On Enhancing Network-Lifetime Using Opportunistic Routing in Wireless Sensor Networks

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1 On Enhancing Network-Lifetime Using Opportunistic Routing in Wireless Sensor Networks Chien-Chun Hung, Kate Ching-Ju Lin, Chih-Cheng Hsu, Cheng-Fu Chou and Chang-Jen Tu Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan Institute for Information Industry, Taipei, Taiwan {shinglee, kenneth, Abstract Lifetime-maximization is the critical concern for wireless sensor networks (WSNs). We notice that two common issues in existing routing schemes for WSNs are that (1) a path may traverse through a fixed set of sensors, draining out their energy quickly, and (2) packet retransmissions over an unreliable link of any fixed-path may consume energy significantly. In this paper, we exploit two natural advantages of opportunistic routing, i.e., path diversity and the improvement of transmission reliability, to develop a distributed routing scheme (called ) for prolonging the network-lifetime of a WSN. Specifically, a new metric is proposed to assist each sensor in determining a suitable set of forwarders as well as their priorities and, thus, enables to extend the network-lifetime. Simulation results show that effectively achieves networklifetime extension compared to other routing protocols. 1 I. INTRODUCTION The advent of research topics in Wireless Sensor Networks (WSNs) over the past few years comes from the diversity of their applications and the challenges of deployment issues. The most pervasive application is data-centric aggregation, in which the sensors propagate the measurement-data toward the sinks that act as data collectors and analysers. Since the batteries of sensors are neither replaceable nor re-chargeable, the operation of a WSN is restricted by the limited energy. Hence, how to enhance the network-lifetime, defined as the the amount of data received by the sinks before the first sensor depletes its energy [1], poses a rigorous issue of WSNs. Several works [2], [3] are proposed to minimize the hop stretch of a routing path (defined as the ratio of the hop distance of a given path to that of the shortest path) in order to reduce the energy cost of end-to-end transmission. Some protocols [1], [4] take a different view for prolonging the network-lifetime. They attempt to sustain the availability of the sensors that have less energy by distributing the traffic load to the ones with much residual energy. All of the above-mentioned works focus on improving energy-efficiency using fixed routing paths; nonetheless, due to the lack of path diversity, those sensors traversed by fixed routing paths may drain out their energy quickly. Although some protocols exploit the concept of selecting multiple paths for a pair of 1 This study is conducted under the Project Digital Convergence Service Open Platform of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China. nodes to improve path diversity, they still distribute traffic load on a specific set of nodes and consume their energy due to the lack of per-hop adaptation of fixed-path routing. In addition, past works assume that the energy cost of transmission over a wireless link is fixed; However, it is problematic in real wireless environments, where retransmissions over an unreliable links may incur additional energy cost to the selected route. Recently, the throughput improvement brought by opportunistic routing (OR) [5] [8] has come into notice. By involving multiple forwarders, OR not only reduces the number of retransmissions, but also introduces randomness for perhop forwarders adaptation. Instead of exploiting fixed-path routing, we believe that per-hop forwarder-adaptation in OR can create path diversity on each hop dynamically and enhance transmission reliability efficiently. Therefore, such advantages are helpful for detouring critical sensors, which have less energy, dynamically, and reducing additional energy consumption caused by retransmissions as well. Though opportunistic routing provides two desirable natural advantages, we observe that the performance of an OR scheme significantly depends on the design of the metric applied in forwarder selection as well as prioritization. Besides, the metrics used in prior OR schemes target only on minimizing end-to-end delay as the ultimate goal for mesh networks. For WSNs, however, how to achieve energy-efficiency, which is the most important concern, is not taken into consideration in such metrics. Thus, this fundamental characteristic in the design issue of deploying OR in WSNs demands a radical routing metric. Moreover, most existing OR schemes, e.g., [6] [9], are able to produce the optimal solution in a reasonable computational time because they only take link reliability into account. Instead, in order to provide energy-efficient routing, we must jointly consider the issues of link reliability and residual node energy, which makes forwarder selection in OR-based WSNs a more challenging problem. Hence, the goal of this work is to develop a lifetime-extended opportunistic routing scheme that allows each sensor to exploit the proposed metric to select and prioritize its forwarders by jointly considering energy capability and link reliability. The contributions of this paper are presented as follows: We propose a metric, called OEC, which can reflect the curtailment of network-lifetime caused by each data

