Trust-based Energy Aware Secure Load Balancing and Resource Provisioning in Fog Computing using a Multi-Objective Optimization Algorithm

Abstract

Fog Computing (FC) is gradually essential in diminishing communication latency and enhancing resource usage for Internet of Things (IoT) tasks; though, critical experiments like resource overloading, security weaknesses, and excessive energy consumption frequently hinder its operational potential. The range of this study includes the expansion of a trust-based, energy-aware secure load matching and resource provisioning system, definitely calculated for decentralized fog architectures using a Multi-Objective Optimization Algorithm (MOA). The methodology incorporates a Many-to-Few (M2F) balancer for effective task distribution, ordering trust in node-task assignments to certify consistency. Resource management is significantly improved through the hybridization of Improved Salp Swarm Optimization (ISSO) and Modified Whale Optimization Algorithm (MWOA) for dynamic provision. To bolster system integrity, an intrusion detection system with offloading mechanisms is implemented alongside Hierarchical Collaborative Federated Learning (HCFL) to raise secure, privacy-preserving node association. Key results from performance assessments showed in the Matrix Laboratory (MATLAB) establish a high 88% Average Resource Utilization (ARU) and a steady 1300s response time. The model's strength is further supported by a precision of 99.5%, an F-measure of 91.0%, and a recall of 78.6% during oppositional testing. The individuality of this study lies in its concurrent optimization of trust, energy, and load metrics within a single meta-heuristic framework. This system offers a practical value for mission-critical IoT ecosystems, such as smart grids and industrial automation, where real-time handling and data reliability are mandatory for keeping complete system strength and performance.