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COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NETWORKS

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In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times.

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COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NETWORKS

  1. 1. ICRTEDC-2014 20 IJCSIT, Vol. 1, Spl. Issue 2 (May, 2014) e-ISSN: 1694-2329 | p-ISSN: 1694-2345 GV/ICRTEDC/05 COMPARISON OF ENERGY OPTIMIZATION CLUSTERING ALGORITHMS IN WIRELESS SENSOR NETWORKS 1 Manpreet Kaur, 2 Jagroop Kaur 1,2 Computer Science Department, UCOE, Punjabi University, Patiala, India 1 manpreetaulakh65@gmail.com, 2 jagroop_80@rediffmail.com ABSTRACT : Fast growth of wireless services in recent years is an indication that considerable value is placed on wireless networks. Wireless devices have most utility when they can be used anywhere at any time. One of the greatest challenges is limited energy supplies. Therefore, energy optimization is one of the most challenging problems in wireless networks. In recent years, Wireless Sensor Networks have gained growing attention from both the research community and actual users. As sensor nodes are generally battery-energized devices, so the network lifetime can be widespread to sensible times. Therefore, the crucial issue is to prolong the network lifetime. In this paper, two Energy Optimization Schemes Clustering and Direct Diffusion for Wireless Sensor Networks (WSN) has been compared on the basis of different parameters like scalability, energy efficiency and reliability etc. on the basis of this comparison we can use better Optimization technique according to the situation. Keywords— mobile ad hoc network, two-tiered architecture, clustering, system lifetime, CH-rotation, Node mobility, In cluster Topology. I. INTRODUCTION WSN consists of spatially distributed sensor to monitor physical or environmental conditions. It described as a network of nodes that cooperatively sense and may control the environment enabling interaction between persons or computers. The Wireless Sensor Network consists of numerous applications for monitoring different environments. Sensor node’s energy source is provided by battery power. The lifetime of a sensor node is expected to be months to years, because replacing or recharging a node is complicated and unfavourable. So efficiently using energy from the nodes has become a crucial challenge. So we here explain different clustering algorithms for optimize use of energy. A. Clustering Neighbouring sensor nodes generally have the data of similar events because they collect events within a specific area. If each node individually transmits the collected data to the sink node, a lot of energy will be wasted to transmit similar data to the sink node. The sensor nodes are organized into a number of clusters in order to avoid such energy wastes. In a clustering mechanism, the nodes that are adjacent geographically are grouped to form a cluster. The cluster head takes care of transferring the data to other clusters within the network. The member nodes report their data to the respective CHs. The CHs aggregate the data and send them to the central base through other CHs. B. Limitations in WSNs, that clustering schemes must consider  Limited Energy  Network Lifetime  Limited Abilities  Application Dependency C. Sensor network components 1) Sensor Node: A sensor node is the core component of a WSN. 2) Clusters: Clusters are the organizational unit for WSNs. 3) Cluster heads: Cluster heads are the organization leader of a cluster. 4) Base Station: It provides the communication link between the sensor network and the end-user. 5) End User: The data for a particular application may make use of the network data over the internet, using a PDA, or even a desktop computer. Fig 1 General Sensor Network Architecture II. CLASSIFICATION OF CLUSTERING ALGORITHMS There are many different classifications based on the characteristics and functionality of the sensors in the cluster:-
  2. 2. 21 ICRTEDC -2014 Fig 2. Classification of Clustering Alrorithms A. According to Cluster-head selection Fig 3 Classification based on Cluster-head 1) Heuristic Algorithms: An heuristic algorithm is an algorithm has goals to Finding an algorithm with reasonable run-time and With finding the optimal solution. 2) Weighted Scheme: These algorithms use a combination of metrics such as the remaining energy, transmission power, etc.,to achieve more generalized goals than single-criterion protocols. 3) Hierarchical schemes: Clustering algorithm in which cluster head candidates compete for the ability to elevate to cluster head for a given round.. If a given node does not find a node with more residual energy, it becomes a cluster head. 4) Grid Schemes: In this algorithm one of the sinks (called the primary sink), dynamically, and randomly builds the cluster grid. The cluster heads are arranged in a grid-like manner. B. According to Algorithm complexity 1) Variable convergence time algorithms: Variable convergence time algorithms showed their suitability for networks having large number of nodes and enable more control of the cluster properties than the constant time ones. 2) Constant convergence time algorithms:- Clustering algorithms that converge completely in a fixed number of iterations, regardless of the size of the nodes population are called constant convergence time clustering algorithms. Fig.4 Classification based on Algorithm Complexity C. According to cluster formation criteria 1) Probabilistic Clustering Approaches: Some algorithm are all probabilistic in nature and their main objective was to reduce the energy consumption and prolong the network lifetime. Some of them follow a random approach for CH election. 