2 transmission. The design of OEC aims at allowing each intermediate sensor to determine its forwarding set and relay sequence for prolonging the network-lifetime. Second, we develop a routing algorithm,, which enables each node to compute its optimal OEC value in a distributed manner and addresses the implementation issues of realizing OR on the proposed OEC metric. The remainder of the paper is organized as follows. Section II introduces related work on opportunistic routing. Section III describes the proposed protocol,, and metric, OEC. In Section IV, we evaluate the performance of via simulations. Finally, Section V concludes this paper. II. RELATED WORK Overview of Opportunistic Routing: In traditional fixed-path routing schemes over wireless networks, each node selects specific nodes to relay data according to a given metric. However, the designated relay nodes may fail to receive data over unreliable wireless links even if the most reliable link is selected. As a result, the sender must retransmit the packets to the relay nodes. In reality, all neighboring nodes in the transmission range of the sender can overhear the relayed packets because of the broadcast nature of wireless channel. Opportunistic routing [5] [8] is proposed to takes advantage of this property to select multiple neighbors as candidates and enable any of those overhearing the packets to forward data. As more than a single node is involved, the number of retransmission can be reduced because the probability that at least one forwarder receives the packets increases. Two key factors that determine the performance of opportunistic routing are candidate selection and relay prioritization. Candidate selection denotes the process of choosing a proper forwarding set from 2 N possible combinations, where N is the number of neighbors, in order to reduce the relay cost. Relay prioritization is to assign each candidate in the forwarding set a priority according to the target metric such that one only needs to relay data if all of those assigned a higher priority than itself fail to forward the packets. Opportunistic Routing in WSNs: [9] is the first attempt to bring the idea of opportunistic routing in sensor networks, aiming to reduce the end-to-end energy cost by selecting the next-hops that have shortest geographic distances to the destination. However, it addresses the problem of minimizing the total energy cost rather than considering the residual energy of each sensor, resulting in the depletions of some sensors quickly. By contrast, our proposed framework focuses on preventing the critical sensors from draining their energy, and, thus, prolonging the lifetime of a WSN. On the other hand, in [10], Kim et al. propose an opportunistic sleep-wake mechanism that enables each sensor to determine the forwarding set that extends the network-lifetime given an allowable end-to-end transmission delay. We note that any routing protocol can operate on top of such a sleepwake scheduling scheme; that is, given a subset of sensors that are awake, an opportunistic routing scheme can be applied to select suitable relay nodes from this subset for networklifetime extension. Hence, the works on sleep-wake scheduling are orthogonal to our goal, and can be combined with our proposed routing protocol. While most of the conventional OR schemes focus on designing the metric for minimizing the end-to-end delay, to the best of our knowledge, this is the first work to investigate a metric that exploits the advantages of opportunistic routing to prolong the network-lifetime of a WSN. III. OEC METRIC AND SCHEME In this section, we present the proposed OEC (Opportunistic End-to-end Cost) metric and describe the framework. A. The OEC metric Prior work on opportunistic routing mainly addresses on the issue of transmission over unreliable links. Therefore, to apply the idea of opportunistic routing to extend lifetime, we propose a new metric as the criterion of forwarder selection and relay prioritization at each routing decision. Here, the lifetime is referred to the amount of data received by the sinks before the first sensor drains out its energy [10]. When designing the OEC metric, we notice that the same amount of energy consumption has different impact on sensors that have different amount of residual energy. For