CAR: Coding-Aware Opportunistic Routing in Wireless Mesh Networks

Transcription

1 : Coding-Aware Opportunistic Routing in Wireless Mesh Networks Hongquan Liu and Yuantao Gu Received Jan., 3 Abstract An intermediate node in inter-flow network coding scheme, such as, needs to know exactly which are the previous hop and next hop of a packet before coding. It is difficult to incorporate inter-flow network coding into Opportunistic Routing (OR) because the next hop of a packet in OR can t be determined in advanced. is proposed in this paper to address this problem. Meanwhile, it aims to maximize the number of native packets coded in each single transmission. Opportunistic transmission may increase coding opportunities and the introduced small forwarding delay in also adds extra network coding chances. This introduced delay is variable. gives the coded packet that consists of a larger number of native packets with a smaller forwarding delay. The forwarder with the largest number of native packets coded together is ultimately selected to send data. Simulations demonstrate that achieves significantly throughput gains and derives a reasonable end-to-end delay in both cross topology and mesh topology under both TCP and UDP traffic. achieves 43% and 36% throughput improvement under TCP traffic and 34% and 5% throughput improvement under UDP traffic, compared to and in cross topology, respectively. also obtains a significant throughout gain in a large scale network (mesh topology). The end-to-end delay of is far smaller than that of and. significantly improves network performance of a WMN. Keywords: Network coding, opportunistic routing, wireless mesh networks. Introduction Wireless Mesh Network (WMN) is an efficient technology for broadband Internet access, but it also suffers low throughput due to the inherent broadcast characteristics like other wireless networks. Network coding and opportunistic routing are two popular mechanisms to improve the performance of WMNs by exploiting the broadcast nature of wireless medium and the path diversity that exists in the underlying network. This work was supported partially by the National Natural Science Foundation of CHINA (8787 and U8353). The authors are with Department of Electronic Engineering, Tsinghua University, Beijing 84, China. The corresponding author of this paper is Yuantao Gu ( gyt@tsinghua.edu.cn).

2 There are mainly two benefits when employing network coding. Firstly, network coding makes intra-flow packet transmission more reliable. For n native packets that need to be sent, source node and intermediate nodes are allowed to generate redundant coded packets before lossy links. The destination node may recover these n native packets as long as it has received n different coded packets, even if some of the coded packets are lost in transmission. By doing this, packet losses are masked and data transmissions are robust []. Secondly, network coding makes inter-flow packet transmission more efficient. A wireless node in promiscuous mode can perform opportunistic listening by exploiting the broadcast nature of wireless channel and obtain the information about what packets its neighbors have sent and received []. An intermediate wireless node tries its best to maximize the number of native packets forwarded in one single transmission as long as all the intended destinations are able to extract their native packets from the coded one. Opportunistic Routing (OR) has emerged as an efficient routing paradigm for improving transmission reliability and efficiency in wireless network. The first implementation of OR was proposed in [3]. Unlike classical unicast routing approaches, the next hop in OR isn t known before a packet is delivered. A packet in source node is delivered by broadcast. All the nodes are ranked based on some metric (e.g., ETX [4], hop count, etc). Any node that overhears this sending packet is regarded as the potential next hop as long as its rank is better than the sender. The best one among those potential next hops that receive this packet is selected as the ultimate next hop. This next hop repeats the same operation until this packet is received by the intended destination node. OR fully embraces the broadcast nature of wireless medium and exploits the multi-user diversity feature in WMNs. With OR, the probability of at least one of the potential next hops correctly receives a packet during one transmission is usually higher than that with classical routing approaches. Therefore, the retransmission probability is reduced and network throughput is increased with OR. As stated above, both network coding and OR can take advantage of the broadcast nature of WMNs. Incorporating intra-flow network coding into OR may complement each other. For instance, a router and its next hops in OR need to exchange state information (i.e., scheduling overhead) between each other for coordination. This scheduling overhead can be partially eliminated by network coding. Assuming there are n native packets that need to be sent at source, next hops along the paths may generate and send a random linear combination of received packets, instead of simply saving and forwarding data. These combination procedures are only based on local packet information and don t require to exchange state information. The intended destination can obtain these n native packets as long as it has received n different coded packets from different paths. MORE [5] is the first practical approach that combines network coding and OR together. MORE and its variants [6 ] are only for intra-flow packet delivery. An intermediate node in inter-flow network coding scheme such as [] needs to know exactly what the previous hop and next hop of a packet is before it starts coding. In contrast, the next hop of a packet in OR protocol can t be determined in advanced. Therefore, it is difficult to incorporate inter-flow

