Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Concurrency and Computation Practice and Experience

DOI: 10.1002/cpe.70168

Authors: Hadi Ghahremani, Masumeh Damrudi, Ali Ghaffari, Kamal Jadidy Aval

Institutions: Islamic Azad University, Tehran, Islamic Azad University North Tehran Branch, Islamic Azad University of Tabriz, Islamic Azad University, Firoozkooh Branch

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4412542278

FWCI: 0.939

Cites: 146

Cited by: 1

Related to: 10