A bi-population Genetic algorithm based on multi-objective optimization for a relocation scheme with target coverage constraints in mobile wireless sensor networks

• Published in: Expert systems with applications 2023-05, Vol.217, p.119486, Article 119486
• Main Authors: Quan, La Van, Hanh, Nguyen Thi, Binh, Huynh Thi Thanh, Toan, Vu Duc, Ngoc, Dang The, Lam, Bui Thu
• Format: Article
• Language: English
• Subjects: Bi-population Genetic algorithm; Mobile wireless sensor network; Multi-objective; NSGA-II; Target coverage
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.119486

Target coverage and lifetime maximization problems are major challenges for mobile wireless sensor networks (MWSN). In this paper, we propose a Multi-Objective formulation for MaxiMizing lifetime with Target Coverage called MO-MMTC, which accounts for the energy fluctuation among mobile sensors after each movement. We prove the formulation to be NP-hard and propose the Enhanced Non-dominated Sorting Genetic Algorithm II (ENSGA-II), a multi-population genetic algorithm, to solve this problem. Experiments are performed to compare ENSGA-II with TV-Greedy, an existing state-of-the-art heuristic for MMTC. Our results show that the proposed algorithm significantly improves many evaluation metrics compared to baseline methods. •Showing a problem that minimizes total movement and maximal movement of sensors.•Modeling the problem to mixed-integer linear programming form.•Proposing a bi-population Genetic algorithm combined with geometrical computing.•Conducting experiments comparing the algorithms and analyzing their results.