Extending wireless connectivity to remote areas is essential for delivering intelligent services in critical sectors, i.e., healthcare, agriculture, and disaster management. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their agile mobility, low deployment cost, and line-of-sight (LoS) communication capabilities. However, efficiently managing UAV resources while integrating sensing, communication, and computing (ISCC) functionalities presents significant challenges. In this paper, we propose a multi-UAV-assisted ISCC framework that simultaneously supports wireless communication links for computational task offloading, remote computing, and active target sensing. A comprehensive system model is developed, and a joint optimization problem is formulated to minimize the weighted sum energy consumption of UAVs and remote users, subject to constraints on latency, power budget, and UAV mobility. To solve the resulting non-convex problem, we design a decomposition-based solution that integrates a convex optimization technique with the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the effectiveness of the proposed framework in achieving energy-efficient operation under practical system constraints.

Integrated terrestrial-nonterrestrial networks have recently gained much attention because they can bridge the gap between the conventional terrestrial infrastructure and nonterrestrial networks. In addition to seamless connectivity, such networks can offer edge computing services to the users with real-time data processing demand. In this article, an integrated terrestrial-nonterrestrial network with multiaccess edge computing (ITNT-MEC) system is considered in which the aerial users (AUEs) share the resources of terrestrial base stations (TBSs) with their existing terrestrial users (TUEs) and that of low-Earth orbit (LEO) satellites with their neighboring satellites. The goal is to minimize the total energy consumption of AUEs, TUEs, and LEO satellites by jointly optimizing the AUE-TBS/LEO satellite association, AUEs’ trajectories, task allocation, as well as network resource allocation. Due to the dynamic nature of network environment and nonconvex characteristics, it is significantly challenging to solve the formulated optimization problem. Therefore, a block coordinate descent (BCD)-based algorithm that integrates deep reinforcement learning (DRL) methods, such as double deep Q-learning network (DDQN), deep deterministic policy gradient (DDPG), and convex optimization methods, is proposed. Simulation results show that the total energy consumption in the proposed approach is reduced by 14%, 26.9%, 34%, 35.8%, 45.5%, and 55.4%, respectively, when compared to the baselines, such as DDPG-based task offloading (DDPG-TO), DDQN-based task offloading (DDQN-TO), DQN-based task offloading (DQN-TO), equal resource allocation (ERA), random association (RA), and fixed trajectory (FT).

This paper investigates a novel network architecture – the 6G integrated terrestrial-non-terrestrial network (ITNTN) with multi-access edge computing (ITNT-MEC). This system aims to bridge the connectivity gap between terrestrial infrastructure and non-terrestrial networks while offering real-time data processing through edge computing. We consider a scenario where aerial user equipments (AUEs) share resources of terrestrial base stations (TBSs) with terrestrial UEs (TUEs). We formulate an optimization problem to minimize the total energy consumption of both AUEs and TUEs. This problem involves joint optimization of AUE association (i.e., TBS or low Earth orbit (LEO) satellite), AUE trajectories, and TBS bandwidth allocation. Due to the dynamic network environment and non-convex optimization characteristics, solving this problem presents a significant challenge. To address this, we propose a novel algorithm that combines block coordinate descent (BCD) with deep deterministic policy gradient (DDPG) and a convex optimization method. Simulation results demonstrate the significant reductions in total energy consumption compared to baseline approaches, achieving improvements of 23%, 36.6%, and 46.5% against DQN-TO, RA, and FT, respectively.

Space-aerial-assisted multi-access edge computing (SA-MEC) has recently been a promising solution to offer the ubiquitous communication and computing services to the resource-constrained internet of things (IoT) devices. Particularly, low earth orbit satellites (LEOSats) and unmanned aerial vehicles (UAVs) having the computing resources onboard assist those devices to compute their generated tasks with the minimum delay under the energy budget. However, due to the existence of inter-cell interference among the devices, they may consume more energy or incur longer delay to offload the tasks to the UAVs. Therefore, the optimal task offloading and resource (channels) allocation should be determined without dissipating much energy and overloading the UAVs. In this work, BCD-based task offloading and resource allocation scheme is proposed to minimize the total task completion latency of the devices by considering the scarce communication resources and energy limitation of devices and UAVs.

Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become one promising solution for energy-constrained devices to run the applications with high computation demand and stringent delay requirement in beyond 5G era. In this work, we study a multi-UAV-assisted two-stage MEC system in which UAVs provide the computing and relaying services to the mobile devices. Due to the limited computing resources, each UAV executes only a portion of the offloaded tasks from its associated MDs in the first stage. Hence, in the second stage, each UAV relays the portions of the tasks to the terrestrial base station (TBS) which has rich computing resources enough to handle all the tasks relayed to it. In this regard, we formulate a joint task offloading, communication and computation resource allocation problem to minimize the energy consumption of MDs and UAVs by considering the limited resources of UAVs and the tolerable latency of the tasks. The formulated problem is a mixed-integer non-convex problem which is NP hard. To solve the formulated optimization problem, we apply the Block Successive Upper-bound Minimization (BSUM) method which guarantees to obtain the stationary points of the non-convex objective function. Finally, the extensive evaluation results are conducted to show the superior performance of our proposed framework.

Task offloading scheme provides opportunistic energy saving for computation-intensive on-vehicle applications. The evolution of the Vehicular Edge Computing (VEC) paradigm has contributed a vast potential that can enhance the performance of such vehicles with energy-hungry and delay-sensitive services. However, determining how much workload to compute locally or offload to the VEC server is still quite challenging. Moreover, when all the vehicles try to offload their computation tasks to the same VEC server, it leads to deterioration in the performance gain due to overburden. Recently, unmanned aerial vehicle (UAV) as the edge server has gained huge attraction due to its well maneuverability and cost efficiency. In this paper, we study the energy-efficient offloading as well as association of the vehicles between the road side unit (RSU) and UAV. First, we formulate the joint offloading and association problem. Next, we decompose the formulated mixed integer linear (MIL) problem into two subproblems and then solve them by using standard convex optimization. Finally, we compare our proposed algorithm with benchmark schemes and the numerical results demonstrate that our algorithm outperforms the benchmark solutions.