Selection of Cluster Heads for Wireless Sensor Network in Ubiquitous Power Internet of Things

Authors

  • Wei Hu College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China
  • Wenhui Yao College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China
  • Yawei Hu College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China
  • Huanhao Li College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China

Keywords:

Wireless sensor network (WSN), ubiquitous power internet of things (UPIoT), cluster head (CH) selection, clustering optimization

Abstract

This paper designs a selection algorithm of cluster heads (CHs) in wireless sensor network (WSN) under the ubiquitous power Internet of Things (UPIoT), aiming to solve the network failure caused by premature death of WSN sensors and overcome the imbalance in energy consumption of sensors. The setting of the cluster head node helps to reduce the energy consumption of the nodes in the network, so the choice of cluster head is very important. The author firstly explains the low energy adaptive clustering hierarchy (LEACH) and the distance and energy based advanced LEACH (DEAL) protocol. Compared with the LEACH, the DEAL considers the remaining nodal energy and the sensor-sink distance. On this basis, the selectivity function-based CH selection (SF-CHs) algorithm was put forward to select CHs and optimize the clustering. Specifically, the choice of CHs was optimized by a selectivity function, which was established based on the remaining energy, number of neighbors, motion velocity and transmission environment of sensors. Meanwhile, a clustering function was constructed to optimize the clustering, eliminating extremely large or small clusters.Finally, the simulation proves that the DEAL protocol is more conducive to prolonging the life cycle of the sensor network. The SF-CHs algorithm can reduce the residual energy variance of nodes in the network, and the network failure time is later, which provides a way to improve the stability of the network and reduce energy loss.

References

Aghera, K.; Pambhar, H.; Tada, N. (2017). MMR-LEACH: Multitier multi-hop routing in LEACH protocol, Proceedings of International Conference on Communication and Networks, 205-214, 2017. https://doi.org/10.1007/978-981-10-2750-5_22

Amirthalingam, K.; Anuratha, V. (2017). Improved LEACH: A modified LEACH for wireless sensor network, IEEE International Conference on Advances in Computer Applications, 255- 258, 2017. https://doi.org/10.1109/ICACA.2016.7887961

Awad, F. H. (2018). Optimization of relay node deployment for multisource multipath routing in wireless multimedia sensor networks using gaussian distribution, Computer Networks, 145, 96-106, 2018. https://doi.org/10.1016/j.comnet.2018.08.021

Bao, X.; Xie, J.; Nan, L.; Li, S. (2014). WRECS: An improved cluster heads selection algorithm for WSNs, Journal of Software, 9(2), 78-89, 2014. https://doi.org/10.4304/jsw.9.2.507-514

Baranidharan, B.; Santhi, B. (2016). DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach, Applied Soft Computing, 40, 495-506, 2016. https://doi.org/10.1016/j.asoc.2015.11.044

Batra, P. K.; Kant, K. (2016). LEACH-MAC: A new cluster head selection algorithm for Wireless Sensor Networks, Wireless Networks, 22(1), 49-60, 2016. https://doi.org/10.1007/s11276-015-0951-y

Chidean, M. I.; Morgado, E.; Del Arco, E.; Ramiro-Bargueno, J., Caamano, A. (2015). Scalable data-coupled clustering for large scale WSN, IEEE Transactions on Wireless Communications, 14(9), 4681-4694, 2015. https://doi.org/10.1109/TWC.2015.2424693

Jia, Y. L.; Chang, X. M. (2017). Cluster heads selection algorithm for wireless sensor networks based on cluster heads sending energy consumption, Computer Engineering and Applications, 53(22), 82-86, 2017.

Lee, J. Y.; Jung, K. D.; Moon, S. J.; Jeong, H. Y. (2017). Improvement on LEACH protocol of a wide-area wireless sensor network, Multimedia Tools & Application, 76(19), 19843-19860, 2017. https://doi.org/10.1007/s11042-016-3732-4

Liang, P.; He, W. (2017). Grid dynamic energy threshold-based cluster header algorithm in wireless sensor network, Chinese Journal of Sensors and Actuators, 30(10), 1583-1588, 2017.

Mehra, P. S.; Doja, M. N.; Alam, B. (2018). Fuzzy based enhanced cluster head selection (FBECS) for WSN, Journal of King Saud University - Science, 2018. https://doi.org/10.1016/j.jksus.2018.04.031

Mittal, N. (2019). Moth flame optimization based energy efficient stable clustered routing approach for wireless sensor networks, Wireless Personal Communications, 104(2), 677-694, 2019. https://doi.org/10.1007/s11277-018-6043-4

Shalabi, M.; Anbar, M.; Wan, T. C.; Khasawneh, A. (2018). Variants of the low-energy adaptive clustering hierarchy protocol: Survey, issues and challenges, Electronics, 7(8), 136- 164, 2018. https://doi.org/10.3390/electronics7080136

Snigdh, I.; Gupta, N. (2016). Quality of service metrics in wireless sensor networks: A survey, Journal of the Institrtion of Engineers, 97(1), 91-96, 2016. https://doi.org/10.1007/s40031-014-0160-6

Srikanth, B.; Kumar, H.; Rao, K. U. M. (2018). A robust approach for WSN localization for underground coal mine monitoring using improved RSSI technique, Mathematical Modelling of Engineering Problems, 5(3), 225-231, 2018. https://doi.org/10.18280/mmep.050314

Sun, L. M.; Li, J. Z.; Chen, Y. (2005). Wireless sensor networks, Beijing: Tsinghua university press, 1-5, 2005.

Thakkar, A. (2017). DEAL: Distance and energy based advanced LEACH protocol, International Conference on Information and Communication Technology for Intelligent Systems, 370-376, 2017. https://doi.org/10.1007/978-3-319-63645-0_41

Wei, X. (2014). Power wireless sensor network clustering routing optimization algorithm research, North China electric power university (Beijing), 2014.

Zahedi, A.; Arghavani, M.; Parandin, F.; Arghavani, A. (2018). Energy efficient reservationbased cluster head selection in WSNs, Wireless Personal Communications Wireless Personal Communications, 100(3), 667-679, 2018. https://doi.org/10.1007/s11277-017-5189-9

Published

2019-05-31

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