With the arrival of the 6G era, there is a rapid increase in computational demands, and multiaccess edge computing (MEC) has emerged as an effective mechanism to satisfy these needs. In disaster-affected or remote areas where ground base stations may struggle to provide service, unmanned aerial vehicle (UAV), and high-altitude platforms (HAPs) can leverage their flexible deployment capabilities to offer MEC services. In this article, we design a layered aerial computing framework comprising user equipments (UEs), UAV, and HAP. Building upon this, we establish a hierarchical offloading computation model and formulate an optimization problem to maximize resource utilization and task scheduling within the model. Specifically, the objective is to minimize task computation delay and maximize the remaining energy of the UAV, i.e., minimize energy consumption, subject to constraints on the total task quantity, delay requirements and UAV energy limitations. Due to the nonconvex and highly complex nature of this objective problem, we propose a deep reinforcement learning-based trajectory optimization and task offloading (DTOTO) algorithm that enables the agent, the UAV, to make correct decisions in complex environments and high-dimensional action spaces. The algorithm is capable of optimizing the UAV’s trajectory to obtain the correct offloading decisions. Additionally, we employ state normalization to improve training efficiency. Simulation experiment results validate the effectiveness of the computing framework and the DTOTO algorithm, and numerical results analyze to evaluate system performance.

Autonomous aerial vehicles (AAV) are regarded as a promising technology to provide additional computing capabilities and wide coverage for Internet of Things (IoT) devices, particularly in cases where these devices are situated beyond the reach of traditional communication infrastructure. This study investigates an AAV-assisted multiaccess edge computing (MEC) network comprising multiple AAVs with edge servers and several IoT Devices (IoTDs). IoTDs with a substantial number of computation tasks can select to offload their tasks to AAV-assisted edge servers to alleviate pressure and costs, while the AAV-assisted edge servers can profit from selling computing resources. The interaction between AAV-assisted edge servers and IoTDs is modeled as a Stackelberg game, where both entities aim to maximize their utility. Employing backward induction, the existence of a unique Nash equilibrium is proved. Subsequently, a Stackelberg game-based distributed computation offloading (SDCO) algorithm is designed to approximate the optimal solution. Finally, extensive simulations validate the effectiveness of the SDCO algorithm, demonstrating superior performance compared to other benchmark methods across diverse scenarios.

Due to the limitations of computing resources and battery capacity, the computation tasks of ground devices can be offloaded to edge servers for processing. Moreover, with the development of the low earth orbit (LEO) satellite technology, LEO satellite-terrestrial edge computing can realize a global coverage network to provide seamless computing services beyond the regional restrictions compared to the conventional terrestrial edge computing networks. In this paper, we study the computation offloading problem in the LEO satellite-terrestrial edge computing systems. Ground devices can offload their computation tasks to terrestrial base stations (BSs) or LEO satellites deployed on edge servers for remote processing. We formulate the computation offloading problem to minimize the cost of devices while satisfying resource and LEO satellite communication time constraints. Since each ground device competes for transmission and computing resources to reduce its own offloading cost, we reformulate this problem as the LEO satellite-terrestrial computation offloading game (LSTCO-Game). It is derived that there is an upper bound on transmission interference and computing resource competition among devices. Then, we theoretically prove that at least one Nash equilibrium (NE) offloading strategy exists in the LSTCO-Game. We propose the game-theoretical distributed computation offloading (GDCO) algorithm to find the NE offloading strategy. Next, we analyze the cost obtained by GDCO's NE offloading strategy in the worst case. Experiments are conducted by comparing the proposed GDCO algorithm with other computation offloading methods. The results show that the GDCO algorithm can effectively reduce the offloading cost.

Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.

