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40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
40120130406013
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40120130406013

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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL JOURNAL OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December, 2013, pp. 107-116 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME PERFORMANCE ANALYSIS OF CLUSTERING PROTOCOLS FOR WIRELESS SENSOR NETWORKS Sachin Gajjar1, 1 Mohanchur Sarkar2, Kankar Dasgupta3 Department of Computer Science and Engineering, Institute of Technology, Nirma University, S.G. Highway, Ahmedabad-382481, India 2 SATCOM and Navigation Applications Area, Space Application Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad - 380015, India 3 Indian Institute of Space Science and Technology, Thiruvananthapuram - 695547, India ABSTRACT The advances in VLSI technologies have made it possible to have integrated devices with processing, storage, sensing, and communication capabilities. These small-scale, low-cost devices when wirelessly connected to form the so called wireless sensor networks open the door of a plethora of commercial and military applications. Extended network lifetime, scalability, and traffic balancing among the nodes in the network are significant requirements for wireless sensor networks. Clustering is one of the effectual techniques for achieving these requirements. This paper surveys, simulates and analyzes well referred clustering protocols which significantly control lifetime of the network. These protocols include Low Energy Adaptive Clustering Hierarchy, Energy Efficient Hierarchical Clustering, clustering protocol by Indranil et. al., Hybrid Energy Efficient Distributed clustering and Distributed Weight based Energy efficient Hierarchical Clustering. The quantitative and qualitative performance metrics presented in the paper for comparison of the clustering protocols can be used to analyze other clustering protocols. These metrics can also be used as design guidelines for developing new wireless sensor networks clustering protocols. Keywords: Wireless Sensor Networks, Clustering Protocols, Survey, Simulation, Analysis. 1. INTRODUCTION The nodes in Wireless Sensor Networks (WSN) have limited computational, storage and power capabilities. They are densely deployed and most of the time left unattended in hostile environments where they may fail, die due to lack of power, physically damaged or may face 107
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME environmental interference. Most of the time it is difficult or impossible to refill or replace their batteries. This demands energy efficient techniques to be used at all the stages of WSN design. One of the important techniques to guarantee prolonged network lifetime is to use clustering. In a clustering protocol the geographically adjacent nodes in a WSN are separated into virtual groups called clusters. A CH works as a local coordinator for its cluster and makes arrangement for intracluster transmission, data aggregation (combining several correlated data signals into a smaller set of information maintaining content of original signals), data forwarding to Base Station (BS), and so on. Clustering offers following advantages: reduces transmission collision by transmission co ordination of CH, offers load balancing by rotating CH, does efficient node resource utilization (when a node dies or joins a cluster, only the cluster members need to update their member information reducing information to be stored and processed), offers good scalability and spatial reuse (non-overlapping and non-neighbor clusters may use same frequency or code set for transmission). This paper surveys, simulates and analyzes well referred clustering protocols which significantly control lifetime of the network. The remainder of paper is organized as follows: Section 2 discusses clustering protocols analyzed in paper, Section 3 presents qualitative and quantitative metrics used to analyze the clustering protocols, Section 4 discusses simulation and analysis of clustering protocols, and finally Section 5 gives conclusion of paper. 2. CLUSTERING PROTOCOLS FOR WIRELESS SENSOR NETWORKS This section introduces the clustering protocols simulated and analyzed in the paper. 2.1 Low Energy Adaptive Clustering Hierarchy (LEACH): LEACH is a distributed hierarchical clustering protocol for WSN [1]. The distributed cluster formation technique enables self organization of nodes and data aggregation at Cluster head (CH) saves communication resources. CH in LEACH are probabilistically selected and CH role is rotated amongst the cluster members (CM) to evenly distribute the energy dissipation across the cluster. The operation of LEACH is divided into rounds consisting of: set up phase where CH selection with cluster formation is done and steady state phase in which nodes send their data to CH which further sends it to BS. During setup phase each node chooses a Random Number (RN) between 0 and 1 and then calculates Threshold T(n) as, P  ,if n ∈ G  1  T ( n) = 1 − P * (r mod ) P  ,otherwise 0  (1) Where P is desired percentage of nodes to be CHs, r is current round, G is set of nodes that has not been CH in past 1/P rounds. If RN<T(n) node becomes CH and broadcasts a CH advertisement message using a non-persistent Carrier Sense Multiple Access (CSMA) Medium Access Control (MAC) protocol [2]. Non-cluster head (NCH) nodes listen to the media and choose to become cluster member based on largest received signal strength of advertisement message which implies nearest CH. NCH nodes transmits cluster join request to chosen CH using a non-persistent CSMA. CH in turn sets up Time Division Multiple Access (TDMA) based transmission schedule for its cluster members and broadcasts it. Each NCH node sleeps all time except during it’s transmit time saving its energy. The steady state operation is broken into frames, where NCH nodes send their data to CH during their allocated transmission slot. 108
