SMoSE: Artificial Intelligence-Based Smart City Framework Using Multi-Objective and IoT Approach for Consumer Electronics Application

This paper introduces an innovative framework at the convergence of Artificial Intelligence (AI), Multi-objective Optimization (MOO), and the Internet of Things (IoT), specifically tailored for applications in consumer electronics within smart cities. The framework seeks to revolutionize urban living by offering intelligent, responsive, and interconnected solutions. In advancing the evolution of smart cities towards enhanced sharing and interconnectedness, this paper scrutinizes smart city data technology grounded in the Internet of Things (IoT) and cloud computing (CC) approaches. Employing machine learning methodologies, particularly the Random Forest (RF) algorithm, facilitates autonomous communication between machines devoid of human intervention. To solve the multi-criteria problem, a hybrid algorithm is proposed, emulating the behavioral traits of the Spotted Hyena Optimization (SHO) and Emperor Penguin Optimization (EPO) algorithms. Experimental results underscore the superior efficiency of the proposed optimization algorithm in comparison with currently employed techniques.