluation results from extensive simulation in ns-2.30 [12]. In our simulation experiment, we compare our NCDS scheme with (1) Blind Flooding; (2) the broadcast algorithm with network coding but not CDS ( NC + NON-CDS for short); (3) CDSbased Broadcasting. The network area is set to m 2 with nodes increasing from 50 to 100. Each node in the network has a constant transmission range of 300 m. The radio propagation model is the two-ray ground reflection

5 model and the radio model assumes a nominal bit rate of 2Mb/sec. The MAC layer scheme follows the IEEE MAC specification. The application traffic is CBR and all the application packets are of 256 bytes each. The broadcast sources are chosen randomly and we set a total of 20 broadcast sessions. The packet sending rate is 2 packets/sec. Two metrics are used: (1) energy gain, defined as the ratio of the total number of transmissions of blind flooding to the number of transmissions used by a considered broadcast scheme, and (2) number of coding operations, defined as the number of times that network coding occurs during the simulation. It is used to evaluate the increase of coding opportunities. The experiment results are the mean value of 10 rounds over different random topologies. Energy Gain Blind Flooding NC + NON CDS CDS based Broadcasting NCDS Number of nodes Fig. 5. Energy gain Consider the energy gain of the algorithms, as shown in Figure 5. As expected, NCDS outperforms the other three algorithms. Without considering coding opportunity lost problem, the energy gain of NC + NON-CDS algorithm is only 1.07, from which we can get that the benefits of network coding cannot be fully exploited as the theoretical analysis shows if we don t consider the coding opportunity lost problem. The energy gain for our NCDS algorithm can be as high as 2.61, which means blind flooding algorithm sends 161% more packets than NCDS. The energy gain of NCDS is composed by two parts - the CDS-based broadcasting energy gain which is about 1.91 and the energy gain brought by network coding which is about Number of Coding Operations NC + NON CDS NCDS Number of nodes Fig. 6. Number of coding operations We show that NCDS does provide more coding opportunities than using network coding on a plain network. Figure 6 shows the number of coding operations of the network coding without CDS scheme, labeled NC + NON-CDS in comparison with NCDS. On average, NCDS increases coding opportunities by about 106%, contributing to a greatly improved energy gain. V. CONCLUSION In this paper, we propose NCDS scheme with the goal of improving the energy gain for wireless ad hoc networks. To do so, our main idea is to apply network coding on CDSbased broadcasting. On one hand, CDS-based broadcasting is an energy efficient scheme itself and CDS can provide a common path for information flows which greatly increase the coding opportunity. On the other hand, network coding makes the transmissions of information flow on the path (provided by CDS) more efficient. Since the execution of network coding algorithm is independent with CDS-based broadcasting approach, we can get an extra network coding energy gain based on CDS-based broadcasting energy gain. To evaluate the performance of NCDS, we carried out a series of experiments in ns-2 and the results show that compared with Flooding, CDS-based broadcast, NCDS gains much higher energy gain by increasing the coding opportunities. ACKNOWLEDGMENT This work was supported in part through National Natural Science Foundation of China (No , No , No ), National Natural Science Foundation of China - Microsoft Research Asia (No ). REFERENCES [1] R. Ahlswede, N. Cai, S. Y. R. Li, and R. W. Yeung, Network information flow, IEEE Transactions on Information Theory, vol. 46, no. 4, pp , [2] S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard and J. Crowcroft, XORs in the Air: Practical Wireless Network Coding, ACM SIGCOMM 2006, September [3] J. Liu, D. Goeckel, and D. Towsley, Bounds on the gain of network coding and broadcasting in wireless networks, in INFOCOM, [4] S. Ni, Y. Tseng, Y. Chen, and J. Sheu. The broadcast storm problem in a mobile ad hoc network, Proc. MOBICOM 99, pp , Aug [5] S. Sengupta, S. Rayanchu, and S. Banerjee, Network Coding-Aware Routing in Wireless Networks, IEEE/ACM Transactions on Networking, vol. 18, no. 4, pp , [6] H. Lim and C. Kim, Flooding in wireless ad hoc networks, Computer Communications Journal, [7] I. Stojmenovic, S. Seddigh, and J. Zunic, Dominating sets and neighbor elimination based broadcasting algorithms in wireless networks, IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 1, Jan. 2002, [8] W. Lou and J.Wu, On reducing broadcast redundancy in ad hoc wireless networks, IEEE Transactions on Mobile Computing, [9] H. Jiang, W. Liu, D. Wang, T. Chen, X. Bai, X. Liu, Y. Wu, and W. Liu, CASE: Connectivity-Based Skeleton Extraction in Wireless Sensor Networks, Proc. IEEE INFOCOM, pp , Apr [10] T.W.Haynes et al., Funcamentals of Domination in Graphs, Marcel Dekker, Inc., A Sireis of Monographs and Text books, [11] C. Perkins, E. Belding-Royer, S. Das, and I. Chakeres, Ad-Hoc On- Demand Distance Vector Routing, in Proc. IEEE WMCSA 99, pp , [12] Network Simulator.