A bi-population Genetic algorithm based on multi-objective optimization for a relocation scheme with target coverage constraints in mobile wireless sensor networks. (1st May 2023)

• Record Type: Journal Article
• Authors: Quan, La Van; Hanh, Nguyen Thi; Binh, Huynh Thi Thanh; Toan, Vu Duc; Ngoc, Dang The; Lam, Bui Thu
• Is Part Of: Expert systems with applications. Volume 217(2023)
• Publication Date: 2023-05-01
• Subjects: Bi-population Genetic algorithm -- Target coverage -- Mobile wireless sensor network -- NSGA-II -- Multi-objective; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119486
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: A concise and factual abstract is required. The abstract should state 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. Highlights: 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.