Quality of Service Enhancement in Mobile Crowdsensing Through Metaheuristic Techniques: A Survey

Abstract

Mobile crowdsensing (MCS) has emerged as a promising paradigm leveraging the widespread availability of mobile devices for large-scale data collection. Ensuring high quality of service (QoS) in MCS is paramount for its effectiveness and reliability. This survey reviews the application of metaheuristic optimization algorithms to enhance QoS in MCS systems, with a focus on adaptive and hybrid optimization techniques for real-time applications. We discuss key QoS metrics, such as accuracy, latency, and reliability, and outline the challenges inherent in maintaining these metrics, including scalability, adaptability to dynamic environments, and energy efficiency. The survey provides a comprehensive overview of various metaheuristic algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA), evaluating their applicability and potential in MCS contexts. Through a systematic review of the literature, we highlight recent advancements and practical implementations of these algorithms, presenting comparative insights and case studies to illustrate their effectiveness in addressing QoS challenges.