Optimizing Energy Consumption and Provisioning for Wireless Charging & Data Collection in Large-Scale WRSNs with Mobile Elements

Gerald Ijemaru; Kenneth Ang; Kah Phooi Seng

doi:10.1109/JIOT.2023.3277667

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Optimizing Energy Consumption and Provisioning for Wireless Charging & Data Collection in Large-Scale WRSNs with Mobile Elements

Journal article

Peer reviewed

Optimizing Energy Consumption and Provisioning for Wireless Charging & Data Collection in Large-Scale WRSNs with Mobile Elements

Gerald Ijemaru, Kenneth Ang and Kah Phooi Seng

IEEE Internet of Things Journal, Vol.10(20), pp.17585-17602

2023

DOI: https://doi.org/10.1109/JIOT.2023.3277667

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Abstract

Data collection

Inductive charging

Wireless sensor networks

Energy consumption

Wireless communication

Data models

Internet of Things

Wireless Rechargeable Sensor Networks (WRSNs) have emerged with strong potentials to address the bottlenecks of energy/lifetime of a WSN. Recent techniques have shown the efficacy of multiple mobile elements (MMEs) in terms of energy consumption optimization. However, new challenges for energy efficient MMEs scheme have emerged due to the emergence of large-scale WRSNs with big sensor-based data systems. Thus, large-scale deployments are currently limited owing to the bottlenecks of energy/lifetime, and mode of deployments of the sensor nodes. This paper proposes a deadline-based multiple mobile elements (DB-MMEs) model exploiting the efficacies of MMEs scheme to optimize energy consumption and provisioning. The DB-MMEs scheme exploits multifunctional wireless Mobile Charging Vehicles (MCVs) for both wireless charging and data collection via a single-hop transmission. The scheme is specifically designed for delay-intolerant applications. None of the existing techniques have considered this approach to minimize latency and optimize energy consumption and provisioning for LS-WRSNs scenarios. The proposed scheme first organizes the sensors into several clusters for wireless charging and data collection. To optimize energy consumption and provisioning and address the challenges of energy/lifetime for LS-WRSNs scenarios, this paper proposes analytical-based approaches to address some critical trade-offs including: (1) determining the optimal amount of energy available for the MCVs; (2) finding the optimal number of MCVs deployed within a given deadline; and (3) finding the optimal number of data collection and charging points (DCCPs). Lastly, the performance of the proposed approach is evaluated through experimental simulations, and the results validate the efficacy of the analytical-based method.

Details

Title: Optimizing Energy Consumption and Provisioning for Wireless Charging & Data Collection in Large-Scale WRSNs with Mobile Elements
Authors: Gerald Ijemaru (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Kenneth Ang (Author) - University of the Sunshine Coast, Queensland, Engage Research Lab
Kah Phooi Seng (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: IEEE Internet of Things Journal, Vol.10(20), pp.17585-17602
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/JIOT.2023.3277667
ISSN: 2327-4662
Organisation Unit: School of Science, Technology and Engineering; Engage Research Lab; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99731498702621
Output Type: Journal article

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