A trust management system for fog computing using improved genetic algorithm

Internet of Things (IoT) devices generate and transfer a vast volume of data in interaction with various objects. These data rely on cloud computing (CC) or fog computing (FC) for processing and calculations. Unlike CC, FC delivers task processing closer to IoT devices. Consequently, FC leads to reduced latency, increased efficiency, ease of deployment, and flexibility. The primary concern between the service requester and provider when agreeing to task offloading is trust. While several types of research are based on intelligent models for trust management, trust management in FC requires direct and indirect trust computation due to the presence of malicious nodes. To ensure security and maintain the quality of service when interacting with fog servers, trust is essential. In this paper, a new trust model for FC is proposed using a combination of the Genetic Algorithm (GA) and Learning Automaton (LA). The crossover operator in GA by LA is improved. The changes in the search space and finding optimal solutions are dependent on the crossover rate. Thus, determining an optimal value for the crossover operator impacts the problem's solutions. The proposed methods evaluation was done in a MATLAB 2020 simulation environment. The results show that the performance of the proposed method with 200 iterations and 1000 tasks is superior in terms of reliability rate, energy consumption, and delay compared to GA and particle swarm optimization (PSO) algorithm.