3 network coding into OR []. In this paper, we present, a Coding-Aware opportunistic Routing (), to address the issue of integration of inter-flow network coding and OR. It utilizes OR to maximize the number of native packets that coded in a single transmission, instead of to increase the successful receiving probability. proactively creates coding opportunities by opportunistic transmission. The router with the largest number of native packets that can be coded together among all the potential next hops is usually selected to send data. tries its best to deliver more packets in each transmission. Network throughput is significantly improved by. is compatible with higer layer protocols and works well with both TCP and UDP flows. We implement in NS and evaluate it in mesh topology with both TCP and UDP traffic. There is nearly no additional overhead for node coordination except for the information of received packets at each router which is piggybacked on header. The rest of this paper is organized as follows. Section II is the related work. Section III presents the basic idea of. The detail of is introduced in section IV. Performance evaluation is given in section V. Finally, section VI concludes the whole paper. Related work [] is the first practical implementation that increases network throughput with inter-flow network coding. A wireless node XORs several native inter-flow packets together and sends it via pseudo-broadcast as long as it finds all the intended next hops given by ETX-like [4] routings are able to extract the packet intended to them. Thus several native packets are delivered in one single transmission. Clearly, network throughput is improved. can only capture the coding opportunities along the paths given by routing protocols. Inspired by, several coding-aware routing protocols [ 5] were proposed. These protocols proposed some new metrics based on which packets are routed via paths with more coding opportunities. [, 3] formulated the coding-aware routing problem as a linear programming. Their proposed approaches are centralized protocols and not suitable for being deployed in WMNs due to the scalability problem. [4, 5] are distributed protocols which makes them easy to be implemented in practical networks. However, The coding opportunities at a node may change over time which may cause that some suboptimal paths are selected by these protocols [ 5]. [6] is also a MAC-layer mixing method like, but it proactively capture coding opportunities by allowing neighbors of a intended next hop to send coded packets. But can only recognize coding opportunities not the quality of coding opportunities. Therefore, the number of native packets coded together in each transmission isn t maximized. [] [7] extended to mix both inter and intra flows. ExOR [3] is the fist practical implementation of OR. ExOR and its variant [8] rely on priority-based forwarding to achieve opportunistic transmission. The router with the lowest 3

