关键词： Mobile edge computing   Physical layer security   Resource allocation  

摘要： With the advent of the fifth generation (5G) era, Mobile edge computing (MEC) has become a promising technology. In order to increase the spectrum utilization efficiency and mitigate interference between users, Orthogonal Frequency-Division Multiple Access (OFDMA) technology is introduced into MEC systems. However, uplink offloading signals from users to servers are vulnerable to malicious eavesdropping. We utilize physical layer security, specifically, jamming caused by servers and co-frequency interference provided by users in non-orthogonal multiple access (NOMA) groups to address this issue. This paper investigates an OFDMA based MEC system which consists of multiple users, multiple servers and an eavesdropper. For this system, an innovative algorithm is designed to minimize latency of the system through optimizing resource allocation and task offloading policy which has lower computation complexity while ensuring its security. The proposed scheme can decrease system latency and ensure secure communication according to experimental results.

关键词： Digital twins   Biological system modeling   Cloud computing   Solid modeling   Sensors   Resource management   Computational modeling   Customers centric network   mobile edge computing   digital twin   smart data-driven modeling   content distribution  

摘要： Recently, the method of non-intelligent vehicles, acting as information consumers, receiving integrated data shared to the cloud by mobile edge nodes and RSUs has garnered significant interest for its potential to enhance road safety. The method can assist the customers in achieving advanced driver assistance system functionalities. Nonetheless, challenges such as resource constraints at RSUs, long communication distances between the cloud and vehicles, and frequent data interactions make it difficult to meet the strict latency requirements in highly mobile and complex traffic environments. To address these challenges, this paper proposes a smart data driven multi-level synchronous digital twin model for vehicle-assisted driving. We make use of multi-level synchronous deduction along with high-level proactive updating to reduce update latency significantly so that it can meet the timeliness requirements for cyber-physical services in differentiated scenario. Additionally, we construct a customer matching value and dynamic vehicle model update system. It solves the resource allocation problem that arises during the update of differentiated twin models for multiple customers when resources of RSUs are constrained. While satisfying the constraints of update latency, it minimizes the cost of vehicle model updates and effectively enhance system efficiency.

关键词： Mobile edge computing   reservation-based   unequal task splitting   distributed offloading  

摘要： Intelligent Cyber-Physical Transportation Systems (ICTS) provide various services through interaction and integration with vehicles, which can improve the safety and efficiency of the transportation system. This is achieved by leveraging Vehicle Edge Computing (VEC) for processing compute-intensive and latency-sensitive tasks in ICTS. However, most studies make offloading decisions by greedily using all available computing resources in the current network, without fully taking into account the impact of the dynamics of resource occupation on offloading decisions. In this paper, a new Reservation-Based Multi-Source Distributed Offloading (ReMuDO) strategy is proposed. With the goal of minimizing the long-term average task completion latency of the system, this strategy employs a Greedy Randomized Adaptive Search Procedures (GRASP) framework in dealing with the problem of multi-source tasks competing for limited computing resources, and uses the reservation-based Sequential Quadratic Programming (SQP) algorithm to achieve unequal task segmentation to maximize system performance. Extensive experimental results show that the proposed ReMuDO strategy can significantly outperform other classical strategies for different parameters.

关键词： Task offloading   mobile edge computing   offline reinforcement learning   mobile sensing  

摘要： Edge servers, which are located in close proximity to mobile users, have become key components for providing augmented computation and bandwidth. As the resources of edge servers are limited and shared, it is critical for the decentralized mobile users to determine the amount of offloaded workload, to avoid competition or waste of the public resources at the edge servers. Reinforcement learning (RL) methods, which are sequential and model-free, have been widely considered as a promising approach. However, directly deploying RL in edge computing remains elusive, since arbitrary exploration in real online environments often leads to poor user experience. To avoid the costly interactions, in this paper, we propose an offline RL framework which can be optimized by using a static offline dataset only. In essence, our method first trains a supervised offline model to simulate the edge computing environment dynamics, and then optimize the offloading policy in the offline environment with cost-free interactions. As the offloading requests are mostly asynchronous, we adopt a mean-field approach that treats all neighboring users as a single agent. The problem can then be simplified and reduced to a game between only two players. Moreover, we limit the length of the offline model rollout to ensure the simulated trajectories are accurate, so that the trained offloading policies can be generalized to unseen online environments. Theoretical analyses are conducted to validate the accuracy and convergence of our algorithm. In the experiments, we first train the offline simulation environment with a real historical data set, and then optimize the offloading policy in this environment model. The results show that our algorithm can converge very fast during training. In the execution, the algorithm still achieves high performance in the online environment.

