This work designs a genetic algorithm (GA)-based UAV trajectory design and offload allocation algorithm that achieves efficient global search through tournament selection, single-point crossover, and Gaussian mutation operators, and validate the necessity and effectiveness of jointly optimizing UAV trajectory and task offloading in urban road scenarios.
Abstract
With the rapid proliferation of internet of vehicles applications, vehicle users in urban road scenarios face ever-increasing demands for low-latency and energy-efficient processing of computation-intensive tasks. The traditional fixed terrestrial infrastructure offers limited coverage in complex urban environments, making it difficult to satisfy the differentiated quality of service requirements of large-scale vehicle populations. To fully exploit the advantages of unmanned aerial vehicles (UAVs) in terms of flexible deployment and on-demand service provisioning, we propose a UAV-assisted mobile edge computing architecture tailored for urban road scenarios. By modeling realistic urban road terrain with varying elevations, we construct a two-tier cooperative network consisting of multiple rotary wing UAVs and ground vehicles. Aiming at maximizing the total system energy consumption, we formulate a mixed integer nonlinear programming problem that minimizes total system energy consumption through joint optimization of UAV flight trajectories and vehicle task offloading decisions while comprehensively accounting for task latency constraints, UAV flight velocity constraints, and the impact of three-dimensional terrain on air-to-ground channels. Considering the high-dimensional, non-convex, mixed integer, and strongly coupled nature of the problem, we design a genetic algorithm (GA)-based UAV trajectory design and offload allocation algorithm. The proposed approach encodes UAV trajectories as real-valued vectors and offloading decisions as binary vectors, employs a penalty function method to handle constraints, and achieves efficient global search through tournament selection, single-point crossover, and Gaussian mutation operators. Simulation results verify that the proposed algorithm converges reliably to feasible solutions under varying task data sizes and vehicle densities and achieves up to 20.6% energy savings compared to the benchmark schemes. The experimental results validate the necessity and effectiveness of jointly optimizing UAV trajectory and task offloading in urban road scenarios.
A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.
Lei Li, Xue Gao, Quansheng Guan· Electronics· 0 citations
Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application.
Buyang Ding, Weijun Ni, Yixing Luo et al.· Electronics· 0 citations
As embodied intelligent agents, uncrewed aerial vehicles (UAVs) support low-altitude urban services, but their endurance is fundamentally constrained by limited onboard battery capacity. Existing solutions in dense urban environments incur high deployment costs, use coarse spatial layouts, and do not scale to large UAV fleets. We instead retrofit existing urban deployable infrastructure (UDI), such as traffic lights, street lamps, and communication base stations, as UAV docking points with charging capability. This UDI-based approach raises two coupled challenges: city-scale docking-point deployment over massive, spatially heterogeneous candidates, and coordinated multi-UAV access under queueing delays and residual-energy safety constraints. We jointly model docking queues, load, and energy consumption, and formulate a multi-objective optimization balancing energy consumption and load. To address these NP-hard deployment and scheduling subproblems, we propose a hierarchical UDI-based docking-point deployment algorithm (HUDD) that generates a scalable docking layout, and a charging access coordination algorithm based on convex relaxation and iterative rounding (CRIR) that coordinates energy-feasible, congestion-aware access for multiple UAVs on the obtained layout. Simulations on realistic urban datasets show that HUDD-CRIR outperforms baseline schemes in terms of energy consumption, response delay, queueing delay, and load distribution.
Wei Yang, Jiajie Xu, Jie Chen et al.· IEEE Transactions on Cogniti...· 0 citations
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.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 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