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A novel energy-aware bio-inspired clustering scheme for IoT communication

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Abstract

Nowadays, the internet of thing (IoT) is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications. Wireless sensor network (WSN) is an important part of IoT, and it is mainly responsible for acquiring and reporting data. As lifetime and coverage area of WSN directly determine IoT performance, how to design a method to conserve nodes energy and reduce nodes death rates become important issues. Sensor network clustering is one efficient method to solve these problems. It divides nodes into clusters and selects one to be cluster head (CH). The data transmission and communication within one cluster are managed by its CH. Many traditional strategies have been designed out, but because of network dynamic feature, machine learning methods become more attractive and many literature are working on them. Particle swarm optimization (PSO) is one evolutionary algorithm. Inspired by this algorithm, we propose a novel energy-aware bio-inspired clustering scheme (PSO-WZ). We firstly initialize CHs combination randomly and assign non-CHs based on division rules. Then, using the fitness function to guide the selection process until the maximum time is reached. Since the division rule is directly related with the network topology and node energy consumption distribution, we design it from two angles: non-CHs and the whole network, to save the energy of each node as much as possible. Meanwhile, in order to balance energy load among nodes, which contributes to lowering nodes reduction and preserving network coverage range, we introduce the Gini coefficient into the objective function. From the results obtained, we conclude that the proposed algorithm is able to keep more nodes alive over time, prolong the network life cycle, and improve the overall performance of IoT further.

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Acknowledgements

This research work is supposed by the National Key R&D Program of China (2018YFB1201500), National Natural Science Founds of China (61602376, 61773313, 61602374, 61702411), National Natural Science Founds of Shaanxi (2017JQ6020, 2016JQ6041), Key Research and Development Program of Shaanxi Province (2017ZDXM-GY-098, 2019TD-014), Science Technology Project of Shaanxi Education Department (16JK1573, 16JK1552).

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  1. Xi’an University of Technology, Xi’an, China

    Yefei Zhang & Yichuan Wang

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  1. Yefei Zhang
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  2. Yichuan Wang
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Correspondence to Yichuan Wang.

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Zhang, Y., Wang, Y. A novel energy-aware bio-inspired clustering scheme for IoT communication. J Ambient Intell Human Comput 11, 4239–4248 (2020). https://doi.org/10.1007/s12652-020-01704-w

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