example, suppose residual energy of two sensors a and b are 5 units and 2 units, respectively. A unit energy consumption costs sensor a 20% of its residual energy, while it costs sensor b 50%. In order to capture the different aspects other than considering each unit of energy consumption equal for all users, we define Scarcity Energy Cost (SE-Cost) of energy consumption EC for a sensor j with residual energy as SE-Cost = EC. (1) The intuition of SE-Cost is to avoid energy depletion of any sensor. For example, suppose that sensor s has two relay candidates, c1 and c2, and each of them has residual energy 1 unit and 5 units, respectively. Assume that it costs sensor c1 1 unit energy to relay the packet to the sinks and it costs sensor c2 3 units; also, according to Eq. (1), the SE-Cost for sensor c1 and sensor c2 is 1 and 0.6, respectively. It is evident that sensor c2 should be chosen as sensor s s next-hop despite the fact that it costs much energy; otherwise, choosing sensor c1 as the next-hop would drain out all its energy, ending the network-lifetime immediately as the result. Therefore, SE-Cost can be viewed as the damage to the network-lifetime, and the the proposed OEC metric aims at minimizing the overall SE- Cost of each end-to-end transmission to prolong the lifetime. In order to compute the overall SE-Cost from a sensor to one of the sinks, we let each node involve all its forwarders OEC values into the computation of its own OEC value. By such a recursive method, the sender can estimate the impact of the utilization of multiple forwarders on energy consumption, and, hence, evaluate the expected end-to-end SE-Cost of sending a unit of data from a sensor to the sink. Specifically, OEC

3 TABLE I NOTATIONS Notation OEC s N s Z s p sj ˆp F pri(k) < pri(j) Description The expected end-to-end transmission cost from node s to any of the sinks The neighboring set of node s The set of all possible forwarding sets; specifically, Z s = 2 Ns The reliability of the link between node s and node j The probability that at least one forwarder of node s has received the packet correctly. Node k has higher priority than node j in terms of the OEC value indicates the opportunistic end-to-end SE-Cost from a node the packet correctly and all nodes in the forwarding s to the sink, which equals the sum of (1) the SE-Cost set F s that have a higher priority than pri(j), i.e., of transmitting data from s, (2) the SE-Cost of receiving pri(i) < pri(j), i F s, fail to receive the packet. That data by all forwarders, (3) the opportunistic end-to-end SE- is, the probability of relaying the packet by forwarder Cost from its forwarders to the sink, and (4) the SE-Cost of j equals p sj k F (1 p s,pri(k)<pri(j) sk), where p sj retransmission. Figure 1 illustrates the above design concept. denotes link reliability of link (s,j). Hence, C fwd sink Hence, given a forwarding set F s of sender s and a priority equals: pri(j) assigned to each forwarder, we define the OEC metric as C fwd sink = OEC j p sj (1 p sk ) k F s,pri(k)<pri(j) OEC s (F s,pri()) = C Tx:s fwd +C Rx:fwd s +C fwd sink +C retx. (5) (2) If all forwarders in F s fail to receive the packet, it costs Table I summarizes the notations used in this paper. the sender OEC s to retransmit it. Since the probability that all forwarders fail to receive the packet is (1 p sj ), the expected cost of retransmission, i.e., C retx, is: C retx = OEC s (1 p sj ). (6) We can combine each term in Eq. (2) and reformulate it as: Fig. 1. Opportunistic End-to-end Cost Each term in Eq. (2) is explained in details as follows. C Tx:s fwd represents the SE-Cost of the sender used to broadcast a unit of data using the transmission power Tx to its forwarders. Therefore, C Tx:s fwd equals: C Tx:s fwd = Tx RE s (3) C Rx:fwd s denotes the total SE-Cost of receiving a unit of data sent from the sender s to a set of forwarders F s. Rx Since each forwarder j consumes the SE-Cost to receive data, where Rx is the receiving power of each receiver, C Rx:fwd s can be set to: C Rx:fwd s = (4) Rx C fwd sink indicates the expected end-to-end SE-Cost that all forwarders accordingly relay the packets to the sink after they receive data from the sender. For those nodes assigned a lower priority, they must wait until all with a higher priority have sent the packets. Hence, forwarder j only needs to relay the packets if it receives OEC s (F s,pri()) = 1 { Tx + Rx ˆp Fs RE s +[ OEC j p sj k F s,pri(k)<pri(j) (1 p sk )]}, where ˆp Fs = 1 (1 p sj ) represents the probability that at least one forwarder receives the packet correctly. Also, note that OEC sink = 0 and F sink =. In order to prolong the active duration of the bottleneck that has the minimum residual energy, we use the expected value of end-to-end SE-Cost as the metric for lifetime extension. Note that existing approaches which aim at maximizing the