Journal of Wireless Networking and Communications
p-ISSN: 2167-7328 e-ISSN: 2167-7336
2012; 2(6): 168-174
doi: 10.5923/j.jwnc.20120206.02
Vipin Pal1, Girdhari Singh2, R P Yadav3, Pavitar Pal4
1Electronics & Communication Engineering Department, Malaviya National Institute of Technology, Jaipur, 302017, India
2Computer Engineering Department, Malaviya National Institute of Technology, Jaipur, 302017, India
3Vice-Chancellor, Rajasthan Technical University, Kota, 324010, India
4Mechanical Engineering Department, BKN Polytechnic College, Narnaul, 123001, India
Correspondence to: Vipin Pal, Electronics & Communication Engineering Department, Malaviya National Institute of Technology, Jaipur, 302017, India.
| Email: | ![]() |
Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved.
Wireless sensor networks are application specific networks composed of large number of sensor nodes. Limited energy resource of sensor nodes make efficient energy consumption of nodes as main design issue. Energy efficiency is achieved from hardware level to network protocol levels. Clustering of nodes is an effective approach to reduce energy consumption of nodes. Clustering algorithms group nodes in independent clusters. Each cluster has atleast one cluster head. Nodes send data to respective cluster heads. Cluster heads send data to base station. Clustering algorithms prolong network lifetime by avoiding long distance communication of nodes to base station. In literature various clustering approaches are proposed. Work of this paper discusses working of few of them and distinguishes them according to operational mode and state of clustering. Work of this paper helps to understand classification of clustering schemes.
Keywords: Wireless Sensor Network, Energy Efficiency, Clustering, Cluster Head, Network Lifetime
Cite this paper: Vipin Pal, Girdhari Singh, R P Yadav, Pavitar Pal, "Energy Efficient Clustering Scheme for Wireless Sensor Networks: A Survey", Journal of Wireless Networking and Communications, Vol. 2 No. 6, 2012, pp. 168-174. doi: 10.5923/j.jwnc.20120206.02.
![]() | Figure 1. Classification of Clustering Schemes |
where P is the desired percentage to become a cluster-head; r, the current round; and G, the set of nodes that have not being selected as a cluster-head in the last 1/P rounds. CHs advertise to network about their status. Sensor nodes receive advertisement and decide CH to join according to receive signal strength of status message. After having information about member nodes, CH decide TDMA schedule and broadcast it to cluster. During steady phase, sensor nodes begin sensing and transmit data to the CHs. CHs aggregate data before sending these data to the base station. After a certain period of time spent on steady phase, network goes into set-up phase again and entering into another round of selecting cluster-heads.LEACH is a fully distributed scheme. Role of CH is rotated among all the sensor nodes to have network load balanced. But the protocol does not guarantee about equal number of CHs in each round and number of member nodes in each cluster. Clusters formed of uneven size makes network load unbalanced.[14] propose an improvement over LEACH by selecting CH not randomly but considering remaining energy when energy level drops below 50% of the initial energy. Cluster head join process is determined not only by received signal strength but also by remaining energy of cluster head. The data is sent by a node only if data satisfies a predefined condition. But scheme does not have view on informality in clusters.LEACH-C[15] is centralized algorithm to form cluster and to assign duty of cluster heads. During set-up phase, nodes send information about respective location and energy level to BS. BS formulates clusters using simulated annealing algorithm[16]. In addition to forming good clusters, BS needs to do load balancing. To do so, BS calculates average node energy and nodes having energy lower than average will not participate in CH selection. Algorithm provides CHs such that nodes minimize their transmission distance and conserve energy. After the formation of clusters and cluster heads, BS broadcasts a message that contains the information of CH ID for each node. The steady phase is same as of LECAH.In LEACH-F[15], clusters are formed using centralized cluster formation algorithm developed for LEACH-C. BS uses simulated annealing to determine optimal clusters and broadcasts the cluster information to the nodes. This broadcast message includes the cluster ID for each node, from which the nodes can determine the TDMA schedule and the order to rotate the CH position. The first node listed in the cluster becomes CH for the first round; the second node listed in the cluster becomes CH for the second round, and so forth. Using LEACH-F, there is no setup required for different rounds.Adaptive Decentralized Re-clustering Protocol (ADRP) [17] selects a CH and set of next heads for upcoming few rounds