Energy efficient protocol in wireless sensor network: optimized cluster head selection model

Springer Science and Business Media LLC - Tập 74 Số 3 - Trang 331-345 - 2020
Turki Ali Alghamdi1
1Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia

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Pandey, O. J., & Hegde, R. M. (2018). Low-latency and energy-balanced data transmission over cognitive small world WSN. IEEE Transactions on Vehicular Technology,67(8), 7719–7733.

Senouci, M. R., & Mellouk, A. (2019). A robust uncertainty-aware cluster-based deployment approach for WSNs: Coverage, connectivity, and lifespan. Journal of Network and Computer Applications,146, 102414.

Vieira, R. G., Cunha, A. M., Ruiz, L. B., & Camargo, A. P. (2018). On the design of a long range WSN for precision irrigation. IEEE Sensors Journal,18(2), 773–780.

Hintsch, T., & Irnich, S. (2018). Large multiple neighborhood search for the clustered vehicle-routing problem. European Journal of Operational Research,270(1), 118–131.

Ahmad, A., Javaid, N., Khan, Z. A., Qasim, U., & Alghamdi, T. A. (2014). Routing scheme to maximize lifetime and throughput of wireless sensor networks. IEEE Sensors Journal,14(10), 3516–3532.

Alghamdi, T. A. (2016). Cluster based energy efficient routing protocol for wireless body area networks. Trends in Applied Sciences Research,11(1), 12–16.

Alghamdi, T. A. (2018). Secure and energy efficient path optimization technique in wireless sensor networks using DH method. IEEE Access,6, 53576–53582.

Krishnan, M., Yun, S., & Jung, Y. M. (2019). Enhanced clustering and ACO-based multiple mobile sinks for efficiency improvement of wireless sensor networks. Computer Networks,160, 33–40.

Radhika, S., & Rangarajan, P. (2019). On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction. Applied Soft Computing,89, 105610.

Jesudurai, S. A., & Senthilkumar, A. (2019). An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications. Cognitive Systems Research,57, 101–106.

Zhao, B., Ren, Y., Gao, D., Xu, L., & Zhang, Y. (2019). Energy utilization efficiency evaluation model of refining unit based on Contourlet neural network optimized by improved grey optimization algorithm. Energy,185, 1032–1044.

Chen, L., Yang, D., Zhang, D., Wang, C., & Nguyen, T.-M.-T. (2018). Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization. Journal of Network and Computer Applications,121, 59–69.

Behera, T. M., Mohapatra, S. K., Samal, U. C., & Khan, M. S. (2019). Hybrid heterogeneous routing scheme for improved network performance in WSNs for animal tracking. Internet of Things,6, 100047.

Yarinezhad, R., & Hashemi, S. N. (2019). Solving the load balanced clustering and routing problems in WSNs with an FPT-approximation algorithm and a grid structure. Pervasive and Mobile Computing,58, 101033.

Saini, A., Kansal, A., & Randhawa, N. S. (2019). Minimization of energy consumption in WSN using hybrid WECRA approach. Procedia Computer Science,155, 803–808.

Wang, L., Lehman, V., Hoque, A. K. M. M., Zhang, B., Yu, Y., & Zhang, L. (2018). A secure link state routing protocol for NDN. IEEE Access,6, 10470–10482.

Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannanc, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks,151, 211–223.

Waqas, M., Malik, S. U. R., Akbar, S., Anjum, A., & Ahmad, N. (2019). Convergence time analysis of OSPF routing protocol using social network metrics. Future Generation Computer Systems,94, 62–71.

Ansari, A. R., & Cho, S. (2018). CHESS-PC: Cluster-HEad selection scheme with power control for public safety networks. IEEE Access,6, 51640–51646.

Faheem, M., Butt, R. A., Raza, B., Ashraf, M. W., Ngadi, A., & Gungorb, V. C. (2019). Energy efficient and reliable data gathering using internet of software-defined mobile sinks for WSNs-based smart grid applications. Computer Standards and Interfaces,66, 103341.

Toor, A. S., & Jain, A. K. (2019). Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks. AEU - International Journal of Electronics and Communications,102, 41–53.

Kaur, S., & Mahajan, R. (2018). Hybrid meta-heuristic optimization-based energy efficient protocol for wireless sensor networks. Egyptian Informatics Journal,19(3), 145–150.

Mohamed, R. E., Ghanem, W. R., Khalil, A. T., Elhoseny, M., Sajjad, M., & Mohamed, M. A. (2018). Energy efficient collaborative proactive routing protocol for wireless sensor network. Computer Networks,142, 154–167.

Sharawi, M. & Emary, E. (2017). Impact of grey wolf optimization on WSN cluster formation and lifetime expansion. In 2017 Ninth international conference on advanced computational intelligence (ICACI), Doha, pp. 157–162.

Jadhav, A. R. & Shankar, T. (2017). Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks. Neural and Evolutionary Computing. arXiv:1711.09389.

Yahiaoui, S., Omar, M., Bouabdallah, A., Natalizio, E., & Challal, Y. (2018). An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks. AEU - International Journal of Electronics and Communications,83, 193–203.

Tianshu, W., Gongxuan, Z., Xichen, Y., & Ahmadreza, V. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software,146, 196–214.

Ennaciri, A., Erritali, M., & Bengourram, J. (2019). Load balancing protocol (EESAA) to improve quality of service in wireless sensor network. Procedia Computer Science,151, 1140–1145.

Sarkar, T. A., & Murugan, S. (2019). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks,25(1), 303–320.

Liu, T., Li, Q., & Liang, P. (2012). An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Computer Communications,35(17), 2150–2161.

Jafari, M., & Chaleshtari, M. H. B. (2017). Using dragonfly algorithm for optimization of orthotropic infiniteplates with a quasi-triangular cut-out. European Journal of Mechanics A/Solids,66, 1–14.

Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation,18, 89–98.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software,69, 46–61.

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67.

Boothalingam, R. (2018). Optimization using lion algorithm: A biological inspiration from lion’s social behavior. Evolutionary Intelligence,11, 31–52.

Ahmad, A., Javaid, N., Qasim, U., Ishfaq, M., Khan, Z., & Alghamdi, T. (2014). RE-ATTEMPT: A new energy-efficient routing protocol for wireless body area sensor networks. International Journal of Distributed Sensor Networks,10, 464010.

Zhu, E., Zhang, Y., Wen, P., & Liu, F. (2019). Fast and stable clustering analysis based on grid-mapping K-means algorithm and new clustering validity index. Neurocomputing,363, 149–170.

Wang, Q., Guo, S., Hu, J., & Yang, Y. (2018). Spectral partitioning and fuzzy C-means based clustering algorithm for big data wireless sensor networks. Journal on Wireless Communications and Networking,2018, 54.

Jafari, H., Nazari, M., & Shamshirband, S. (2018). Optimization of energy consumption in wireless sensor networks using density-based clustering algorithm. International Journal of Computers and Applications,2018, 1–10.

Moorthi, M., & Thiagarajan, R. (2020). Energy consumption and network connectivity based on Novel-LEACH-POS protocol networks. Computer Communications,149, 90–98.