1) Non-probabilistic Clustering Approaches: Here criteria for CHs election and cluster formation, based on the nodes’ proximity (connectivity, degree, etc.) and on the information received from other closely located nodes. Fig.5 Classification based on Algorithm Complexity III. OVERVIEW OF CLUSTERING ALGORITHMS A. Linked Cluster Algorithm (LCA) LCA, was one of the very first clustering algorithms developed. In LCA, each node is assigned a unique ID number and has two ways of becoming a cluster head. The first way is if the node has the highest ID number in the set including all neighbour nodes and the node itself. The second way, assuming none of its neighbour are cluster heads, then it becomes a cluster head. B. Weighted Clustering Algorithm (WCA) WCA a corresponding weight-based protocol was proposed where the CH election process is based on the computation of a “combined weight” Wv for each node, which takes into account several system parameters such as the node degree, the transmission power, mobility, and the remaining energy of the node: Wv = w1Tv +w2Dv +w3Mv +w4Pv. The node with the smallest weight in its neighbourhood is chosen as a CH. C. Low Energy Adaptive Clustering Hierarchy (LEACH)
  3. 3. ICRTEDC-2014 22 It’s an hierarchical, probabilistic, distributed, one-hop protocol. Initially a node decides to be a CH with a probability “p” and broadcasts its decision. After its election, each CH broadcasts an advertisement message to the other nodes and each one of the other (non-CH) nodes determines a cluster to belong to, by choosing the CH that can be reached using the least communication energy. A node becomes a CH for the current rotation round if the number is less than following threshold. D. Energy Efficient Clustering Scheme(EECS) It is a distributed, k-hop hierarchical clustering algorithm aiming at the maximization of the network lifetime. Initially, each sensor node is elected as a CH with probability “p” and announces its election to the neighbouring nodes within its communication range. Consequently, any node that receives such CH election message and is not itself a CH, becomes a member of the closest cluster. E. Hybrid Energy-Efficient Distributed Clustering (HEED) The algorithm is divided into three phases. In Initialization phase, percentage value, Cprob, used to limit the initial CH announcements to the other sensors. CHprob = Cprob * Eresidual/Emax, where Eresidual is the current energy in the sensor, and Emax is the maximum energy.In Repetition phase every sensor goes through several iterations until it finds the CH that it can transmit to with the least transmission power. In Finalization phase each sensor makes a final decision on its status. It either picks the least cost CH or pronounces itself as CH. F. PEGASIS Power-Efficient GAthering in Sensor Information Systems is a data-gathering algorithm. The algorithm presents the idea that if nodes form a chain from source to sink, only 1 node in any given transmission time-frame will be transmitting to the base station. Data-fusion occurs at every node in the sensor network allowing for all relevant information to permeate across the network. G. GROUP In this algorithm one of the sinks (called the primary sink), dynamically, and randomly builds the cluster grid. The cluster heads are arranged in a grid-like manner. Forwarding of data queries from the sink to source node are propagated from the Grid Seed (GS) to its cluster heads, and so on. The GS is a node within a given radius from the primary sink.. IV. PERFORMANCE ANALYSIS OF CLUTERING ALGORITHMS Two major areas:- A. Power, Energy and Network Lifetime 1) WCA: • In terms of energy consumption, the algorithm tries to achieve the most stable cluster architecture. • This reduces system updates and hence computation and communication costs Fig 6.Comparison of Reaffiliations for Heuristic Algorithms 2)LEACH: • No overhead is wasted making the decision of which node becomes cluster head as each node decides independent of other nodes. • Each node calculates the minimum transmission energy to communicate with its cluster head and only transmits with that power level. 3)PEGASIS: • During a given round, only 1 node in the network is transmitting data to the base station • Since each node communicates with its nearest neighbour, the energy utilized by each node is also minimized. 4) GROUP : • Energy conservation is achieved by the lower transmission distance for upstream data. • In GROUP, data is transmitted short ranges along the upstream path. Fig 7.Comparison of GROUP and LEACH Algorithms 5)HEED: • In this algorithm, network life time is prolonged through:Reducing the number of nodes that compete for channel access; Cluster head updates, regarding cluster topology. B.Quality and Reliability of the Links 1) WCA: • In terms of quality and reliability, the WCA algorithm has the flexibility to be adapted to many applications, assigning different weights to the parameters of the combined weight 2)LEACH:
