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An intelligent fault detection approach based on reinforcement learning system in wireless sensor network

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Abstract

The Internet of Things (IoT) has developed a well-defined infrastructure due to commercializing novel technologies. IoT networks enable smart devices to compile environmental information and transmit it to demanding users through an IoT gateway. The explosive increase of IoT users and sensors causes network bottlenecks, leading to significant energy depletion in IoT devices. The wireless network is a robust, empirically significant, and IoT layer based on progressive characteristics. The development of energy-efficient routing protocols for learning purposes is critical due to environmental volatility, unpredictability, and randomness in the wireless network’s weight distribution. To achieve this critical need, learning-based routing systems are emerging as potential candidates due to their high degree of flexibility and accuracy. However, routing becomes more challenging in dynamic IoT networks due to the time-varying characteristics of link connections and access status. Hence, modern learning-based routing systems must be capable of adapting in real-time to network changes. This research presents an intelligent fault detection, energy-efficient, quality-of-service routing technique based on reinforcement learning to find the optimum route with the least amount of end-to-end latency. However, the cluster head selection is dependent on residual energy from the cluster nodes that reduce the entire network’s existence. Consequently, it extends the network’s lifetime, overcomes the data transmission’s energy usage, and improves network robustness. The experimental results indicate that network efficiency has been successfully enhanced by fault-tolerance strategies that include highly trusted computing capabilities, thus decreasing the risk of network failure.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Tariq Mahmood, Jianqiang Li & Sirajuddin Qureshi

  2. Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, 54000, Pakistan

    Tariq Mahmood, Suhail Ashfaq Butt & Allah Ditta

  3. Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China

    Jianqiang Li

  4. Computer Science Division, University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan

    Yan Pei

  5. Department of Computer Science, Sukkur IBA University, Sukkur, 65200, Pakistan

    Faheem Akhtar

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  1. Tariq Mahmood
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  2. Jianqiang Li
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Correspondence to Yan Pei.

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Mahmood, T., Li, J., Pei, Y. et al. An intelligent fault detection approach based on reinforcement learning system in wireless sensor network. J Supercomput 78, 3646–3675 (2022). https://doi.org/10.1007/s11227-021-04001-1

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