Dynamic fog node placement optimization using adaptive dynamic pufferfish optimization for real-time IoT networks

Abstract

The exponential growth of Internet of Things (IoT) deployments demands fog computing architectures that position computational resources at the network edge to ensure low-latency processing. However, optimal fog node placement in dynamic environments remains a significant challenge due to node mobility, random failures, and fluctuating traffic patterns, which require continuous adaptation to maintain service quality. This paper introduces the Dynamic Pufferfish Optimization Algorithm (D-POA), a bio-inspired metaheuristic that leverages behavioral strategies observed in pufferfish species to adaptively reposition fog nodes in real-time, balancing exploration of the solution space with refinement of promising configurations. D-POA formulates the placement problem as a continuous multi-objective optimization that simultaneously addresses network connectivity, area coverage, and movement costs to minimize service disruption during reconfiguration. Comprehensive experimental evaluation across five dynamic scenarios demonstrates that D-POA achieves 97.8% connectivity and 98.4% coverage while reducing movement costs by 38–57% compared to baseline algorithms, with near-linear scalability maintaining over 96% solution quality across networks ranging from 50 to 1000 nodes.