MHCF-CECSO: A Novel High-Performance Clustering Framework for Industrial IoT
Yunping Gong, Chaoqun Li, Xiao Sheng Fang
Abstract
With the rapid development of the Industrial Internet of Things (IIoT), the industrial wireless sensor network (IWSN) with strong information perception capability and strict service quality requirements has been derived. The robust operation of the network plays a crucial role in the collection and transmission of important industrial information. Therefore, it is imperative to solve the problems of short network life and low Quality of Service (QoS). Considering these problems, this article comprehensively considers the energy consumption, remaining energy, packet loss rate, and delay, and designs a new multiobjective clustering model to accurately and reasonably elect cluster heads. Based on the advantages of the model, a novel multiobjective high-performance clustering framework (namely, MHCF-CECSO) is proposed to improve the overall performance of IWSN. Specifically, in MHCF-CECSO, a novel chaotic multilevel elite clone snake optimization method is designed to enhance the optimal clustering mechanism. To further improve the clustering efficiency of MHCF-CECSO, chaos optimization is designed to perform an initial search for the snake group to enhance the diversity of the snake group and the algorithm’s ability to escape from local optimum. In addition, a new multilevel elite cloning strategy is designed, which effectively improves the convergence speed of MHCF-CECSO. Comparing experiments with three state-of-the-art clustering schemes are conducted in four different scenarios, and the results show that the proposed MHCF-CECSO outperforms the other three comparison schemes in terms of network lifetime, energy consumption control, and reliability.