关键词： Mobile edge computing   Task priority   Computation offloading   Task migration   Dynamic programming  

摘要： Mobile edge computing (MEC) solves the problem of limited resources by offloading computation-intensive tasks to edge servers. However, as important factors affecting offloading decisions, task priority and migration strategy are often overlooked. This paper considers a MEC system with multiple servers and multi-priority tasks, aiming to minimize the completion delay of low-priority tasks by constructing a task offloading and migration model. Each server has two priority queues, one for high-priority tasks and another for low-priority tasks, both priority tasks arrive randomly. The migration behavior of low-priority tasks among multiple edge servers is modeled using PRP M/G/1 queuing model. Furthermore, the task offloading and migration problem is formulated as a sequential decision-making problem, and an algorithm based on dynamic programming is designed to find the optimal offloading and migration strategy. Through simulation experimental analysis, the algorithm proposed in this paper has better performance in reducing task completion delay and migration times compared with RSAS, MTCC and PSO.

关键词： Processor scheduling   Collaboration   Servers   Delays   Optimization   Cloud computing   Bandwidth   Internet of Things   Energy consumption   Artificial intelligence   Mobile edge computing   multi-layer collaboration   deep learning   resource scheduling   performance evaluation  

摘要： In the application of the Internet of Things (IoT), existing cloud edge collaboration technologies face the problem of poor coordination of heterogeneous resources. In this article, we propose CFEMC, which is a novel Cloud-Fog-Edge Multi-layer Collaboration resource scheduling framework for IoT. First, we design a collaborative resource scheduling framework based on semi-distributed artificial intelligence. It can achieve collaborative optimization of cloud/edge computing resource allocation under the constraints of high reliability and low latency. Second, we present a workflow applications scheduling strategy based on the proposed collaborative resource scheduling framework. This can solve the problem of unstable computing performance and transmission bandwidth during the scheduling process. Finally, the extensive and real data supported simulation results show that CFEMC has advantages in terms of energy consumption, delay and throughput compared with other benchmark strategies. Against CEC Hu et al. 2023 and PSO Zeng et al. 2022, the average throughput increases by 16.37% and 24.21%, and the total queuing delay decreases by 54.23% and 58.12%, respectively.

关键词： Autonomous aerial vehicles   Delays   Predictive models   Optimization   Servers   Training   Heuristic algorithms   Data models   Computational modeling   Resource management   Digital twin   mobile edge computing   deep reinforcement learning   generative and predictive models  

摘要： The emerging digital twin (DT) technology involves creating a digital clone of the physical world through techniques such as computer simulation and machine learning. This facilitates the management and decision-making of the physical world. In this correspondence, we propose a DT-assisted deep reinforcement learning (DRL) method to optimize the decision of computation offloading in an autonomous aerial vehicles (AAV)-based communication system. Our focus is on minimizing task processing delay by jointly optimizing task offloading and the AAV's flight path. This problem can be effectively addressed using the DRL method. However, it necessitates frequent interactions with the environment, which may lead to increased resource consumption and risks associated with some "bad" actions. To mitigate these challenges, our proposed DT comprises a predictive model to anticipate the reward and next stage of the physical environment. Additionally, a generative model is introduced to enhance sample efficiency, with these two models realized by a fully connected neural network and a variational auto-encoder, respectively. Subsequently, we generate a hybrid experience replay buffer to facilitate DRL training, resulting in faster convergence, improved performance, and fewer environmental interactions. Numerical results demonstrate that our proposed method achieves superior delay performance and training efficiency compared to state-of-the-art methods.

关键词： Aerial computing   Mobile edge computing   Unmanned aerial vehicle   High-altitude platform   Offloading decision   Resource allocation  

摘要： Aerial computing can facilitate the successful execution of tasks, ensuring low latency for Internet of things (IoT) devices. It gains greater significance and practicality by offering both edge and cloud computing services for IoT applications. However, in aerial computing, resources such as computing power, energy, and bandwidth are limited and constrained. Consequently, certain tasks must be offloaded to different platforms for sustained operation. Hence, the offloading decision (OD) and resource allocation (RA) are closely interconnected. Currently, numerous research efforts are underway to address efficient offloading and resource management. However, a comprehensive review of OD and RA in aerial computing platforms is yet to be explored. This study presents a thorough survey of OD and RA in aerial computing platforms, extensively reviewing and comparatively discussing various algorithms and approaches. The discussion delves deep into key design issues and also explores unresolved challenges, providing possible future directions. Our work can help researchers in studying and designing efficient methods for task offloading and resource management across different application scenarios.

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： 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.