Global Scheduling of Internet of Things Applications on Cloud Platforms: A Community-Driven Approach using Advanced Metaheuristic for Improved User Satisfaction

Abstract: The increasing proliferation of Internet of Things (IoT) applications has intensified the demand for efficient scheduling mechanisms within cloud computing environments. Traditional scheduling approaches often struggle to handle the heterogeneity, scalability, and dynamic nature of IoT workloads, resulting in suboptimal Quality of Service (QoS) and diminished user satisfaction. This paper proposes a novel global scheduling framework that integrates a community-driven approach with hybrid metaheuristic optimization to address these challenges. The framework groups IoT users into communities based on behavioral and service characteristics, enabling context-aware resource allocation. Furthermore, a hybrid optimization algorithm combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is developed to enhance scheduling efficiency by balancing exploration and exploitation capabilities. The proposed system is evaluated through simulation, demonstrating significant improvements in latency reduction, resource utilization, and overall user satisfaction compared to conventional methods. The findings highlight the effectiveness of integrating user-centric intelligence with advanced optimization techniques for next-generation IoT- cloud systems.

Keywords: IoT Scheduling, Cloud Computing, Hybrid Metaheuristics, QoS Optimization, User Satisfaction