Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer

As one of the most crucial scenarios of the Internet of Things, wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for the remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment,...

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Bibliographic Details
Published in:IEEE sensors journal 2022-11, Vol.22 (21), p.1-1
Main Authors: Yao, Yin-Di, Wen, Qin, Cui, Yan-peng, Zhao, Feng, Zhao, Bo-Zhan, Zeng, Yao-Ping
Format: Article
Language:English
Subjects:
Algorithms
Army Ant Search Optimizer (AASO)
Audio data
Coverage Enhancement
Data collection
Intelligent sensors
Internet of Things
Monitoring
Multimedia
Optimization
Particle swarm optimization
Sensor phenomena and characterization
Sensors
Swarm Intelligence
Wireless Multimedia Sensor Networks (WMSNs)
Wireless sensor networks
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Summary:As one of the most crucial scenarios of the Internet of Things, wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for the remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ant, this paper propose a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3203147