The integration of sensing, communication, and computation (ISCC) is a critical technology that will support various emerging wireless services in future 6G networks. The unmanned aerial vehicles (UAVs) equipped with edge servers can be used as an aerial service platform in intelligent transportation systems (ITSs) to offer ISCC services to vehicles. This article studies an aerial UAV network comprising a central UAV and secondary UAVs to realize sensing of the global ITS environment and data fusion computation through collaborative UAVs. To enhance the service performance of ISCC, we maximize the success rate of ISCC services and the energy efficiency of UAVs by jointly optimizing bandwidth allocation, power allocation, and computing capacity control while ensuring the sensing and data processing latency requirements. Leveraging the network architecture and collaboration requirements of UAVs, we propose the multi-UAV collaborative Air-ISCC (MCAI) algorithm based on the asynchronous advantage actor-critic algorithm, which obtains the optimal ISCC service policy by co-training a deep reinforcement learning model with multiple UAVs. Sufficient experimental results show that MCAI enhances energy efficiency by 10.51% to 80.12% compared with the baselines. Moreover, MCAI exhibits good scalability, strengthening its feasibility in real scenarios.

Unmanned aerial vehicle (UAV)-based edge computing has been widely applied in intelligent transportation systems (ITSs) owing to its ease of deployment and high mobility. In this article, we study intelligent over-the-air computation (AirComp) in UAV swarm-assisted ITS. To develop a holistic service framework for UAV swarm, we consider the heterogeneity of Internet of Things Devices (IoTDs) and UAVs. We model the 3-D deployment of UAVs, service configuration, bandwidth allocation, the control of computing capacity, and transmission power as a joint optimization problem. To tackle this complex problem, we first propose a dual time-scale architecture based on deep reinforcement learning (DRL). This architecture enables UAVs to achieve seamless coverage of IoTDs on larger time scales, while collaborative UAVs dynamically provide services on smaller time scales. Next, we propose an intelligent AirComp algorithm D2IAC based on distributed DRL to obtain the optimal UAV deployment and dynamic service policies on different time scales. The D2IAC algorithm consists of three subalgorithms, i.e., TD3-based UAV deployment (TBUD), UAV services configuration (USC), and REINFORCE-based dynamic service (RBDS). Sufficient experimental results show that the proposed algorithm can achieve 3-D deployment of UAVs with coverage improvement from 9% to 36% compared to clustering, center layout, and random algorithms. Regarding dynamic services, compared with the deep deterministic policy gradient algorithm, greedy, fixed, and random strategies, the service durations of UAV swarm are improved by 32.95%–93.72% and the resource utilization is improved by 36.19%–49.61%.

Unmanned aerial vehicles (UAVs)-assisted multiaccess edge computing (MEC) has emerged as a promising solution in B5G/6G networks. The high flexibility and seamless connectivity of UAVs make them well suited for providing enhanced communications coverage and efficient computing support. Particularly, in situations where ground facilities may be compromised or communication is unreliable. In this article, we study joint dynamic service switching and resource allocation for multiple UAVs in MEC network. We consider the heterogeneity of tasks and UAVs and model the dynamic service process of UAVs as a sequential decision problem based on the Markovian decision process. To enable dynamic and intelligent UAV service, we first propose a centralized dynamic service algorithm DDPG-based centralized (DDBC) based on deep reinforcement learning. However, given the training difficulties of the centralized algorithm, we propose a more promising distributed learning algorithm FLBF, which combines federated learning. We conduct extensive simulations to evaluate the effectiveness and advantages of the proposed algorithms. Our results show that DDBC and FLBF significantly reduce the system cost by 17.99%–35.72% and 12.30%–31.26%, respectively, compared to the comparative algorithms. Furthermore, FLBF can effectively improve the convergence speed with guaranteed learning performance, indicating its suitability for model training in UAV-assisted MEC networks.