关键词： Data driven model   informer   mobile edge computing   quantum particle swarm optimization   task offloading.  

摘要： Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive ***,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational *** limitation becomes notably significant in complex industrial networks of high-speed *** by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge *** link switching mechanism is executed by the train according to the attributes of the completed *** task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional ***,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time *** simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a key technology for supporting low latency applications close to the end user. Users can access application servers in MEC instead of routing to the Internet by passing through a core cellular network. Few security challenges arise as the traffic does not traverse through the core network, and these can be solved by providing authentication services in the MEC. However, authentication and application mobility issues arise in the case of multiple MECs where a user is mobile and needs continuous service from application servers, without needing to establish a new session and providing authentication information repeatedly to every new MEC the user connects with. In this work, we propose two solutions, a TC3A (Token-based Cookie transfer & 3rd-party Authentication) and a TS3A (Token-based State transfer & 3rd-party Authentication) for resolution of authentication and application mobility issues while achieving low latency. We conducted experiments on a testbed that had MECs deployed in a real-time cellular network (emulated via OpenAirInterface) and performed user handover between two MECs. The experimental results show that TC3A and TS3A successfully re-authenticate the users, without provision of login credentials with target MEC, while reducing the latency by approximately 49.76-59.72% as compared to simple login method. The TC3A and TS3A also eliminate the need of keeping multiple accounts for applications at different MECs and most importantly provide application service continuity, through state transfer during cross-system handover, which is not provided by a simple login method. TC3A provides the application service continuity without any loss of session state, which is suitable for applications that cannot afford state loss, and TS3A provides the same while reducing the latency by 47.05-51.25% as compared to TC3A, which is suitable for applications that require low latency. © 2004-2012 IEEE.

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： mobile edge computing   unmanned aerial vehicle   reflecting intelligent surface  

摘要： The new applications surge with the rapid evolution of the mobile *** explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local *** is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading *** edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low *** this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC *** energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the *** that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based *** combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be ***,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.

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： Task analysis   Energy consumption   Servers   Optimization   Delay effects   Industries   Mobile handsets   Mobile edge computing   industry 50   task scheduling   offloading strategy   particle swarm optimization  

摘要： With the development of network and communication technology, artificial intelligence, distributed computing and beyond fifth-generation communications, Industry 5.0 is booming and obtains rapid growth. To improve the processing efficiency of intensive tasks, Mobile Edge Computing (MEC) technology can facilitate task offloading from mobile devices to edge servers. Traditional methods do not fully consider that applications are usually composed of dependency-aware tasks, which neglect the impact of task dependencies on offloading strategies and lead to low efficiency in task scheduling. This paper proposes a joint optimization of energy consumption and time delay for dependency-aware task offloading with mobile edge computing. First, in order to minimize the energy consumption and task processing of mobile device, a dependency-aware task offloading model is established. Secondly, the dependencies between tasks are analyzed to construct a Directed Acyclic Graph (DAG), and an algorithm based on topological ordering is introduced to obtain possible solutions for task scheduling. Furthermore, to minimize the total cost, an improved Particle Swarm Optimization (PSO) algorithm is used to obtain the optimal task offloading decision and MEC server selection optimization. Experimental results demonstrate that the proposed strategy can reduce the time cost and energy consumption compared to existing typical methods for tasks with different dependencies effectively.

关键词： Consumer electronics   Performance evaluation   mobile edge computing   auction   task offloading  

摘要： This work presents a novel offloading approach to mobile edge computing (MEC). MEC aims to bring computation, storage, and network control closer to the edges of the network, such as base stations and access points, to support resource-limited mobile devices like smartphones and wearable devices. Conventional MEC solutions assume a priori knowledge on the number of processing cycles required by a computational task, which is often impractical. To address this challenge, we propose an online energy-efficient auction (OEA) algorithm for MEC systems consisting of multiple servers and mobile devices. The OEA algorithm takes into account computing tasks with both certain and uncertain processing cycle numbers, and considers both energy consumption and energy harvesting. OEA adopts a combinatorial optimization framework, translated into a series of one-shot procurement auctions optimized using the Vickrey-Clarke-Groves mechanism. The performance of the OEA algorithm is validated through theoretical analysis and simulation studies, demonstrating that it outperforms benchmark strategies.

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： cache   DDPG   mobile edge computing   UAV  

摘要： Mobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade-off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache-aided multi-terminal multi-CAP MEC network with non-ideal channel estimation, where there are N$$ N $$ mobile terminals and M$$ M $$ CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache.

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.