The unmanned/uncrewed aerial vehicle (UAV) assisted mobile edge computing (MEC) technology has become a viable and flexible solution for providing computation offloading and energy charging services for ground users, especially in scenarios with terrible direct links. Therefore, latency has become one of the crucial design issues subject to the energy limitations of the UAV and users. Motivated by this, we study a latency-aware air ground hybrid MEC system with an assistant UAV and a ground base station (GBS) to serve and charge multiple users under both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols. The task completion latency minimization problems are formulated by jointly optimizing the time slot scheduling, CPU frequency allocation, UAV's three dimensional (3D) trajectory design, transmit power allocation, as well as the number of required time slots. To address the formulated mixed integer non-convex optimization problems, we introduce an efficient alternating optimization algorithm with a double-loop structure. In the outer loop, we constantly adjust the number of time slots by employing the bisection search method and determine the search range via feasibility check. In the inner loop, we first transform the original subproblem into an equivalent problem that maximizes the minimum computation completion ratio of the users. Then we further deconmpose this transformed problem into four subproblems, which can be solved by a proposed iterative algorithm. Extensive experiments are con ducted to illustrate the efficacy and superiority of the proposed algorithm over the other benchmark schemes in minimizing the task completion latency, particularly in scenarios where the computing resource is limited or the density of users is high.

Integrated sensing, communications, and computation (ISCC) is becoming increasingly critical, particularly for enabling advanced intelligent applications. This paper proposes an ISCC framework for edge-intelligent networks, where edge intelligent devices (EIDs) cooperatively sense multiple mobile targets and simultaneously offload radar sensing data to a base station (BS) equipped with an edge server for processing. To address the time-varying nature of the network, we develop an online resource management strategy that maximizes the long-term average weighted sum rate (AWSR), subject to queue stability, average power constraints, and quality-of-service (QoS) requirements. Using the Lyapunov drift-plus-penalty framework, the original stochastic optimization problem is decomposed into a sequence of deterministic subproblems across time slots. At each time slot, sensing scheduling, transmit beamforming for both sensing and communications, receive beamforming for radar echoes, and computing resource allocation at the BS are jointly optimized through an efficient alternating optimization algorithm based on the current system state. Simulation results validate the effectiveness of the proposed online strategy, showing superior performance over baseline methods and revealing the influence of key parameters. In particular, a trade-off is observed between the AWSR and queue backlogs, which can be flexibly tuned via control parameters.

The unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) technology is opening up new opportunities in the integrated space-air-ground in the 5 G era and beyond. However, providing low-latency services solely from an overall perspective cannot ensure a high quality of experience (QoE) for user equipments (UEs). Therefore, we propose a service experience-oriented cooperative caching framework, where the UAVs can effectively serve each UE by providing communication and computing resources. A novel metric called service experience ratio is defined to reflect the experience at the UEs. Under the constraints of UAV's energy budget and delay requirements, we consider jointly optimizing task offloading, resource allocation, trajectory planning, and service caching placement to maximize the service experience ratio. Since the original problem is a mixed- integer non-convex programming problem with a fractional structure, it is challenging to be solved in polynomial time. Based on Dinkelbach's method and convex optimization theory, we simplify the problem model and propose a four-stage alternating iterative service ratio maximization algorithm to solve this problem. Besides, we also analyze the convergence and complexity of our proposed algorithm. Numerical results demonstrate that the service experience ratio achieved by the proposed algorithm is 19%-34% higher than the comparative works.

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.