Trust-Aware Multi-Objective Resource Provisioning in Fog Computing Using NSGA-II: A Comparative Optimization Framework

The rapid growth of the Internet of Things (IoT) has made it necessary to create efficient and secure resource management frameworks in situations that use fog computing. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this research proposes a trust-based resource provisioning strategy to overcome the difficulties of dynamic resource availability, security vulnerabilities, and trust management in decentralized fog networks. The proposed model optimizes key performance metrics such as latency, trust level, and energy efficiency while balancing security and system performance. An adaptive trust-driven approach dynamically adapts resource distribution to current network circumstances, enhancing security and reliability. Simulation results demonstrate that the NSGA-II-based model outperforms conventional optimization techniques like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) in terms of decreased computing time and energy consumption , making it a viable solution for efficient fog computing resource management.