Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer
Yindi Yao, Qin Wen, Yanpeng Cui, Feng Zhao, Bozhan Zhao, Yaoping Zeng

TL;DR
This paper introduces a novel swarm intelligence algorithm inspired by army ant behavior to optimize coverage in wireless multimedia sensor networks, addressing complex deployment challenges and improving data collection effectiveness.
Contribution
The paper proposes the Army Ant Search Optimizer (AASO), a new swarm intelligence algorithm inspired by army ant behavior, for maximizing coverage in WMSNs, outperforming existing methods.
Findings
AASO demonstrates superior exploration and exploitation capabilities on benchmark tests.
Coverage enhancement using AASO yields better coverage effects than existing approaches.
The algorithm effectively handles the NP-hard coverage optimization problem in WMSNs.
Abstract
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…
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