Joint Task Offloading and Resource Allocation for UAV Swarm Networks

Dynamic Multiobjective Co-Optimization of Unmanned Aerial Vehicle Placement, Task Scheduling, and Resource Allocation for Remote Sensing

This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the total energy consumption of UAVs. Addressing the shortcomings of existing research, which often overlooks the computational capabilities of UAVs, optimizes only a single aspect, and fails to account for environmental dynamics, this work formulates the problem as a dynamic multiobjective optimization problem. A hybrid optimization framework named DSG, integrating swarm intelligence and evolutionary algorithms, is proposed. The framework first derives a closed-form optimal resource allocation solution for given deployment and scheduling strategies through theoretical analysis. It then employs an improved dynamic multiobjective evolutionary algorithm (DMOEA) to co-optimize UAV positions (continuous variables) and task scheduling (discrete variables). Experimental results demonstrate that DSG achieves significantly better normalized hypervolume performance than comparative algorithms across various system scales [number of UAVs, access points (APs), and sensors] while exhibiting good stability and scalability. This provides an effective solution for the efficient co-optimization of UAV-assisted edge computing in dynamic environments.

Task allocation and joint resource optimization for multi-UAV integrated sensing and communication based on the MDP-PPO algorithm

This paper investigates the task assignment and joint resource optimization problem for integrated sensing and communication (ISAC) in multi-UAV systems. To support the cooperative execution of detection, tracking, and communication tasks, a unified optimization framework is formulated by jointly considering task assignment, power allocation, and bandwidth allocation. Specifically, the UAV set, task set, and task-specific performance models are first established, and the system state is characterized by task priority, remaining power, remaining bandwidth, and task completion status. Then, task assignment constraints, UAV power constraints, bandwidth constraints, and task-type constraints are incorporated into the optimization problem. By introducing task-priority weights and a task-balancing factor, the objective is formulated as the maximization of the overall joint performance of the system. Since the considered problem involves discrete task assignment variables, continuous resource allocation variables, and nonconvex coupled constraints, it is difficult to solve efficiently using conventional optimization methods. To address this issue, the problem is modeled as a Markov decision process (MDP), and a proximal policy optimization (PPO)-based solution framework is developed to jointly determine UAV selection, power allocation, and bandwidth allocation actions. Simulation results demonstrate that, compared with P-DQN, SAC, and PADDPG, the proposed framework achieves superior joint performance, thereby verifying its effectiveness for multi-UAV ISAC joint optimization.

Collaborative Task Offloading Optimization in Multi-UAV-Assisted Mobile Edge Computing

MADRL-Based Resource Allocation for UAV-Assisted Mobile Edge Computing

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).

Multi-Objective Distributed Task Allocation for UAV Swarms with Limited Interactions

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.

Energy-Efficient Task Offloading and Load Balancing for Multi-UAV-Assisted Vehicular Networks

The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.