In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining i ) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame, and ii ) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

With the exponential growth of data traffic, the use of caching and device-to-device (D2D) communication has been recognized as an effective approach for mitigating the backhaul bottleneck in unmanned aerial vehicle (UAV)-assisted networks. In this paper, we investigate the issue of secure transmission in a cache-enabled UAV-relaying network with D2D communications in the presence of an eavesdropper. Specifically, both UAVs and D2D users are equipped with cache memory, which can pre-store some popular contents to collaboratively serve users. Considering the fairness among users, we formulate an optimization problem to maximize the minimum secrecy rate among users, by jointly optimizing the user association and UAV scheduling, transmission power, and UAV trajectory over a finite period. The joint design problem is a non-convex mixed-integer programming problem. To efficiently solve this problem, we propose an alternating iterative algorithm based on the block alternating descent and successive convex approximation methods. Specifically, the user association and UAV scheduling, UAV trajectory, and transmission power are optimized alternately in each iteration, and the convergence of the algorithm is proven. Extensive numerical results show that the proposed joint design scheme significantly outperforms other benchmark schemes in terms of the secrecy rate.

In this paper, the joint sensor activation and mobile charging vehicle scheduling for wireless rechargeable sensor network (WRSN) based industrial Internet of Things (IIoT) is studied. In the proposed framework, an optimal sensor set is selected to collaboratively execute a bundle of heterogeneous industrial tasks (e.g., production-line monitoring), meeting the quality-of-monitoring (QoM) of each individual task, and we consider that a mobile charging vehicle (MCV) is scheduled for recharging sensors before their charging deadlines, i.e., time instants of running out of their batteries, in order to prevent from any potential service interruptions (which is one of the key features of IIoT). Our goal is to jointly optimize the sensor activation and MCV charging scheduling for minimizing the system energy consumption, subject to tasks' QoM requirements, sensor charging deadlines and the energy capacity of the MCV. Unfortunately, solving this problem is nontrivial, because it involves solving two tightly coupled NP-hard optimization problems. To address this issue, we design a novel scheme integrating reinforcement learning and marginal product based approximation algorithms, and prove that it is not only computationally efficient but also theoretically bounded with a guaranteed performance in terms of the approximation ratio. Simulation results show the feasibility of the proposed scheme and demonstrate its superiority over counterparts.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to complement fixed charging stations. Currently, the strategy of MCSs is to move towards the EVs with insufficient energy (IEVs) only after being requested, which is not efficient. However, similar to online car-hailing services, more IEVs could be charged and the charging expenses could be reduced if idle MCSs can actively move towards the potential charging positions. In this paper, the problem of placements of idle MCSs in an IoEV is investigated in order to enhance the proportion of charged IEVs and reduce the charging expenses of IEVs. To this end, we propose a Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) to help the idle MCSs to predict the future charging positions, by exploiting the historical routes of MCSs which contain rich information regarding the charging demand of IEVs. In the proposed framework, the historical routes are trained locally by each MCS, and then the local model parameters and charging records are periodically uploaded to an edge server for a global parameter aggregation. Then, idle MCSs decide their placements according to the predicted charging positions (potential charging positions). The training time can be largely shortened, because the distributed learning on each MCS is executed in parallel. Extensive simulations and comparisons demonstrate the performance superiority of FL-PDMIM. Specifically, with the proposed federated learning-based predictions, the waiting time of IEVs to be served can be significantly shortened, and FL-PDMIM enhances the proportion of charged IEVs and reduces the charging expenses of IEVs effectively.