An integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station that discusses deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.
Abstract
Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) extend the lifetime of wireless rechargeable sensor networks (WRSNs) by delivering energy on demand. This article presents an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. A prioritized charging queue captures node urgency and service cost through residual energy, traffic load, estimated UAV travel time, and flight-direction alignment. This bidirectional coupling ensures that scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the queue. ISAC-assisted estimation of UAV distance, speed, and position updates travel-time predictions under mobility uncertainty. A time-allocated partial charging policy distributes limited hover time across queued nodes according to criticality. Simulations show gains in energy usage efficiency, travel distance, and charging delay compared with representative baselines. We discuss deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
This paper addresses the self-sustainable operation of unmanned aerial vehicle (UAV) swarms in dense urban 6G networks, where both communication reliability and energy replenishment are strongly affected by building-induced blockage. Although solar harvesting and laser wireless power transfer (WPT) can extend UAV operation, they are tightly coupled with UAV mobility: a communication-favorable position may not be feasible for laser charging, while a charging-oriented position may degrade ground node (GN) service. To capture this coupling, we develop a blockage-aware self-sustaining UAV swarm framework that integrates communication, solar harvesting, safety-compliant laser WPT, and UAV mobility under urban blockage. In the proposed model, buildings serve as common geometric constraints that determine both UAV–GN line-of-sight (LoS) connectivity and laser charging feasibility. We formulate a joint optimization problem of user association, UAV trajectory, laser charging decisions, and battery states to maximize the minimum spectral efficiency among GNs while ensuring sustainable energy operation. To address the resulting nonconvex mixed-integer nonlinear problem, we develop a tailored convexification framework for the coupled communication–charging–mobility design. Simulation results reveal that UAVs adapt their mobility according to solar availability: they prioritize short-range LoS communication when solar energy is sufficient, while moving toward safety-compliant and blockage-free laser charging regions under limited solar harvesting. These results highlight the need for joint mobility control that balances communication service and energy replenishment under building-induced blockage.
Urban low-altitude Unmanned Aerial Vehicles (UAVs) often require intermediate recharging during long-duration missions, and mobile Automated Guided Vehicles (AGVs) provide a flexible ground-side recharging solution in obstacle-constrained urban environments. Although AGV–UAV wireless energy coordination frameworks exist, appropriate matching cannot rely solely on nearest-distance or maximum-energy rules, as flight energy consumption, reachable safety margins, post-charge task feasibility, and low-altitude trajectory executability are tightly coupled. To address this issue, this paper proposes an energy-aware AGV matching method for efficient UAV recharging. For each candidate AGV, a feasible trajectory is generated under obstacle avoidance and altitude constraints, and the corresponding flight energy consumption, arrival energy, received charging energy, and post-charge available energy are quantitatively evaluated. A post-charge energy threshold is introduced to filter out AGVs that are reachable but unable to support subsequent mission execution after recharging. In addition, altitude regularization is employed to suppress excessive vertical maneuvers and improve the practicality of low-altitude trajectories. The proposed method is evaluated using three representative scenarios and three randomly generated scenarios against four baseline approaches. Results show that this method provides more reliable and mission-consistent decisions, avoids inefficient charging choices, and substantially reduces vertical motion.
Xiaolin Mou, Jiaxin Zhuang, Nan Li et al.· Electronics· 0 citations
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
Wireless power transfer (WPT) technique is promising for addressing the energy bottleneck of conventional wireless sensor networks (WSNs). Most existing work focuses on optimizing either the deployment of static chargers or the trajectory design of mobile chargers when the device-to-be-charged is static. However, when devices are mobile, the charging optimization becomes more challenging and remains insufficiently explored. In this work, we investigate the charging optimization for mobile devices, where some devices move along their scheduled routes and some static wireless chargers are deployed to charge the devices within a certain area. The objective is to minimize the time-averaged total power consumption of all chargers over a finite time horizon, while minimizing the time-averaged proportion of devices whose real-time battery levels fall below a given safety threshold. To address the spatiotemporal charging, partial observability of device-side information, and trade-off between chargers’ power minimization and devices’ battery level maintenance, we propose D2C-MASAC, a decoupled-dual constrained multi-agent soft actor-critic algorithm that combines decoupled policy learning with adaptive Lagrangian adjustment under constrained multi-agent reinforcement learning (MARL). Simulation-based validation is conducted in a 100m $\times 100$ m wireless rechargeable sensor network (WRSN) with 9 or 12 static chargers and 5, 10, or 20 mobile devices. The training process uses three random seeds and the final evaluation uses 50 independent test episodes. We define non-equal-weighted (NEW) and equal-weighted (EW) composite scores over three evaluation metrics, i.e., the average total power, the average safety threshold violation ratio, and the distribution of device battery levels. D2C-MASAC outperforms six MARL baselines and six optimization-based or heuristic-based benchmarks, achieving NEW/EW gains of 26.66%/28.07% and 35.96%/40.12%, respectively.
Yihao Shao, Xiuling Zhang, Riheng Jia et al.· IEEE Transactions on Network...· 0 citations
While wireless energy transfer offers a practical solution to the problem of energy scarcity in Wireless Sensor Networks (WSNs), the efficient management of Wireless Rechargeable Sensor Networks (WRSNs) in smart environments still faces two main inhibiting challenges: the inefficiency of single-charger architectures in large-scale deployments (lack of scalability) and the absence of adaptive, real-time scheduling mechanisms. Motivated by this, this paper introduces the Objective-Oriented Intelligent Charging Coordinator (OOICC), an innovative scheduling and charging resource allocation framework designed to overcome these limitations through a comprehensive two-layer strategy. By fundamentally rethinking network management, OOICC first dynamically determines the optimal number of Mobile Chargers (MCs) required for scalable coverage, moving beyond the conventional limitations of single chargers. Second, it establishes a hybrid offline-online decision-making architecture that synergistically combines strategic planning with operational adaptability. At the core of OOICC lies a formally defined multi-objective optimization framework that incorporates designer-specified characteristics into a comprehensive ten-fold objective function. This ensures the balanced pursuit of critical metrics such as charging latency, node survival rate, and network throughput. The coordinator operates through an intelligent workflow whereby the offline phase employs fuzzy clustering guided by a metaheuristic optimizer to create an optimal initial network partition and MC assignment. This framework actively interacts with an online phase, where an intuitive fuzzy logic engine performs context-aware charging scheduling by processing live network data in real-time. Through extensive simulations, OOICC demonstrates superior and consistent performance compared to established benchmarks. The method achieves significant improvements in charging response time, node survival rate, energy consumption efficiency, network stability, and data throughput. Quantitatively, the proposed framework outperforms the TSFM, CFMCRS, FLCSD, ESS, and NJNP methods by 8%, 48%, 68%, 73%, and 89%, respectively, confirming its efficacy as a robust and scalable solution for next-generation WRSN management.
Fakhrosadat Fanian, M. Kuchaki Rafsanjani, A. Borumand Saeid· Scientific Reports· 0 citations
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