based on residual energy of each nodes and average energy of cluster. A round in ADRP has two phases as shown in Fig. 2: initial phase and cycle phase.In the initial phase, nodes send status of their energy and location to BS. BS partitions the network in clusters and selects a CH for each cluster. BS also selects a set of nodes as next heads to avoid re-clustering for few rounds. So a node in a cluster can be in one state out of three: cluster head, next head, and member. In the cycle phase, each CH distributed constituted TDMA schedule to node. Nodes send data to CH according to allotted time slot. CH receives and aggregates data and sends it to BS. In re-cluster stage, nodes transit to cluster head from set of next heads without any assistance from BS. Previous CHs are now member nodes. If set of next heads is empty, initial phase is executed again.The set of next heads avoids re-clustering for few rounds and conserve energy of nodes. But no new nodes can be added until next initial phase. If a node in the next head list is dead, then the sets for cluster will be uneven. Then when the initial phase will be executed?Energy-Balanced Unequal Clustering (EBUC)[18] is a centralized protocol that organize network in unequal clusters and CHs relay data of other CHs via multi-hop routing. PSO is applied at BS to select high energy nodes for CH role and for formation of clusters with unequal nodes as shown in Fig. 3. Clusters closer to BS are formed of small size to consume less intra-cluster energy and hence are ready for inter-cluster communication energy consumption.At the starting of first set-up phase, nodes send their energy and location information to BS. Operation of clustering is done by BS. BS computes average energy level of each node and uses this information for CH selection and cluster member nodes. BS estimates energy consumption of each node at end of round that is used for next round. So nodes do not need to send information message to BS again. Inter cluster multi hop routing depends upon a cost function that uses the distance between CHs, distance of relay CH to BS and residual energy of relay CH. At the end of set-up phase, BS broadcasts information about clusters and multi-hop routing. At the beginning of steady phase, CH broadcasts TDMA schedule to cluster. Node sends data to CH according to allotted TDMA time slot. CH aggregates collected data and sends to BS via multi-hop inter-cluster routing. ![]() | Figure 2. Operation in ADRP |
![]() | Figure 3. Cluster Formation in EBUC |
RSSi denotes nodei’s received signal strength for the signal broadcasted by the base station, RSSmax is a constant which is determined by the location of base station, and the function D is used to estimate the distance between nodei and the base station.The scheme has advantage of applicable for both homogenous and heterogeneous energy nodes as it handles heterogeneous energy issue. But the protocol consumes memory of nodes by maintaining a table their neighbourhood nodes. Updating of table consumes energy of nodes.Distributed Energy-Efficient Clustering (DEEC) [20] is an energy-efficient clustering scheme for heterogeneous wireless sensor network. In DEEC, CH selection is probabilistic based on the ratio of the residual energy of each node and the average energy of the network. So the nodes with high residual energy have more chances for selection of CH as compared to low energy nodes. DEEC consider two-level heterogonous network. The heterogeneity of nodes is based on energy of nodes. There are two types of sensor nodes: Advanced nodes and Normal nodes. Advanced nodes have high initial energy as compared to normal nodes.In DEEC the initial and residual energy level of the nodes are considered for CH selection which require global knowledge of networks. In DEEC to avoid global knowledge of networks by each node, ideal value of network life-time is estimated which is helpful to compute the reference energy that each node expends during a round. Energy Efficient Heterogeneous Clustered (EEHC) scheme [21] extend the node heterogeneity up to three levels. Nodes are of three types; Super nodes, Advance nodes and Normal nodes. Super nodes have highest energy among all; hence have highest chances of selection for CH. EEPSC (Energy-Efficient Protocol with Static Clustering)[22] is a base station assisted static clustering scheme. Each round of EEPSC consists of set-up phase, responsible node selection phase and steady phase. The set-up phase is executed once at beginning of network operation to partition the network. For k desired number of CHs, base station broadcasts k-1 different messages with different transmission powers. All the sensor nodes receiving k=i (1<=i<=k-1) message set their cluster ID to i. Remaining sensor nodes in the network that has not joined any cluster, set their cluster ID to k as shown in Fig. 4 and inform to base station.![]() | Figure 4. Network partition into clusters |
