Retransmission or redundancy: Transmission reliability study in wireless sensor networks

Transcription

1 . RESEARCH PAPER. SCIENCE CHINA Information Sciences April 2012 Vol. 55 No. 4: doi: /s Retransmission or redundancy: Transmission reliability study in wireless sensor networks WEN Hao 1, LIN Chuang 1,RENFengYuan 1,ZHOUJia 1, YUE Yao 2 & HUANG XiaoMeng 1 1 Department of Computer Science and Technology, Tsinghua University, Beijing , China; 2 Department of Computer Science, Cornell University, Upson Hall 4161, NY, USA Received January 10, 2010; accepted October 12, 2010; published online November 2, 2011 Abstract As an application-driven network, wireless sensor network generally requires data reliability to maintain detection and response capabilities. Although two approaches, namely, retransmission and redundancy, have been proposed to enhance data reliability, the theoretical work is required to evaluate their impact on transmission reliability and energy efficiency. In this paper, we offer a comprehensive theoretical study on packet arrival probability and average energy consumption for both approaches. Our analysis and simulations indicate that when loss probabilities remain low or moderate, erasure coding, a typical redundancy-based scheme, is more energy efficient than retransmission. We also demonstrate that the resistance capability of erasure coding against packet loss weakens as the hop number increases, which motivates us to propose two improved mechanism based on the idea of the loss detection and the loss recovery. Finally, the simulation results carried out using TOSSIM verify the performance of different mechanisms both in the chain topology and the tree topology. Keywords reliability, energy efficiency, retransmission, redundancy, erasure coding Citation Wen H, Lin C, Ren F Y, et al. Retransmission or redundancy: Transmission reliability study in wireless sensor networks. Sci China Inf Sci, 2012, 55: , doi: /s Introduction In recent years, wireless sensor networks (WSNs) have received much attention from both academia and industry. The development of WSNs is largely driven by the numerous military, industrial, and scientific applications. Furnished with limited power resources, they allow cost-effective sensing for many different typical applications (e.g. environmental monitoring), most of which generally require high data reliability to maintain the detection and response capabilities. However, unlike in the traditional internet, transmission in WSNs is particularly influenced by the quality of the wireless channel. For instance, the loss rates can be as high as 30% in WSNs [1]. In order to achieve transmission reliability, the most straightforward choice is retransmission-based mechanism, which includes end-to-end one and hop-by-hop one. Since the end-to-end way has been proven to be inefficient in WSNs [2], hop-by-hop way is widely adopted to address the reliability problem over lossy wireless links [3 5]. Corresponding author ( chlin@tsinghua.edu.cn) c Science China Press and Springer-Verlag Berlin Heidelberg 2011 info.scichina.com

2 738 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No. 4 Another solution to enhance reliability is introducing redundancy into message delivery [6,7]. Using erasure coding, which is a typical redundancy scheme, we encode M source packets into M + R packets for transmission. At the destination, we can reconstruct M original messages if we receive at least M out of M + R encoded data packets. If the degree of data redundancy, represented by R, is sufficiently large compared with the loss probability, high reliability can be obtained in the WSNs. Reed-solomon codes and LDPC codes are popular types of erasure codes. Previous work has been done on reliable transport in an experimental way, whereas there still lacks theoretical work to evaluate the impact of different approaches on data reliability and energy efficiency. To provide a theoretical study for a general comparison, rather than case-by-case study, we explore the delivery reliability of retransmission and redundancy in this paper. To fully ground their feasibility and benefit, we also compare their energy efficiency, which is fundamentally imperative for WSNs. The rest of this paper is outlined as follows. In Section 2 we briefly introduce assumptions for the whole paper. Then we provide an analytical framework to represent the packet delivery performance of different approaches in Section 3. The theoretical evaluations and simulations of erasure coding and retransmission are carried out in Sections 4 and 5. Further discussion and conclusions are given in last two sections. 2 Assumptions In this paper, we discuss the reliability problem based on the following assumptions: 1) We mainly focus on the energy consumption of data transmission instead of coding process. This assumption utilizes the fact that the power consumed by radio communication is dominant in WSNs. For example, the ratio of the energy spent in sending one bit of data to the energy spent in executing one instruction varies from 1500:1 to 2700:1 for Rockwell s WIN nodes, 220:1 to 2900:1 for the MEDUSA II nodes, and around 190:1 for the MICA nodes. Authors in [8] show that the communication energy of the μamps sensor node to transmit and receive per useful bit for an RC=1/2 code is 168 nj while the energy to decode is estimated to be 25 pj. They conclude that the use of redundancy codes has little impact on the energy consumption of communication. Therefore based on simple XOR or modular operation, Erasure coding is entirely feasible in terms of processing power for WSNs [9]. 2) The packet latency caused by erasure coding is acceptable for nodes to deal with. The correctness of this assumption comes from the fact proved by the previous work. In the experiment on MICA2 motes [6], encoding or decoding one code word requires about average 1.7 ms, which is considerably smaller than the transmission time of a packet (20 ms) by an order of magnitude. Therefore, each coding step can be done well before the next coding occurs. 3 Theoretical analysis The main contribution of our paper is to build an analytical model to investigate the packet delivery reliability and energy efficiency under different schemes. Focusing on comparison and evaluation, two metrics are proposed in our paper, i.e., the packet arrival probability and the average energy consumption for one successful arrival packet. The former indicates whether the delivery approach fulfills the required reliability, namely, The number of arrival packets p e = The number of all packets sent from the sender, (1) while the latter indicates how much energy it costs for one packet to be successfully delivered, namely, E avg = Expected total energy consumption. (2) Delivered packets In other words, we define the energy consumption of goodput instead of throughput. The whole analysis is carried on in three steps. First, we study the behavior of simple transmission without any additional mechanism. Then we discuss how retransmission influences delivery performance. Finally, we explore the impact of erasure coding based on the two metrics. Generally, the superscript

