Multiobjective Optimization for Multi-UAV-Enabled WRSNs With PADs

With the advancements in wireless power transfer (WPT) technology, wireless rechargeable sensor networks (WRSNs) have emerged as a prominent research area. In the studies of WRSNs, there has been growing interest in using unmanned aerial vehicles (UAVs) as mobile chargers (MCs) to overcome geographi...

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Published in:IEEE sensors journal Vol. 25; no. 22; pp. 42292 - 42305
Main Authors: Zhang, Tianle, Jia, Riheng, Li, Minglu
Format: Journal Article
Language:English
Published: New York IEEE 15.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Ant colony optimization
Autonomous aerial vehicles
Base stations
Batteries
Capacitive sensors
Charging
Charging scheduling
Completion time
Energy consumption
Intelligent sensors
multiobjective optimization
Multiple objective analysis
multiunmanned aerial vehicle (UAV)
Optimization
power access docker (PAD)
Recharging
Sensor phenomena and characterization
Sensors
Unmanned aerial vehicles
Wireless networks
Wireless power transmission
wireless rechargeable sensor networks (WRSNs)
Wireless sensor networks
ISSN:1530-437X, 1558-1748
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Summary:With the advancements in wireless power transfer (WPT) technology, wireless rechargeable sensor networks (WRSNs) have emerged as a prominent research area. In the studies of WRSNs, there has been growing interest in using unmanned aerial vehicles (UAVs) as mobile chargers (MCs) to overcome geographical constraints. Recently, a fixed wireless charging station called the power access docker (PAD) has been introduced in the UAV-based WRSNs to extend the operating range of the UAV in WRSNs by recharging the UAV. However, existing studies on WRSNs with PADs are predominantly limited to single-UAV systems. Thus, this article focuses on jointly designing the sensor allocation and flight path for multi-UAV-enabled WRSNs, where multiple PADs are integrated to replenish energy to UAVs during their mission of charging terrestrial sensors. The goal of this work is to simultaneously minimize the maximum task completion time and the total energy consumption of all dispatched UAVs. First, we propose a load balancing-based insertion method (LBIM) combined with ant colony optimization (ACO) to generate a feasible initial solution set. This designed approach primarily handles the recharging decisions of UAVs under the energy depletion situation and ensures that each UAV will not drain out its battery before returning to the base station (BS), whereas balancing the workload among all UAVs. Next, we develop an innovative enhanced multiobjective adaptive large neighborhood search (EMO-ALNS) algorithm to iteratively optimize the sensor allocation for each UAV and the visiting order of sensors and PADs based on the feasible initial solution. Extensive simulation results validate the effectiveness and superiority of the proposed algorithm compared to exiting arts. Specifically, the maximum task completion time can be reduced by up to 20.8%, and the total energy consumption can be reduced by up to 5.9%.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3616136