关键词： 5G network   call flow   load balancing   mobile edge computing   task allocation scheme   task verification  

摘要： Mobile edge computing is an important element in ensuring the efficiency of the 5G network as a whole, as it enables data storage and computing at the network edge. Existing solutions do not fully address the issues of load distribution between computing nodes, and most solutions do not offer methods for verifying computations and controlling errors. Accordingly, this paper aims to develop an approach to the organization of mobile edge computing in a 5G mobile network that would authenticate distribution servers and computing nodes, manage the process of distributing computing nodes, have a procedure for verifying the correctness of calculations, and take into account the parameters of computing nodes during distribution. To achieve this goal, we propose to use the developed method. The method of load balancing and selection of computing nodes for edge computing via 5G allows for identifying available nodes and distributing computing blocks among them. It also provides mutual authentication of elements and includes a method of data verification and error detection for the MEC system. The provided solution allows for controlling errors during calculations and protecting the server from incorrect data. These methods are optimized according to minimum network resources and computing time criteria. These improvements increase the efficiency of mobile edge computing in a 5G network. © А. Аstrakhantsev, L. Globa, О. Fedorov, D. Degtiarov, Y. Romanko, K. Romanii, 2024ã.

关键词： Mobile edge computing  

摘要： With the rapid development of the Internet of Things (IoT), the importance of data transmission security and efficiency has become increasingly significant. Traditional consensus mechanisms, particularly the Practical Byzantine Fault Tolerance (PBFT) algorithm, face notable challenges in the context of blockchain-based edge computing due to scalability issues. Specifically, as the number of nodes increases, PBFT experiences high communication complexity, which leads to significant performance degradation. To tackle this challenge, this paper proposes a novel consensus algorithm named ACH-PBFT (Adaptive Clustering Hierarchical) that employs a hierarchical cluster structure and node fault tolerance check to enhance the security and efficiency of blockchain-based edge computing in the IoT. ACH-PBFT adaptively selects an appropriate primary node and forms multiple sub-node clusters, facilitating a more scalable consensus process. The system reduces the probability of malicious nodes becoming primary nodes through mutual verification between the primary node cluster and the sub-cluster consensus. Experimental results indicate that the ACH-PBFT algorithm achieves a 19.83% increase in execution speed, a 23.67% improvement in throughput, and an average reduction of 23.05% in communication overhead compared to a similar double-layer PBFT algorithm lacking hierarchical clustering. In the experiments testing against malicious nodes, the ACH-PBFT algorithm exhibited an average increase of 24.73% in the probability of successfully achieving consensus. Overall, this research provides a practical and effective solution for implementing secure and efficient blockchain-based IoT edge computing using a multi-layer architecture to achieve flexible deployment and efficient resource utilization in edge computing environments. © 2001-2012 IEEE.

关键词： Mobile edge computing  

摘要： With the rapid development of internet of everything, current Mobile Consumer Electronics (MCEs) can support complex computing due to their specialized data-handling capacity. Thus, federated Machine Learning (ML) and edge computing are integrated to leverage such capacity of MCEs to enhance large model training. This paper studies motivations of all participants that take part in large model collaborative optimization from a global perspective, and proposes the positive promotion pattern that collaborates MCEs, Edge Computing Servers (ECSs), and the centralized computing center to support large model collaborative optimization by introducing the idea of computing economic. We firstly propose the novel three-layered framework for large model collaboratively optimizing based on MCEs constructed federated ML. Secondly, we introduce the economic factor into the large model optimization by devising algorithms to assess consumptions of all participants and dynamically adjust incentive benefits according to the qualities of trained models. Finally, we design the scheme to encourage more MCEs to train local models by adaptively adjusting the ECS pricing strategy with the ECS future economic benefit trend considered. Experimental results validate that our proposed approach is able to optimize the global accuracy and improve the economic benefit more efficiently than the state of the art. © 1975-2011 IEEE.

