关键词： Mobile edge computing   Task offloading   Energy optimization   Optimal stopping  

摘要： Mobile edge computing provides task offloading services for users close to edge servers, greatly reducing the computing pressure of users, decreasing the energy consumption and delay of task to be offloaded. This paper studies the problem of minimizing the average energy consumption of task offloading based on the premise that data on wireless links has delay requirements and variable offloading rate. Firstly, by utilizing the random variation characteristics of wireless channel quality, a task offloading average energy consumption minimization problem based on variable offloading rate is constructed. Then, it is transformed into an optimal stopping rule problem and the existence of the optimal stopping rule is proved. Finally, the optimal offloading rate threshold based on variable offloading rate of the edge terminal at each channel detection time is obtained by solving the approximate optimal stopping rule, realizing the task offloading energy consumption optimization strategy based on variable offloading rate using the optimal stopping theory. In simulation, the strategy proposed in this paper is compared with those in related literature in terms of the average energy consumption, the average offloading ratio, and the average scheduling period. The results showed that the proposed strategy had lower average energy consumption and higher average offloading ratio, achieving good energy optimization effects.

关键词： Optimization   Satellites   Resource management   Qubit   Throughput   Logic gates   Internet of Things   Computers   Energy consumption   Low earth orbit satellites   Satellite-aerial-terrestrial integrated network   mobile edge computing   quantum computing   generalized Benders' decomposition   Lyapunov optimization  

摘要： In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

关键词： Autonomous aerial vehicles   Optimization   NOMA   Three-dimensional displays   Resource management   Energy consumption   Trajectory   Symbols   Interference   Delays   Mobile edge computing   unmanned aerial vehicle   resource allocation   3D position optimization   computation efficiency  

摘要： In this article, the computation efficiency (CE) optimization of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) is addressed in the presence of imperfect successive interference cancelation. Specifically, joint design schemes of resource allocation (RA) and three-dimensional (3D) position are developed to improve the CE while ensuring the fairness of groundusers. In particular, we apply the max-min fairness criterion and optimize the beamforming (BF), power allocation (PA), local CPU frequency and UAV position jointly via two-step optimization method. Namely, we first optimize the 3D position by using an efficient iteration algorithm based on the alternating optimization and concave-convex procedure methods. Then, the joint design of BF, PA and CPU frequency is solved by an efficient iteration algorithm based on the block coordinate descent, sub-gradient methods and convex optimization tool. Additionally, a lower-complexity suboptimal PA scheme with closed-form expression for each iteration is developed. Simulation results indicate that the proposed two design schemes of joint RA and position are effective.

关键词： Servers   Costs   Medical services   Energy consumption   Resource management   Delays   Edge computing   Real-time systems   Optimization   Cloud computing   Smart healthcare system   mobile edge computing   deep reinforcement learning   offloading strategy   data-driven cognitive computing  

摘要： With the widespread adoption of the Internet of Things (IoT) and various smart medical devices, the volume of medical data has dramatically increased, making the processing of medical Internet of Things (IoMT) data increasingly challenging. Due to the integration of edge computing and cloud computing, IoMT can allocate increased computing and storage resources in proximity to the terminal, addressing the low-latency requirements of computationally intensive tasks. While existing initiatives have shifted services to edge servers, they have not taken into account the joint impact of task priorities and mobile computing services on Mobile Edge Computing (MEC) networks. Fortunately, the rapidly advancing field of Artificial Intelligence (AI) has proven effective in some resource allocation applications in recent years. In this article, we propose a mobile edge computing-based intelligent healthcare multitasking processing system aimed at addressing the issue of service prioritization in medical scenarios. Considering energy consumption and latency, we present a multi-objective task-aware service offloading algorithm under the framework of end-edge-cloud collaborative IoMT systems, employing deep deterministic policy gradients (DDPG). Adaptability to the diversity of different services is achieved through dynamic adjustments based on various business types and system requirements. Finally, the effectiveness of DDPG for IoMT is validated using real-world data.