4 remaining cost to the ultimate destination is usually chosen to forward packets. MORE [5] first combines network coding and OR together. MORE and its variants [6 9] are only for intra-flow packet delivery. CORE [9] incorporates localized inter-flow network coding into OR to improve the throughput performance of a WMN. It aims to maximize the coding opportunities at the next hop. To achieve this goal, XORed packet with the most number of intended receivers is allowed to be sent. CORE isn t optimal because it can t make sure that the number of native packets that coded in each transmission is maximized. 3 System model A wireless mesh network is modeled as a directed graph G = (V, E), where V is the set of mesh nodes and E is the set of wireless links. All the wireless nodes in the network are communicated with each other via a same channel. Therefore, there is radio frequency interference among them. Radios on each node are omnidirectional and have a communication distance(d c ) and a interference distance (D i ), where D i > D c. Only one of the nodes in an interference range can send packets at the same time. Let S be the set of unicast flows, and each flow s S has a source node, Src(s) V and a destination node, Dst(s) V, and is associated with a rate x s. 4 Basic idea aims to maximize the number of native packets that coded in a single transmission with opportunistic transmission. The potential coding opportunity may be significantly reduced by classical routing. To illustrate, let s consider the example in Fig. where packet p goes from node A to node C, packet p goes from node C to node A, and another packet p 3 goes from node E to node B. As shown in Fig. (a), with classical routing such as shortest-path routing or ETX-like [4] routing, the disjoint paths may be chosen. In this case, there is no coding opportunity. On the other hand, if we use a opportunistic routing decision as illustrated in Fig. (b), packets p, p and p 3 will be received by all the neighbors of the senders. Then nodes B, D and E all have coding opportunities. Nodes B and E can forward p p, and node D can forward p p p3. All the coding opportunities are exploited. To maximize the number of native packets that coded in a single transmission, sets different coding opportunities with different priorities. The more native packets that can be coded together there are, the higher priority this coding opportunity is. Therefore, the coding opportunity on node D has a higher priority and p p p3 is forwarded by. Clearly, the network throughput is improved. combines distributed inter-flow network coding with opportunistic routing to improve the throughput of a WMN. All the wireless nodes are set in the promiscuous mode. When a native packet is sent, all the potential next hops receive this packet by opportunistic listening. The one with the largest number of native packets that coded together is chosen 4

5 (a) Classical routing (b) Opportunistic routing Figure : Example: effect of routing decision on the number of native packets that coded in a single transmission. to send packet. The potential next hops of this coded packet receive and decode this packet. Then they start to find native packets that can be coded in a single transmission and the one with the largest number is chosen again. This process is continued until all the packets are received by their intended destinations. 5 design In this section, the design details of are introduced. is designed to maximize the number of native packets coded together in each transmission and ultimately increase network throughput. In order to address this issue, we have to deal with the following challenges. Choose proper next hops. Due to the multi-user diversity, a packet may be received by all the neighbors of the sender via opportunistic transmission. The key question is how to choose the set of next hops for this packet to maximize coding chances. Recognize coding opportunities. There may be multiple flows at a node. should know what flows and what packets to code together. In other words, it should know whether the intended next hops can decode a coded packet. Choose best next hop. The next hops of a native packet are all allowed to send this packet. should choose the one with the largest number of native packets that coded together as the best next hop to send data. Meanwhile, other potential next hops of these native packets in this coded packets shouldn t send them. consists of three components to address the challenges above, which are opportunistic transmission, coding opportunity recognition and priority-based forwarding. Table defines the terms used in the rest of this study. 5

6 Table : Definitions of terms used in this study Term Native Packet Coded or XORed Packet Flow Id Packet Id PrevHops(i) NextHops(i) Default Nexthop(i) Coding Group Output Queue(i) Sent Queue(i) Neighbor Packet List (m, i) Overheard Packet List (m, i) Definition Non-coded packet A packet which is the XOR of several native packets A 3-bit hash of the flow s IP source and destination addresses The packet s IP sequence number The set of potential previous hops for flow i at each node The set of potential next hops for flow i at each node the next hop with the shortest path to the destination of flow i at each node The group of flows that may be coded together at each node The packet queue of flow i sorted by packet id at each node, where it keeps the packets of flow i it needs to forward The packet queue of flow i sorted by packet id at each node, where it keeps the recent N packets of flow i it has forwarded The sorted packet list of flow i at each node, where it keeps the recent N packets of flow i at node m The sorted packet list of flow i at each node, where it keeps the recent N overheard packets of flow i that node m may have received 5. Opportunistic transmission In order to effectively exploit the multi-user diversity of opportunistic transmission, should carefully choose the NextHops and PrevHops for each flow at each node. Like classical shortest path routing protocols (e.g., DSDV, etc), collects the information of hop count to other nodes at each node. In this study, we assume that all nodes are equipped with GPS receivers, therefore they have the geo-positions of themselves. also collects the geo-positions of all nodes in the network at each node. Every node also saves the hop count information of its neighbors to other nodes. 6