关键词： Servers   Task analysis   Cloud computing   Surveillance   Energy consumption   Cameras   Mobile handsets   Intelligent surveillance system   mobile edge computing   cloud computing   task offloading  

摘要： In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.

关键词： Mobile edge computing   Mobility prediction   Relay assistance   Computing offloading   Unequal splitting of tasks  

摘要： Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation -intensive and time -sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi -source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi -source tasks while satisfying the quality of service requirements. To address the Non -deterministic Polynomial -time hardness (NP -hardness) of the problem, we design a Relay -Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay -assisted nodes can enhance performance in poor scenarios and ensure low -latency multi -tasking under various conditions, especially reducing latency by 39% compared to local computing.

关键词： Task analysis   Massive MIMO   Servers   Optimization   Wireless communication   Energy consumption   Resource management   Cell-free massive MIMO   mobile edge computing   task offloading   optimization  

摘要： A mobile edge computing (MEC) aided cell-free massive MIMO network is investigated in this paper that aims at optimizing the task offloading process from the wireless devices to the MEC server. Partial computation offloading strategies are designed that aim at optimizing either the aggregated latency or the energy consumption by considering the finite computational capability of the MEC server and assuming that the tasks are subject to a maximum latency constraint. The proposed optimization approach considers the joint optimization of both the per-task offloading ratio and the corresponding computational resources allocated to the tasks at the MEC server. This optimization is performed by taking into account the computing capabilities of both the wireless devices and the MEC server and both the UL and DL spectral efficiencies provided by the cell-free massive MIMO network. The proposed optimization problems are shown to be convex and, inspired by the well-known waterfilling algorithm, optimal solutions based on low-complexity iterative algorithms are devised. Extensive numerical results reveal the potential of the proposed MEC-enabled cell-free massive MIMO network.

关键词： Satellites   Task analysis   Low earth orbit satellites   Space-air-ground integrated networks   Aircraft   Servers   Delays   Mobile edge computing   space-air-ground integrated network   collaborative offloading   online learning   6G system  

摘要： In 6G systems, space-air-ground integrated network (SAGIN), relying on space/aerial communications to complement terrestrial networks, is developed to achieve worldwide connectivity and multi-service access especially in remote areas. However, the heavy workload of the resource-limited satellites may raise an important issue about reducing service coverage and quality. In this article, we propose a collaborative edge computing framework for SAGIN-aided 6G system, in which the LEO satellites are considered as both "servers" and "users". Excepting provide services for ground users/devices, the LEO satellites is able to offload the tasks to nearby aircrafts via one-hop link or offload them to the cloud server along multi-hop satellite path. To minimize the long-term task completion delay of LEO satellites, a stochastic optimization problem is formulated by considering the variation of the space/aerial environment. The Lyapunov-based optimization method is developed to solve the problem and the delayed online learning technique is adapted to predict dynamic task arrival and queue length of satellites and aircrafts. Numerical results confirm the effectiveness of the proposed collaborative offloading scheme for reducing tasks completion delay of LEO satellites while guaranteeing computation efficiency.

关键词： Task analysis   Servers   Resource management   Processor scheduling   Consumer electronics   Interference   Wireless communication   Mobile edge computing   online   deadline-sensitive   resource allocation   competitive ratio  

摘要： With the remarkable prosperity in smart consumer electronics (CEs), Mobile Edge Computing (MEC) has emerged as a pivotal technology to tackle the prevalent latency issues faced by smart CEs. Consequently, this has produced multiple deadline-sensitive tasks, imposing stringent computational time requirements on the infrastructure of 6G wireless communication systems. To meet the deadline demands of tasks when offloading, research efforts have focused on improving the scheduling mode. However, a majority of modes are offline, which is unrealistic for MEC servers to schedule tasks or reserve resources according to the global information (i.e., the time and quantity of tasks released by CEs) grasped in advance. To address this concern, we propose an online data-driven scheduling mechanism maximizing the revenue of deadline-sensitive tasks in the MEC-enabled consumer electronics system. Given that the released time and deadline time are fixed, but the execution process are flexible and the execution process involves two network resources, we design a two-step online resource allocation (TORA) algorithm comprising an online spectrum allocation sub-algorithm and an online computing resource allocation sub-algorithm. Moreover, we derive a precise competitive ratio to evaluate the performance of our TORA algorithm. Finally, through extensive simulations, we demonstrate its superiority in improving system revenue.

关键词： Sensors   Task analysis   Resource management   Hardware   Delays   Servers   Wireless sensor networks   Integrated sensing and communication   mobile edge computing   resource allocation   action recognition  

摘要： Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.