关键词： Energy minimization   federated learning   machine learning based algorithms   mobile edge computing   UE selection and aggregator placement  

摘要： To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption;and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We third deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

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： Internet of vehicle   Mobile edge computing   Budget   Offloading strategy   Latency  

摘要： Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing, which provides enormous communication services in the network. In this research, we explore a multiuser smart Internet of Vehicles (IoV) network with mobile edge computing (MEC) assistance, where the first edge server can assist in completing the intense computing jobs from the vehicular users. Many currently available works for MEC networks primarily concentrate on minimising system latency to ensure the quality of service (QoS) for users by designing some offloading strategies. Still, they need to account for the retail prices from the server and, as a result, the budgetary constraints of the users. To solve this problem, we present a Dueling Double Deep Q Network (D3QN) with an Optimal Stopping Theory (OST) strategy that helps to solve the multi-task joint edge problems and minimises the offloading problems in MEC-based IoV networks. The multi-task-offloading model aims to increase the likelihood of offloading to the ideal servers by utilising the OST characteristics. Lastly, simulators show how the proposed methods perform better than the traditional ones. The findings demonstrate that the suggested offloading techniques may be successfully applied in mobile nodes and significantly cut the anticipated time required to process the workloads.

关键词： Edge computing   Edge computing enabled IoT   Authentication   Key agreement   Provably  

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： Mobile edge computing  

摘要： Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service. © 2024, CC BY.

关键词： Mobile edge computing   5G   Bilateral teleoperation   ROS   Robot control   Edge analytics  

摘要： Human-centric bilateral teleoperation of robots over a network enables a human controller to perform assistive or corrective control to modify the state of a remotely placed robot. However, the timeliness of control depends on dynamics such as communication latency, bandwidth and quality/quantity of feedback information. In various specific use cases, inadequate compliance to communicate expeditious control could result in hazards. In this paper, we demonstrate the architecture of the split-control mechanism to enable edge-based control in bilateral teleoperation. Edge computing allows a robotic setup to benefit from the introduction of intelligent algorithms at the edge. We characterize the notion of feasibility of edge-based analytics by demonstrating two scenarios viz., a command controlled see-saw-like robot (with a low-complexity data/intelligence to capture the delay from cloud to edge);and a haptic glove controlled robotic hand (with an ML-based intelligence algorithm without incorporating the edge-to-cloud delay). We report a practical implementation of a 5G Mobile Edge Computing system that is leveraged to implement the specified split-control mechanism to detect and avoid certain unwanted events arising from the large latency between a robot and a remote human controller. Experimental results show that the proposed edge-based split-control system notably aids the human-centric control mechanism. We, then, introduce the notion of edge-fiability to capture the limits of edge-based prediction and control approach.

关键词： Deep reinforcement learning (DRL)   LEO satellite networks   Lyapunov optimization   mobile edge computing   stochastic computation offloading  

摘要： The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite-terrestrial channels and the random arrival characteristics of ground users' (GUs) tasks bring new challenges for managing the LEO satellite's communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.

关键词： WBAN   IoT   Blockchain   Mobile Edge Computing   eHealth   Smart Agent   AI  

摘要： Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.

关键词： Multi-UAVs   Mobile edge computing   Trajectory planning   Non-dominated solutions   Pareto optimality   K-means clustering algorithm  

摘要： The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs us

关键词： Mobile edge computing   Radar beam blockage correction   Image restoration   Deep learning  

摘要： In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing approximate radial information interpolation and supplementing missing data, often fail to effectively exploit potential patterns in massive radar data, for the large volume of data precludes a thorough analysis and understanding of the inherent complex patterns and dependencies through simple interpolation or supplementation techniques. Fortunately, edge computing possesses certain data processing capabilities and cloud center boasts substantial computational power, which together can collaboratively offer timely computation and storage for the correction of radar beam blockage. To this end, an edge-cloud collaborative driven deep learning model named DenMerD is proposed in this paper, which includes dense connection module and merge distribution (MD) unit. Compared to existing models such as RC-FCN, DenseNet, and VGG, this model greatly improves key performance metrics, with 30.7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} improvement in Critical Success Index (CSI), 30.1%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} improvement in Probability of Detection (POD), and 3.1%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \u