Optimized Energy Efficient- Hierarchical Clustering Based Routing (OEE-HCR) For Wireless Sensor Network (WSN)
DOI:
https://doi.org/10.4108/ew.6504Keywords:
Optimized Energy Efficient-Hierarchical Clustering Based Routing, OEE-HCR, Routing protocol, Whale Optimization Algorithm, WOA, Wireless Sensor Network, WNNAbstract
The study into Wireless Sensor Network (WSN) has grown more crucial as a result of the many Internet of Things (IoT) applications. Energy – Harvesting (EH) technology can extend the lifespan of WSN; however, because the nodes would be difficult to get to during energy harvesting, an energy-efficient routing protocol should be developed. The use of clustering in this study balances energy consumption across all Sensor Node (SN) and reduces traffic and overhead throughout the data transmission phases of WSN. Cluster Head (CH) selection step of the Optimized Energy Efficient-Hierarchical Clustering Based Routing (OEE-HCR) technique involves sending data to the closest CH. In order to analyse and transmit each cluster data, CH will need to use more energy, which will hasten and asymmetrically deplete the network. Whale Optimization Algorithm (WOA) algorithm is introduced for the best number of clusters formation with dynamically selecting the CH. Experimentation analysis, results are measured using First Node Dead (FND), the Half Node Dead (HND), Last Node Dead (LND), and Maximum Lifetime Coverage (MLC) at the time of number of data transmission rounds performed in routing algorithms.
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Wu, H., Miao, Z., Wang, Y. and Lin, M.,(2015) Optimized recognition with few instances based on semantic distance. The Visual Computer, 31, pp.367-375.
Lin, B., Guo, W., Xiong, N., Chen, G., Vasilakos, A.V. and Zhang, H.,(2016) A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Transactions on Network and Service Management, 13(3), pp.581-594.
Ma, J., Wang, S., Meng, C., Ge, Y. and Du, J.,(2018) Hybrid energy-efficient APTEEN protocol based on ant colony algorithm in wireless sensor network. EURASIP Journal on Wireless Communications and Networking, pp.1-13.
Zheng, H., Guo, W. and Xiong, N., (2017) A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12), pp.2315-2327.
Manchanda, R. and Sharma, K., (2017) Energy efficient compression sensing-based clustering framework for IoT-based heterogeneous WSN. Telecommunication Systems, 74(3), pp.311-330.
Zhang, J., Lin, Z., Tsai, P.W. and Xu, L. (2020) Entropy-driven data aggregation method for energy-efficient wireless sensor networks. Information Fusion, 56, pp.103-113.
Singh, D., Kumar, B., Singh, S. and Chand, S., (2019) SMAC-AS: MAC based secure authentication scheme for wireless sensor network. Wireless Personal Communications, 107, pp.1289-1308.
Zhao, L., Qu, S. and Yi, Y., (2018) A modified cluster-head selection algorithm in wireless sensor networks based on LEACH. EURASIP Journal on Wireless Communications and Networking, pp.1-8.
Sanhaji, F., Satori, H. and Satori, K., (2019) Cluster head selection based on neural networks in wireless sensor networks. International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 1-5.
Mody, S., Mirkar, S., Ghag, R. and Kotecha, P.,(2021) Cluster Head Selection Algorithm For Wireless Sensor Networks Using Machine Learning. International Conference on Computational Performance Evaluation (ComPE), pp.445-450.
Gheisari, M., Yaraziz, M.S., Alzubi, J.A., Fernández-Campusano, C., Feylizadeh, M.R., Pirasteh, S., Abbasi, A.A., Liu, Y. and Lee, C.C., (2022)An efficient cluster head selection for wireless sensor network-based smart agriculture systems. Computers and Electronics in Agriculture, 198, pp.1-26.
Hamzeloei, F. and Dermany, M.K., (2016) A TOPSIS based cluster head selection for wireless sensor network. Procedia Computer Science, 98, pp.8-15.
Han, B., Ran, F., Li, J., Yan, L., Shen, H. and Li, A., (2022) A novel adaptive cluster based routing protocol for energy-harvesting wireless sensor networks. Sensors, 22(4), pp.1-16.
Rathore, R.S., Sangwan, S., Prakash, S., Adhikari, K., Kharel, R. and Cao, Y., (2020) Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs. EURASIP Journal on Wireless Communications and Networking, pp.1-28.
Ahmed, G., Zou, J., Fareed, M.M.S. and Zeeshan, M., (2016) Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Computers & Electrical Engineering, 56, pp.385-398.
Sah, D.K. and Amgoth, T., (2020)A novel efficient clustering protocol for energy harvesting in wireless sensor networks. Wireless Networks, 26(6), pp.4723-4737.
Mirjalili, S. and Lewis, A.,(2016) The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
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