关键词： Digital twin (DT)   mobile edge computing   vehicular ad-hoc networks (VANETs)   blockchain   synchronized privacy-preserving authentication (SPPA)  

摘要： Digital Twin (DT) technology, by performing simulation, analysis, and prediction over the data mapped to digital space, can create a digital replica of the physical object. It can be combined with edge computing or cloud computing to provide broad vehicle-to-everything applications and improve the service quality of vehicular ad-hoc networks (VANETs). In this article, DT technology and mobile edge computing are integrated into VANETs to introduce a framework of mobile digital twin edge network-driven VANETs (MDTEN-Driven VANETs). Moreover, facing the security and privacy challenges in the framework, we propose a synchronized privacy-preserving authentication (SPPA) scheme. In SPPA, we first design a synchronized anonymous certificateless aggregate signature (SA-CLAS) to achieve the authentication with time state synchronization and the privacy preservation of the real identity. Furthermore, to deal with malicious vehicles, we adopt blockchain technology and devise a smart contract algorithm to manage the public information of vehicles. The security analysis demonstrates that SA-CLAS is existentially unforgeable under adaptive chosen message attacks, and SPPA can satisfy the necessary security requirements. The performance evaluation shows the efficiency and practicality of SA-CLAS and SPPA. Besides, the designed smart contract is implemented in an Ethereum test network, which presents an acceptable blockchain consumption.

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： 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： 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

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   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： 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.