, pp.37-42 http://dx.doi.org/10.14257/astl.2015.95.08 An Energy Efficient Clustering in Wireless Sensor Networks Se-Jung Lim 1, Gwang-Jun Kim 1* and Daehyon Kim 2 1 Department of computer engineering, 2 Department of Marine & Civil Engineering, Chonnam National University, 50 Daehak-ro, Yeosu-Si, Jeollanam-do, 550-749, Republic of Korea 146279@live.jnu.ac.kr, {kgj, daehyon}@jnu.ac.kr Abstract. In WSNs, sensor nodes which are capable of sensing, computing and communication are randomly deployed in inaccessible terrains or disaster relief operations. Since their power sources are either battery or non-rechargeable, they have to rely on the limited supply of energy. In order to operate for a given mission time as long as possible, a more effective use of energy is the major challenge. In this paper, we proposed an energy efficient clustering (EEC) to prolong the network lifetime in WSNs. We considered the distribution of sensor nodes and then divided the network field into several regions using a hierarchical strip in order to elect cluster headers. The simulation results illustrate that our proposal achieves a longer network lifetime via an effective energy consumption. Keywords: Wireless sensor networks, Clustering, Data gathering 1 Introduction Wireless sensor networks (WSNs) are composed of a large number of sensor nodes which are capable of sensing, computing and communication. Sensor nodes are randomly deployed in inaccessible terrains or disaster relief operations. They have to rely on the limited supply of energy since their power sources are either battery or non-rechargeable. Simultaneously, they must at least operate for a given mission time or as long as possible [1-3]. Hence, a more effective use of energy is the major challenge, and an energy efficient way in WSNs is necessary. Low Energy Adaptive Clustering Hierarchy (LEACH) [4] is one of energy efficient algorithms in WSNs. In LEACH, because being decided by a probability, cluster headers can be concentrated at some regions in the network field. This means that non-header nodes which are located far away from cluster headers need more energy for sending data. The goal of our study is to perform an energy efficient clustering to prolong the network lifetime. We consider the distribution of sensor nodes and divide the network field into several regions by using a hierarchical strip. In each region, we elect a cluster header. By this * Corresponding Author ISSN: 2287-1233 ASTL Copyright 2015 SERSC
it is possible to balance the regional distribution of cluster headers. We used the OMNeT++ simulator for evaluating performance. Simulation measurements are used to verify our proposal. 2 Related Work 2.1 LEACH Low Energy Adaptive Clustering Hierarchy (LEACH) [4] is one of cluster-based energy efficient algorithms in WSNs. In an advertisement phase, each node chooses a random number between 0 and 1 and compares with threshold T (n). If this random number is less than the threshold, it becomes a cluster header for the current round. The threshold value is calculated by (1) To calculate the threshold, a desired percentage of cluster headers (P), the current round (r), and the set of sensor nodes that have not been cluster headers in the last 1/p rounds (G) are used. After receiving the advertisement messages from all the cluster headers, each non-header node determines its own cluster based on the received signal strength measurements and transmits this information that it will be a member of the cluster to its cluster headers in cluster set-up phase. After clustering, each cluster header broadcasts TDMA schedules for providing the order of transmission. Each non-header node sends its data to the cluster header by the allocated TDMA slot. When all data have been received, the cluster header performs data processing such as data fusion and aggregation, and transmits to the sink. 2.2 Hierarchical Strip Tree Ballard [5] strip tree uses rectangular strips of arbitrary orientation for a hierarchical representation for curves in fields such as geography and computer-aided circuit design. The vector-based strip tree algorithm that were modified from Ballard are designed [6-8] in geographic information systems (GIS) environment. 38 Copyright 2015 SERSC
3 Proposed Algorithm 3.1 Network field division phase The aim of this phase is to divide the network field by using hierarchical strip. The number of divisions is decided in accordance with the deployment density of sensor nodes and the number of alive nodes. In each region, we allocate the local sequence numbers for sensor nodes based on distance from a sink. Each node can know the distance to other nodes, and thereby control its own transmission power. 3.2 Cluster set-up phase In this phase, we describe the election method of cluster headers. In each region, we use the number of rounds r mod N (N is the number of sensor nodes in each region) in order to elect a cluster header according to the local sequence number. In each region, the elected cluster header and others construct their cluster. 3.3 Data transmission phase Each cluster header creates a schedule for sensor nodes within each region based on time-division multiple access (TDMA) to gather data, and sensor nodes transmit the gathered data to the cluster header during their allocated time. The cluster header the composite signal is transmitted to a sink. After a round is completed, Cluster set-up and Data transmission phases repeat per round. When there is a fault node, the cluster header sends this fault information to the sink, and then the network field division phase is performed. 