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME 2.2 Energy-efficient hierarchical clustering (EEHC): EEHC can be considered as an extension of LEACH with multi-hop intra clusters and a hierarchy of CHs to route data to the BS [3]. EEHC is a distributed randomized clustering protocol that organizes the nodes into clusters with a hierarchy of CHs. The operation of EEHC protocol is based on two-stage clustering: single-level clustering and multilevel clustering. In the single-level clustering stage, each node becomes a volunteer CH with a predefined probability p and announces it to its neighbor nodes within k hops communication range. A NCH node receiving this announcement becomes a CM of the closest cluster. Nodes that are neither CHs nor belonging to a cluster become forced CHs. If a node does not receive a CH advertisement within a certain time duration t, it assumes that it is not within k hops of any volunteer CH and becomes a forced CH. At the second stage, the same mechanism is repeated recursively at each level of CHs. Data transmission to the sink is performed using multi-hop routing through CH organization in a multi-level hierarchy rooted at the BS. The authors provide methods to compute the optimal values of p and k parameters that ensure minimum consumed energy. 2.3 Protocol by Indranil et. al.: Indranil et. al. propose CH selection using fuzzy logic [4]. The operation of protocol is divided into rounds. In each round all the nodes transmit their location and residual energy information to BS which runs fuzzy logic to decide the CH using node’s residual energy, centrality with respect to the cluster and concentration of node in a given region as input parameters. The output of the fuzzy logic is chance of node to become CH. Node with the highest chance value becomes CH. The BS broadcasts the ID of node that has been selected as CH. The steady state phase is similar to LEACH. 2.4 Hybrid Energy-Efficient Distributed clustering (HEED): HEED is another distributed clustering protocol in which CHs are selected periodically based on sensor node’s residual energy and intra-cluster communication cost which is a function of cluster size [5]. If the power level for intra-cluster communication is fixed for all nodes and it is required to distribute traffic load amongst CHs, then cost is proportional to node degree. It is proportional to 1/node degree, if it is required to create dense clusters. If power levels for intra-cluster communication are variable then cost is Average Minimum Reachability Power (AMRP) defined as, M ∑ MinPwr i AMRP = i =1 (2) M where MinPwri is minimum power level required by a node vi, 1 < i < M (number of nodes within cluster range), to communicate with a CH u. AMRP is the mean of minimum power levels required by nodes within the cluster range to reach the CH. The operation of HEED is divided into: initialization phase, repetition phase and finalization phase. Clustering is initiated every TCP+TNO seconds to select new CHs. TCP is time required to form a cluster and TNO is the time gap between end of TCP and start of a succeeding TNO. In the initialization phase each sensor node sets its probability of becoming a CH, CHprob, given as, CH prob = C prob × 109 Eresidual Emax (3)
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME where Cprob is initial percentage of CHs, Eresidual is residual energy and Emax is maximum energy of the node. In the repetition phase, every node goes through several iterations until it finds CH with the least cost. If it does not find a CH it elects itself as a CH and broadcasts message to its neighbors informing them its status change. In each iteration the node doubles its CHprob value. The execution of repetition phase stops when CHprob reaches one. In the finalization phase each node makes a final decision on whether to become a CH based on its CHprob value or to join a cluster. 2.5 Distributed weight-based energy efficient hierarchical clustering protocol (DWEHC): DWEHC is protocol that constructs multilevel clusters [6]. Nodes in DWEHC are location aware and transmit at fix power levels. Clusters are multi-level with CH and its first level child node, second level child node, and so on. TDMA is used for intracluster communication and 802.11 based MAC mechanism is used by CHs for data transmission to BS [7]. The operation of DWEHC is divided into: initialization phase, cluster generation phase and finalization phase. During initialization phase, each sensor node broadcasts its (x,y) coordinates. It also discovers neighbors lying inside its transmission range. It then calculates its weight as below,  wweight (s) =   ∑  u∈Nα ,c( s ) (R − d )  Eresidual (s) × 6R  Einitial (s)  (4) where R is the cluster range fixed for the entire network; d is the distance between node s and its neighbor node u; Nα,c (s) is the set of the neighbor nodes of s, α is the transmitter power factor equal to 2 or 4, and c is a constant; Eresidual(s) is the residual energy and Einitial(s) is the initial energy of the node s. Each node then broadcasts its weight. Node with the largest weight among all its neighbors becomes a temporary CH. A temporary CH can become an actual CH only if a certain percentage of its neighbors elect it as their CH. This percentage is 100% in the first iteration and in subsequent six iterations, it is decreased to (6-i)/6 where i is the number of iterations and i<6. At this phase, the neighboring nodes are said to be the first-level child members with respect to the CH. Each node then checks with its NCH neighbor to discover its minimum cost for reaching CH. The sensor node uses its neighboring distance to decide whether to change its membership level or not to reach a CH using minimum communication energy. In this process a node may switch to a new CH different from the original one it had selected. The process goes on until all nodes agree upon the best energy-efficient intra-cluster topology. In the finalization phase, the cluster generation process is repeated one more time. The cluster generating process (including finalization) runs at the most seven times as each sensor node can have a maximum of six neighbors. 