关键词： Unmanned aerial vehicles   Non-orthogonal multiple access   Time-division multiple access   Mobile edge computing   Physical layer security   Friendly jammer  

摘要： The surging demand for Internet of Things applications requires efficient resource allocation and security robustness to safeguard sensitive data from eavesdropping threats. Hence, this article investigates the efficacy of secrecy data offloading in a system that employs unmanned aerial vehicle (UAV)-assisted mobile-edge computing. To concurrently enhance the system's secure performance and mitigate the passive aerial eavesdropper (PAE)'s impact, the system incorporates a hybrid non-orthogonal multiple access (NOMA)-timedivision multiple access (TDMA) framework along with the deployment of a friendly jammer (FJ). To assess the system's secrecy offloading performance, the closed-form expression of a metric termed secrecy successful computation probability (SSCP) is derived that considers the implications of imperfect channel state information (iCSI) and imperfect successive interference cancellation (iSIC) for practical model evaluation. Additionally, an optimization approach, grounded on Ananya algorithm, is formulated with the objective of maximizing SSCP by establishing UAV's locations and altitude, the power-allocation ratio (PAR), and the offloading ratio. Numerical findings are implemented by considering a variety of parameters including the transmission power of edge devices (EDs) and FJ, iCSI-iSIC scenarios, UAV's locations and altitude, task-bits lengths, the system bandwidth, fading parameters, the PAR, and the offloading ratio. Eventually, comparative findings demonstrate the hybrid NOMA-TDMA scenario's superiority, while also highlighting Ananya algorithm effectiveness on enhancing the multi-user system's secrecy offloading efficacy.

关键词： Mobile edge computing   Relay-assisted computing offloading   Execution cost   Resource allocation   Block coordinate descent  

摘要： Motivated by the limited computing and storage resources of smart devices (SDs), existing resource management schemes under the traditional mobile edge computing (MEC) system couldn't fully utilize the network resources. This article proposes a novel relay-assisted MEC architecture to provision SDs, where an idle relay device assumes the dual role of providing fee-based communication and computing services. At the same time, tasks generated by applications exhibit three distinct processing modes: local execution, relay offloading, and edge computing. To minimize the execution cost associated with communication and computing services from the user's perspective, joint computation offloading, relay selection strategy, computation resource allocation, and spectrum resource allocation (JORA) problem is formulated, which is a non-convex problem. Subsequently, a two-stage iterative algorithm is designed to solve the JORA problem using block coordinate descent, reconstruction linearization technology, and convex optimization methods. Simulation results show the effectiveness of the proposed JORA algorithm, which can significantly save the execution cost for edge users.

关键词： Eavesdropping   Resource management   Jamming   Security   Delays   Energy consumption   Scheduling   Computational modeling   Quality of service   Physical layer security   Mobile edge computing   physical layer security   task scheduling   multi-agent reinforcement learning   resource allocation  

摘要： This paper focuses on distributed resource management and task scheduling for multi-access MEC networks against the intelligent eavesdropping jammer (IEJ). Due to the lack of a central controller, the problem of joint task scheduling and network resource allocation is formulated as a distributed multi-user hybrid-integer non-convex *** optimization objective is to maximize users' satisfaction while meeting the Quality of Service (QoS) requirements of tasks and ensuring the high-reliable demands of data offloading. To overcome the challenge of partial observability for users, the channel observation matrix and Gramian Angular Field (GAF) are utilized to preprocess the limited channel state information and to mine the potential time-frequency characteristics of the external environment. Moreover, the hierarchical architecture and parallel networks are introduced for a parameterized redesign of the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) method to improve the decision accuracy. Finally, simulation results demonstrate the superiority of the proposed algorithm over existing methods in terms of delay, energy consumption, and security.