Due to the limited battery capacity and computational resources of mobile devices, computation-intensive tasks generated by mobile devices can be offloaded to edge servers for processing. This paper investigates the multi-user task offloading and resource pricing issues in Autonomous aerial vehicle (AAV)-assisted Multi-Access Edge Computing (MEC) systems. The optimization objectives is optimizing the utility of the server and the utility of the Edge Users (EUs), with decision variables encompassing the offloading strategies of EUs and the pricing strategies of the server. We divide the entire optimization problem into two parts. When optimizing the server's utility, server energy consumption is a crucial metric; hence, in the first part, we formulate the user allocation problem with the goal of minimizing the server's overall energy consumption. Utilizing game theory, we transform the user allocation problem into a multi-user non-cooperative game and prove the existence of a Nash Equilibrium (NE). The Game-based User Allocation (GBUA) algorithm is proposed to obtain the user allocation strategy. After addressing the user allocation problem, we consider the simultaneous optimization of both server and EUs utility. Therefore, in the second part, we model the server and EUs's engagement using the Stackelberg game model and employ backward induction to verify the presence of a Stackelberg Equilibrium (SE). Additionally, we propose the Resource Pricing and Task Offloading (RPATO) algorithm, based on game theory, to obtain the SE solution. Finally, extensive experiments are conducted to validate the effectiveness of the proposed algorithms, and numerous comparative algorithms are tested to prove the advancement and innovation of our proposed algorithms.

With the swift progress of Internet of Things (IoT) technologies, the number of IoT devices has grown exponentially, leading to an increasing demand for computational power and system stability. Mobile edge computing (MEC) is a powerful solution that allows IoT devices to offload data to the edge for computing. In situations involving disasters or complex terrains, establishing ground-based stations may be challenging in providing computational services. Edge computing frameworks built with unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) can provide airborne computational services for IoT devices situated in environments with disasters or complex terrains. In this article, we design a three-tier framework consisting of ground users (GUs), UAVs, and HAP, offering MEC services for GUs. Considering the randomness and dynamism of task arrivals and the wireless communication quality of devices, we propose an algorithm supporting nonorthogonal multiple access (NOMA) communication in aerial access networks. The objective of the algorithm is to reduce the overall energy consumption of the system while ensuring system stability. Employing stochastic optimization techniques, we convert the task offloading and resource allocation problem into several parallel solvable subproblems. We also provide a theoretical analysis of the algorithm. Through a series of comparative experiments, we demonstrate the feasibility and effectiveness of our proposed dynamic energy-efficient computation offloading (DEECO) algorithm.

Edge caching is crucial in the Internet of Things (IoT) by accelerating content delivery and reducing latency, offering significant advantages. However, inappropriate incentive mechanisms may prevent third-party edge caching nodes from caching data. To address the incentive challenges in edge caching, this paper proposes a contract theory approach to resolve the incentive issues between content providers (Google and Microsoft) and edge caching nodes. Initially, utilizing the framework of contract theory, the security service quality of edge caching nodes is classified into a finite number of types, and transactions between content providers and edge caching nodes are modelled. Subsequently, contract packages containing popular data content and corresponding rewards are designed for different types of edge caching nodes. Utilizing the revelation principle of contract theory addresses the problem of incomplete information in the system, enabling content providers to maximize revenue. A blockchain-based reputation mechanism is employed to identify abnormal nodes within edge caching nodes. Numerical results demonstrate that, compared to other mechanisms, our proposed contracts can effectively incentivize the participation of edge caching nodes, significantly enhance content providers’ revenue, and improve content delivery efficiency and effectiveness.

As the Internet of Things (IoT) becomes ubiquitous and the demand for high-quality services increases, the limitations of traditional network models are starting to show due to endpoint resource constraints. Edge Computing, as an emerging computing paradigm, can significantly improve data processing efficiency and quality of experience by deploying edge servers with computing resources near terminal devices. However, the computing process can generate a lot of carbon emissions due to energy consumption. How to reduce energy consumption and therefore reduce carbon emissions while ensuring service latency is still a pressing issue to resolve. This article focuses on how to reasonably utilize technologies, such as mobile edge computing (MEC), nonorthogonal multiple access (NOMA), and small cell networks (SCNs) in the IoT environment, while considering service latency and energy-saving carbon reduction requirements. We model the problem as a stochastic problem involving local devices, small base stations (SBS), and macro base stations (MBSs), aiming to simultaneously ensure service latency and minimize carbon dioxide emissions. Due to uncertainties, such as task arrival rates and channel conditions, we convert the stochastic problem into a deterministic problem using mathematical optimization theory. Then, we propose an online algorithm called Carbon-aware dynamic task offloading (CADTO) which can solve the problem brought by NOMA and obtain a dynamic offloading strategy. Through theoretical analysis and simulation experiments, we prove that the CADTO algorithm can effectively reduce energy consumption and carbon emissions while ensuring service quality.