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Published VersionCC BY V4.0, Open Access
Journal article
Swarm Intelligence Techniques for Mobile Wireless Charging
Electronics, Vol.11(3), pp.1-28
2022
Abstract
This paper proposes energy-efficient swarm intelligence (SI)-based approaches for efficient mobile wireless charging in a distributed large-scale wireless sensor network (LS-WSN). This approach considers the use of special multiple mobile elements, which traverse the network for the purpose of energy replenishment. Recent techniques have shown the advantages inherent to the use of a single mobile charger (MC) which periodically visits the network to replenish the sensor-nodes. However, the single MC technique is currently limited and is not feasible for LS-WSN scenarios. Other approaches have overlooked the need to comprehensively discuss some critical tradeoffs associated with mobile wireless charging, which include: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall available network energy. These important tradeoffs are investigated in this study. Thus, this paper aims to investigate some of the critical issues affecting efficient mobile wireless charging for large-scale WSN scenarios; consequently, the network can then be operated without limitations. We first formulate the multiple charger recharge optimization problem (MCROP) and show that it is N-P hard. To solve the complex problem of scheduling multiple MCs in LS-WSN scenarios, we propose the node-partition algorithm based on cluster centroids, which adaptively partitions the whole network into several clusters and regions and distributes an MC to each region. Finally, we provide detailed simulation experiments using SI-based routing protocols. The results show the performance of the proposed scheme in terms of different evaluation metrics, where SI-based techniques are presented as a veritable state-of-the-art approach for improved energy-efficient mobile wireless charging to extend the network operational lifetime. The investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.
Details
- Title
- Swarm Intelligence Techniques for Mobile Wireless Charging
- Authors
- Gerald Ijemaru (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringKenneth Ang (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJasmine Kah Phooi Seng (Author) - Queensland University of Technology
- Publication details
- Electronics, Vol.11(3), pp.1-28
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/electronics11030371
- ISSN
- 2079-9292
- Copyright note
- This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99608307802621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Physics, Applied