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

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 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.

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

“…Experimental results demonstrated the effectiveness of the proposed improved algorithms. Yao et al [15], addressing the optimization problem of maximizing coverage. Inspired by the predation behavior of Army Ants, this paper introduces a novel swarm intelligence (SI) technique called the Army Ant Search Optimizer (AASO) to tackle the problem of maximizing coverage.…”

“…Most existing research on WSNs coverage optimization has overlooked the variability in coverage requirements within the monitoring target area [13][14][15][16][17]. It generally assumes that the monitoring area has uniform coverage requirements.…”

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

“…Usually, random deployment results in a poor distribution density balance of nodes, resulting in coverage holes in UWSNs that cannot be effectively connected. When the network is not connected, the ground receiving station and the underwater node cannot communicate normally, and the data cannot be obtained, thus causing partial paralysis of the UWSNs [6]. Limited by the actual cost of underwater hardware, deploying as few sensors as possible to collect more information is an ideal option that has usually been pursued [7].…”