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∗ 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 † E-mail: {shinglee, kenneth, ccf}@cmlab.csie.ntu.edu.tw §
[email protected] ∗
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† Department
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 EFFORT) 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 EFFORT to extend the network-lifetime. Simulation results show that EFFORT effectively achieves networklifetime extension compared to other routing protocols. 1
I. I NTRODUCTION 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
•
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, EFFORT, 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, EFFORT, and metric, OEC. In Section IV, we evaluate the performance of EFFORT via simulations. Finally, Section V concludes this paper. II. R ELATED W ORK 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 2N 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: GCF [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 EFFORT 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 M ETRIC AND EFFORT S CHEME In this section, we present the proposed OEC (Opportunistic End-to-end Cost) metric and describe the EFFORT 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 REj as SE-Cost =
EC . REj
(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 SECost 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
TABLE I N OTATIONS Notation OEC s Ns Zs psj pˆF 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, |Zs | = 2Ns 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 s to the sink, which equals the sum of (1) the SE-Cost of transmitting data from s, (2) the SE-Cost of receiving data by all forwarders, (3) the opportunistic end-to-end SECost from its forwarders to the sink, and (4) the SE-Cost of retransmission. Figure 1 illustrates the above design concept. Hence, given a forwarding set Fs of sender s and a priority pri(j) assigned to each forwarder j∈Fs , we define the OEC metric as
the packet correctly and all nodes in the forwarding set Fs that have a higher priority than pri(j), i.e., pri(i) < pri(j), ∀i∈Fs , fail to receive the packet. That is, the probability Q of relaying the packet by forwarder j equals psj · k∈Fs ,pri(k)
