Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs. (9th September 2022)

• Record Type: Journal Article
• Authors: Prakash, P. Suman; Janardhan, M.; Sreenivasulu, K.; Saheb, Shaik Imam; Neeha, Shaik; Bhavsingh, M.
• Other Names: Donta Praveen Kumar Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-09-09
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/8373343
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Because wireless sensor networks (WSNs) have low-constrained batteries, optimizing the network lifetime is a primary challenge. Rechargeable batteries are a solution to prolong the lifetime of a sensor node instead of restricting their functionalities to save energy. Wireless energy transmitters have the added benefit of providing a charger for the batteries of the sensor nodes in the WSN. However, scheduling one or more charging vehicles efficiently to recharge multiple sensor nodes is challenging. In this context, this paper provides a solution to recharge the sensor nodes using charging vehicle scheduling in WSNs through a mixed linear programming approach. Initially, we identify a heuristic value of each sensor node based on their residual energy, distance from a charging vehicle, available data packets, and other metrics. Further, a set of nodes is recharged by identifying the best charging vehicle to prolong their lifetimes, as well as the lifetime of the network as a whole. We simulated the proposed approach using a Python simulator, tested using different performance metrics, and compared using the recently published works. We notice the superior performance of the proposed work under various metrics in time and query-driven WSNs.