The rapid evolution of swarm intelligence and edge computing has highlighted the potential of Uncrewed Aerial Vehicle (UAV) swarms for data-driven services. However, heterogeneous capabilities and time-varying communication conditions pose significant challenges for efficient resource orchestration. This letter proposes a novel method for joint communication topology formation and computation offloading in heterogeneous UAV networks to optimize task completion time and energy consumption. A graph attention network is employed for swarm feature extraction, and the communication topology and task offloading ratios are determined by proximal policy optimization. Successive convex approximation is further applied for bandwidth and power allocation. Simulation results demonstrate that the proposed framework effectively reduces task completion time and energy consumption compared with other benchmarks.
This paper proposes a joint optimization algorithm for trajectory control and task offloading ratios based on multi-agent deep reinforcement learning. By jointly optimizing the flight trajectories of unmanned aerial vehicles (UAVs), user scheduling strategies, and task offloading ratios, the decoupled coordination of resource allocation and trajectory planning is achieved, thereby minimizing system delay and weighted energy consumption. An enhanced multi-agent proximal policy optimization algorithm, named FMAHPPO, is designed. Compared with existing benchmark algorithms, the FMAHPPO algorithm significantly reduces the total system overhead and effectively improves the energy efficiency and task processing success rate of multi-UAV swarms. This research provides a valuable theoretical foundation and algorithmic support for the collaborative management of edge resources in future space-air-ground integrated networks (SAGIN).
Unmanned Aerial Vehicles (UAVs) have experienced rapid development due to their advantages of low cost, high efficiency, flexibility, reliability, and strong environmental adaptability. To fully leverage the potential of UAV swarms in multi-task scenarios, optimizing task allocation has become a crucial direction to enhance the efficiency of UAV swarms. While existing task allocation methods have achieved promising results, frequent inter-UAV information exchange can impose substantial communication overhead in distributed UAV swarms, particularly in resource-constrained applications such as emergency rescue and mountainous operations. In this context, to reduce inter-UAV interactions while maintaining the solution quality of task allocation, this paper first analyzes the factors affecting communication interactions between UAVs. Based on this analysis, we utilize historical bidding information to infer other UAVs’ positions and employ estimation strategies to resolve task conflicts, thereby reducing communication iterations. Furthermore, we propose a bidding-based grouping method to eliminate ineffective communication interactions. Finally, we introduce a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure. Simulation results demonstrate that the proposed algorithm significantly reduces inter-UAV interactions while preserving the number of allocated tasks, with a maximum observed increase of only approximately 8% in task waiting time across the evaluated simulation settings.
Wei-xing Xia, Peng Chen, Feifei Song et al.· Drones· 0 citations
This paper presents a joint communication–computation optimization (JCCO) framework for energy-efficient resource allocation in edge-assisted swarm robotics. The proposed framework jointly optimizes task offloading, bandwidth allocation, transmission power, and computational workload under latency and energy constraints. A distributed optimization strategy combined with a lightweight deep reinforcement learning controller enables adaptive and scalable decision making for resource-constrained robotic swarms. The simulation results demonstrate that the proposed method reduces the total swarm energy consumption by up to 41% while maintaining more than 99% deadline satisfaction across varying swarm sizes and communication conditions. The framework further achieves improved fairness, lower communication overhead, and efficient embedded deployment suitability for TinyML-enabled robotic platforms.
Amir Ijaz, Hashem Haghbayan, E. Nigussie et al.· Journal of Sensor and Actuat...· 0 citations
High-performance networking is essential for Unmanned Aerial Vehicle (UAV) swarms to accomplish complex, coordinated missions. A central challenge in UAV swarm networking is managing concurrent multi-hop transmissions, where traditional protocols often struggle due to routing path conflicts and co-channel interference. To address this, we propose a novel multi-agent reinforcement learning (MARL)-based cross-layer transmission framework that maximizes system throughput by jointly optimizing network-layer routing, link-layer resource allocation, and UAV trajectories. We decouple this complex joint optimization problem and solve it with a routing-prioritized iterative scheme. For the routing sub-problem, an MARL approach is designed for agents to collaboratively plan concurrent routing paths. The non-convex resource allocation and trajectory sub-problems are handled using successive convex approximation (SCA). Experimental results demonstrate that our proposed framework significantly outperforms existing benchmarks in system throughput, end-to-end delay, and packet delivery ratio.
Yang Shen, Bing Li, Rongqing Zhang· IEEE Transactions on Wireles...· 0 citations
— Unmanned Aerial Vehicles (UAVs) have emerged as flexible relay platforms capable of enhancing wireless connectivity in beyond-5G and 6G networks. This paper investigates the joint optimization of UAV trajectory and power allocation to maximize end-to-end throughput under practical mobility and power constraints. The problem is highly non-convex due to the strong coupling between trajectory variables and transmission power. To address this challenge, we develop a penalty-based metaheuristic framework that incorporates a constraint-handling mechanism into the Bat Algorithm (BAT). Simulation results show that the proposed BAT-based approach achieves significant throughput improvement, efficient power allocation, and fast convergence compared with baseline convex optimization and heuristic schemes. These findings highlight the potential of BAT for reliable and energy-efficient UAV-assisted communication in future wireless networks.
Pham Thi Quynh Trang· Journal of Communications· 0 citations
Unmanned aerial vehicles play an increasingly important role in the low-altitude domain by collecting and transmitting aerial images. However, the inter-dependency between UAV motion and communication strategies has been largely overlooked. To address this gap, we propose a UAV motion-aware image capturing and communication (MICC) system that dynamically optimizes data offloading by jointly considering scenario variability and communication resource allocation. Specifically, we formulate an MICC optimization problem to maximize transmission accuracy and efficiency by adaptively controlling down-sampling ratios, compression ratios, and transmit power. Considering its non-convex nature, we first develop a geometric programming based algorithm (GP-MICC) to obtain high-fidelity solutions. Recognizing its high computational cost, which hinders real-time deployment, we further propose a fast learning-based optimization algorithm (FLO-MICC). Extensive experiments demonstrate that GP-MICC achieves excellent transmission performance, while FLO-MICC reduces computational time by over 12x with minimal performance loss, making it suitable for dynamic UAV scenarios.