minimum residual energy of all sensors attempt to detour the bottleneck for preserving its energy; however, detouring may incur additional energy cost, resulting in potential damage to the energy usage in the near future. Therefore, we aim at lifetime-maximization by reducing the SE-Cost for each sensor, while reducing the overall energy cost of each transmission in the meantime. We note that since we involve energy consumption of both the sender s and its forwarders in F s during the computation process of the OEC value, the value of OEC s (F s,pri()) may vary with different forwarding sets. Besides, OEC s (F s,pri()) also varies with the priority pri(i) of each forwarder i F s because pri(i) determines the probability that forwarder i should consume OEC i to relay the packet and, hence, affects

4 Algorithm 1 Candidate-Selection(s) 1: A Neighbor-Extraction(N s ) 2: F s Candidate-Inclusion(A) 3: OEC s s.computeoec(f s ) 4: return F s Algorithm 2 Neighbor-Extraction(N s ) 1: A N s 2: while A φ do 3: OEC s s.computeoec(a) 4: a i EXTRACT-MAX-OEC(A) 5: if OEC s OEC ai then 6: A A {a i } 7: else 8: return A 9: end if 10: end while the value of C fwd sink. Therefore, we further define the optimal OEC value of sender s as OEC s = min {OEC s(f s,pri())} (7) F s Z s,all possible pri(), where Z s is all possible forwarding sets for s. Specifically, each senderscan select the optimal forwarding set Fs and the optimal relay sequence pri (i), i Fs, so that the expected end-to-end scarcity energy cost from sender s to the sink OEC s = OEC s (Fs,pri ()) can be minimized. This also means that forwarding the packets by the optimal forwarding setfs can achieve the minimum expected end-to-end SE-Cost, and, hence, prolong the network-lifetime. B. Framework We now focus on implementation issues of realizing the proposed opportunistic routing protocol () based on the OEC metric, which represents the expected end-to-end SE- Cost of each data forwarding. We describe three components of : (1) procedure of OEC computation, (2) candidate selection and relay prioritization, and (3) data forwarding and OEC updating. The first component enables each sensor to compute its optimal OEC in a distributed manner. The second one lets each sensor locate its optimal forwarding set from its neighbors and determine the relay sequence. The last explains how the selected forwarders cooperate with each other to relay data and update the OEC value subsequently. 1) Procedure of OEC Computation: Once a WSN is initiated, each sensor s periodically broadcasts probing packets for locating its neighboring set N s and estimating link reliability p sj associated with each neighbor j N s. Recall that OEC sink = 0 and the forwarding set F sink =. Meanwhile, the initial OEC value of all other nodes is set to. The procedure of OEC computation starts from the sink, while each sensor computes its OEC value once it receives the OEC information from its neighbors. In order to avoid duplicating the procedure of OEC computation, we let each sensor delay Algorithm 3 Candidate-Inclusion(A) 1: for all a i A do 2: IEC ai = s.computeoec({a i }) 3: end for 4: F s φ 5: OEC s 6: while A φ do 7: a i EXTRACT-MIN-IEC(A) 8: if s.computeoec(f {a i }) < OEC s then 9: OEC s s.computeoec(f {a i }) 10: F s F {a i } 11: A A {a i } 12: else 13: return F s 14: end if 15: end while the notification propagation for a period of time proportional to its current OEC once it receives an updating notification. Such a delay-based mechanism ensures that only one sensor propagates the notification in the network at a time, and also guarantees that each sensor can find its optimal forwarding set Fs and OEC value when it in turn propagates the notification. Therefore, a sensor does not need to update its forwarding set and OEC after it propagates the notification, and, hence, only needs to propagate the notification once. The procedure of OEC computation terminates as all the sensors finish its OEC computation and notification propagation. 2) Candidate Selection and Relay Prioritization: Next, we present how a sensor finds the optimal forwarding set from a subset of neighborsn s providing it their OEC values, and how it determines the optimal relay sequence for the given optimal forwarding set. Note that our forwarder-selection issue can be viewed as a 0-1 integer programming problem, which is NPhard. That is, when computing OEC, we regard 1 as selecting the neighbor as one of its forwarders, and 0 otherwise. Unlike the prior OR metrics, our forwarder-selection problem also restricts each sensor to consuming no more than its residual energy and, hence, cannot be solved in polynomial time. We propose a two-stage heuristic forwarder-selection algorithm (shown in Algorithm 1), which enables each sensor to efficiently decide its forwarding set that performs almost as well as the optimal one does. First, in Extraction Stage shown in Algorithm 2, each sensor prunes the neighbors that must not be its forwarder, narrowing the possible candidates down to a smaller set. Assume that s initially selects A = N s as its forwarding set; it then gets that the corresponding OEC value equals OEC s (A,pri ()). If the node a i A that has the maximum OEC value, i.e., a i = argmax a A OEC a, has a greater or equal OEC value than OEC s (A,pri ()), i.e., OEC ai OEC s (A,pri ()), based on Theorem??, we can extract a i from A such that A = A\{a i }. This pruning procedure repeats until a j A,OEC aj < OEC s. Next, in order to decide whether node s should select neighbor j A N s as a forwarder, we define another metric