7 When a node wants to forward a packet, any neighbor that may potentially facilitate this packet forwarding to the destination and increase network coding chances should be included in the NextHops of this packet at this node. Based on this principal, NextHops(i) at node n should satisfy the following constraints. Any node in NextHops(i) is the neighbor of node n. The hop count to the destination of flow i at any node in NextHops(i) is less than or equal to that at node n. Nodes in NextHops(i) are closer to the destination of flow i than node n. Default Nexthop(i) is must be included in NextHops(i). If the destination of flow i is in NextHops(i), NextHops(i) only consists of this destination node. Similarly, PrevHops(i) at node n should satisfy the following constraints. Any node in PrevHops(i) is the neighbor of node n. The hop count to the destination of flow i at any node in PrevHops(i) is greater than or equal to that at node n. Nodes in PrevHops(i) are further to the destination of flow i than node n. By doing this, a native packet is closer to its destination after each transmission. Meanwhile, coding opportunities are maximized. 5. coding opportunity recognition There are three steps to process inter-flow network coding which are saving native packets, collecting packet information of neighborhood and recognizing coding opportunity. 5.. Save native packets Each flow maintains an output queue and a new received packet of flow i is buffered in Output Queue(i) at each node. 5.. Collect packet information of neighborhood inserts a variable-length network coding header in each packet to help deliver packet information, as shown in Fig.. It has two ways to collect the packet information of neighborhood. 7

8 MAC Header Header IP Header ENCODED_LEN SRC_IP DEFAULT_ NEXTHOP REPORT_LEN FLOW_ID LAST_SEQ DST_IP PKT_ID BitMap Coded Packets Information Reception Reports Figure : header. Reception reports. When node n receives a packet from its neighbor m, it can get the ids of recently heard packets of all the flows at node m by reception reports in the header, as shown in Fig.. For example, a report of the form {,, } means that the largest packet id of flow at node m is, and node m also has packets 99, 97, 94 and 9 of flow. Node n then inserts these packet ids in the Neighbor Packet List (m, ) Snooping. When a node receives a packet from node m, it guesses whether its neighbors can receive one of the native packets in this coded packet according to the positions of them. This procedure is illustrated in Algorithm. Algorithm Snooping Procedure for Neighbor n = to M && n m do for Flow i = to ENCODED LEN do if n NextHops(i), then flag = for j = to ENCODED LEN && j i do flag = flag & (n PrevHops(j)? : ) end for if flag ==, then Insert the PKT ID of flow i into Overheard Packet List (n, i) end for end for return 8

9 5..3 Recognize coding opportunity In order to facilitate coding opportunity recognition, first creates a set of Coding Group which are the flows that can potentially coded together, when a node receives a new flow. This procedure is illustrated in Algorithm. Algorithm Creating Coding Group Procedure Coding Group Set = When receiving a new flow i, for Group k = to S Coding Group Set do if j Group k, PrevHops(i) NextHops(j) && NextHops(i) PrevHops(j), then Insert i in Group k end for Insert Group {i} in Coding Group Set Sort Coding Group Set by group size When wants to find native packets that can be coded together, it just needs to search in the Coding Group Set. It searches from large group (i.e., a group with a large number of flows) to small group and it will stop searching when it judge that, if it combines the head packets in the Output Queue of flows in the current group, all the flows have at least one potential next hop can decode this XORed packet. This searching coding native packets procedure is illustrated in Algorithm 3. Algorithm 3 Searching Coding Native Packets Procedure for Group k = S to Coding Group Set do flag = for i = k.begin to k.end, do if m PrevHops(j), j Group k\{i}, the head of Output Queue(i) Neighbor Packet List (m, j) Overheard Packet List (m, j) then continue else flag = break if flag == then return all the heads of Output Queue of flows in Group k end for return 9

