To achieve the objectives of efficient wireless charging and timely data collection in Wireless Rechargeable Sensor Networks (WRSNs), this paper proposes a joint scheduling scheme for UAV that optimizes both charging and data collection tasks. First, the network area is divided into sparse and non-sparse grids. A Sparse Grid Fusion Algorithm based on Minimum Enclosing Circle (SGFMC) is introduced to reduce the number of UAV hovering points in sparse grids, thereby improving energy replenishment efficiency. Then, for non-sparse grids, a clustering approach based on minimizing energy radiation area is employed to shrink the energy coverage of UAV, effectively shortening the distance between the UAV and sensor nodes and enhancing energy efficiency. Next, the charging priority of cluster head nodes is determined by jointly considering their residual energy and the UAV’s vertical displacement during flight. This not only reduces UAV’s energy consumption during vertical movement but also ensures fairness in charging. A binary search-based allocation strategy is then applied to optimally distribute UAV energy among cluster head nodes. Furthermore, to minimize the energy consumption of cluster heads when transmitting data to the UAV during data collection, an adaptive UAV hovering altitude adjustment strategy is introduced. Simulation results demonstrate that, compared with existing algorithms such as HEC, HSA, and SA, the proposed SGFMC strategy significantly improves UAV flight distance efficiency and overall energy utilization.

Utilizing Unmanned Aerial Vehicles (UAVs) for simultaneous Wireless Power Transfer (WPT) and data collection is an effective method for sustaining large-scale sensor networks. However, most existing schemes typically offload data only after the UAV completes its entire service round, inevitably leading to information staleness and excessive transmission latency. To address the inherent trade-off between long-duration energy replenishment and low-latency data reporting, this paper proposes a multi-UAV based Energy replenishment and Data collection strategy for Low data uploading Delay (EDLD). First, a Uniform-Sized Clustering strategy (USC) based on grids is proposed to partition the network into load-balanced clusters. Crucially, to facilitate timely data backhaul without waiting for mission completion, we construct a Data Transmission Strategy (DTS). This strategy enables UAVs to dynamically adopt one of the modes for data transmission, i.e., the “directly Return with Data (RD)” and the “Multi-hop Data Backhaul (MDB) via UAVs” to minimize transmission delay while ensuring that UAVs retain sufficient residual energy for subsequent tasks. On this basis, a Multi-UAV Task Planning (MTP) algorithm is presented to generate optimal flight trajectories and determine efficient departure timings. By synchronizing the arrival times of UAVs with the energy demands of nodes, it effectively mitigates node energy depletion while achieving workload balancing among multiple UAVs. Extensive simulation results demonstrate that EDLD significantly outperforms typical benchmarks by reducing average data collection latency and improving node survival rates under various network configurations.

This paper investigates the integration of Unmanned Aerial Vehicles (UAV) with Internet of Things (IoT) infrastructure to enhance Mobile Edge Computing capabilities in urban environments. While UAVs offer promising solutions for mobile edge computing, their deployment in high-rise urban areas presents significant challenges, particularly in computational resource balancing, energy-efficient trajectory planning, and dynamic IoT service provisioning. We propose a comprehensive low-altitude UAV-assisted mobile edge computing framework that jointly optimizes UAV trajectory planning, the assignment of offloaded tasks to specific UAVs, and the strategic deployment and energy management of the UAV fleet to maximize system utility. We first formulate this as a multi-objective optimization problem and prove its NP-hardness due to its non-convex and integer linear programming nature. To tackle this challenge, we develop a decomposition-based approach that systematically addresses the coupled variables. We then propose a novel Variable Strategy Reinforcement Learning-based Lin-Kernighan-Helsgaun algorithm that synergistically combines Q-learning, Sarsa, and Monte Carlo methods with the LKH algorithm. The proposed solution is further enhanced by incorporating two refined trajectory optimization mechanisms, the Trajectory Refining Algorithm and the Service-Oriented Segmented Trajectory Refining Algorithm, specifically designed to improve the robustness and reliability in solving the Computation Offloading Trajectory Optimization Problem. Extensive simulation results demonstrate that our proposed algorithms consistently outperform state-of-the-art approaches, achieving faster convergence, higher energy efficiency for UAVs, and lower computational latency for IoT devices.

The increasing frequency of forest fires has become a serious threat to both ecological environments and public safety. Recent advances in unmanned aerial vehicle (UAV) technology have provided new opportunities for monitoring field environments, owing to UAVs’ high maneuverability and real-time data collection capabilities. To address the challenges of real-time performance and energy efficiency in forest fire data collection networks, our work integrates UAVs with Internet of Things (IoT) technologies to efficiently collect and process environmental parameters and human activity information. The aim of our study is to develop an instantaneous and energy-efficient forest fire data collection system that rapidly predicts and responds to fire risks by optimizing UAV data acquisition and trajectory planning. First, we propose a model based reinforcement learning framework and a dataset difficulty definition method to refine the training sample distribution. Subsequently, an adaptive learning mechanism is introduced to gradually increase dataset difficulty, thereby accelerating model convergence to better adapt to environmental changes. Furthermore, considering the dual challenges of information freshness and UAV energy constraints, a novel node partitioning strategy is designed to decompose the overall problem into multiple subproblems for cooperative solution. Extensive simulation results demonstrate that the proposed method outperforms existing approaches in terms of real-time capabilities and energy efficiency, validating its effectiveness for forest fire detection and warning systems.

This article studies multiple unmanned aerial vehicles (multi-UAVs)-enabled wireless-powered Internet of Things (IoT), where a group of UAVs is dispatched as mobile power sources to charge a set of ground IoT devices. Different from the conventional radio-frequency (RF) wireless power transfer (WPT) systems, magnetic resonance-coupled (MRC) WPT systems can guarantee high power transfer efficiency without the complete alignment, which is remarkable. In this article, we extend the charging range by the wired connection between the energy receiving systems and IoT devices. Due to the restriction of carriable energy on the UAVs, designing the shortest possible trajectory for each UAV is necessary. We formulate it as a multidepots multi-UAVs trajectory optimization problem, jointly with constraints of the UAV's energy capacity and the area of the target region, to maximize the resource utilization of UAVs. To tackle this nonconvex problem, we decompose it into two subproblems, i.e., hovering locations selection and multi-UAVs trajectory optimization. For the first subproblem, we propose two approximation algorithms to obtain the near-optimal solution in the sparse networks. Then, we adopt a heuristic algorithm, a memetic algorithm-based variable neighborhood search (MAVNS), to achieve the quasioptimal trajectory rapidly. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithms. New insights are investigated on the estimation of feasibility that whether the given UAVs with energy capacity constraint can fully charge ground IoT devices within open areas.