5 Independent End-to-end Cost (IEC) as follows: IEC j = 1 p sj ( Tx RE s + Rx )+OEC j = OEC s ({j}). (8) IEC j represents the OEC value of sender s while j is the only forwarder of s. Second, in Inclusion Stage shown in Algorithm 3, s initializes its forwarding set F s as an empty set φ, and iteratively chooses the neighbor a A that has the smallest IEC value and puts it into F s until the OEC s value cannot be improved, i.e., decreased. After executing Algorithm 2 and Algorithm 3, s gets its final forwarding set F s, and computes its final OEC value by OEC s = OEC s (F,pri ()). Specifically, pri (i) is the optimal relay sequence that assigns priority to each node i F according to their OEC values in an ascending order. 3) Data Forwarding and OEC Updating: When a sensor s needs to send or relay packets, it broadcasts those packets, which can be overheard by the candidates in its forwarding set F s. Each forwarder in F s sequentially relays the packets according to the optimal relay sequence pri () of F s if it receives data from the sender correctly. Once a forwarder in F s relays the packets, it issues an ACK message that notifies sender s to terminate data forwarding. In addition, that forwarder can piggyback the information about its residual energy, link reliability, and its updated OEC value in the ACK message. Upon receiving ACK, sender s can update its OEC s value based on the information embedded in ACK. IV. PERFORMANCE EVALUATION We evaluate the performance of through the simulations implemented in NS2, which is modified to simulate energy expenditure of each sensor. We choose MICAz [11] as the hardware setting in our simulations, and parameter setting is based on [12]. We consider a multi-sink application, where transmission is considered successful no matter which sink receives the packets. We have done many experiments including single or multi-sinks case, while all the trends observed in the multi-sink cases still persist for the singlesink case. However, we do not present them all in the interest of brevity. In each simulation, each sensor starts transmission toward the sinks; therefore, the number of transmission pairs equals the number of sensors in the network. The transmission is considered successful when all the packets are received by the sinks. In addition, the simulation terminates once any sensor depletes its energy. Unless specifically stated, the experimental parameters are set according to Table II. Our simulations compare the following protocols: is the proposed routing scheme which incorporates the advantages of opportunistic routing to sensor networks for network-lifetime prolonging. [1] is a fixed-path routing scheme for lifetime extension. To avoid energy depletion of critical sensors, selects a single fixed route that consumes the minimum energy cost for each transmission pair. [9] is an opportunistic routing protocol that utilizes geographic information to forward data to its neighbors TABLE II NS2 SIMULATION PARAMETERS Hardware setting MICAz [11] Transmit power 0 dbm Transmission range Up to 80 meters Data rate 250 Kbps Packet size 46 Bytes (25 Bytes for payload) Data size 10 KBytes for each transmission Area of field 500m x 500m Number of sink nodes 4 Number of sensors 175, 200, 225, 250, 275, 300, 325, 350 Initial energy 50 Joule Transmission interval 1000 seconds that are geographically close to the destination. Their goal is to reduce the total transmission power of each end-toend transmission, instead of lifetime extension. We compare the performance of above schemes in terms of the following metrics: 1) network-lifetime, which is defined as the data size that had been received by any sink before the first sensor depletes its energy, 2) energy cost per bit, which shows the total energy cost of transmission a bit to the sinks, and 3) delay, which is the average time of transferring each data from a source to the sinks. A. Lifetime Enhancement In this simulation, we set the initial energy of each sensor to 2 Joules such that there must be certain sensor draining out its energy during the simulation time. Fig. 2 shows that the data size received by the sinks before