4 Simulation In this section, we evaluate the performance of our proposed an energy efficient clustering (EEC) using OMNeT++ simulation tools. All simulations are run in the same network size (100m x 100m), and the number of sensor nodes increases from 50 up to 200 (cluster headers is with around 5% of sensor nodes). In three different scenarios according to the location of the sink, we measured the network lifetime in terms of the round when the first sensor node fails: (50, 200), (50, 300), and (50, 400). Our assumptions about simulation environment as follows: a sink is located far away from sensor nodes and fixed. Sensor nodes are randomly distributed in the network field, and the location of them is fixed. All sensor nodes are homogeneous and know their exact geographic location. They can send data to the sink and adjust their transmission range. Each node has same initial energy, and an energy is restricted. The energy of sensor nodes cannot be recharged. Table 1 shows the parameters used Copyright 2015 SERSC 39
for evaluating the performance in our simulation. We applied the same radio model used in LEACH for transmitting and receiving a message. Table 1. Parameters Symbol Value Description E elec 50 nj/bit The transmitter or receiver circuitry Є amp 100 pj/bit/m 2 The transmitter amplifier E agg 5 nj/bit/message The energy for data aggregation k 2000 bit Message size d meter Distance between two nodes In Table 2, E TX (k, d) is energy consumption for transmitting a k-bit message and a distance d, and E RX (k) is the formula to compute energy consumption for receiving a k-bit message. In case of the E TX (k, d), the distance d assumes d 2 energy loss caused by channel transmission. Table 2. Radio model Radio model Transmitting receiving Formulas E TX ( k, d) = E TX-elec (k) + E TX-amp (k, d) E TX (k, d) = E elec * k + Є amp * k * d 2 E RX (k) = E RX-elec (k) E RX (k) = E elec * k 4.1 Simulation results When the number of sensor nodes is different, Table 3 shows the network lifetime, according to the location of the sink. Our EEC shows better performance in terms of the network lifetime. Table 3. Network lifetime for different number of sensor nodes Location of the sink (50, 200) (50, 300) (50, 400) Protocol Number of sensor nodes 50 100 150 200 EEC 764 965 961 1220 LEACH 482 511 565 527 EEC 417 625 780 802 LEACH 208 284 287 294 EEC 258 416 549 501 LEACH 131 166 170 163 Table 4. Average residual energy for different number of sensor nodes Location of the sink (50, 200) Protocol Number of sensor nodes 50 100 150 200 EEC 0.394 0.409 0.466 0.401 LEACH 0.367 0.352 0.387 0.326 40 Copyright 2015 SERSC
(50, 300) (50, 400) EEC 0.404 0.416 0.358 0.386 LEACH 0.414 0.367 0.367 0.352 EEC 0.385 0.412 0.328 0.409 LEACH 0.369 0.355 0.345 0.363 In LEACH, since cluster headers can be concentrated in some regions in the network field, non-header nodes spend more energy for sending data to the cluster header. However, in our proposal, since a cluster header is elected in each divided region, it is possible to achieve balanced regional distribution among cluster headers. We measured an average residual energy until the first sensor node fails. As shown in Table 4, there are no significant differences between the two algorithms. This means a balance of energy consumption in comparison with the results of the measurement for the network lifetime in Table 3. 5 Conclusion An effective use of energy is the major challenge in WSNs. In this paper, we proposed an energy efficient clustering to prolong the network lifetime in WSNs. We considered the distribution of sensor nodes and then divided the network field into several regions using a hierarchical strip. In each region, we elected one cluster header for each round in accordance with the allocated local sequence numbers. Thereby it was possible to achieve the balanced regional distribution among cluster headers. In the simulation, we measured the network lifetime and an average residual energy when the first sensor node fails. The simulation results illustrated that our proposal achieves a longer network lifetime via more effective energy consumption than LEACH. Acknowledgements. This work (Grants No. C02208260100420951) was supported by Business for Academic-industrial Cooperative establishments funded Korea Small and Medium Business Administration in 2015. References 1. Akyildiz, I.F., Weilian Su, Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communicatoins Magazine 40(8), pp.102-114 (2002) 2. Holger, Karl, Andreas, Willig.: Protocols and architectures for wireless sensor networks. John Wiley & Sons Inc (2005) 3. Ameer Ahmed Abbasi, Mohamed Younis.: A survey on clustering algorithms for wireless sensor networks. Computer Communications 30, pp.2826 2841 (2007) 4. Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. System Sciences, the 33rd Annual Hawaii International Conference (2000) Copyright 2015 SERSC 41
5. Ballard D.: Strip-trees: a hierarchical representation for curves. Communications of the Association for Computing Machinery 14: 31CL21 (1981) 6. Buttenfield, B.P.: A Rule for Describing Line Feature Geometry. In Buttenfield, B. and McMaster. R. (Eds.) Map Generalization: Making Rules for Knowledge Representation. London: Longman, pp.150-171 (1991) 7. Buttenfield, B.P.: Transmitting Vector Geospatial Data across the Internet. Lecture Notes in Computer Science, Vol. 2478, pp.51-64 (2002) 8. Barbara P. Buttenfield and Eric B. Wolf.: The Road and the River Should Cross at the Bridge Problem: Establishing Internal and Relative Topology in an MRDB. 10th ICA Workshop on Generalization and Multiple Representation, Moscow, Russia (2007) 42 Copyright 2015 SERSC