关键词： Extended reality   immersive experience   5G-advanced   mobile edge computing   quality of experience  

摘要： Extended reality (XR) would become the main enabler of future immersive experience services. The challenge today is how to make this new set of immersive experience services running through a mobile network such as beyond 5G efficiently in terms of latency and Quality of experience (QoE). Academics and industry have already started to study and provide solutions towards enabling XR services in beyond 5G networks. These solutions affect both the network and the service elements by either introducing new ones or modifying existing ones. In the present paper, we proffer an extensive survey in this topic with details on the solutions proposed by academia per part of the network, i.e. radio access, core, edge etc. Moreover, we provide a thorough overview of 3rd Generation Partnership Project (3GPP) standards in Releases 17 and 18 focusing on the proposed solutions, Key Performance Indicators (KPIs), services and network elements. Finally, we summarize the lessons gained from this survey and draw the attention regarding open affairs for further research exploration for enabling XR services in beyond 5G networks and towards 6G.

关键词： Real-time systems   Motion capture   Training   Accuracy   Cameras   Data communication   Monitoring   Multi-access edge computing   Convolutional neural networks   Attention mechanisms   Mobile edge computing   kinect   Tai Chi   graph convolutional network   attention mechanism  

摘要： This study aims to develop an efficient 24-type Tai Chi motion recognition system by using Kinect and mobile edge computing (MEC) technology, to improve the teaching and training effect of Tai Chi. First, by recruiting Tai Chi experts and beginners to conduct experiments, a large number of 24-type Tai Chi movement data are collected to ensure the representativeness and generalization ability of the dataset. Second, in an experimental environment equipped with Kinect devices and MEC nodes, a Tai Chi action recognition model by utilizing Graph Convolutional Network with attention mechanism is constructed. The experimental results reveal that the proposed algorithm achieves 95.17% accuracy in action recognition, markedly higher than the prediction accuracy of the Convolutional Neural Network algorithm. In terms of data transmission, the proposed algorithm's average delay is stable at 0.53 seconds when the data volume is 6Mb, and the packet loss rate is maintained below 7.7%. The conclusion shows that the proposed algorithm not only has excellent performance in recognition accuracy and real-time performance but also has high stability and reliability in network data transmission performance, which provides effective technical support for Tai Chi's digital teaching and training.

关键词： Mobile edge computing  

摘要： While improving network performance, Mobile Edge Computing (MEC) faces significant security threats due to User Equipment (UE) vulnerabilities and insecure communication protocols. This enables attackers to insert malicious nodes into the entire system and propagate worms such as the Mirai, enabling activities like DDoS attacks and cryptocurrency mining. To reduce security risks, it is necessary to identify worm sources and predict their propagation accurately. However, in practice, there exist a large number of stealthy worms (nodes that infect stealthy worms work without exhibiting any abnormal behavior), making it challenging to accurately analyze their source and predict their propagation. To address this problem, our paper introduces a prediction scheme designed to analyze the propagation of stealthy worms originating from unknown sources. First, a Bayes rule-based observation algorithm is designed to correct false-negative nodes and increase the accuracy of identifying the propagation sources. Then, we design a branch-and-bound algorithm to effectively reduce the traversal space and improve the efficiency of calculating the upper and lower bounds. Finally, a series of experiments in four real networks are conducted to show the accuracy and efficiency of our scheme. © 1975-2011 IEEE.

关键词： Protocols   Servers   Security   Authentication   Passwords   Costs   Internet of Things   security   mobile edge computing   session key  

