an energy-based clustering algorithm for wireless

An Energy-based Clustering Algorithm for Wireless Sensor Networks Jin Wang 1 , Xiaoqin Yang 1 , Yuhui Zheng 1 , Jianwei Zhang 2 , Jeong-Uk Kim 3 1 Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China 2 School of Math and Statistic, Nanjing University of Information Science & Technology, Nanjing 210044, China 3 Department of Energy Grid, Sangmyung University, Seoul 110-743, Korea {wangjin, xqyang, yhzheng, zhangjw}@nuist.edu.cn; jukim@smu.ac.kr Abstract. Energy efficient routing is one of the key issues to prolong lifetime of wireless sensor networks (WSNs). The number of cluster and the distribution of cluster heads (CHs) have a major impact on the network performance. Energy-based clustering algorithm can greatly improve energy efficiency of WSNs because it adopts a multi-hop communication in each cluster. Besides, the neighborhood of the sink node (SN) will perform direct transmission to relieve the workload of CHs. Simulation results show that our algorithm can largely reduce the total energy consumption and prolong the network lifetime compared to other algorithm like LEACH. Keywords: wireless sensor networks, energy level, cluster number, clustering 1 Introduction Wireless sensor networks (WSNs) are composed of large number of low-cost and tiny sensors. It is a distributed and self-organized network where sensor nodes will locally carry out sensing, processing and transmitting operations in an autonomous and unattended manner. WSNs have broad applications such as military surveillance and tracking, environment monitoring and forecasting, healthcare etc.[1,2]. Energy consumption and energy-balancing are one of the primary research issues for WSNs. Since node’s energy is limited and non-rechargeable, how to improve energy efficiency and balance energy has become more and more important. Clustering algorithm is an energy-efficient technology for WSNs. In practice, CHs can process, filter and aggregate data sent by cluster members. In this paper, we focus on studying the influence of cluster number on network performance. We propose an Energy-based Clustering Algorithm (ECA) for WSNs. Based on [6], we discuss the overall energy consumption under different numbers of clusters and get the optimal cluster number. Then we divide the sensor field into k equal regions where we choose nodes with the highest residual energy as CHs and adopt multi-hop manner to transmit data. Thus, we can ensure uniform distribution of CHs in entire network and relieve the workload of CHs. 16 2 Related Work Directed diffusion (DD)[3] is a query-based routing protocol for WSNs. By using data aggregation, caching and reinforcement techniques, the appropriate link is dynamically selected from the candidates. Rumor routing protocol [4] uses forwarding query messages randomly to reduce the overhead of route establishment. Hierarchical structure routing protocols are suitable for WSNs since they can not only provide good scalability but also perform data aggregation. Low-energy adaptive clustering hierarchy (LEACH)[5] is a clustering-based protocol which utilizes randomized rotation of local CHs to evenly distribute the energy load across the network. Compared with other ordinary routing protocols like DD, it can prolong the network lifetime up to 8 times. However, the 5% of CHs are randomly selected and CHs transmit data directly to SN. To address this deficiency, LEACH-C algorithm [6] centralize to choose CHs through the sink nodes. Power-Efficient gathering in sensor information systems (PEGASIS)[7] is known as an improved version of LEACH. It uses chain structure to connect and select the nearest neighbor to communicate. Besides, each chain selects only one node as the head communicating with SN. A hybrid, energy-efficient, distributed (HEED)[8] clustering protocol was proposed which considers node’s residual energy and the cost of communication within the cluster during CHs selection. It can not only minimize the control overhead, but also prolong network lifetime since CHs are well distributed. The author in [9] introduced an energy efficient heterogeneous clustered scheme based on weighted election probabilities of each node to become CHs according to the residual energy. The author in [10] introduced an adaptive decentralized re-clustering protocol (ADRP). In ADRP, CHs and next heads are elected on residual energy of each node and the average energy of each cluster. The author in [11] proposed a novel distributed clustering algorithm where CHs are elected following a three-way message exchange between each sensor and its neighbors. Sensor’s eligibility to be elected cluster head is based on its residual energy and its degree. To enhance lifetime, the author in [12] proposed an energy efficient clustering protocol (EECPL) which organizes sensors into clusters and uses ring topology to send data packets. In [13], the author proposed a hop-based energy aware routing algorithm to save and balance energy consumption. 3 Energy Consumption and Optimal Cluster Number We adopted the energy consumption model which is called first order radio model. The definitions and units of radio parameters are listed in Table 1. Table 1. Radio parameters. Parameter Definition Unit elec E Energy dissipation to run the radio device 50 nJ/bit fs ε Free space model of transmitter amplifier 10 pJ/bit/m 2 17 mp ε Multi-path model of transmitter amplifier 0.0013 pJ/bit/m 4 l Packet length 2000 bits 0 d Distance threshold mpfs εε m Each node consumes the following Tx E amount of energy to transmit an l-bits message over distance d and the Rx E amount of energy to receive the message. { 0 2 0 4 , , ),( dddllE dddllE Tx fselec mpelec dlE <+ ≥+ = ε ε ; elecRx lElE = )( (1) According to first order radio model, each cluster consumes the cluster E amount of energy to communicate with SN. To minimize the total energy consumption total E , we use the conclusion in [10] to get the optimal