Energy efficient cluster-based routing protocol using leach and charged system search algorithm in WSN


  • P. Nandhini Dept. of Computer Science, Kongu College of Ar¬ts and Science, Karur - 6.
  • A. Suresh Principal, Siri PSG Arts and Science College for Women, Sankari - 637 301





Charged System Search (CSS), Clustering, Cluster Head (CH) Selection, Energy, Low Energy Adaptive Clustering Hierarchy (LEACH), Routing, Received Signal Strength Indicator (RSSI) and Wireless Sensor Network (WSN).


There are several sensor nodes in a wireless sensor network (WSN). Their energy, storage and processing abilities are constrained. One important task associated with the sensor nodes is to gather the data and relay it to the base station (BS). Thus, for designing effective data collection techniques in WSN, the critical factor is the network lifespan. This is because every sensor node has restricted energy resource. The literature presents a scheme for data collection based on clustering which can effectively save energy .The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for accumulating clusters and changing CH positions so that the energy is uniformly disseminated throughout the nodes. The literature specifies that the design of an energy-balanced clustering for peak network lifespan of WSN is a Non-deterministic Polynomial (NP)-hard problem. In the recent past, several meta-heuristic approaches on which the clustering schemes are based have been suggested for solving the NP-hard problem. Nonetheless, these clustering schemes suffer from uneven consumption of power. This research suggests an optimized Cluster Head (CH) selection algorithm that makes use of Charged System Search (CSS), for solving the aforementioned issue. It has been shown via empirical outcomes that compared to LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol, this suggested scheme provides better throughput. Thus the suggested CSS optimized CH selection is promising for extending the network lifespan.




[1] Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: development of energy-efficient LEACH Protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking, 2015(1), 76.

[2] Rajagopal, A., Somasundaram, S., Sowmya, B., & Suguna, T. (2015). Soft computing-based cluster head selection in wireless sensor network using bacterial foraging optimization algorithm. World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 9(3), 379-384.

[3] Dembla, D., & Mehta, S. H. (2013). Energy efficient leach protocol for wireless sensor network (ee-leach). IJITKMIVolume6, Number2, 165-169.

[4] Pal, V., Singh, G., & Yadav, R. P. (2015). Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Computer Science, 57, 1417-1423.

[5] Manjusha, M. S., & Kannammal, K. E. (2014). Efficient Cluster Head Selection Method For Wireless Sensor Network. International Journal of Computational Engineering Research, 4(2), 43-49.

[6] Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. sensors, 12(8), 11113-11153.

[7] Jadhav, A. R., & Shankar, T. (2017). Whale Optimization Based Energy-Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks. arXiv preprint arXiv:1711.09389.

[8] Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H. K., & Riad, A. M. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19(12), 2194-2197.

[9] Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127-140.

[10] Ari, A. A. A., Gueroui, A., Yenke, B. O., & Labraoui, N. (2016, January). Energy efficient clustering algorithm for wireless sensor networks using the ABC metaheuristic. In Computer Communication and Informatics (ICCCI), 2016 International Conference on (pp. 1-6). IEEE.

[11] Sirdeshpande, N., & Udupi, V. (2017). Fractional lion optimization for cluster head-based routing protocol in wireless sensor network. Journal of the Franklin Institute, 354(11), 4457-4480.

[12] Shankar, A., & Jaisankar, N. (2016, December). Security Enabled Cluster Head Selection for Wireless Sensor Network Using Improved Firefly Optimization. In International Conference on Soft Computing and Pattern Recognition (pp. 176-192). Springer, Cham.

[13] Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461-1474.

[14] Baskaran, M., & Sadagopan, C. (2015). Synchronous firefly algorithm for cluster head selection in WSN. The Scientific World Journal, 2015.

[15] Vijayalakshmi, K., & Anandan, P. (2018). A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Computing, 1-8.

[16] Rajagopal, A., Somasundaram, S., & Sowmya, B. (2018). Performance Analysis for Efficient Cluster Head Selection in Wireless Sensor Network Using RBFO and Hybrid BFO-BSO. International Journal of Wireless Communications and Mobile Computing, 6(1), 1.

[17] Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony Search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101-109.

[18] Shankar, T., Karthikeyan, A., Sivasankar, P., & Rajesh, A. (2017). Hybrid approach for optimal cluster head selection in wsn using leach and monkey search algorithms. Journal of Engineering Science and Technology, 12(2), 506-517.

[19] Kannan, G., & Raja, T. S. R. (2015). Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network. Egyptian Informatics Journal, 16(2), 167-174.

[20] Kaur, A., Singh, E. S., & Kaur, N. (2015). Review of LEACH Protocol and Its Types. International Journal of Emerging Engineering Research and Technology, 3 (5), 20-25.

[21] Kumar, Y., & Sahoo, G. (2014). A charged system search approach for data clustering. Progress in Artificial Intelligence, 2(2-3), 153-166.

[22] Kaveh, A., & Talatahari, S. (2011). Hybrid charged system search and particle swarm optimization for engineering design problems. Engineering Computations, 28(4), 423-440.

[23] Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3), 267-289.

[24] Tabrizian, Z., Ghodrati Amiri, G., & Hossein Ali Beigy, M. (2014). Charged system search algorithm utilized for structural damage detection. Shock and Vibration, 2014.

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