  4. 4. 23 ICRTEDC -2014 • The CSMA mechanism is used to avoid collisions.Periodic rotation of cluster heads extend the network lifetime, guaranteeing full connectivity in the network for longer periods than conventional algorithms. 3)PEGASIS: • In PEGASIS each node communicates with its nearest neighbour. This implementation may be more susceptible to failure due to gaps in the network. 4)EECS: • Since EECS offers improved energy utilization throughout the network, full connectivity can be achieved for a longer duration. 5)GROUP: • When a node fails in its attempt to communicate with its cluster head it will send a broadcast message to search and establish a new cluster head. 6)HEED: • This is because HEED uses intra-cluster communication cost in selecting its cluster heads. Therefore the node distribution does not impact the quality of communication. V. COMPARISON OF PROPOSED ALGORITHMS We compare clustering Algorithms LCA, WCA, LEACH, EECS, HEED, PEGASIS, GROUP on the basis of Time complexity, Node mobility, Cluster overlap, In cluster topology, Cluster count, Clustering process, Ch-rotation, multilevel parameters to choose best one approach. Table 1.COMPARISON of CLUSTERING ALGORITHMS Clustering Approaches Time Complexity Node Mobility LCA Variable Possible WCA Constant No LEACH Constant Limited EECS Constant Limited HEED Constant No PEGASIS Variable No GROUP Variable No Clustering Approaches Clustering process CH- Rotation Multilevel LCA Distributed No No WCA Distributed No No LEACH Distributed Yes No EECS Distributed Yes No HEED Distributed Yes No PEGASIS Hybrid Yes No GROUP Hybrid No No VI. CONCLUSION In wireless sensor networks, the energy limitations , Quality of Service metrics expose reliability issues when designing recovery mechanisms for clustering schemes. Hierarchical routing and data gathering protocols, regarded as the most efficient approach to support scalability in WSNs. To prolong the lifetime of the network, the combined need for fast convergence time and minimum energy consumption led to appropriate fast distributed probabilistic (clearly random or hybrid) clustering algorithms which quickly became the most popular and widely used in the field. VII. FUTURE WORK Further improvements in reliability should examine possible modifications to the re-clustering mechanisms following the initial cluster head selection. In addition, other mechanisms such as the ability of nodes to maintain membership in auxiliary clusters can reinforce the current state of sensor network reliability. ACKNOWLWDGEMENT First and foremost, I would like to thank God almighty for life itself. All that I have is due to His grace and I give all glory to him. With deep sense of gratitude I express my sincere thanks to my esteemed and worthy supervisor Er. Jagroop Kaur, Assistant professor, Department of Computer Engineering, Punjabi University, Patiala for her valuable guidance in carrying out this work. REFERENCES [1] A.A. Abbasi and M. Younis,” A survey on clustering algorithms for wireless sensor networks”, Computer Communications, 30, 2826–2841, 2007. [2] K. Sohrabi et al., “Protocols for self-organization of a wireless sensor network”, IEEE Personal Communications, 7(5), 16–27, 2000. [3] R. Min et al.,” Low power wireless sensor networks”, in Proceedings of International Conference on VLSI Design, Bangalore, India, January 2001. [4] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy Efficient Communication Protocol for Wireless Micro Sensor Networks,” Proceedings of IEEE HICSS, Jan 2000. [5] C. F. Chiasserini, I. Chlamtac, P. Monti, and A. Nucci, “Energy Efficient Design of Wireless Ad Hoc Networks,” Proceedings of European Wireless, Feb 2002. [6]S. Bandyopadhyay and E. J. Coyle, “An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks,” IEEE INFOCOM, April 2003. [7] D. J. Dechene, A. El Jardali, M. Luccini, and A. Sauer “A Survey of Clustering Algorithms for Wireless Sensor Networks”,Manual for University Course [8] Ossama Younis, Marwan Krunz, and Srinivasan Ramasubramanian “Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges” Clustering Approaches Cluster Overlap In Cluster Topology Cluster Count LCA No 1-hop Variable WCA No k-hop Variable LEACH No 1-hop Variable EECS No 1-hop Variable HEED No 1-hop Constant PEGASIS No 1-hop Constant GROUP No k-hop Controlled
  5. 5. ICRTEDC-2014 24 [9] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirc” Wireless sensor networks:a survey”; COMNET Vol. 38(2002), pp. 393422 [10] Do-hyun Nam, hong-ki min,” An Efficient Ad-Hoc Routing Using a Hybrid Clustering Method in a Wireless Sensor Network”, Wirelessand Mobile Computing, Networking and Communications, pp. 60-60, Oct 2007 [11] Ma Chaw Mon Thein, Thandar Thein ,” An energy efficient Cluster Head Selection for Wireless Sensor network”, 2010 Int. Conf. on Intelligent systems, Modelling and simulation

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