Extending the Lifetime of Wireless Sensor Networks Using the Fuzzy Clustering Algorithm Based on Trust Model
Subject Areas : Fuzzy logic, fuzzy set theory, and many-valued logic
1 - Department of English, Shahrekord branch, Islamic Azad University, Shahrekord, Iran
Keywords: Cluster head, Fuzzy clustering, Network lifetime, Trust model, Wireless sensor network ,
Abstract :
Wireless sensor networks (WSNs) are the safest and most widely used existing networks, which are used for monitoring and controlling the environment and obtaining environmental information in order to make appropriate decisions in different environments. One of the very important features of wireless sensor networks is their lifetime. Two important factors come to mind to increase the lifetime of networks: These factors are maintaining the coverage of the network and reducing the energy consumption of sensor nodes simultaneously with the uniform consumption of energy by all of them. Clustering, as the optimal method of data collection, is used to reduce energy consumption and maintain the coverage of the network in wireless sensor networks. In clustered networks, each node transmits acquired data to the cluster head to which it belongs. After a cluster head collects all the data from all member nodes, it transmits the data to the base station (sink). Given that fuzzy logic is a good alternative for complex mathematical systems, in this study, a fuzzy logic-based trust model uses the clustering method in wireless sensor networks. In this way, cluster-head sensors are elected from among sensors with high reliability with the help of fuzzy rules. As a result, the best and most trusted sensors will be selected as the cluster heads. The simulation results in MATLAB software show that in this way, in comparison with K-Means, FCM, subtractive clustering, and multi-objective fuzzy clustering protocols, the energy consumption in clustered nodes will decrease and the network’s lifetime will increase.
[1] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, F. Silva, “Directed diffusion for wireless sensor networking”, ACM/ IEEE Transactions on Networking, 2002, vol. 11, pp. 2-16.
[2] Seyyit Alper Sert, Hakan Bagci, Adnan Yazici, “MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks”, Applied Soft Computing, May 2015, vol. 30, pp. 151-165.
[3] A. Mahani, “Journal of Applied Research in Electrical Engineering A Novel Energy-Efficient Weighted Multi-Level Clustering Protocol Ebrahim Farahmand,” vol. 1, no. 1, pp. 69–78, 2022, doi: 10.22055/jaree.2021.36169.1019
[4] N. I. Sarkar, D. P. Singh, and M. Ahmed, “A survey on energy harvesting wireless networks: Channel capacity, scheduling, and transmission power optimization,” Electron., vol. 10, no. 19, pp. 1–20, 2021, doi: 10.3390/electronics10192342.
[5] M. Lotfinezhad, B. Liang, “Effect of partially correlated data on clustering in wireless sensor networks”, Proceedings of the IEEE International Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON), Citeseer, 2004, pp. 172–181.
[6] C. Nakas, D. Kandris, and G. Visvardis, “Energy efficient routing in wireless sensor networks: A comprehensive survey,” Algorithms, vol. 13, no. 3, pp. 1–65, 2020, doi: 10.3390/a13030072.
[7] Santosh.Irappa. Shirol, Ashok Kumar. N, Kalmesh.M. Waderhatti, “Advanced-LEACH Protocol of Wireless Sensor Network”, International Journal of Engineering Trends and Technology (IJETT), June 2013, Volume 4 Issue 6.
[8] O. Younis, S. Fahmy, “Distributed clustering in ad hoc sensor networks: a hybrid, energy-efficient approach”, Proceedings of the IEEE 23rd Joint Annual Conference of Computer and Communications Societies (INFOCOM), Hong Kong, vol.1, 2004; an extended version appeared in IEEE Transactions Mobile
Computing, vol. 3, No. 4, 2004, pp. 366–379. [9] L. Xuxun, “A survey on clustering routing protocols in wireless sensor networks”, Sens. J. 12 (8), 2012, pp. 11113–11153, http://dx.doi.org/10.3390/s120811113.
[10] Vaishali R. Patel, Rupa G. Mehta, “Impact of Outlier Removal and Normalization Approach in Modified k-Means Clustering Algorithm”, IJCSI International
Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, September 2011. [11] S. Suganthi, N. Umapathi, M. Mahdal, and M. Ramachandran, “Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network,”
Sensors, vol. 22, no. 5, pp. 1–13, 2022, doi: 10.3390/s22051736. [12] I. Gupta, D. Riordan, S. Sampalli, “Cluster-head election using fuzzy logic for wireless sensor networks”, Proceedings of the IEEE 3rd Annual Communication Networks and Services Research Conference, 2005, pp. 255–260.
[13] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, 1981, New York.
[14] J. C. Bezdek and S. K. Pal, “Fuzzy Models for Pattern Recognition: Methods that Search for Structure in Data”, IEEE Press, 1992, New York.
[15] Nameirakpam Dhanachandra, Khumanthem Manglem and Yambem Jina Chuna, “Image Segmentation Using K-Means Clustering Algorithm and Subtractive
Clustering Algorithm”, Procedia Computer Science 54, 2015, pp. 764-771. [16] Wang Y, Zhao Q, Zheng D, “Energy-driven adaptive clustering data collection protocol in wireless sensor networks”, International conference on intelligent mechatronics and automation, Chengdu, China, 2004, p. 599–604.
[17] M. Majid et al., “Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review,” Sensors, vol. 22, no. 6, pp. 1–36, 2022, doi: 10.3390/s22062087.
[18] P. K. Mishra and S. Kumar, “Wireless Sensor Network for Underground Mining Services Applications,” Sens. Technol., pp. 452–478, 2020, doi: 10.4018/978-1-7998-2454-1
[19] S. Sadeghi, N. Soltanmohammadlou, and F. Nasirzadeh, “Applications of wireless sensor networks to improve occupational safety and health in underground mines,” J. Safety Res., vol. 83, pp. 8–25, Dec. 2022, doi: 10.1016/j.jsr.2022.07.016.
[20] F. Kyoomarsi, H. Khosravi, E. Eslami, P. K. Dehkordy, and A. Tajoddin, “Optimizing text summarization based on fuzzy logic,” Proc. - 7th IEEE/ACIS Int. Conf. Comput. Inf. Sci. IEEE/ACIS ICIS 2008, conjunction with 2nd IEEE/ACIS Int. Work. e-Activity, IEEE/ACIS IWEA 2008, pp. 347–352, 2008, doi: 10.1109/ICIS.2008.46.
