Swarm Intelligence-Based Performance Optimization for Wireless Sensor Networks for Hole Detection

Abstract

Extensive research into maintaining coverage over time has been spurred by the growing need for wireless sensor networks to monitor certain regions.  Coverage gaps brought on either haphazard node placement or failures pose the biggest threat to this objective.  In order to identify and fix coverage gaps, this study suggests an algorithm based on swarm intelligence.  Using both local and relative information, the swarm of agents navigates a potential field toward the nearest hole and activates in reaction to holes found.  In order to spread out effectively and speed up healing, the agents quantize their perceptions and approach holes from various angles. The need for wireless sensor networks to monitor certain areas has grown, leading to many studies on maintaining coverage over time. Random node deployment or failures create coverage gaps, which pose the biggest threat to this objective.  A swarm intelligence-based approach is proposed in this paper to identify and fix coverage deficiencies. Even with Their encouraging performance and operational quality, WSNs are susceptible to various security threats. The security of WSNs is seriously threatened by sinkhole attacks, one of these. In this research, a detection strategy against sinkhole attacks is proposed and developed using the Swarm Intelligence (SI) optimization algorithm. MATLAB has been used to implement the proposed work, and comprehensive Models have been run to assess its effectiveness in terms of energy consumption, packet overhead, convergence speed, detection accuracy, and detection time. The findings demonstrate that the mechanism we have suggested is effective and reliable in identifying sinkhole attacks with a high rate of detection accuracy.