Discrete Army Ant Search Optimizer-Based Target Coverage Enhancement in Directional Sensor Networks

Yao, Yindi; Wen, Qin; Cui, Yanpeng; Zhao, Bo-Zhan

doi:10.1109/lsens.2022.3158274

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…The work of [30] proposed a constrained artificial fish-swarm algorithm to optimize the sensor distribution by regarding the sensing centroid as artificial fish, which improved the coverage rate in the monitoring area. Utilizing discrete army ant search optimizer (AASO) in [31], the authors maximized the coverage of the target of DSNs. Fan et al [32] defined the objective function about the ideal weighted coverage rate and searched the solution space by the quantum genetic algorithm.…”

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

et al. 2022

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As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for 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 ants, this article proposes 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.

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confidence: 99%