Q-Learning Based MEP Search Algorithm and Coverage Enhancement Strategy in IoT-Enabled Intrusion Detection

Yao, Yindi; Tian, Yuying; Li, Xiong; Yang, Xuan; Zhao, Bozhan; Yang, Ying

doi:10.1109/jsen.2023.3335939

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2023.3335939

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Q-Learning Based MEP Search Algorithm and Coverage Enhancement Strategy in IoT-Enabled Intrusion Detection

Yindi Yao,

Yuying Tian,

Xiong Li

et al.

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2024

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Cited by 2 publications

References 27 publications

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Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

Yao,

Yang,

Tian

et al. 2024

IEEE Sensors J.

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Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

Yao,

Yang,

Tian

et al. 2024

IEEE Sensors J.

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A Method to Optimize Deployment of Directional Sensors for Coverage Enhancement in the Sensing Layer of IoT

Wang,

Xiong

2024

Future Internet

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Directional sensor networks are a widely used architecture in the sensing layer of the Internet of Things (IoT), which has excellent data collection and transmission capabilities. The coverage hole caused by random deployment of sensors is the main factor restricting the quality of data collection in the IoT sensing layer. Determining how to enhance coverage performance by repairing coverage holes is a very challenging task. To this end, we propose a node deployment optimization method to enhance the coverage performance of the IoT sensing layer. Firstly, with the goal of maximizing the effective coverage area, an improved particle swarm optimization (IPSO) algorithm is used to solve and obtain the optimal set of sensing directions. Secondly, we propose a repair path search method based on the improved sparrow search algorithm (ISSA), using the minimum exposure path (MEP) found as the repair path. Finally, a node scheduling algorithm is designed based on MEP to determine the optimal deployment location of mobile nodes and achieve coverage enhancement. The simulation results show that compared with existing algorithms, the proposed node deployment optimization method can significantly improve the coverage rate of the IoT sensing layer and reduce energy consumption during the redeployment process.

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Q-Learning Based MEP Search Algorithm and Coverage Enhancement Strategy in IoT-Enabled Intrusion Detection

Cited by 2 publications

References 27 publications

Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

Improved Genetic Algorithm-Based MEP Search Strategy for DSNs Intrusion Detection

A Method to Optimize Deployment of Directional Sensors for Coverage Enhancement in the Sensing Layer of IoT

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