the first depletion in and is much larger than that in GCL because they consider residual energy of each sensor as selecting relay nodes, and, therefore, avoid energy depletion by detouring the critical nodes. Besides, can further outperform because it exploits the advantages of opportunistic routing to reduce the cost of retransmission and energy consumption by adaptively involving multiple forwarders in each hop relay. The figure also reveals that the improvement of and increases when the network becomes denser because more sensors can be substituted for the critical sensors to avoid consuming residual energy of those bottleneck nodes. We observe that network density has only minor impact on the lifetime of since, no matter how dense the network is, would select the certain best forwarders in terms of its distance metric and, thus, drain out their energy. Fig. 3 shows the cumulative distribution function of residual energy. Since both and take residual energy into consideration, they can distribute the traffic load across all the sensors, and, hence, decrease the variation of residual energy among all sensors. By contrast, selects the forwarding set only based on the distance metric, the static forwarder-selection becomes radical damage to the networklifetime. Therefore, drains out some sensors energy while some other ones remain high energy. Fig. 4 compares the average energy cost per bit that verifies efficiency of energy usage. requires the least energy to transmit a bit of data among all the schemes. Compared

6 Network-lifetime (MBytes) Energy cost (mjoule/bit) Number of sensors Number of sensors Fig. 2. Network-lifetime vs. Density Fig. 4. Efficiency of Energy Usage Cumulative distribution function (CDF) End-to-end delay (second) Residual energy (Joule) Fig. 3. CDF of Residual Energy Number of sensors Fig. 5. End-to-end Delay with which aims at maximizing geographic advancement at each hop, the metric SE-Cost used in can indicate energy consumption of each transmission and, thus, utilize the energy more efficiently. Compared with, exploits the advantages of OR, i..e, path diversity and perhop adaptation, to improve transmission reliability and reduce energy consumption of retransmission. B. Path Quality In this simulation, we compare the end-to-end delay of each protocol. Fig. 5 shows that achieves lower delay in comparison with and because it can progressively reduce the number of retransmissions by involving multiple forwarders in per-hop adaptation. produces high endto-end delay because it excessively detours around critical sensors. The metric used in mainly focuses on maximizing geographic advancement; however, it does not consider the cost of retransmissions and, hence, incurs high end-to-end delay due to frequent retransmission. V. CONCLUSIONS In this work, we propose, which is a distributed routing protocol that exploits the advantages of opportunistic routing to enhance network-lifetime for WSNs. The operation of is based on a novel metric called OEC, which (a) jointly considers the energy cost of end-to-end transmission, the residual energy of each sensor and transmission reliability at each intermediate node, and (b) can be efficiently computed from the sink to each node in a recursive fashion. Simulation results show that achieves network-lifetime enhancement and energy-cost reduction compared with other schemes. REFERENCES [1] J.Park and S.Sahni, An online heuristic for maximum lifetime routing in wireless sensor networks, Computers, IEEE Transactions on, [2] B.Karp and H.T.Kung, GPSR: greedy perimeter stateless routing for wireless networks, in ACM MobiCom 00. [3] M.J.Tsai, H.Y.Yang, and W. Huang, Axis-based virtual coordinate assignment protocol and delivery-guaranteed routing protocol in wireless sensor networks, in IEEE INFOCOM 07. [4] C.Wu, R.Yuan, and H.Zhou, A novel load balanced and lifetime maximization routing protocol in wireless sensor networks, in IEEE VTC 08 Spring. [5] S.Biswas and R.Morris, Exor: opportunistic multi-hop routing for wireless networks, in ACM SIGCOMM 05. [6] K.Zeng, W.Lou, and Y.Zhang, Multi-rate geographic opportunistic routing in wireless ad hoc networks, in IEEE MILCOM 07. [7] K.Zeng, W.Lou, and H.Zhai, On end-to-end throughput of opportunistic routing in multirate and multihop wireless networks, in IEEE INFO- COM 08. [8] R.Laufer, D.-Ferriere, H., and L.Kleinrock, Multirate anypath routing in wireless mesh networks, in IEEE INFOCOM 09. [9] K.Zeng, J. W.Lou, and D. Brown, On geographic collaborative forwarding in wireless ad hoc and sensor networks, in IEEE WASA 07. [10] J.Kim, X.Lin, N.B.Shroff, and P.Sinha, On maximizing the lifetime of delay-sensitive wireless sensor networks with anycast, in IEEE INFOCOM 08. [11] MICAz Datasheet, [12] M.C.Vuran and I.F.Akyildiz, Cross-layer analysis of error control in wireless sensor networks, in IEEE SECON 06.