关键词： Dynamic Task Offloading   Mobile Edge Computing   Computer Access Point Selection   Fitness-Based Piranha Foraging Optimization   Algorithm   Adaptive Deep Reinforcement Learning  

摘要： The computationally intensive tasks are processed by mobile devices which include data processing, virtual reality, and artificial intelligence. The computational resources of the mobile devices are very low so they are suited to perform all tasks with low latency. Mobile Edge Computing (MEC) is a cutting-edge computing model that offloads computation-intensive tasks to MEC servers to increase the capability of computing in Mobile Devices (MDs). Due to the extensive use of Wireless Local Area Networks (WLAN), each MD can use numerous Wireless Access Points (WAPs) to offload tasks to a server. In this research work, the task offloading problem is determined by considering the delay-sensitive task along with edge load dynamics to reduce the long-term cost. The distributed algorithm based on Adaptive Deep Reinforcement Learning (ADRL) is introduced, where every device is analyzed for offloading decisions without knowing the task model of other devices. The parameters in the model are optimized using the Fitness-based Piranha Foraging Optimization Algorithm (F-PFOA) to enhance the performance of the model. Finally, the evaluation is done by using the various metrics to showcase the effectiveness of the proposed model, and it gives the throughput is 93.5, which is enhanced than other existing models. Thus, the simulation outcome with a greater number of mobile devices and corresponding edge nodes showed that the developed optimization minimizes the dropped task's ratio and average task delay respectively. The result of the designed model outperformed better than other available models.

关键词： Costs   Energy consumption   Optimization   Servers   Power control   Resource management   Computational modeling   Vectors   Heuristic algorithms   Deep reinforcement learning   Mobile edge computing   partial offloading   power control   deep reinforcement learning   differential evolution algorithm  

摘要： Mobile Edge Computing (MEC) enhances computational efficiency by reducing data transmission distance, yet optimizing resource allocation and reducing operational cost remain critical challenges as the number of users grows. This paper investigates a multi-user partial computation offloading system under the time-varying channel environment and proposes a novel deep reinforcement learning-based framework to jointly optimize offloading strategy and power control, aiming to minimize the weighted sum of latency and energy consumption. Due to the problem's multi-parameter, highly coupled, and non-convex characteristics, a deep neural network is firstly utilized to generate offloading ratio vectors, which are then discretized using an improved k-Nearest Neighbor (KNN) algorithm. Based on the quantized offloading actions, the Differential Evolution (DE) algorithm is employed to seek the optimal power control. Finally, the optimal action and state vectors are stored in an experience replay pool for subsequent network training until convergence, producing the optimal solution. Numerical results demonstrate that the proposed improved quantization method avoids the additional action exploration while accelerating convergence. Furthermore, the proposed algorithm significantly lowers user devices latency and energy consumption, outperforming other schemes and providing more efficient edge computing services.

关键词： Computational modeling   Predictive models   Autonomous aerial vehicles   Consumer electronics   Adaptation models   Spatiotemporal phenomena   Prediction algorithms   Accuracy   Performance evaluation   Correlation   Lightweight large model   autonomous aerial vehicles   consumer electronics   mobile edge computing   traffic prediction  

摘要： Autonomous Aerial Vehicles (AAV) have emerged as an important category of consumer electronics, offering versatile applications and enhanced operational efficiency. In the context of Mobile Edge Computing (MEC), AAVs can improve task execution and service quality by providing dynamic allocation of computational and data processing resources at the network edge. However, one critical issue in AAV-assisted MEC is task traffic prediction, which directly impacts the efficiency of task offloading strategies. Inspired by the recent success of large models, we propose a novel Lightweight Large Model with Temporal-Spatial Attention (L-LMTSA), designed to maximize task traffic prediction accuracy and minimize the overall computation latency. Specifically, L-LMTSA integrates a Temporal Attention Model (TAM) and a Spatial Attention Model (SAM) to capture multi-dimensional information, such as task flow location and temporal characteristics. In addition, we develop a pre-trained lightweight GPT-2 model by employing a one-dimensional convolutional input encoding layer and fine-tuning its parameters for predictive accuracy. Additionally, we optimize the AAV's trajectory and task offloading strategy using a three-dimensional artificial potential field informed by the predicted task traffic. Extensive experiments demonstrate that L-LMTSA not only improves training speed and prediction accuracy but also significantly reduces computational latency, resulting in more efficient AAV-assisted MEC experiences for consumers.