Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this paper, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. Firstly, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Secondly, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.

In this paper, we aim to enhance the throughput of a reconfigurable intelligent surface (RIS)-assisted and wireless-powered network by enabling user devices (UDs) to cooperatively adapt their channel access strategies. The base station (BS) simultaneously receives data and provides radio frequency (RF) energy to the UDs. Each UD equipped with a passive RIS employs non-orthogonal multiple access (NOMA) or backscattering for data transmissions. Moreover, the UD can assist other UDs' data transmissions through RIS's phase reconfiguration. We seek to maximize the overall throughput by optimizing UDs' channel access modes, time allocations, and the beamforming strategies of the RISs and the BS. Throughput maximization is challenging due to uncertain channel information, dynamic traffic demands, and limited energy supply. We propose a hierarchical learning approach to address access mode selection and transmission control in two steps. We first employ a multi-agent attention-enhanced deep reinforcement learning approach to update each UD's channel access strategy by interacting with the dynamic network. Then, we efficiently update the UDs' time allocation and the beamforming strategies of the RISs and the BS to further enhance throughput. Simulation results validate that our proposed scheme achieves improved throughput and learning efficiency, and exhibits superior adaptability against the varying traffic demands, compared to benchmarks.

Mobile crowdsensing is considered as a promising technology to exploit the computing and sensing capabilities of the decentralized wireless sensor nodes. Typically, the quality of information obtained from crowdsensing is largely affected by various factors, such as the diverse requirements of crowdsensing tasks, the varying quality of information across different crowd workers, and the dynamic changes of channels conditions and the sensing environment. In this paper, considering the dynamics’ of the crowd workers, we focus on a spatial-temporal crowdsensing model and aim to maximize the value of information at the point of interest, by optimizing the recruiting range and time duration for the crowd workers. In particular, the crowdsensing system includes a mobile access point (MAP) and a set of wireless sensor nodes. As the information requester, the MAP can broadcast its crowdsensing task and then estimate the value of information by collecting the responses from the sensing nodes. Each sensing node in the crowdsensing task will receive a payment from the MAP. We aim to maximize the utility of the information requester by optimizing the recruiting range and waiting time for the sensing nodes. We firstly define a set of value metrics to characterize the MAP’s value of information. The optimal recruiting range can be obtained in closed-form expressions. Furthermore, considering the aging effect, we propose a gradient-based method to maximize the spatial-temporal value of information. Specifically, we first determine the optimal recruiting time for the requester and then choose the optimal recruiting range within each time slot. Via simulation, we first compare the sum, max, and min values of information at the requester, and then verify the effectiveness of the gradient-based method to optimize the recruiting time and range to maximize the value of information.