A Novel Multi-Objective Service Scheduling Framework for Fog-Driven Agricultural Internet of Things
Timely, context-aware service recommendations are most significant for maximizing system efficiency and resource utilization in fog-enabled Internet of Things (IoT) for smart agriculture. Classical Grey Wolf Optimization (GWO) algorithms are prone to premature convergence and lack robustness for multi-objective and dynamic environments. This study presents an Enhanced Grey Wolf Optimization (EGWO) algorithm that incorporates adaptive weighting and nonlinear exploration-exploitation control. The EGWO algorithm uses a dynamic alpha-beta-delta hierarchy for weighting and a cosine-based decay for the control parameter, ensuring better convergence and global search efficiency. In the fog-based service recommendation environment, EGWO optimizes multiple conflicting objectives, including latency, energy consumption, trust, and service utility. A comparison is made between standard GWO, PSO, and DE using a simulated agricultural fog-IoT network. The results demonstrate that EGWO achieves faster convergence speed, higher recommendation quality, and uniform behavior across all test scenarios. The method is suitable for practical application in real-time agricultural use cases, ensuring guaranteed optimal service allocation and improved decision support at the network's edge.