Summary In mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q‐network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy Multi-objective Optimization Problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved Multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.

The application of unmanned aerial vehicles (UAVs) in IoT networks, especially data collection, has received extensive attention. Due to the urgency of the mission and limitation of the network cost, the mission completion time and number of UAVs are research hotspots. Most studies mainly focus on the trajectory optimization of the UAV to shorten the mission completion time. However, under different data collection modes, flying mode (FM) and hovering mode (HM), the collection time will also greatly affect the mission completion time. This paper studies the data collection from ground IoT devices (GIDs) in Multi-UAV enabled IoT networks. The problem of data collection is formulated to minimize the aerial cost and maximum mission completion time of UAVs by optimizing mission allocation, UAV trajectory, and UAVs’ flying speeds. In view of the complexity and non-convexity of the formulated problem, we propose a heuristic-based approximation algorithm to optimize the mission allocation of UAVs. Then, we specifically optimize the trajectory of the UAV for GIDs to minimize the flight time and collection time. Since the UAV’s flying speed affects the mission completion time, the successive convex approximation (SCA) technique is adopted to optimize it. Simulation results show that our scheme achieves the performance of near-optimal solution.

The unmanned aerial vehicle (UAV) is widely used in some scenes with high requirements for information freshness. Due to the limited endurance of the UAV, especially in the scenes with large area and dense sensor nodes (SNs), it is difficult for one UAV to complete the data collection task under the condition of ensuring the freshness of SNs’ information. Therefore, multiple UAVs are required to cooperate to participate in data collection. In this paper, we study the multi-UAV assisted data collection problem to improve information freshness. We use the Age of Information (AoI) to measure the freshness of information, mainly including the SNs’ uploading time, the UAVs’ flight time and the data offloading time. The data collection problem is formulated to minimize the SNs’ peak AoI and average AoI in multi-UAV assisted wireless sensor networks. Since the problem is complex, we introduce a start-to-end strategy comprising of association and planning to minimize two SNs’ AoIs through an iterative three-step process. Firstly, the locations of data collection points (CPs) at which the UAVs hover to collect data and the SN-CP association are determined based on a density-based clustering algorithm. Secondly, the CPs are clustered to form CP clusters, and the CP-UAV association is established. Finally, based on the results of the above two steps, the flight trajectories of the UAVs are optimized by improved ant colony (ACO) algorithm subject to the limited endurance capability. The simulation results show the proposed strategy can optimize the peak-AoI and ave-AoI of SNs to improve the freshness of information.

In recent years, multi-access edge computing (MEC) has become a hot topic. In this paper, we study a task offloading problem combined with bandwidth allocation in multi-access edge computing. Based on alliance game, we formulate bandwidth allocation to minimize the dissatisfaction of alliances. Then, we formulate the task offloading decision-making to minimize the delay. The delay consists of communication delay and execution delay. To solve the offloading problem, we convert the dissatisfaction of alliance into a vector, and obtain the Pareto optimal through multi-objective particle swarm algorithm. Then, we use Branch and Bound method to construct the propagation tree to facilitate decision-making. To evaluate the edge servers in the tree, we build an evaluation matrix and transform the matrix to a set of evaluation index which is used on task offloading decision-making. A large number of experimental results show that our algorithm is better than compared algorithm.

The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function as part of evaluating the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption and improves Quality of Service (QoS).