10 5.3 Priority-based forwarding A native packet is received by a set of next hops. introduces variable and small forwarding delays to make as many native packets as possible are coded in a single transmission. It gives the coded packet that has a larger number of native packets with a smaller forwarding delay. In other words, a coded packet with a larger number of native packets is given with a higher priority to be forwarded. Once a native packet is forwarded by one of the NextHops, the other nodes in the NextHops move this native packet from Output Queue to Sent Queue when they overhear this coded packet. There are no overhead packets for coordination between the set of NextHops in. Each intermediate node has a forwarding timer (T ). The interval of this timer, T, is proportional to the number of encodable native packets (denoted by S) and it is set by (). { T if S 4, = () T/S else, where T is a constant time interval and is set to a large-enough value (e.g.,.s) to increase coding opportunities and make the coded packet with largest number of native packets coded together to be first transmitted. We describe our decoding and encoding procedure in Algorithm 4. Intermediate nodes that aren t the Default Nexthop of a flow aren t allowed to forwarded a single native packet of this flow, as shown in Algorithm 4. By doing this, packets are forwarded along shortest paths if there are no coding opportunities. In terms of TCP flow, ACK packets are forwarded along shortest paths without opportunistic transmission to reduce transmission delay. ACK packets are coded with themselves alone. 6 Performance evaluation In this section, simulation results under NS- are presented. is evaluated in various scenarios and compared with and under both TCP flows and UDP flows. The MAC -layer protocol is IEEE 8.b. The transmission range is set to 5m and the interfering (carrier sensing) range is 55m. The packet size is set to bytes and the duration of all the flows in simulation is 5s. We use a cross topology and a 5 5 mesh topology to test the performance of, as seen in Fig. 3. The distances in the two orthogonal directions in these two topologies are set to 5m.

11 Algorithm 4 Decoding and Encoding Procedure Overhear a packet for i = to ENCODED LEN do k = the hash of SRC IP[i] and DST IP[i] if this node NextHops(k) at previous hop then Check whether packet PKT ID[j], j {,,..., ENCODED LEN}\{i} is in the Output Queue or Sent Queue if true then XOR these packets with this packet and get native packet PKT ID[i], save it in Output Queue(k). Packet Group = that returned by Algorithm 3, its size is S, set T by () if forwarding timer(t ) isn t started then Start it else if this node = DEFAULT NEXTHOP[i]then Send a decoding failure notification to the sender of this packet if previous hop NextHop(k) then Move packet PKT ID[i] from Output Queue(k) to Sent Queue(k), if exist end for for i = to REPORT LEN do k = FLOW ID[i] if previous hop NextHop(k) then Move packets {LAST SEQ[i] BitMap[i]} from Output Queue(k) to Sent Queue(k), if exist end for Forwarding Timer(T ) Expires Packet Group = that returned by Algorithm 3, its size is S if S > then XOR these native packets and send it, Move these packets from Output Queue to Sent Queue else Among all the flows whose Default Nexthop is this node, pick the one whose Output Queue header is the earliest received Send the header of this flow

12 5m 5 5m 3 4 (a) (b) Figure 3: Topologies for simulation. (a) Cross topology. (b) Mesh topology.

