Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed.

To exploit the massive amounts of onboard data in vehicular networks while protecting data privacy and security, federated learning (FL) is regarded as a promising technology to support enormous vehicular applications. Despite that FL has great potential to improve the architecture of intelligent vehicular networks, the mobility of the vehicles and the dynamic nature of wireless channels make the integration of FL and vehicular networks more challenging. In this paper, we propose a vehicle mobility- and channel dynamic-aware FL (MADCA-FL) scheme to fit vehicular networks and enhance learning performances. This novel scheme enables the RSU to select appropriate vehicles and weightedly average the local models. Afterward, MADCA-FL formulates a problem to maximize the model accuracy while assuring the latency and energy restrictions, by jointly optimizing the computation and communication resources. With a mixed- integer non-linear programming structure, the problem is NP-hard. Firstly, we utilize the successive convex approximation algorithm to handle the non-convexity, and then apply the Lagrange multiplier method and the block coordinate descent method to obtain the optimal solution. Extensive experiments are conducted to confirm the effectiveness of our proposed scheme.

In this paper, we consider an Internet of Things (IoT) system where the employed unmanned aerial vehicle (UAV) carries edge computing server to perform data collection and execution for multiple IoT nodes (INs). For such a network, UAV trajectory and uplink transmission power optimization are integrated to minimize the Age of Information (AoI) of the results returned to all ground INs subject to energy consumption limitations. Due to the non-convex nature of the formulated problem, it is divided into two subproblems, which are respectively solved by Lagrangian dual and convex optimization methods, and block coordinate descent method is applied to solve the overall problem. Simulation results show that the proposed algorithm achieves the lowest average AoI of all INs compared with other schemes. The results also reveal the relationship between the average AoI and the number of INs and advantages of the proposed scheme.

The unmanned aerial vehicles (UAVs) have been widely used in civilian environments, due to its high flexibility, low cost and ease of deployment. In this paper, an UAV-enabled mobile edge computing (MEC) system is studied, in which the UAV serves as an aerial mobile base station to provide services for a group of ground user equipments (UEs) with computation task requests. We jointly optimize the time allocation, resource allocation and the UAV flying trajectory to minimize the time required for the UAV to complete the task, subject to the constraints of different kinds of resources, energy and velocity. Due to the non-convexity of the formulated problem, we first transform it to a feasibility check problem and then divide it onto three convex optimization subproblems. By using the block coordinate descent method and the successive convex approximate (SCA) method, we propose an efficient iterative algorithm to solve the three subproblems alternately with ensured convergence. Extensive simulation results show that the proposed joint optimization algorithm reduces the task completion time compared with other schemes.