3. PERFORMANCE METRICS 3.1 Quantitative metrics • Goodput: It is the percentage of useful information excluding the control packets delivered to the BS. • Throughput: It is the number of bytes per second received at BS. • Overhead: It is the total number of control packets measured in bytes required for protocol operation. 110
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME • Network settling time: It is time required for group of nodes to self organize and transmit first message. • Latency: It is the time lapse between start of data dissemination from source nodes to its arrival at BS. • Network Lifetime: For a network with thin node deployment it is time until first node dies; for intense deployment it is time till half of nodes are alive, for surplus deployment it is time till last node dies. • Residual Energy: Energy available in node at a particular instance of time is called residual energy. • Network scalability: The maximum number of nodes that protocol can scale to while preserving reliable communication. 3.2 Qualitative metrics • Adaptation to Transmission media: Protocol design should provide solutions for wireless transmission media problems like hidden, exposed terminal problem, High BER, fading, spatial reuse. • Fairness: For BS to have knowledge of entire sensed area, protocols should provide fair bandwidth allocation and access among all nodes. • Heterogeneity: Protocol should consider the heterogeneity between BS and sensor node in terms of power, storage and processing capabilities at the time of work distribution among nodes and BS. • Time Synchronization: Time synchronization either strict or loose is required for synchronizing sleeping cycles of nodes and BS. 4. SIMULATION AND ANALYSIS All the simulations were carried out using Matlab [8]. Table I shows the basic simulation settings for the simulations carried out in this paper. To reduce the occasionalism, twenty simulations with different seeds were carried out for each scenario and average values were adopted as the results. TABLE I Simulation Parameters Parameter Description Nodes 1000 Network size 1000m x 1000m BS location (0,0) Packet length 6400 bytes Initial energy 0.5 J Radio amplifier energy Radio electronics energy 111 10 nJ/bit/m2 50 nJ/bit
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME 4.1 Quantitative analysis: In Fig. 1 the comparison of total residual energy of the network for the first 260 rounds is presented. The performance of DWEHC is better compared to others. This is because in DWEHC, source nodes relay messages through their parents to the CH and in other protocols nodes directly communicate with the CH. In direct communication the energy consumption increases with the increase in distance between the sensor node and its CH. The total residual energy rate is least for LEACH protocol because it uses probabilistic method for CH selection which does not take into account any of the node parameters. As a result there might be cases when nodes with less residual energy and/or the ones which are far from the BS might be selected as CH. This in turn increases the energy consumption of the network. Fig. 2 shows the number of dead nodes and Fig. 3 shows aggregated data send to BS for the first 260 rounds. The number of dead nodes is minimum and aggregated data send to BS is maximum for DWEHC because as explained earlier energy consumption in DWEHC is minimum. HEED shows an intermediate performance in terms of residual energy, number of dead nodes and data received at the BS. This is because HEED uses sensor node’s residual energy and intracluster communication cost for CH selection. However, HEED’s clustering process requires several iterations and in each iteration a lot of packets are broadcasted which increases the overhead and degrades its performance compared to DWEHC. Protocol by Indranil et. al. requires each node to send its location and energy information to the BS for each round which degrades its overall performance. EEHC uses a probabilistic method for CH selection and hence shows an inferior performance compared to all other protocols. Fig. 1. Total residual energy of the network for the first 260 rounds 112
  • 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME Fig. 2. Number of dead nodes for the first 260 rounds Fig. 3. Total amount of data received at the BS for the first 260 rounds For examining the scalability of protocols the number of nodes in the network was varied from 200 to 1000 and packet received at the BS was measured. Fig. 4 shows that as the number of source increases aggregated packets received at BS increases. This effect is maximum in DWEHC 113