cluster number opt k and the formula is shown as the follows. k N k R llEE k N lEE k N EE fselecTxelecmemberCHcluster ) 2 ()1()1( 2 ε +++−≈−+= (2) k R NlKElkENlENEkEkEE fsTxelecelecmemberCHclustertotal 2 2 2 ε ++−=+== (3) mp fs opt N d R k ε ε 2 2 = (4) 4 Our Proposed Energy-based Clustering Algorithm (ECA) 4.1 Cluster Formation Before CHs selection, we firstly divide the network into k equal regions according to the optimal cluster number. Cluster head only manages the data collected from the region and then relay the aggregated data to SN. Besides, neighbor nodes of SN will perform direct transmission to SN. After cluster formation, we assign a random initial energy level to each sensor. To balance the energy consumption levels, we use the initial energy levels to select the CH-candidate nodes. Upon being selected, each CH-candidate transmits a packet and advertises its ID and residual energy level. A CH-candidate monitors advertisements from others and defers from acting as a CH if a higher energy level is reported by another. Finally, candidate with the highest residual energy level will become CH. Other nodes in this region will become the member of this cluster. 18 4.2 Intra-cluster Multi-hop Routing Setup We adopt a multi-hop communication protocol to save energy and set a threshold d. If the distance is smaller than d, it transmits data to CHs directly; otherwise, it will find an adjacent node as the relay node. We choose the relay node based on distance and residual energy. Suppose i s is far away from CHs and chooses j s as its relay node. We adopt a free space propagation channel model to deliver an l-bits packet to CHs. The energy consumption and the link cost link E are defined respectively as follows. )),(),((3),( 22 CHssdssdllEssE jjifselecji ++= ε (5) 22 ),(),(),( CHssdssdssE jjijilink += (6) To avoid the nodes near CHs depleting their energy quickly, we also consider the residual energy of the relay node. So the cost is defined as follows. ]1,0[, ))(max( )())(max( )1( )),(),(max( ),(),( )(cos 22 22 ∈ − ∗−+ + + ∗= ωωω jE jEjE CHssdssd CHssdssd jt jji jji (7) After each node has chosen the minimum cost node as its relay node, an intra- cluster route is constructed. 5 Performance Evaluation We use MATLAB simulator to evaluate the performance of our algorithm. There are [200,500] nodes evenly deployed in a [150,200,300] circular area. The initial energy level of each node is 2J. SN is placed in the centre of the network. The transmission radius can be adjusted from 80 to 100 meters based on node density. 5.1 Network Topology According to Formula (4), when N=200 and R=150, k is equal to 16. Fig.1 illustrates this point that the energy consumption is the least when k=16. Fig.1. Comparison of energy consumption. Fig.2. Network topology. 19 Then we divide the network into 16 equal regions and select nodes with the highest residual energy level to act as CHs. The neighborhood of SN has been marked with stars. The network topology is built as shown in Fig.2. 5.2 Total Energy Consumption and Network Lifetime Fig.3 shows the energy consumed by ECA and LEACH and ECA consumes less energy than LEACH so that we can achieve the goal to save energy. Besides, the number of “Alive node” in one region over simulation time is illustrated in Fig.4. Here, we define the network lifetime as the period of time until the first node depletes its energy. Fig.5 shows that the lifetime of ECA in one region is around 2087 rounds, whereas the lifetime of LEACH is around 1478 rounds. Thus ECA has a better performance in extending network lifetime. Fig.3. Energy consumption. Fig.4. Network lifetime. 6 Conclusions In this paper, energy-based clustering algorithm (ECA) is proposed to reduce the energy consumption. In ECA, we compare the overall energy consumed by the network with different cluster number. And then we divide the network and assign an initial energy level to each node. In each region, we select the node with highest residual energy level from the candidate nodes to act as cluster head. In each cluster, we adopt a multi-hop communication protocol between cluster members to reduce the cost of long distance transmission. Simulation results demonstrate that ECA can effectively reduce the energy consumption of the entire network so that the network lifetime is largely prolonged. 20 Acknowledgement This research work was supported by a grant (07-HUDP-A01) from the High-tech Urban Development Program funded by Ministry of Land, Transport and Maritime Affairs of Korean government. It was also supported by the National Natural Science Foundation of China (61173072) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. Prof. Jeong-Uk Kim is the corresponding author. References 1 I.F.Akyildiz, W.Su, Y.Sankarasubramaniam, and E.Cayirci, “Wireless sensor networks: A survey” , Comput. Netw., vol.38, no.4, pp.393-422, 2002. 2 K.Akkaya and M.Younis, “A survey on routing protocols in wireless sensor networks”, in the Elsevier Ad Hoc Network, vol.3, no.3, pp.325-349, 2005. 3 C.Intanagonwiwat, R.Govindan, and D.Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks”, Proc. 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom'00), pp.56-67, Aug. 2000. 4 D.Braginsky, and D.Estrin, “Rumor Routing Algorithm For Sensor Networks” ， WSNA’02, September 28,2002. 5 W. Heinzelman, A.Chandrakasan, and H. 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An Energy-based Clustering Algorithm for Wireless Sensor Networks Jin Wang 1 , Xiaoqin Yang 1 , Yuhui Zheng 1 , Jianwei Zhang 2 , Jeong-Uk Kim 3 1 Jiangsu Engineering Center. energy is limited and non-rechargeable, how to improve energy efficiency and balance energy has become more and more important. Clustering algorithm is an energy-efficient technology for WSNs. In. of wireless sensor networks (WSNs). The number of cluster and the distribution of cluster heads (CHs) have a major impact on the network performance. Energy-based clustering algorithm can greatly