3 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No used in this paper stands for mechanism types: s is for simple transmission, r is for retransmission, and c is for erasure coding; the subscript stands for distance type: h is for hop-by-hop and e is for end-to-end. 3.1 Simple transmission Given a multihop transmission, an n-hop path can be represented as a concatenation of n single links. We define p as the probability of a successful transmission across one hop. Then the successful delivery probability across n hops is p n. The expected total energy consumption for all M packets is: E s e = εmpn n + n εmp k 1 (1 p)k, (3) k=1 where ε denotes the energy consumed by transmitting one data packet (including sending and receiving), εmp n n is the energy consumption of all the packets delivered through n hops, and εmp k 1 (1 p)k is the energy consumption of all lost packets that are dropped on the k-th hop. Owingtothefactthat (3) can be rewritten as (1 p) n εmp k 1 (1 p)k = εm(1 p n np n (1 p)), (4) k=1 E s e = εmp n n + εm 1 pn np n (1 p) 1 p Based on (2), the average energy consumption for one successful arrival packet is: 3.2 Retransmission = εm(1 pn ). (5) (1 p) E s avg = Es e Mp n = ε(1 pn ) p n (1 p). (6) As a popular mechanism, hop-by-hop retransmission is usually adopted to increase the transmission reliability. Before discussing this mechanism, we should note that for each one-hop transmission, an ACK packet is sent back when one data packet is received. Thus the loss of an ACK packet will also cause a retransmission. Since the loss of a data packet and the loss of an ACK packet will both trigger a retransmission, the one-hop loss probability p hl canbeexpressedas p hl =1 p p ack, (7) where p and p ack are the probabilities of a successful transmission across one hop for a data packet or an ACK, respectively. The relationship between the required one-hop arrival probability p r h and the maximal number of retransmissions x is p r h =1 (p hl) x. (8) Then, we obtain the average energy consumption in a failed one-hop transmission when a loss event occurs: Eh l = ε (1 p) 1+ε p(1 p ack) 2, (9) (1 p)+p(1 p ack ) ε(1 p) where (1 p)+p(1 p ack ) denotes the energy consumption when a data packet is lost, and 2εp(1 p ack ) (1 p)+p(1 p ack ) the energy consumption when an ACK packet is dropped while the data packet arrives successfully.

