Enhancing IoT Systems with Bio-Inspired Intelligence in fog computing environments

Abstract

In the context of deploying delay-sensitive Internet of Things (IoT) applications, the fog computing paradigm faces critical challenges in optimal node placement to ensure both connectivity and coverage. Traditional optimization approaches often fail to effectively balance these competing objectives in dynamic IoT environments. This paper presents a novel bio-inspired approach using the Pufferfish Optimization Algorithm (POA) to solve this multi-objective optimization problem. Our solution uniquely leverages a two-stage optimization process, inspired by pufferfish predator response mechanisms, to dynamically balance global exploration and local exploitation. Experimental evaluation across varied network configurations demonstrates that POA significantly outperforms existing state-of-the-art algorithms in both network connectivity and coverage metrics (p < 0.001). The proposed algorithm exhibited robust performance across different communication ranges, while maintaining optimal network connectivity and comprehensive coverage of edge devices. These results demonstrate POA's effectiveness of the POA in optimizing fog node placement for real-world IoT applications.