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<p>The Industrial Internet of Things (IIoT) makes possible intelligent industrial automation, real-time monitoring, and some “smart” infrastructure via Industrial Wireless Sensor Networks (IWSNs). Still, a lot of existing routing protocols end up with excessive energy usage, poor adaptability when the network conditions changes, and not enough protection from malicious behavior. Also, many approaches try to handle energy efficiency and security, like two separate things, and this tends to shorten the network lifetime and makes data transmission less dependable. To address these gaps, this paper introduces a fresh routing framework named Progressive Chimp Optimization with Q-Learning Neural Network (PCOQLNN). In the framework, a PCOA is used to handle energy-aware cluster head (CH) selection and route choosing, while the QLNN learns, step-by-step, better routing decisions as the network state shifts. A trust-aware mechanism is added to spot and isolate malicious nodes, which should strengthen routing security and improve how reliably packets get through. Unlike the usual routing schemes, PCOQLNN tries to tune hyper parameter values together inside one unified process. The performance is checked against several recent routing techniques. Results show that PCOQLNN extends network lifetime by 3.42%, boosts residual energy by 66.7%, and raises packet success score by 0.38% when compared to the best baseline. Also, the framework keeps a larger count of alive nodes and supports more reliable data delivery, even when conditions are not perfectly stable. These results confirm that PCOQLNN provides a secure, adaptive, and energy-efficient routing solution for next-generation IIoT environments.</p>

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Designing of Secured and Optimized Routing Algorithm for Improving Network Lifetime in IIoT

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • M. Sathya,
  • V. Palanisamy,
  • M. Vanitha,
  • P. Muthukumar

摘要

The Industrial Internet of Things (IIoT) makes possible intelligent industrial automation, real-time monitoring, and some “smart” infrastructure via Industrial Wireless Sensor Networks (IWSNs). Still, a lot of existing routing protocols end up with excessive energy usage, poor adaptability when the network conditions changes, and not enough protection from malicious behavior. Also, many approaches try to handle energy efficiency and security, like two separate things, and this tends to shorten the network lifetime and makes data transmission less dependable. To address these gaps, this paper introduces a fresh routing framework named Progressive Chimp Optimization with Q-Learning Neural Network (PCOQLNN). In the framework, a PCOA is used to handle energy-aware cluster head (CH) selection and route choosing, while the QLNN learns, step-by-step, better routing decisions as the network state shifts. A trust-aware mechanism is added to spot and isolate malicious nodes, which should strengthen routing security and improve how reliably packets get through. Unlike the usual routing schemes, PCOQLNN tries to tune hyper parameter values together inside one unified process. The performance is checked against several recent routing techniques. Results show that PCOQLNN extends network lifetime by 3.42%, boosts residual energy by 66.7%, and raises packet success score by 0.38% when compared to the best baseline. Also, the framework keeps a larger count of alive nodes and supports more reliable data delivery, even when conditions are not perfectly stable. These results confirm that PCOQLNN provides a secure, adaptive, and energy-efficient routing solution for next-generation IIoT environments.