4 740 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No. 4 We can now express the average energy consumption for one data packet through one hop as x Eh r =(p hl ) x xeh l + (p hl ) i 1 pp ack [(i 1)Eh l +2ε]. (10) i=1 In the first term of (10), (p hl ) x represents the probability that all x transmissions through one hop fail and the corresponding energy consumption is xeh l. In the last term, (p hl) i 1 pp ack expresses the probability that the i-th retransmission is successful while the previous i 1 transmissions all fail, and (i 1)Eh l +2ε indicates the corresponding energy consumption. Therefore, for the whole M packets under hop-by-hop retransmission, the expected total energy consumption through n hops is E r e = n i=1 M(p r h )i 1 E r h, (11) where M(p r h )i 1 is the number of delivered packets after (i 1)-th hop. Similarly based on the definition in (2), the average energy consumption for one successfully delivered packet is: 3.3 Erasure coding E r avg = Er e M(p r h )n = n i=1 (p r h) i n 1 E r h. (12) For every original M packet, N = M + R packets will be produced using erasure coding. Due to the redundancy, if we get at least M encoded packets, we can reconstruct the original M packets. However if we get less than M, recovery is impossible to execute. Thus the whole N packets should be taken into account to define the successful arrival probability as M+R p c e = C(M + R, k)(p n ) k (1 p n ) M+R k. (13) k=m For all N packets, which are encoded from M source packets, the expected total energy consumption through n hops is n Ee c = ε(m + R)pn n + ε(m + R)p k 1 (1 p)k = ε(1 pn )(M + R), (14) (1 p) k=1 where ε(m + R)p n n is the energy consumption of all the encoded packets delivered through n hops, and ε(m + R)p k 1 (1 p)k is the energy consumption of all loss packets that are dropped on the i-th hop. Based on the definition in (2), we obtain the average energy consumption for one successfully delivered packet under erasure codes as: Eavg c = Ec e M p c = ε(1 pn )(M + R) e p c e(1 p)m. (15) For fairness of comparison, the M + R encoded packets have been normalized to the original M packets. 4 Analytical results The analytical model given above is utilized to compare the successful packet arrival probability and the average energy consumption of three different delivery schemes. In the comparison, the default maximum number of retries allowed after a transmission failure, namely x, is three [10], and the typical erasure coding with M =4andR = 3 is chosen [6]. Actually, the group size M is determined by many factors such as memory space and bandwidth of wireless channels, which depend on hardware and application environment. Constrained by the space, we only consider M = 4 without loss of generality. In terms of lengths of packets, we adopt parameters of S-MAC [11], where the header, payload and CRC fields have 6 B, 30 B and 2 B respectively. Thus ACK is 8 B and data packet is 38 B.

5 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No Reliability Given lengths of data packet and ACK, we can approximate the relationship between loss probabilities of a data packet and an ACK as p lack p l = 1 p ack 1 p = 1 (1 b)lack 1 (1 b) l data bl ack bl data 1/5, (16) where b is the bit error ratio in a packet. Then we can get p ack 4+p 5 in (7). To verify the performance of the three mechanisms, we show in Figure 1 the packet arrival probability versus the value for packet loss probability p l (i.e., 1 p) in a one-hop channel. From Figure 1, we can see that when p l is below 0.5, the arrival packet probability using erasure coding is larger than the simple approach and almost as large as the retransmission approach. Obviously, the tolerance of moderate packet loss by erasure coding can be attributed to its redundancy. However, if more than R packets of the whole M + R ones are lost, the rest arrival ones are useless and the energy is wasted. Therefore when p l continues to increase, e.g. 0.5 in Figure 1, erasure coding does not keep its positive effect. Considering higher than 60% of loss probabilities in WSNs are rare [1], redundancy-based transmission is believed to be feasible and effective. Figure 2 depicts the arrival probability ratio between erasure coding and simple transmission as a function of the number of hops. It is observed that under different hops, the arrival probability ratio is above 1 only at some value ranges for p l (e.g., for hop 3). When p l goes beyond the boundary of that range, the reliability of erasure coding will dramatically deteriorate. Therefore we take the turning point of the p l value as the reliability boundary (e.g., 0.2 for hop 3), which precisely indicates the reliability range of erasure coding. In Figure 2, it is also obvious that the resistance capability of erasure coding against packet loss drops as the hop number increases. For instance, the reliability boundary can be as high as 0.5 in the one hop situation, while it rapidly drops to 0.1 when hop number increases to Energy efficiency To visualize the energy efficiency distinction, in Figure 3 we show the average energy consumption per successfully delivered packetof the three mechanisms, normalized with respect to the energy consumption of transmitting one ACK. From the figure, it is clear that in order to achieve more reliable transmission, more energy is indispensable for additional mechanisms, i.e., retransmission and redundancy. In other words, there exists a tradeoff between reliability and energy efficiency, which is the crucial problem we discuss for WSNs in this paper. Compared with the result of simple approach, the uptrend of results for erasure coding and retransmission under different loss probabilities indicates that high lossy wireless channel will boost energy consumption no matter which scheme is adopted. Additionally, we also note in Figure 3 that the energy consumption of erasure coding is less than hop-by-hop retransmission when p l is below When p l increases beyond 0.55, it is observed that erasure coding cannot keep its superiority in energy efficiency over retransmission. However, since the loss probabilities in WSNs generally change below 30%, we conclude that erasure coding is more energy efficient than retransmission in a practical wireless channel. To further investigate the characteristic of energy efficiency, we report in Figure 4 the energy consumption ratio between retransmission and erasure coding across different hops. Similar to reliability analysis, there exists a turning point of the p l value which indicates the energy efficiency range of erasure coding mechanism compared with hop-by-hop retransmission. Take hop=3 for example: when p l goes across the boundary of that range, i.e. 0.2, the energy efficiency of erasure coding will quickly worsen. Thus we define the turning point of the p l value as the energy efficiency boundary (EEB) of erasure coding. In Figure 4 we also see that with the increase of the number of hops, the EEB is decreasing. As a result of the loss accumulation in a longer multi-hop channel, it is harder for destination node to receive at least M encoded packets for reconstruction.

