Bio-Inspired Algorithms for Self-Healing and Adaptation in Sensor Networks

Bio-inspired algorithms are becoming more and more popular as a way to solve challenging problems in sensor networks wit in the framework of self-healing and adaptation. This study offers a novel approach to enhancing sensor network performance using Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA). To demonstrate the superior performance of the proposed strategy, we compare it against six industry standard methodologies. The presented work majorly focusing on resource use, accuracy, efficiency, speed, and reliability. Hence, the results of the experiment show that the recommended technique is better in many important areas. It performs better than traditional methods in several areas, including as resource use, efficiency, and accuracy and reliability. In addition to efficiently managing energy, the PSO module encourages self-organization. The ACO enhances routing and fault tolerance. The GA optimizes node placement and recovery mechanisms to function in everchanging network conditions. The suggested technique offers a comprehensive answer to the problem of sensor network selfhealing and adaptation, making it an excellent option for a wide range of practical uses.