Unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) networks provide ubiquitous communication and computing capacity for mobile users compared with terrestrial networks. In crowd management, the UAV base station(UAV-BS) collect computation tasks from the Internet of Things (IoT) devices and process tasks with terrestrial MEC networks cooperatively. However, energy efficiency(EE) and user mobility are the bottlenecks of UAV performance. Therefore, it is crucial to maximizing the energy efficiency(EE) of UAVs. In this paper, we propose an energy-efficient UAV-enabled MEC network composed of IoT devices, the UAV-BS, edge cloud, and the data center, and propose a Green-UAV-CoCaCo algorithm to jointly optimize communications, caching, and computation for EE of UAV. Specifically, we design a UAV trajectory model based on a greedy algorithm to predict the user’s coordinates and choose the proper edge server for task offloading. Then, the UAV-CoCaCo algorithm is proposed to maximize the EE of the task caching and offloading. Simulation results demonstrate the effectiveness of the proposed algorithm.

With the advancements in technologies such as 5G, Unmanned Aerial Vehicles (UAVs) have exhibited their potential in various application scenarios, including wireless coverage, search operations, and disaster response. In this paper, we consider the utilization of a group of UAVs as aerial base stations (BS) to collect data from IoT sensor devices. The objective is to maximize the volume of collected data while simultaneously enhancing the geographical fairness among these points of interest, all within the constraints of limited energy resources. Therefore, we propose a deep reinforcement learning (DRL) method based on Graph Attention Networks (GAT), referred to as “GADRL”. GADRL utilizes graph convolutional neural networks to extract spatial correlations among multiple UAVs and makes decisions in a distributed manner under the guidance of DRL. Furthermore, we employ Long Short-Term Memory to establish memory units for storing and utilizing historical information. Numerical results demonstrate that GADRL consistently outperforms four baseline methods, validating its computational efficiency.

The blooming of cloud computing leads to the rapid expansion of data center networks (DCN). Conventional link state routing algorithms like OSPF are widely adopted in data center networks, however, those routing algorithms bring great control overheads and long convergence time. Recently, topology-aware routing methods are considered to be efficient especially in data center networks with regular topologies. Lots of topology-aware routing methods (e.g., Fat-Tree and BCube) have been proposed for specific data center network topologies. This paper first proposes a formalized method to describe regular topologies and a regular Topology Description Language (TPDL) based on this method. TPDL is well designed to accurately define regular network topologies in a clear way leveraging their regularities. Based on the Software-Defined Networking (SDN) technology, this paper also proposes a novel topology-aware routing scheme: cRetor (controller-side REgular TOpology Routing scheme). Different from other topology-aware routing methods, cRetor is a TPDL-based general routing method, which means it is expected to work on different kinds of regular topologies. In this scheme, TDPL files are used as a priori knowledge to build an initial topology in the SDN controllers, which eliminates the process of topology discovery via Link Layer Discovery Protocol (LLDP) and hence relieves the bandwidth and processing burdens on controllers. Besides, we also apply the A-star algorithm to SDN controllers to speed up the routing selection, where TPDL’s distance formulas act as the heuristic function. The experimental results show that cRetor outperforms LLDP-based SDN, OSPF and DCell in routing calculation performance, convergence speed, routing overheads and fault tolerance.

Aerial computing paradigms, particularly those involving UAVs as access points and radio towers, show significant promise for local data analysis and real-time service provision in aerial access networks. However, the limited battery life of UAVs, compounded by the high-energy demands of communication tasks and prolonged computation delays, presents a significant challenge. Deep reinforcement learning (DRL) enables UAVs to autonomously optimize their operations, reducing both energy consumption and latency. Nevertheless, the prolonged learning process of DRL can lead to inefficiencies, especially in dynamic environments where the equitable participation of UAVs is crucial. To address these issues, we introduce a fairness-oriented federated learning (FL) scheme that employs importance sampling to select UAVs for training, ensuring equitable utilization of each UAV's data. Furthermore, we integrate this FL fairness scheme into the design of DRL algorithms, termed FedDRL. This algorithm jointly optimizes the computation capabilities and bandwidth allocation of UAVs to minimize system costs. Numerical results demonstrate the fairness of FedDRL in fFL networks. Specifically, compared to other state-of-the-art DRL algorithms (e.g., TD3 and DDPG), FedDRL reduces system costs by 43.82% and 49.45%, respectively.