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

TL;DR

This paper introduces a novel discrete army ant search optimizer (DAASO) to improve target coverage in directional sensor networks, addressing perception blind spots caused by environmental constraints and sensor limitations.

Contribution

The paper proposes a new DAASO algorithm inspired by army ants to enhance coverage in directional sensor networks, outperforming existing methods.

Findings

DAASO significantly improves coverage compared to existing algorithms.

Experimental results validate the effectiveness of DAASO across various sensor parameters.

Coverage enhancement is achieved in challenging environments with blind spots.

Abstract

Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.