Research on Wireless Sensor Network Coverage Path Optimization Based on Biogeography-Based Optimization Algorithm

Chen, Guojun; Qin, Xiangdong; Fang, Ningsheng; Xu, Wenbo

doi:10.1155/2021/7826132

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…However, when the monitoring area is large, some data may be lost and delayed. Chen et al [13] used the biogeography-based optimization algorithm to better optimize the WSN coverage path. However, the operation speed of this algorithm is relatively slow, and in the initial stage of network deployment, part of the energy is sacrificed in exchange for minimizing the energy consumption after the network is stable.…”

Section: Related Workmentioning

confidence: 99%

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

Xiao,

Huang,

Zhang

et al. 2024

IET Communications

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At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption.

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Section: Related Workmentioning

confidence: 99%

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

Xiao,

Huang,

Zhang

et al. 2024

IET Communications

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“…Biogeography-based optimization (BBO) is widely used in the field of cluster intelligent optimization because its search method has a better incentive mechanism for population evolution. In [80], through the research and improvement of the BBO algorithm, it will make full use of the ability of the BBO algorithm to sense interactive data in multidimensional and high-dimensional problems to achieve the optimal construction of the WSN coverage path. The move-in and move-out operation and mutation operation of the BBO algorithm enable WSN to find an efficient coverage path.…”

Section: Coverage Solutions Based Ai Techniques In Wsnsmentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

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Research on Ink Color Matching based on Stearns Noechel and BBO Optimization Algorithm

Chen

2021

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

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Research on Wireless Sensor Network Coverage Path Optimization Based on Biogeography-Based Optimization Algorithm

Cited by 4 publications

References 23 publications

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

Research on Ink Color Matching based on Stearns Noechel and BBO Optimization Algorithm

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