摘要： The emergence of edge computing has revolutionized the landscape of modern applications, offering remarkable advantages such as low-latency and high-bandwidth abilities by decentralizing computation closer to data sources. Despite these advantages, providing secure software regarding communication between users and mobile edge computing (MEC) servers introduces a challenge owing to the sensitive nature of the transmitted data. In response, numerous authentication protocols have been proposed to maintain the reliability of software communication within the MEC environment. In this paper, we introduce RAP-MEC, a novel technique developed to enhance software quality by setting up session key for encrypted communication between users and MEC server. RAP-MEC incorporates functionalities for password change and single sign-on, enabling seamless access to MEC server services. Leveraging physical unclonable functions, symmetric encryption, elliptic curve cryptography, and hash functions, RAP-MEC aims to significantly reduce communication cost [24.85% to 72.73%] and execution time [34.66% to 76.64%]. Through rigorous security analysis, we assess RAP-MEC's resilience against various threats, including DoS, replay, man-in-the-middle, and impersonation attacks. Our findings underscore RAP-MEC's robustness, ensuring the integrity and confidentiality of communications within the MEC environment.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) can reduce the latency of cloud computing successfully. However, the edge server may fail due to the hardware of software issues. When the edge server failure happens, the users who offload tasks to this server will be affected. How to recover the services for these affected users quickly and effectively is challenging. Moreover, considering that the server failure is continuous and temporary, and the failed server can be repaired, the previous works cannot handle this problem effectively. Therefore, in this paper, we propose the fast, online, distributed, and temporary failure recovery algorithm (FODT) for MEC. In FODT, when edge sever failure happens, only the affected APs recalculate their user-server allocation strategies and the other access points (APs) do not change their strategies. For the affected APs, the strategies before server failure are reused to reduce complexity and latency. When the failed server is repaired, the influenced APs reuse the strategies before server failure to offload task to this server. Based on this approach, the FODT can achieve better performance than previous works, includes higher failure recovery efficiency and lower complexity with acceptable approximate ratio. © 2023, CC BY.

关键词： Mobile edge computing  

摘要： Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G, can achieve massive connections and high spectrum efficiency. Motivated by the benefits of NOMA, integrating NOMA with model split in MEC to reduce the inference latency further becomes attractive. However, the NOMA based communication during split inference has not been properly considered in previous works. Therefore, in this paper, we integrate the NOMA into split inference in MEC, and propose the effective communication and computing resource allocation algorithm to accelerate the model inference at edge. Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, and the variables of subchannel allocation and model split are discrete, the gradient descent (GD) algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is proposed to reduce the complexity of GD algorithm that caused by the parameter discrete. Additionally, the properties of the proposed algorithm are also investigated, which demonstrate the effectiveness of the proposed algorithms. © 2023, CC BY.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush-Kuhn-Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms.

关键词： cache allocation policy   mobile augmented reality   mobile edge computing   reinforcement learning   user preference  

摘要： In mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q-network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

关键词： Mobile edge computing  

摘要： This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS. While usually ignored, the control aspects related to channel estimation (CE), resource allocation (RA), and control signaling play a fundamental role in the user-perceived delay and energy consumption. In general, the higher the resources involved in the control operations, the higher their reliability;however, this introduces an overhead, which reduces the number of resources available for computation offloading, possibly increasing the overall latency experienced. Conversely, a lower control overhead translates to more resources available for computation offloading but impacts the CE accuracy and RA flexibility. This paper establishes a basic framework for integrating the impact of control operations in the performance evaluation of the RIS-aided MEC paradigm, clarifying their trade-offs through theoretical analysis and numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： mobile edge computing   industrial internet of things   computation offloading   task queues   dueling deep Q-network  

摘要： Industrial Internet of Things (IIoT) is a promising mechanism of Industry 4.0. Mobile edge computing (MEC) is an emerging mechanism that is an enabler for IoT applications, which applies computation offloading mechanism to offload workload from IoT devices to MEC servers to enhance computing quality. This paper addresses the computation offloading problem under a heterogeneously loaded MEC-enabled IIoT network, which specifically applies one-hop offloading for devices and task queue for devices and servers. We first formulate our computation offloading problem as a Markov Decision Process, then design a dueling deep Q-network-based computation offloading method for better optimizing the value function for specific state and advantage function for specific action. Experimental results verify that our scheme reduces more energy consumption (30%), latency (50%) and task dropped rate (50%) of IIoT devices, compared with other popular methods.