Volume 7 , Issue 1 , PP: 08-17, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ankita Srivastava 1 * , P. K. Mishra 2
Doi: https://doi.org/10.54216/IJWAC.070101
Large number of small sensor nodes exists in WSN’s for sensing and collecting information from the environment. In today’s time, these sensor nodes were applied in under water, military area, health care, earthquake sensing and in dedicated areas with recent technologies. Sensor nodes have limited life time and have supplementary network life. Network lifecycle depends on many factors such as connectivity, residual energy, topology types, single hop, multi hop, distance from base station, distance to cluster heads and much more. Among the various solutions given, clustering is considered to be good solution and optimal cluster head selection leads to efficient energy consumption. This paper proposes fuzzy based multi-attributes clustering that balances load among sensor nodes and also gives energy efficient clustering. Here we have used some attributes such as delay, residual energy, distance to CH, standard deviation to average network lifetime and standard deviation to residual energy. Results and experimental analysis validates that the proposed methods outperforms other compared algorithms.
Fuzzy Rules , Clustering , MADM , Load-Balance , Network Lifetime.
[1] Heinzelman, W.B., Chandrakasan, A.P. and Balakrishnan, H., 2002. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 1(4), pp.660-670.
[2] El Khediri, S., 2022. Wireless sensor networks: a survey, categorization, main issues, and future orientations for clustering protocols. Computing, pp.1-63.
[3] Younis, O., Krunz, M. and Ramasubramanian, S., 2006. Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE network, 20(3), pp.20-25.
[4] Mamalis, B., Gavalas, D., Konstantopoulos, C. and Pantziou, G., 2009. Clustering in wireless sensor networks. In RFID and sensor Networks (pp. 343-374). CRC Press.
[5] Shi, S., Liu, X. and Gu, X., 2012, August. An energy-efficiency Optimized LEACH-C for wireless sensor networks. In 7th International Conference on Communications and Networking in China (pp. 487-492). IEEE.
[6] Younis, O. and Fahmy, S., 2004. HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on mobile computing, 3(4), pp.366-379.
[7] Ye, M., Li, C., Chen, G. and Wu, J., 2005, April. EECS: an energy efficient clustering scheme in wireless sensor networks. In PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005. (pp. 535-540). IEEE.
[8] Kumar, D., Aseri, T.C. and Patel, R., 2009. EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. computer communications, 32(4), pp.662-667.
[9] Srivastava, A. and Mishra, P.K., 2022. Multi-attributes based energy efficient clustering for enhancing network lifetime in WSN’s. Peer-to-Peer Networking and Applications, 15(6), pp.2670-2693.
[10] Srivastava, A. and Mishra, P.K., 2021, December. Multiple-Parameter Based Clustering for Efficient Energy in Wireless Sensor Networks. In International Conference on Advanced Network Technologies and Intelligent Computing (pp. 15-24). Springer, Cham.
[11] Sharma, R., Vashisht, V. and Singh, U., 2022. eeFFA/DE-a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks. International Journal of Advanced Intelligence Paradigms, 21(1-2), pp.129-157.
[12] Maratha, P. and Gupta, K., 2022. Linear optimization and fuzzy-based clustering for WSNs assisted internet of things. Multimedia Tools and Applications, pp.1-25.
[13] Trinh, C., Huynh, B., Bidaki, M., Rahmani, A.M., Hosseinzadeh, M. and Masdari, M., 2022. Optimized fuzzy clustering using moth-flame optimization algorithm in wireless sensor networks. Artificial Intelligence Review, 55(3), pp.1915-1945.
[14] Jayaraman, G. and Dhulipala, V.R., 2022. FEECS: Fuzzy-Based Energy-Efficient Cluster Head Selection Algorithm for Lifetime Enhancement of Wireless Sensor Networks. Arabian Journal for Science and Engineering, 47(2), pp.1631-1641.
[15] Kumar, A. and Kumar, A., 2022. Multi criteria decision making based energy efficient clustered solution for wireless sensor networks. International Journal of Information Technology, pp.1-10.
[16] Jagadeesh, S. and Muthulakshmi, I., 2022. Hybrid Metaheuristic Algorithm -Based Clustering with Multi-Hop Routing Protocol for Wireless Sensor Networks. In Proceedings of Data Analytics and Management (pp. 843-855). Springer, Singapore.
[17] Alaei, M. and Yazdanpanah, F., 2020. A ِDistributed Fuzzy-based Clustering Scheme to Optimize Energy Consumption and Data Transmission in Wireless Sensor Networks. Journal of Soft Computing and Information Technology, 9(3), pp.229-243.
[18] Le-Ngoc, K.K., Tho, Q.T., Bui, T.H., Rahmani, A.M. and Hosseinzadeh, M., 2022. Optimized fuzzy clustering in wireless sensor networks using improved squirrel search algorithm. Fuzzy Sets and Systems, 438, pp.121-147.
[19] A. Sariga , J. Uthayakumar, Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 33-46 (Doi : https://doi.org/10.54216/IJWAC.010102
[20] Senthil Murugesan , Krishna Venkata , Narendra Mupparaju , Rayudu Kommi, Quality of Service Enhancement in Wireless LAN and MANET, International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 2 , (2020) : 08-18 (Doi : https://doi.org/10.54216/IJWAC.010201)
[21] Khalily-Dermany, M., 2022. Multi-criteria itinerary planning for the mobile sink in heterogeneous wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, pp.1-20.
[22] Ramesh, D. and Karegowda, A.G., 2022. Firefly and Grey Wolf search based multi-criteria routing and aggregation towards a generic framework for LEACH. International Journal of Information Technology, 14(1), pp.105-114.
[23] Subramani, N., Mohan, P., Alotaibi, Y., Alghamdi, S. and Khalaf, O.I., 2022. An efficient metaheuristic-based clustering with routing protocol for underwater wireless sensor networks. Sensors, 22(2), p.415.
[24] Jagadeesh, S. and Muthulakshmi, I., 2022. Hybrid Metaheuristic Algorithm-Based Clustering with Multi-Hop Routing Protocol for Wireless Sensor Networks. In Proceedings of Data Analytics and Management (pp. 843-855). Springer, Singapore.
[25] Balasubramanian, D.L. and Govindasamy, V., 2023. Energy Aware Farmland Fertility Optimization Based Clustering Scheme for Wireless Sensor Networks. Microprocessors and Microsystems, p.104759.
[26] Seyfollahi, A., Taami, T. and Ghaffari, A., 2023. Towards developing a machine learningmetaheuristic-enhanced energy-sensitive routing framework for the internet of things. Microprocessors and Microsystems, 96, p.104747.
