Efficient sensor network management for asset localization. (November 2018)

• Record Type: Journal Article
• Authors: Soeanu, Andrei; Ray, Sujoy; Berger, Jean; Debbabi, Mourad
• Is Part Of: Computers & operations research. Volume 99(2018)
• Page Start: 148
• Page End: 165
• Publication Date: 2018-11
• Subjects: Asset localization -- Wireless sensor network -- Multi-dimensional assignment; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.06.001
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Asset localization over resource constrained wireless sensor network (WSN). WSN management to assign heterogeneous sensors to localize assets with minimized error. Localization problem extension considering an overall WSN energy budget restriction. Heuristic solution approach using evolutionary learning and meta-heuristic improvements. Abstract: Asset localization represents an important application over wireless sensor networks (WSN) with a wide area of applicability ranging from network surveillance to search and rescue operations. In this paper, we address a research problem of network management where resource constrained sensors, in terms of capacity, sensing range and energy, are assigned to multiple targets in order to optimally localize assets with minimized error. We consider a heterogeneous network of omnidirectional sensors, each of which has an individual capacity to focus on a number of targets and a specific range to accurately estimate its distances to the targets that it is focusing on. A proper localization of each target requires a minimum of K (typically three) sensors where the target location is estimated using the intersection of the K range circles. We further analyze the problem under the constraint of a globally specified overall WSN energy budget which limits the possible assignments for the capacitated sensors. Restricting the energy budget leads to a trade-off between energy conservation and localization performance. In this context, we propose a heuristic solution approach leveraging evolutionary learning followed by meta-heuristic improvements based on target swapping among sensors. This approach actually minimizes a quantifier that is composed of the total localization area for all targets in addition to a penalty for each target if it is assigned less than minimum sensors. We provide an illustrative case study for the proposed approach and assess its effectiveness experimentally via benchmark results obtained on a data-set derived from known vehicle routing problem instances.