关键词： Mobile edge computing   Computation offloading   System-centric energy efficiency   Latency   Optimization   DELAY MINIMIZATION   EDGE   MAXIMIZATION   OPTIMIZATION   AWARE  

摘要： Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users' computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.

关键词： Mobile edge computing   Unmanned aerial vehicle   Multi-agent deep reinforcement learning   Trajectory optimization   Offloading strategy   RESOURCE-ALLOCATION   DRL  

摘要： As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost.

关键词： federated learning   internet public opinion   mobile edge computing   semantic communication  

摘要： Building a public opinion monitoring system can detect public opinion crises in advance and deal with crisis public relations in a timely manner. Semantic communication is a new type of communication technology with development potential, which reduces the amount of data required for transmission by mining semantic information in information sources. In this paper, a multi-user semantic communication system based on federated learning deployment is used to realize data transmission in edge computing scenarios. Use the data from the client side to train the deep learning model more effectively. This paper also evaluates the performance of the proposed semantic communication system. The model trained by federated learning can achieve an effect close to centralized training and protect user privacy.

关键词： Edge computing   Edge computing enabled IoT   Authentication   Key agreement   Provably   KEY EXCHANGE   INTERNET   SCHEME   THINGS  

摘要： Edge computing server deployed at the edge of the network has promoted the development of the Internet of Things (IoT) but also brings some security problems. Traditional IoT security solutions have not met the requirements of the current rapid growth of equipment, real-time and lightweight. Therefore, the development of new authentication and key agreement is becoming an important means to solve the security problems of the IoT. To address this challenge, we propose an authentication framework for the edge computing-enabled IoT environment. This framework aims to facilitate the establishment of secure communication among devices and between devices and edge servers. This authentication framework achieves secure communication and identity verification among edge computing-enabled IoT devices and between devices and edge servers by incorporating a portion of the local private key during registration and leveraging a distributed blockchain network. Formal and informal security analyses of the proposed protocol show that the protocol effectively responds to numerous security threats while satisfying various security attributes. In addition, the study provides a comprehensive assessment of the computational and transmission overheads associated with the protocol. The results show that the protocol's commendable performance in terms of both computational efficiency and transmission cost-effectiveness.

关键词： Internet of vehicle   Mobile edge computing   Offloading strategy   Latency   Energy consumption   Smart IoV  

摘要： The current task offloading technique needs to be performed more effectively. Onboard terminals cannot execute efficient computation due to the explosive expansion of data flow, the quick increase in vehicle population, and the growing scarcity of spectrum resources. As a result, this study suggests a task-offloading technique based on reinforcement learning computing for the Internet of Vehicles edge computing architecture. The system framework for the Internet of Vehicles has been initially developed. Although the control centre gathers all vehicle information, the roadside unit collects vehicle data from the neighborhood and sends it to a mobile edge computing server for processing. Then, to guarantee that job dispatching in the Internet of Vehicles is logical, the computation model, communications approach, interfering approach, and concerns about confidentiality are established. This research examines the best way to analyze and design a computation offloading approach for a multiuser smart Internet of Vehicles (IoV) based on mobile edge computing (MEC). We present an analytical offloading strategy for various MEC networks, covering one-to-one, one-to-two, and two-to-one situations, as it is challenging to determine an analytical offloading proportion for a generic MEC-based IoV network. The suggested analytic offload strategy may match the brute force (BF) approach with the best performance of the Deep Deterministic Policy Gradient (DDPG). For the analytical offloading design for a general MEC-based IoV, the analytical results in this study can be a valuable source of information.

关键词： Mobile edge computing   Vehicular fog computing   Urban intelligent video systems   Task placement   ICVs   ALLOCATION   UAV  

摘要： With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper.

关键词： Mobile edge computing   Task offloading   Task scheduling   Particle swarm optimization   Genetic algorithm  

摘要： Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18-31% and can be applied to scenarios with tight task deadlines.

关键词： Virtual machine placement   Container consolidation   Mobile edge computing   Similarity   Spectral property  

摘要： An optimized container consolidation is a key factor in Mobile Edge Computing. It is a challenging problem as its complexity is NP-hard. In addition to structural properties of a network which reveal nodes features and their connectivity, we believe that the spectral properties of correlation between rows of similarity matrix of the network could be more beneficial in placement algorithm. Most existing literature focus on reducing energy consumption in cloud or edge environments based on structural properties of the given network. However, they undermine the concept of locality which could affect excessive network traffic. Our proposed method utilizes structural and spectral properties of the network to solve the problem. We expected to improve locality factor and link reduction as well as reducing the problem space by utilizing a centroid-based approach and a queue. Our proposed algorithm preserves topology architecture without consuming more energy in comparison to the existing popular algorithms.

关键词： Mobile edge computing   Status update system   Age of information   Priority queueing model   Stochastic hybrid system   PEAK AGE   QUEUE  

摘要： Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

关键词： Mobile edge computing   Task dependency   Computing offloading   Directed acyclic graph  

摘要： Mobile edge computing offloads computing-intensive applications from resource-constrained terminal devices to adjacent edge servers to meet users' latency and energy consumption requirements. Most existing studies do not consider the dependencies between applications, leading to the wastage of computing resources. As the number of request users increases, edge servers with limited resources cannot meet the needs of all users. However, there are a large number of idle computing resources on the user side that are not utilized. Aiming at the problem of computing offloading of dependent tasks in this scenario, we establish an end-edge collaboration-dependent task offloading model and propose an offloading algorithm that balances task completion time and energy consumption. Firstly, we solve the problem of collaboratively matching request users by considering user mobility and computing requirements. Secondly, we determine the scheduling order of tasks according to the dependencies between tasks. Finally, we propose a hybrid artificial bee colony algorithm to solve the problem of task offloading. The results show that our algorithm saves 19.9% in average task completion time compared to an offloading strategy that does not consider device-to-device.