  • 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME because it uses a multihop cluster architecture and limits the maximum number of child nodes a parent can have. Scalability of LEACH is minimum because of the two hop architecture for data transfer to the BS. Table 2 gives the qualitative analysis of the clustering protocols presented in the paper. LEACH has the best goodput performance among all the protocols because it requires minimum control packets (CH advertisement and join requests) to be sent by the nodes. DWEHC has the best throughput performance as the number of dead nodes over rounds is minimum in case of DWEHC. The network settling time is maximum in Indranil’s protocol since each node requires to send its parameters to the BS for running the CH selection algorithm. The average data latency is maximum in EEHC due to its multihop architecture. The network lifetime of DWEHC is maximum as energy consumption in it is minimum as explained earlier. Fig. 4. Total amount of data received at BS for varying number of nodes TABLE II Quantitative analysis of the protocols 114
  • 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME 4.2 Qualitative analysis: Table 3 gives qualitative analysis of the clustering protocols presented in the paper. To reduce inter-cluster interference LEACH uses direct-sequence spread spectrum (DSSS) and transmitter-based code assignment in which the BS assigns a unique spreading code to each cluster [9]. The nodes in the cluster then transmit their data to the CH using this spreading code and the CH filters all received energy using this spreading code. Use of TDMA for intra-cluster communication makes DWEHC and LEACH comparatively fair protocols. Inranil’s protocol runs the computationally intensive algorithm for CH election at the BS in and hence it considers heterogeneity between the nodes and BS. Use of TDMA and DSSS requires tight global time synchronization among nodes in LEACH. Indranil too requires tight timing synchronization among nodes as each node has to periodically transmit its parameters to the BS. DWEHC requires local time synchronization. The major drawback of DWEHC is that each node requires location information to run DWEHC which in turn increases node’s energy consumption. Use of AMRP for CH selection in HEED provides a unified mechanism for CH selection and balances the energy distribution of the nodes but the periodic neighbor updates increases the protocol overhead. Use of fuzzy logic selects the best CH in Indranil’s protocol but again each node is required to periodically send its parameters to the BS which increases the energy consumption of the nodes. TABLE II Qualitative analysis of the protocols 115
  • 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 6, November - December (2013), © IAEME 5. CONCLUSION WSNs due to their versatile applications have attracted attention of researchers in recent years. There has been much research for addressing issues related to WSN. Clustering plays an important role in the performance of a WSN, and research associated with it is always in spotlight. Many clustering protocols have been proposed to solve issues like scalability, load balancing and spatial reuse. This paper surveys, simulates and analyzes LEACH, EEHC, Indranil’s protocol, HEED and DWEHC clustering protocols. The paper discusses each clustering protocol in terms of their objective, operation and analyzes them using qualitative and quantitative metrics. With this survey it is seen that clustering protocols for WSN still have many important issues to examine which include stability of the cluster structure, control overhead involved in cluster formation and its maintenance, traffic load and distribution among different clusters. Through the detailed study of these five clustering protocols, we can conclude that DWEHC can perform comparatively well because it provides energy efficiency by creating a minimum power cluster topology, it offers scalability with multihop cluster architecture and limit on maximum number of child nodes a parent can have. The weak points of the clustering protocols discussed facilitates researchers to work on overcoming them and thereby offer more efficient and effective clustering protocols for WSNs and evaluate them using the quantitative and qualitative metrics presented for comparison framework. REFERENCES [1] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, An Application-Specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, 1(4), 2002, 660-670. [2] Alberto Leon-Garcia and Indra Widjaja, Communication Networks Fundamental concepts and key architectures, (McGraw-Hill Publications, USA, 2001), 424-431. [3] S. Bandyopadhyay and E. J. Coyle, An energy efficient hierarchical clustering algorithm for wireless sensor networks, Proc. of IEEE INFOCOM, 2003, 1713-1723. [4] I. Gupta, D. Riordan, and S. Sampalli, Cluster-head election using fuzzy logic for wireless sensor networks, Proc. of 3rd Annual Communication Networks and Services Research Conference, 2005, 255–260. [5] O. Younis and S. Fahmy, Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, IEEE Transactions on Mobile Computing, 3(4), 2004, 366–379. [6] Ping Ding, JoAnne Holliday and Aslihan Celik, Distributed energy efficient hierarchical clustering for wireless sensor networks, Proc. of the 1st IEEE International Conference on Distributed Computing in Sensor Systems, 2005, 322–339. [7] Charles Severance, IEEE 802.11: Wireless Is Coming Home, Journal of Computer, 32(11), 1999, 126-127. [8] Matlab guide [Online] Available http://www.mathworks.com/. [9] L. Hu, Distributed code assignments for CDMA packet radio networks, IEEE/ACM Transactions on Networking, 1(6), 1993, 668–677. [10] Yogesh V Patil, Pratik Gite and Sanjay Thakur, “Automatic Cluster Formation and Assigning Address for Wireless Sensor Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 116 - 121, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [11] Neeraj Tiwari, Rahul Anshumali and Prabal Pratap Singh, “Wireless Sensor Networks: Limitation, Layerwise Security Threats, Intruder Detection”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 2, 2012, pp. 22 - 31, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. 116

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