A Multi-objective Cluster Head Selection Optimization Algorithm for Industrial Wireless Sensor Networks

Yang, Zhen; Wu, Di; Li, Tong; Feng, Weite; Tu, Shanshan

doi:10.1109/icivc52351.2021.9526976

Cited by 1 publication

(8 citation statements)

References 27 publications

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“…These tests stack JOCP against multiple baselines: LEACH, 13 SEP, 26 EAMMH, 27 LEACH-ABF, 28 and MOCHSOM. 29 Each test is simulated using MATLAB R2017b to mimic a sensor network in an infinite cooling system. The specific IWSN design constraints are based on MOCHSOM.…”

Section: Experiments Setupmentioning

confidence: 99%

“…The specific IWSN design constraints are based on MOCHSOM. 29 Initially, the DLSEA algorithm sets varying sensor network CH distributions with a (population size U is 20). Then, the crossover and mutation rates are determined to be 1.00 and 1∕M, respectively.…”

Section: Experiments Setupmentioning

confidence: 99%

“…All parameters are identical to those used by MOCHSOM. 29 We evaluate the performance of the DLSEA algorithm and the efficiency of the network through three primary experimental criteria: the net-…”

Section: Experiments Setupmentioning

confidence: 99%

“…The baseline included LEACH, 13 SEP, 26 EAMMH, 27 LEACH-ABF, 28 and MOCHSOM. 29 Figure 12 portrays the network node distribution for different baselines upon the completion of the network execution. For the LEACH, EAMMH, and LEACH-ABF networks, a considerable number of central nodes fail, resulting in a deficiency in information collection from the median area in the later stages.…”

Section: Different Wsn Clustering Protocol Experimental Setting and A...mentioning

confidence: 99%

“…Our study consists of three experiments, each intended to evaluate the performance of the JOCP protocol. These tests stack JOCP against multiple baselines: LEACH, 13 SEP, 26 EAMMH, 27 LEACH‐ABF, 28 and MOCHSOM 29 . Each test is simulated using MATLAB R2017b to mimic a sensor network in an infinite cooling system.…”

Section: Experiments Setup and Analysismentioning

confidence: 99%

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JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm

Wu,

Yang,

et al. 2023

Concurrency and Computation

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SummaryIndustrial Wireless Sensor Networks (IWSNs) have gained significant popularity for their ability to improve plant productivity and production efficiency through self‐organization and rapid deployment. However, the challenge of achieving reliable and sustainable data transmission remains due to the large amount of heterogeneous data generated by large‐scale IWSNs. In this paper, we present a systematic approach that addresses this challenge by focusing on data transmission clustering strategies, optimal cluster head selection, and routing design. We propose a novel Jointly Optimized Clustering Protocol (JOCP), which enhances cluster head selection by considering multiple critical factors that impact the IWSN life cycle. JOCP incorporates two key modules: the many‐objective clustering model and the double‐layer selection evolutionary algorithm. Specifically, the many‐objective clustering model considers cluster head selection from different perspectives, including maximum node survival cycle, minimum node distance, minimum network overall energy consumption, and balanced cluster energy consumption, with the aim of extending the network life cycle. Additionally, the double‐layer selection evolutionary algorithm optimizes the many‐objective clustering model to select appropriate cluster heads. Through performance verification, we demonstrate that the JOCP protocol effectively enhances the network life cycle and increases the number of surviving nodes compared to baseline clustering algorithms. Our research provides a comprehensive solution to the challenges associated with reliable and sustainable data transmission in large‐scale IWSNs, highlighting the potential for improved performance in industrial applications.

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Section: Experiments Setupmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%