13 6. Cross topology In this topology, there are four -hop flows that send packets from sources to destinations, as seen in Fig. 3(a). We first study the performance of under TCP traffic. Fig. 4(a) is the aggregate throughput of TCP traffic supported by IEEE 8.,, and. far outperforms and in throughput. The throughput gain of relative to 8. is 56.8%, which is nearly 4 times to that (4.9%) of, as seen in Fig. 4(b). This means that is more effective in seizing network coding opportunities than and, as demonstrated in Fig. 4(c). Fig. 4(c) shows the distribution of the number of native packets in a single transmission at node 5 in Fig. 3(a). We can see that, under protocol, most of the coded packets at node 5 consist of 4 native TCP packets, and the result is a high throughput gain (56.8%). Under TCP traffic, achieves 43% and 36% throughput improvement compared to and, respectively. introduces a variable and small delay at each forwarder and the added delay has two benefits. First, it increases coding opportunities. Second, it allows the node with the largest number of native packets coded in a packet has the highest priority to access the medium. As a result, is better than other approaches at creating and seizing coding opportunities, as demonstrated in Fig. 4. We also examine the effects of average end-to-end delay and fairness of and compare it with 8., and. As shown in Fig. 5(a), the average end-to-end delay of is smaller and much more stable than that of 8., and. The reason is that is more effective to forward packets and it make packets wait less time in the queue. We use the fairness index define in () to evaluate the fairness performance. The more close to this index is, the more fairness it is. The fairness feature of 8. and is less stable compared with and. The index of is bigger than which means the wireless medium is more fairly allocated to different flows by. tries its best to forward more inter-flow packets in a transmission. Therefore, the resource is more fairly allocated by. F I = ( N i= f i) N N i= f, () i where f i is the rate of flow i and N is the total number of flows in simulation. Next, let us examine the performance of our proposed protocol under UDP traffic. The interval of UDP packets is set to.s. As seen in Fig. 6(a)(b), still far outperforms and and its throughput gain is 76.8%. Fig. 6(b) shows that the achieved throughput gains of and are much higher than those in Fig. 4(b), which mean that and is more suitable for UDP traffic forwarding. In other words, compared with, and aren t good at dealing with bi-direction flows (e.g., TCP flows) that involve both large and small packets. Under UDP traffic, achieves 34% and 5% throughput improvement compared to and, respectively. Fig. 4(c) shows the PDF of the number of UDP packets coded in a single transmission 3

14 Average end-to-end dela Fairness index (a) (b) Throughput (# of pkt) 3.6 x Throughput gain(%) (a) (b) (c) Percentage x x 3x 4x 4 Figure x 4 4: Results of TCP traffic in cross topology. (a) Total throughput. (b) Throughput x 3.5 x gain. (c) Distribution of number 3x of native packets coded together. 3 4x.5 Pkt.5.5 UDP TCP Average end-to-end delay (s) Fairness index (a) (b) 3.6 x 4 4 Figure 5: Effects of delay and fairness of TCP traffic in cross x 4 topology. (a) Average x endto-end delay. 3. (b) The effect of fairness. 4x x 5 3x Throughput (# of pkt).4 Throughput gain(%) Pkt (a) (b) (c) UDP 4.99

15 .99 Fairness index at node.975. Clearly, in seizing coding opportunities, is much better than and is better than under UDP traffic. As shown in Fig. 7(a), the end-to-end delay is inversely proportional to the throughput because a faster transmission makes packets wait.96 less time in queue, as explained above. far outperforms the other three approaches in end-to-end delay. In the effect of fairness, is also the best one among these four approaches,.95 as the same as that under TCP traffic, as illustrated in Fig. 7(b). 8. Throughput (# of pkt) x5 TCP.98 4 x 4 4 Throughput gain(%) x x x x x 4x 4x x (a) (b) (c) 8.4 x (a) x (b) (c) 3x 4 4x x 8 x 3x 4x gain. (c) Distribution 4 of number of native packets coded together. 8 Figure 6: Results of UDP traffic in cross topology. (a) Total throughput. (b) Throughput Pkt Throughput (# of pkt).6 x Throughput (# of pkt) Pkt 4 x 4 Average end-to-end delay (s) 8 4 Throughput gain(%) 8 Throughput gain(%) 8. (a) Figure 7: Effects of delay and fairness of UDP traffic in cross topology. (a) Average endto-end delay x5 Number (b) of 4-hop The TCP flows effect of fairness. TCP Fairness index / 6 Percentage Average end-to-end delay (s) (b).4.6 x Mesh topology We use a 5 5 mesh topology to test the performance of in supporting variable number Throughput (# of pkt) Throughput gain(%) of flows in a larger scale wireless network. The flows are 4 hop and their number are changed among, 4, 8, and 6. Average end-to-end delay (s) (a) (b) (c) (d) Again, we first examine the performance of under TCP traffic. and slightly outperform 8. in mesh topology and the aggregate throughout achieved by is significantly greater than 8., and, as shown in Fig. 8(a). Fig. 8(b) Percentage 9 Fairness index 5

