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.

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.

Mobile augmented reality (MAR) applications are gaining popularity. There are significant challenges in handling the latency-sensitive and computation-intensive tasks generated by MAR applications on mobile devices. The emergence of mobile edge computing (MEC) provides a new idea to improve the computing capability of resource-constrained mobile terminals. In this paper, we study the task offloading and cache placement of MAR tasks in multi-MEC server cooperation system. The problem of the task offloading and cache placement is formulated to maximize the hit ratio and minimize the service latency under the constraints of MEC server's computing resources and cache space. To solve this problem, we propose a task offloading and cache placement (MOTOCP) algorithm based on multi-objective artificial bee colony. Pareto optimal relation is introduced in the optimization process to find the optimal solution. Extensive evaluation verifies that our proposed algorithm has better performance.