6 742 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No. 4 Figure 1 Arrival probability (hop=1). Figure 2 Ratio of delivery probability for multihop. Figure 3 Energy consumption (hop=1). Figure 4 Ratio of energy for multihop. 4.3 Further results From the above analysis, we conclude that redundancy-based approach is a feasible and efficient mechanism for reliable transmission. However, we also realize that cumulative delivery loss across multiple hops would degrade performance of data transmission and increase energy consumption. Hence it is important to scrutinize the impact on two kinds of boundaries from the perspective of hop number. Figure 5 depicts the reliability boundaries (RB) and energy efficiency boundaries (EEB) for erasure coding of various redundancies across different numbers of hops. It is obvious that two boundaries of different redundancy are sensitive to hop change when the hop number is below 3, while they asymptotically drop when hop number is larger than 3. It is easily deduced from (13) that the resistance capability of erasure coding against packet loss weakens as hop number increases. In fact, when the arrival probability p n diminishes due to hop increases, it is more likely that more than R of M + R packets would drop, which will ultimately cause p c e to decrease. This phenomenon is similar to the retransmission-based case: the end-to-end retransmission performs fine across many hops in high reliable links but not effective and efficient in dynamic harsh conditions. Instead, the hop-by-hop retransmission is widely adopted to address the reliability problem over lossy wireless links. In order to avoid the loss accumulation in a multi-hop channel, we propose that relay nodes should take responsibility for loss detection and recovery. This proposal utilizes the fact that the power consumed by radio communication is dominant in WSNs and erasure coding is entirely feasible in terms of processing power for WSNs. Just as concluded in [8], the use of redundancy codes has little impact on the energy consumption of communication. From Figure 5, we also observe the influence of redundancy degree. As the number of redundant packets increases, the RB and EEB of erasure coding both keep growing. However, the increase of redundancy packets does not practically affect the energy efficiency boundary when R is greater than 3. Especially when the redundant packet R is equal to 4, EEB becomes even lower than RB. In other words, when R

7 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No Figure 5 Boundaries for different erasure coding. Figure 6 One-hop simulation result. Table 1 Simulation parameters for two scenarios Channel Total packets Radio range Energy for one packet Size of data packet Size of ACK AWGN and BPSK 30 m 5 normilized unit 1 normilized unit 38 bytes 8 bytes Node ID 2, 3, 5, 6, 7, 9 4, 8, 10 11, 12 Sending rate 0.1 pkt/s 0.2 pkt/s 0.5 pkt/s increases beyond one threshold value, e.g. 3 for M =4, erasure coding has to sacrifice energy efficiency for more data redundancy and reliability. 5 Simulation This section evaluates the performance of the three approaches using the TOSSIM simulator: simple transmission without any additional mechanism, erasure coding-based transmission, and hop-by-hop retransmission. In simulation, each node communicates within additive white Gaussian noise channel using BPSK modulation. The common simulation parameters are list in Table 1. Considering the practical channel conditions and our above analysis, we mainly provide the experimental results of packet loss probabilities between 0 and 60%. All results related to the energy consumption are normalized with respect to the energy consumed by transmitting one packet. 5.1 Chain topology To verify the theoretical results, we first compare three mechanisms in a multi-hop chain topology, where nodes are deployed in a line with equal 20 m distance. Simulation results are given by sending 5000 packets from one source node to the sink node with 1 pkt/s. Similar to the analysis part, the default maximum number of retries for hop-by-hop retransmission is three, and the typical erasure coding with M=4 and R=3 is chosen. Due to space constraints, we only provide some parts of results and other experiments are also validated. Figure 6 show energy consumptions in one hop scenario under various channel conditions: simulation results of simple transmission and erasure coding approximately match theoretical results. However, the experimental data of retransmission are a little different from the theoretical analysis as the result of two reasons: 1) Since the loss of a data packet and an ACK packet would both trigger a retransmission, we define that a successful transmission includes sending one data packet and receiving ACK packet in Section 3. 2) The delay among intermediate nodes, which may trigger unwanted retransmissions, will bring in more energy consumption. Although sending rates are properly set to avoid congestion, it is hard to completely avoid this problem. This can also be verified that the differences between simulation results and theoretical results are mainly in high loss conditions, i.e. larger than 0.3 in Figure 6. To further verify the analytical accuracy as the number of hops changes, the results under the packet loss probability 0.05 are given in Figures 7 and 8. It is obviously observed that our theoretic analysis is