16 Average end-to-end delay (s) shows the throughout gains relative to The gains achieved by and are relatively very small which means they aren t good at supporting TCP traffic. On the contrary, still obtains a significant throughout gain in large scale network compared with that in cross topology. This means packets are drained much faster in the network by compared with the other three approaches. As a result, the average end-to-end (a) achieved by is also much smaller, as shown in Fig. 8(c). When the number of flows in the network is small, the effects of fairness of the four approaches are all very well. As the number of flows increases, the fairness index of drops quickly, followed by. 5x5 The fairness of is relatively stable as the number of flows increases. TCP Fairness index (b) Throughput (# of pkt).6 x Throughput gain(%) 8 4 Average end-to-end delay (s) Fairness index (a) (b) (c) (d) / 6 Figure 8: Results of TCP traffic in mesh topology. (a) Total throughput. (b) Throughput gain. (c) Average end-to-end delay. (d) The effect of fairness. Next, let us study the performance of under UDP traffic in mesh topology. We change the interval of UDP packets from.s to.5s which makes the traffic changes from heavy to light. Fig. 8(a) shows the achieved throughout gains to 8. by, and. There are hardly any throughout gains when the traffic is light (i.e., the interval of UDP packets is big and the number of flows is small) because the network itself can afford the traffic even without the improved protocols. When the interval goes small and the number of UDP flows increases, the traffic in the network is getting heavy and the achieved throughout gains by, and are getting bigger. Again, far outperforms and in throughout. Fig. 8(b) shows the effect of average end-to-end delay under 6 UDP flows. We can see that the delay decreases as the traffic changes from heavy to light. is still the best one and the achieved delay is also very stable because packets are drained much faster in the network by it, as seen in Fig. 8(b). We can see that, in a relatively larger scale of network, the achieved performance of is still very well. 7 Conclusion An intermediate node in inter-flow network coding scheme, such as, needs to know exactly which are the previous hop and next hop of a packet to make coding decision. It is 6

17 Throughp Average end UDP Throughput gain(%) Interval of UDP packets Average end-to-end delay (s) Interval of UDP packets (a) (b) Figure 9: Results 5 of UDP traffic in mesh topology. (a) Throughput gain. (b) Average end-to-end delay under 6 UDP flows. Throughput gain(%) 5 difficult to incorporate inter-flow network coding into OR because the next hop of a packet in OR can t be determined in advanced. is proposed in this paper to address this Interval of UDP packets problem. Meanwhile, it is able to maximize the number of native packets coded in each single transmission. Opportunistic transmission increase the coding opportunities and the introduced small forwarding delays also add extra network coding chances. This introduced delay is variable. gives the coded packet that has a larger number of native packets with a smaller forwarding delay. In other words, a coded packet with a larger number of native packets is given with a higher priority to be forwarded. The forwarder with 3 / 6 the largest number of native packets coded together is ultimately selected to send data. Simulations demonstrate that achieves significantly throughput gains and derives a reasonable end-to-end delay in both cross topology and mesh topology under both TCP and UDP traffic. achieves 43% and 36% throughput improvement under TCP traffic and 34% and 5% throughput improvement under UDP traffic, compared to and in cross topology, respectively. also obtains a significant throughout gain in a large scale network (mesh topology). The end-to-end delay of is far smaller than that of and. significantly improves network performance of a WMN. References [] J. Sundararajan, D. Shah, and M. Medard, Network Coding Meets TCP: Theory and Implementation, Proceedings of the IEEE, vol. 99, no. 3, pp. 49-5,. [] S. Katti, H. Rahul, W. Hu, and D. Katabi, XORs in the air: practical wireless network coding, IEEE/ACM Trans. Networking, vol. 6, no. 3, pp , 8. [3] S. Biswas and R. Morris, Opportunistic routing in multi-hop wireless networks, SIG- COMM Comput. Commun. Rev., vol. 34, no., pp , 4. 7

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