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<p>For Industrial Wireless Sensor Networks (IWSNs) serving industrial environmental monitoring tasks, clustering optimization, the core technology for network performance tuning, is a well-recognized NP-hard problem that directly determines the energy efficiency and communication reliability of the entire system. Such IWSNs are typically deployed in large-scale, unattended industrial fields to collect real-time, high-precision environmental data including air quality, water pollution levels and soil parameters. However, inherent constraints like limited node energy supply and unstable wireless links in these scenarios often lead to incomplete data collection and delayed early warning, which directly undermine the reliability of environmental monitoring. To address this challenge, this paper proposes CCNCSO-CRP, a novel energy-efficient clustering routing protocol based on a multi-objective clustering model that jointly considers four key metrics: total network residual energy, average transmission delay, packet loss rate, and the distance from cluster heads to the base station. The protocol is built on the newly designed Chaotic Clonal Niche Cockroach Swarm Optimization (CCNCSO) algorithm, which integrates chaotic initialization and evolutionary strategies including clonal selection and niche preservation to enhance population diversity and convergence speed. Extensive experimental validations are conducted on the CEC2008 and CEC2020 benchmark test suites, where the CCNCSO algorithm outperforms classical meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Osprey Optimization Algorithm (OFA), Komodo Mlipir Algorithm (KMA), Grey Wolf Optimizer (GWO), and Artificial Bee Colony (ABC). Furthermore, experimental evaluations under various IWSN environmental monitoring scenarios show that CCNCSO-CRP outperforms state-of-the-art protocols including LEACH-C, VSSLS-SIACR, and FOAEAUC-SARP by at least 11.5% in extending network lifetime, reduces average delay by no less than 9.5%, and cuts packet loss rate by a minimum of 40.9%. These results validate the effectiveness and superiority of the proposed protocol in improving clustering efficiency and network stability for environmental monitoring IWSNs, and provide reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.</p>

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A multi-objective energy-efficient clustering routing protocol for environmental monitoring in industrial IoT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月11日

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  • Yunpeng Lv,
  • Tingfa Zhou,
  • Bo Zhou,
  • Yahui Shan

摘要

For Industrial Wireless Sensor Networks (IWSNs) serving industrial environmental monitoring tasks, clustering optimization, the core technology for network performance tuning, is a well-recognized NP-hard problem that directly determines the energy efficiency and communication reliability of the entire system. Such IWSNs are typically deployed in large-scale, unattended industrial fields to collect real-time, high-precision environmental data including air quality, water pollution levels and soil parameters. However, inherent constraints like limited node energy supply and unstable wireless links in these scenarios often lead to incomplete data collection and delayed early warning, which directly undermine the reliability of environmental monitoring. To address this challenge, this paper proposes CCNCSO-CRP, a novel energy-efficient clustering routing protocol based on a multi-objective clustering model that jointly considers four key metrics: total network residual energy, average transmission delay, packet loss rate, and the distance from cluster heads to the base station. The protocol is built on the newly designed Chaotic Clonal Niche Cockroach Swarm Optimization (CCNCSO) algorithm, which integrates chaotic initialization and evolutionary strategies including clonal selection and niche preservation to enhance population diversity and convergence speed. Extensive experimental validations are conducted on the CEC2008 and CEC2020 benchmark test suites, where the CCNCSO algorithm outperforms classical meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Osprey Optimization Algorithm (OFA), Komodo Mlipir Algorithm (KMA), Grey Wolf Optimizer (GWO), and Artificial Bee Colony (ABC). Furthermore, experimental evaluations under various IWSN environmental monitoring scenarios show that CCNCSO-CRP outperforms state-of-the-art protocols including LEACH-C, VSSLS-SIACR, and FOAEAUC-SARP by at least 11.5% in extending network lifetime, reduces average delay by no less than 9.5%, and cuts packet loss rate by a minimum of 40.9%. These results validate the effectiveness and superiority of the proposed protocol in improving clustering efficiency and network stability for environmental monitoring IWSNs, and provide reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.