Reputation Based Resource Allocation for IoT in Fog Computing Environments

The widespread adoption of Internet-of-Things (IoT) applications has led to the development of the Fog computing paradigm, which enables the seamless integration of mobile-edge and cloud resources. Efficiently allocating and provisioning application tasks in these environments is challenging due to limited resource capacities, IoT mobility, network hierarchy, resource heterogeneity, and stochastic behaviours. In this interconnected world, the demand for more computing devices, especially for delay-sensitive applications, is crucial. In this paper, we propose a community-based fog computing system that leverages a reputation-based resource distribution scheme for efficient task allocation across Fog Nodes (FNs). The system is structured into a three-layer architecture comprising the end layer, fog layer, and cloud layer. The end layer consists of IoT devices that generate tasks for processing. The fog layer implements a reputation mechanism to evaluate the reliability of fog nodes, organizing them into high, medium, and low communities based on performance metrics. Tasks are then allocated to fog nodes based on their reputation and the priority of the tasks. This architecture ensures optimal resource utilization, improves load balancing, and maintains the Quality of Service by dynamically allocating resources based on real-time reputation scores. The simulation results of the proposed algorithm were compared with FCFS, GfE, Detour, and PSG-M algorithms. For 10 to 50 FNs executing 500 tasks, the proposed algorithm demonstrated the following average improvements over PSG-M: 20.36% in makespan, 12.74% in energy consumption, 51.15% in cost, 11.2% in deadline violation, and 2.81% in resource utilization.