In an air–ground integrated network (AGIN), low-altitude unmanned aerial vehicles (UAVs) and a high-altitude platform (HAP) operate synergistically to support computationally expensive and delay-critical applications of mobile ground devices (GDs). UAVs obtain tasks from GDs, execute the tasks, and offload some of the tasks to the HAP. In AGINs, the trajectory control of a UAV swarm should provide optimal coverage to randomly distributed mobile GDs. The limited resources of UAVs, such as energy, computation, caching, and bandwidth, result in further challenges. Therefore, a joint optimization problem is formulated in this study to minimize the task execution delay and energy consumption of UAVs by optimizing the UAVs trajectory, GD association, task-offloading ratio, and resource allocation. The limited resources, maximum task execution delay, task queue size, and mobility of UAVs are regarded as key constraints. Solving the problem is intricate owing to the complex mixed-integer nonlinear constraints coupled with a large continuous and discrete decision space. To track the dynamics in AGINs and efficiently solve the problem above, we utilize a swarming behavior-integrated multi-agent gated recurrent unit-based actor and multi-head attention-based critic network (SMA-GAC) framework. Results of simulative evaluation show that the proposed SMA-GAC outperforms baseline methods.

Collaborative unmanned aerial vehicle (UAV) swarm networks can effectively execute various emerging missions such as surveillance and communication coverage. However, due to high mobility and constrained transmission range, packet routing encounters mutual interferences, link breakages, and unexpected delays. In such networks, routing performance is coupled with trajectory control, frequency allocation, and relay selection. In this study, we propose a joint trajectory control, frequency allocation, and packet routing (JTFR) algorithm, in which link utility is maximized by considering the link stability, signal-to-interference-plus-noise ratio, queuing delay, and residual energy of UAVs. The proposed JTFR employs adaptive distributed multi-agent deep deterministic policy gradient coupled with the swarming behavior to obtain the optimal solution. For each UAV, an actor network is established by utilizing a long short-term memory-based state representation layer containing two-hop neighbor information to adopt the dynamic time-varying topology. Subsequently, a scalable multi-head attentional critic network is set up to adaptively adjust the actor network policy of each UAV by collaborating with neighbors. The extensive simulation results show that JTFR outperforms existing routing protocols by 30-60% less end-to-end delay, 15-32% better packet delivery ratio, and 20-46% less energy consumption.

Aerial surveillance using unmanned aerial vehicles (UAVs) provides an on-demand and cost-effective solution to smart-city monitoring needs, owing to their three-dimensional positioning adjustment and autonomy. The optimal deployment of a UAV swarm, also known as a flying ad hoc network (FANET), to achieve on-demand coverage of mobile ground targets (MGTs) is challenging in terms of controlling UAV mobility to maximize coverage while maintaining quality of service. Data routing from UAVs to a base station (BS) without awareness of the updated topology causes link breakages, excessive retransmissions, high congestion, and energy holes. Therefore, we propose a joint topology control and routing (JTCR) protocol comprising three modules to perform crowd surveillance. The first JTCR module provides virtual force-based mobility control (VFMC), which controls the mobility of UAVs to track MGTs, ensuring stable bi-connectivity. The second module provides energy-efficient mobility-aware fuzzy clustering that clusters the FANET to aggregate the sensed data to each cluster head (CH) by utilizing the UAV mobility provided by the VFMC. The third module provides topology-aware Q-routing, which routes the aggregated data from CH UAVs to the BS by selecting an optimal path in terms of delay, path stability, and energy consumption. According to our performance study, the proposed JTCR outperforms existing routing protocols in terms of tracking-coverage rate, connectivity rate, the number of retransmissions, packet delivery ratio, end-to-end delay, and energy consumption. This is mainly enabled by the realistic mobility control of the UAV swarm at the reasonable cost of control overhead.