8 744 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No. 4 Figure 7 Arrival probability of p l =0.05 (simulation). Figure 8 Energy of p l =0.05 (simulation). Figure 9 Arrival probability of the tree topology. Figure 10 Energy of the tree topology. entirely accurate: for all three mechanisms, the analytical results (solid lines) completely coincide with the simulation results (dashed lines). 5.2 Tree topology In this part, we evaluate the performance of different mechanisms in a more practical topology. Consider anetworkofn sensor nodes, with each node identified by an integer in the range from 1 to N. We adopt the tree topology that has also been used in [12]. The distance between nodes among the tree is 20 m. The 1-th node is the base station, and other nodes send data to it. The sending rates of all nodes listed in Table 1 are carefully chosen to avoid congestion as the transmission reliability is the main focus of this paper. Simulation results are obtained after averaging the metrics over 50 runs and each run lasts 1000 s. When discussing erasure coding-based transmission in section 4, we point out that relay nodes should take responsibility for loss detection and recovery to avoid the loss accumulation in a multi-hop channel. Therefore two improved redundancy-based scheme are proposed: erasure coding with loss detection (ECLD) and erasure coding with loss recovery (ECLR). The intermediate nodes using ECLD mechanism will detect the integrity (i.e., whether more than M packets of M + R ones in one group are successfully received) and only relay the encoded groups that can be reconstructed. This detection is to avoid forwarding useless groups and save energy. The intermediate nodes using ECLR mechanism will not only detect the integrity of received groups but also try to reconstruct every group if the loss event is detected. The feasibility and necessity of this rule comes from two facts: First, considering the dynamic complexity of the application environment, poor cumulative packet delivery performance across multiple hops will degrade performance of data transport and expend significant energy. Second, as we state in the assumption, it is reasonable to prevent the risk of packet loss at the low energy cost of coding, which will eventually save energy. All simulation results are given in Figures 9 and 10. From the figures, we can obtain characteristics of every mechanism: 1) At most conditions, retransmission is more reliable than any other mechanism. However, it also consumes more energy than ECLD in high loss rates (below 0.4) and than all erasure