![]() | Figure 5. Clustering in QAC |
| [1] | I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor networks: a survey”, Computer Networks, vol. 38 (4), 2002, pp. 393-422. |
| [2] | D. Estrin, R. Govindan, J. S. Heidemann, S. Kumar, “Next century challenges: Scalable coordination in sensor networks”, in Proc. MOBICOM, 1999, pp. 263-270. |
| [3] | A. Arora, P. Dutta, S. Bapat, V. Kulathumani, H. Zhang, V. Naik, V. Mittal, H. Cao, M. Demirbas, M. Gouda, Y. Choi, T. Herman, S. Kulkarni, U. Arumugam, M. Nesterenko, A. Vora, M. Miyashita, “A line in the sand: a wireless sensor network for target detection, classification, and tracking”, Computer Networks,vVol. 46 (5), 2004, pp. 605-634. |
| [4] | A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, J. Anderson, “Wireless sensor networks for habitat monitoring”, in Proc 1st ACM international workshop on Wireless sensor networks and applications (WSNA '02), New York, NY, USA, 2002, pp. 88-97. |
| [5] | L. Selavo, A. D. Wood, Q. Cao, T. I. Sookoor, H. Liu, A. Srinivasan, Y. Wu, W. Kang, J. A. Stankovic, D. Young, J. Porter, “Luster: wireless sensor network for environmental research”, in Proc. SenSys, 2007, pp. 103-116. |
| [6] | A. Milenkovic, C. Otto, E. Jovanov, “Wireless sensor networks for personal health monitoring: Issues and an implementation”, Computer Communication, vol. 29 (13-14), 2006, pp. 2521-2533. |
| [7] | J. Tavares, F. J. Velez, J. Ferro, “Application of wireless sensor networks to automobiles”, Measurement Science Review, Vol. 8 (3), 2008, pp. 65-70 |
| [8] | A. Flammini, P. Ferrari, D. Marioli, E. Sisinni, A. Taroni, “Wired and wireless sensor networks for industrial applications”, Journal of Microelectronics, vol. 40 (9), 2009, pp. 1322-1336. |
| [9] | G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, “Energy conservation in wireless sensor networks: A survey”, Ad Hoc Network, vol. 7 (3), 2009, pp. 537-568. |
| [10] | A. Abbasi, M. Younis, “A survey on clustering algorithms for wireless sensor networks”, Computer Communication, vol. 30 (14-15), 2007, pp. 2826-2841. |
| [11] | D. Wei, H. Chan, “Clustering ad hoc networks: Schemes and classifications”, in Proc. 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, 2006, pp. 920-926. |
| [12] | P. Rentala, R. Musunuri, S. Gandham, U. Saxena, “Survey on sensor networks”, 2001. |
| [13] | W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, in Proc. 33rd Hawaii International Conference on System Sciences-Volume 8, HICSS '00, Washington, DC, USA, 2000, pp. 1-10. |
| [14] | K. Y. Jang, K. T. Kim, H. Y. Youn, “An energy efficient routing scheme for wireless sensor networks”, in Proc. International Conference on Computational Science and its Applications, (ICCSA 2007), 2007, pp. 399-404. |
| [15] | W. Heinzelman, A. Chandrakasan, H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks”, IEEE Transactions on Wireless Communications, vol. 1 (4), 2002, pp. 660-670. |
| [16] | T. Murata, H. Ishibuchi, “Performance evaluation of genetic algorithms for fowshop scheduling problems”, in Proc. First IEEE Conference on IEEE World Congress on Computational Intelligence, Evolutionary Computation, 1994, pp. 812-817. |
| [17] | F. Bajaber, I. Awan, “Adaptive decentralized re-clustering protocol for wireless sensor networks”, Journal of Computer System Science, vol. 77 (2), 2011, pp. 282-292. |
| [18] | C. jiang JIANG, W-ren SHI, min XIANG, X-lun TANG, “Energy-balanced unequal clustering protocol for wireless sensor networks”, The Journal of China Universities of Posts and Telecommunications, vol. 17 (4), 2010, pp. 94-99. |
| [19] | M. Liu, J. Cao, G. Chen, X. Wang, “An energy-aware routing protocol in wireless sensor networks”, Sensors, vol. 9 (1), 2009, pp. 445-462. |
| [20] | Q. Li, Z. Qiungxin, W. Mingwen, “Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks”, Computer Communication, vol. 29, 2006, pp. 2230-2237. |
| [21] | K. Dilip, A. Trilok C., R. B. Patel, “EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks”, Computer Communication, vol. 32, 2009, pp. 662-667. |
| [22] | A. S. Zahmati, B. Abolhassani, A. Asghar, B. Shirazi, A. S. Bakhtiari, “An energy-efficient protocol with static clustering for wireless sensor networks”, 2007. |
| [23] | A. Durresia, V. Paruchuri, L. Barolli, “Clustering protocol for sensor networks”, in Proc. 20th International Conference on Advanced Information Networking and Applications, 2006,2006. |
| [24] | S. Deng, L. Shen, X. Zhu, “Energy-efficient data aggregation protocol based on static clustering for wireless sensor networks”, in Proc. PIERS Proceedings, 2008, pp. 470-473. |
| [25] | S. Hussain, A. W. Matin, “Base station assisted hierarchical cluster-based routing”, in Proc. International Conference on Wireless and Mobile Communications, 2006. |
| [26] | W. Chen, W. Li, H. Shou, B. Yuan, “A QoS-based adaptive clustering algorithm for wireless sensor networks”, in Proc. IEEE International Conference on Mechatronics and Automation, 2006, pp. 1947-1952. |