Meta-heuristic-based guidance model for node localization in wireless sensor networks. (December 2022)

• Record Type: Journal Article
• Authors: Alomari, Abdullah
• Is Part Of: Computers & electrical engineering. Volume 104:Part B(2022)
• Publication Date: 2022-12
• Subjects: Wireless sensor networks -- Path planning -- Optimization -- Mobile communication -- Meta-heuristic algorithms; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108437
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: An important requirement in many applications in Wireless sensor networks (WSNs) is the location of nodes within the network. Manual location provision options have become practically impossible, necessitating the use of self-localization approaches for the provision of location information. In one such approach, the mobile-assisted localization approach, a mobile anchor (MA) goes through the network and provides updated location information to the nodes. However, the implementation of solutions with highly MAs is limited in many real-time applications, with typical networks having irregular and unpredictable obstacles, vast distances between nodes, and large energy requirements. To make MAs more efficient in providing localization information, we proposed the Flow Direction Path Planning (FDPP) model, where the planning for movement is achieved using meta-heuristic physics-based algorithms following a real-time approach. The model shows better outcomes than traditional models, with improved localization accuracy, ratios, precision, and lower computational times. Highlights: The main contribution is in combining a metaheuristic model with irregular obstacles. Most previous works in the field consider optimal scenarios which are unrealistic. The work's novelty lies in the development, and testing of FDPP in several metrics.