9 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No coding-based mechanisms in low and moderate loss rates (between 0 and 0.2). 2) With the help of loss detection, ECLD can avoid forwarding useless groups and is more energy efficient than pure erasure coding, while their arrival probabilities are exactly the same. 3) Based on loss recovery, ECLR mechanism removes the loss accumulation in a multihop channel and even achieves almost the same level of reliability as the retransmission way in low and high loss rates (i.e., ). Additionally, it is the most energy efficient when the loss probability is below In summary, ECLR indicates better performance on energy efficiency than retransmission while achieves the same level of reliability under low or moderate loss conditions (below 0.45). Since the loss probabilities in WSNs generally change below 30%, we conclude that erasure coding is a feasible solution for reliable transmission in cost-sensitive WSNs. 6 Further discussion For reliability-required applications in WSN, such as environmental monitoring, both retransmission and redundancy will inevitably cause transmission delay [3]. However, erasure coding would not bring more delay than retransmission. The time consumed by coding can be almost ignored compared with that of communication as we stated in section 2, not to mention the latency caused by different sleeping schedule algorithms [11,13]. As for real-time applications which are more sensitive to latency, our mechanism is also more flexible than retransmission by decreasing redundancy degrees. Furthermore, memory usage is another crucial concern in WSN. In fact, the total amount of memory usage in our experiment is below 1k bytes, which includes operation table, coding matrix, packet buffers and other costs. Considering that the packet buffer is shared by application and that operation table can be stored in program memory, the memory actually occupied by the erasure code component is less than 68 bytes, which also has been verified in [6]. Finally, our analytical framework can be easily extended to compare transmission latency between retransmission and erasure codes if we replace energy-related parameters with latency-related ones. However, constrained by space, this aspect is outside the scope of this paper. 7 Conclusions In this paper, we have presented a simple and realistic analytical model to analyze the packet arrival probability and average energy consumption of retransmission and erasure coding. With the help of the proposed model, the comprehensive impact on reliability and energy efficiency of two approaches is quantitatively evaluated. Notable is the energy efficiency of erasure coding, especially erasure coding with loss recovery, while this kind of redundancy mechanism achieves acceptable reliability under low or moderate loss conditions. Although the one-hop superiority of the data redundancy scheme in terms of energy is verified in practical loss channel conditions, our analytic and experimental results indicate that its performance advantage weakens with an increase in hop number or under high loss rates. Therefore two improved mechanisms based on loss detection and loss recovery are proposed and their performances are evaluated in simulations compared with other mechanisms. The results shows that ECLR mechanism is not only more energy efficient but also keeps the same level of reliability as retransmission in practical loss channels. Thus we believe it is a feasible solution for reliable transmission in energy-sensitive WSNs. Finally, we have to emphasize that the purpose of this paper is not to criticize retransmission method. Instead, we only aim to analyze the feasibility of redundancy and retransmission in terms of reliability and energy efficiency. To make a full-fledged transport protocol, we plan to combine the redundancy mechanism with the congestion control scheme in future.

10 746 Wen H, et al. Sci China Inf Sci April 2012 Vol. 55 No. 4 Acknowledgements This work was supported in part by National Natural Science Foundation of China (Grant Nos , , ), National Grand Fundamental Research Program of China (Grant No. 2009CB320504), National High-Tech Research & Development Program of China (Grant Nos. 2008AA01Z212, 2010CB328105), and National Science and Technology Major Project of China (Grant Nos. 2009ZX , 2009ZX ). References 1 Zhao J, Govindan R. Understanding packet delivery performance in dense wireless sensor networks. In: Proceedings of the 1st International conference on Embedded Networked Sensor Systems, Los Angeles, Balakrishnan H, Padmanabhan V, Seshan S, et al. A comparison of mechanisms for improving TCP performance over wireless links. IEEE/ACM Trans Networking, 1997, 5: Wang C, Sohraby K, Li B, et al. A survey of transport protocols for wireless sensor networks. IEEE Network, 2006, 20: Stann F, Heidemann J. RMST: Reliable data transport in sensor networks. In: Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, Wan C Y, Campbell A T, Krishnamurthy L. PSFQ: A reliable transport protocol for wireless sensor networks. In: Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, Kim S, Fonseca R, Culler D. Reliable transfer on wireless sensor networks. In: The 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, Santa Clara, Kumar R, Paul A, Ramachandran U, et al. On Improving wireless broadcast reliability of sensor networks using erasure codes. In: Proceeding of Second International Conference on Mobile Ad-hoc and Sensor Networks, Hong Kong, Shih E, Calhoun B H, Cho S H, et al. Energy-efficient link layer for wireless microsensor networks. In: Proceedings IEEE Computer Society Workshop on VLSI, Orlando, Xu Y, Lee W, Xu J. Analysis of a loss-resilient proactive data transmission protocol in wireless sensor networks. In: 26th IEEE International Conference on Computer Communications IEEE INFOCOM, Anchorage, IEEE. IEEE Standard TM. IEEE Press: New York Ye W, Heidemann J, Estrin D. An energy-efficient MAC protocol for wireless sensor networks. In: Proceedings of 21st Annual Joint Conference of the IEEE Computer and Communications Societies IEEE INFOCOM, New York, Rangwala S, Gummadi R, Govindan R. et al. Interference-aware fair rate control in wireless sensor networks. In: Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Pisa, Luo J, Jiang L G, He C. An analytical model for SMAC protocol in multi-hop wireless sensor networks. Sci China Inf Sci, 2010, 53: