A WSN comprises more minimum-cost and minimum-power sensor nodes. In a particular area all the sensor nodes are located and form a WSN in terms of self-organizing. Usually they have the capability to employ at any of the particular or deprived environment that public cannot secure. Nevertheless, the broadcast of data between nodes in an effectual manner is approximately not probable because of the several intricate issues. The clustering is a famous method to create data transmission high effectual. Moreover, the clustering model partitions the SNs into several clusters. In network, each cluster has exclusive CHN that transmit the information to other SNs in cluster. In such cases, it is the important task of any clustering technique to select the optimal CH in several constraints like delay, less energy utilization, and hitherto. This article presents a new CHS model to increase the network lifetime and energy effectiveness. Furthermore, this article presents a novel Fruit Fly Optimization Method and WNN to select the optimal CH in WSN.
Tao Han, Lijuan Zhang, Sandeep Pirbhulal, Wanqing Wu, Victor Hugo C. de Albuquerque,"A novel cluster head selection technique for edge-computing based IoMT systems",Computer Networks, Volume 158, 20 July 2019, Pages 114-122.
Q. Ni, Q. Pan, H. Du, C. Cao and Y. Zhai, "A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 1, pp. 76-84, 1 Jan.-Feb. 2017.
A. R. Ansari and S. Cho, "CHESS-PC: Cluster-HEad Selection Scheme With Power Control for Public Safety Networks," in IEEE Access, vol. 6, pp. 51640-51646, 2018.
T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand and A. H. Gandomi, "Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5132-5139, June 2019.
Ramin Yarinezhad, Seyed Naser Hashemi,"A routing algorithm for wireless sensor networks based on clustering and an fpt-approximation algorithm”, Journal of Systems and Software, Volume 155, September 2019, Pages 145-161.
W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000.
M. Ettus, “System capacity, latency, and power consumption in multihop-routed SS-CDMA wireless networks,” in Proceedings RAWCON 98. 1998 IEEE Radio and Wireless Conference (Cat. No.98EX194), 1998, pp. 55–58.
Ying-Chang Liang, Yonghong Zeng, E. C. Y. Peh, and Anh Tuan Hoang, “Sensing-Throughput Tradeoff for Cognitive Radio Networks,” in 2nd International Conference on Computer Science and Network Technology (ICCSNT), 2008, vol. 7, no. 4, pp. 660–665.
W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wirel. Commun., vol. 1, no. 4, pp. 660–670, 2002.
S. Lindsey, C. Raghavendra, and K. M. Sivalingam, “Data gathering algorithms in sensor networks using energy metrics [PEGASIS],” IEEE Trans. Parallel Distrib. Syst., vol. 13, no. 9, pp. 924–935, 2002.
A. Manjeshwar and D. P. Agrawal, “TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks,” in Proceedings 15th International Parallel and Distributed Processing Symposium, 2001, pp. 2009– 2015.
A. Manjeshwar and D. P. Agrawal, “APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks,” in Proceedings International Parallel and Distributed Processing Symposium, 2002, pp. 195–202.
C. H. Lin and M. J. Tsai, “A comment on „HEED: A Hybrid, Energy-Efficient, Distributed clustering approach for ad hoc sensor networks,‟” IEEE Trans. Mob. Comput., vol. 5, no. 10, pp. 1471–1472, 2006.
M. Lawanyashri, Balamurugan Balusamy, S. Subha,"Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications", Informatics in Medicine Unlocked, Volume 8, 2017, Pages 42-50.
Lei Lei, Wei Chen, Yu Xue, Wei Liu,"A comprehensive evaluation method for indoor air quality of buildings based on rough sets and a wavelet neural network",Building and Environment, Volume 162, September 2019.
W.Brajula and Praveena S," Energy Efficient Genetic Algorithm Based Clustering Technique for Prolonging the Life Time of Wireless Sensor Network",Journal of Networking and Communication Systems (JNACS)Volume 1, Issue 1, October 2018.
S Vinusha and J.S. Abinaya,"Performance analysis of the Adaptive Cuckoo Search Rate Optimization Scheme for the Congestion Control in the WSN",Journal of Networking and Communication Systems (JNACS)Volume 1, Issue 1, October 2018.
L.H.Qin, S. X. Li, J. L.Han,D. X.Niu, andZ. J. Zhu, “Application of BP neural network optimized by FOA in the forecasting of summer air-conditioning cooling load,”
G. S. Ding and H. Zou, “New improved fruit fly optimization algorithm,” Computer Engineering and Applications, vol. 52, no. 21, pp. 168–174, 2016.
W.Guo and Z. Zhao, “Anovel hybrid BND-FOA-LSSVMmodel for electricity price forecasting,” Information (Switzerland), vol. 8, no. 4, 2017.
W. X. Yu, Y. G. He, X. M. Wu, and K. Gao, “Method of analog circuit fault diagnosis based on FOA-neural network,” Computer Engineering and Applications, vol. 51, no. 22, pp. 22– 27, 2015.
Y. Falamarzi, N. Palizdan, Y. F. Huang, and T. S. Lee, “Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs),” AgriculturalWater Management, vol. 140, pp. 26–36, 2014.
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