关键词： Training   Servers   Processor scheduling   Convergence   Scheduling   Optimization   Energy consumption   Stability analysis   Correlation   System performance   Mobile edge computing   task scheduling   low latency   energy consumption   deep deterministic policy gradient  

摘要： The Mobile Edge Computing (MEC) system is crucial in modern networks, particularly for applications requiring low latency and high energy efficiency. Efficient task scheduling in MEC environments to balance these requirements is challenging. This paper presented a method that improves the Deep Deterministic Policy Gradient (DDPG) algorithm for better performance in MEC systems. Stability is enhanced through a soft update mechanism for target network parameters and an experience replay buffer to store past experiences, reducing sample correlation and improving training efficiency. It also proposed a reward function considering both latency and energy consumption, comparing the effects of linear and nonlinear reward functions on system performance. The task scheduling process includes state representation, action selection, environment feedback, network updates, and target network soft updates. The experimental results indicate that by analyzing the performance of different reward functions, a nonlinear reward function has been determined to achieve faster convergence. The improved DDPG algorithm outperforms traditional task scheduling algorithms in terms of latency and energy consumption under different task loads, and also has significant advantages in robustness. The ablation experiment further verified the positive contribution of each improved part to the overall performance. At Task load 10, the latency of DDPG, DDPG+Soft update, DDPG+Experience reply buffer, and this paper model were 267 ms, 263 ms, 260 ms, and 250 ms, respectively, with energy consumption of 988J, 974 J, 969 J, and 965 J. The improved DDPG algorithm in this paper not only effectively solves the task scheduling problem in MEC systems, but also provides important references for future research.

关键词： Sensors   Deep learning   Roads   Computational modeling   Cloud computing   Training   Servers   Multi-access edge computing   Internet of Vehicles   mobile edge computing   the Internet of Vehicles  

摘要： Recently, the internet of vehicles (IoVs), mobile edge computing (MEC), and deep learning have attracted many research attentions in the applications of autonomous driving. MEC can help to reduce the network load and transmission delay by offloading the computing tasks to the powerful edge servers while deep learning can effectively improve the accuracy of obstacle detection to enhance the stability and safety of automatic driving. In this article, we first present a comprehensive overview of distributed deep learning based on edge computing over IoVs. Then, the related key techniques, including the distributed characteristics of IoVs, mobile edge collaborative computing architecture for high quality of service (QoS) requirements in terms of vehicle transmission delay and energy consumption, distributed deep learning, and its applications for vehicular networking, are discussed. Finally, the article identifies several important open challenges, opportunities, and potential research directions to provide a reference for readers in this field.

关键词： Mobile edge computing  

摘要： This paper studies the design of vehicular edge computing networks (VECNs) with multiple moving vehicles and roadside units (RSUs). Uniquely, our study reflects a pragmatic situation where wireless channels are time-varying in the duration of task offloading and vehicles can travel with inconstant speeds in a real-world scenario. By jointly optimizing transmit power and time allocation for task offloading as well as computation task partition in the VECN, our goal is to minimize the cost at the vehicles for energy consumption on task offloading and computing, and rent on task computing service at RSUs. However, solving the formulated optimization problem directly is impossible due to the requirement of noncausal vehicular position information (VPI) and noncausal channel state information (CSI) between vehicles and RSUs. To address this issue, a path prediction model is adopted to predict the noncausal VPI, based on which the noncausal CSI can be estimated. Then, a novel receding horizon optimization method is proposed to transform the original problem into a sequence of tractable problems. Despite this, the problems remain complex due to the computationally prohibitive task of identifying the optimal task offloading duration at each vehicle in a centralized manner. To overcome this difficulty, the consensus alternating directions method of multipliers is proposed to solve the problem in a distributed manner with low computational complexity. Numerical results show that our proposed scheme can save at most 30% of monetary cost as compared with existing baseline schemes. © 2014 IEEE.

关键词： Mobile edge computing  

摘要： Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users' quality of service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing. IEEE

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   integrity   data right confirmation   consensus   merkle tree  

摘要： As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance.

关键词： Mobile edge computing   resource allocation   resource allocation   mobile edge server   mobile edge server   edge device   edge device   edge device  

摘要： Mobile Edge Computing (MEC) has the potential to provide computational resources to edge devices. The Edge device (Ed) does not have enough computational resources, so it offloads its tasks to nearby Mobile Edge Servers (MES), while sometimes offloading tasks to MES causes MES to overload. The overload state of MES is when the data occupies all the CPU cores and memory of MES, so MES rejects requests for data offloading from other Eds. In this research, we have considered a multi-server, multi-device scenario, for which we have proposed an algorithm called CPU Memory Mobility Resource Allocation (CMMRA) that does not reject Ed from providing computational resources and encourages them to migrate the task to another MES in case the current MES is overloaded. The algorithm increases the service rate of a communication area, the rejection rate of the devices is also reduced, the total processing time of MES in this research is less as compared to others, and the migration in our proposed algorithm is greater than others. As we consider the mobility of devices and many devices are requesting computational resources, if the current MES is busy, it migrates the task to an underloaded MES for computation.