关键词： Servers   NOMA   Resource management   Interference   Downlink   5G mobile communication   Base stations   Non-orthogonal multiple access (NOMA)   mobile edge computing   user allocation   game theory  

摘要： Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

关键词： Location privacy protection   Maritime network   Mobile edge computing   Task floading  

摘要： Mobile Edge Computing(MEC)can support various high-reliability and low-delay applications in Maritime Networks(MNs).However,security risks in computing task offloading *** this study,the location privacy leakage risk of Maritime Mobile Terminals(MMTs)is quantified during task offloading and relevant Location Privacy Protection(LPP)schemes of MMT are considered under two kinds of task offloading *** single-MMT and single-time offloading scenario,a dynamic cache and spatial cloaking-based LPP(DS-CLP)algorithm is proposed;and under the multi-MMTs and multi-time offloading scenario,a pseudonym and alterable silent period-based LPP(PA-SLP)strategy is *** results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location ***,extending the alterable silent period,increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in ***,the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.

关键词： Internet of things   Artificial intelligence   Mobile edge computing   Remote loading   Service migration  

摘要： In mobile edge computing (MEC) systems, enhancing the learning capabilities of edge nodes through Artificial Intelligence (AI) can improve the efficiency of dynamically allocating resources. For the scenario of edge service migration, various tasks in lightweight IoT devices are offloaded to edge nodes and the services on edge nodes are migrated adaptively to another available node nearer to the users while they move. To speed up network application loading during service migration, this paper proposes an intelligent trace-driven predicting approach (ITPA) that improves the efficiency of I/O scheduling and the hit ratio of caching when migrating services between resource-constrained edge nodes. First, based on the characteristics of sequential access to the binary codes of an application during its startup progress, the request loading list is generated by tracing key I/O requests at that phase. Then, an intelligent algorithm is designed to search and select the key I/O requests in the loading list. Finally, the efficiency of data acquisition is improved by implementing a prefetch strategy for the client side and a three-level caching strategy for the server side. Experimental results show that the ITPA reduces the service startup time during stateless migration.

关键词： Tensors   Training   Convolutional neural networks   Data models   Computational modeling   Servers   Collaborative work   Federated learning   mobile edge computing   traffic sign image recognition   convolutional neural networks  

摘要： Traffic sign recognition and autonomous vehicles computing are a few of the innovative applications which are emerging in the domain of mobile edge computing. Distributed machine learning in the form of Federated Learning (FL) has been applied to mobile edge computing through a range of methodologies and techniques for intelligent feature classification approaches. The challenges that research on such FL methods is facing is twofold: identify an optimal distributed architecture and algorithm components to each side to meet the demand of heavy data processing, and enhance the algorithm components with heuristics that fit to the problem domain and optimize the key parameters of the algorithms. In this prospect, we present a Federated Learning implementation based on a neural network architecture with emphasis to traffic sign image recognition. Our benchmark was tested with two FL strategies seeking an optimal performance model and in reference to a corresponding data set. We present the results of this work while we define the scope of future improvements to our model.

关键词： Mobile edge computing   Time slot allocation   Wireless power transfer   User cooperation  

摘要： Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the "double near-far" effect. Therefore, in order to further overcome the influence of the "double near-far" effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme;the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm. (C) 2022 Elsevier B.V. All rights reserved.

关键词： D2D   mobile edge computing   power optimization   task offloading  

摘要： A smart grid is composed of power lines and power equipment that are connected by the information interaction system composed of a large number of sensing equipment, as well as the signal transmitting and receiving equipment. At present, the intensity of power equipment access in the smart grid is increasing, and the traditional information interaction system cannot guarantee the time delay symmetry of the power equipment. With the rapid development of the 5th generation mobile networks, it is possible to apply 5G Communication Network to the smart grid information interaction system. In order to minimize the delay difference between the power equipment, this paper proposes a joint assignment scheme of computing task proportion and power based on symmetric delay optimization by using the mobile edge computing(MEC) and the device to device(D2D) technologies in the 5G communication network. The complex original problem is decomposed into sub-problems, and the linear programming method is used to solve them. Thus, the transmitted power and the task allocation ratio of terminal power equipment can be optimized. The simulation results show that the proposed joint assignment scheme of the proportion of computing tasks and the transmission power can effectively reduce the delay difference and improve the delay symmetry of the information interaction system. And compared with other schemes, the proposed scheme has better time delay symmetry. © 2022 Journal of Nanjing Institute of Posts and Telecommunications. All rights reserved.

关键词： Mobile edge computing   blockchain   image sharing   user equipment assignment   service entity deployment  

摘要： Integrating dense small cell (DSC) networks with mobile edge computing is employed by SG to tackle the contradiction between the computation limitations of user equipment (UE) and the stringent latency requirement of services. This paper investigates the service-oriented edge resource allocation problem in DSC networks, determining where to deploy the service entity, how many service entities should he deployed at each edge cloud, and how to assign the UEs to service entities. The problem is challenging for the following three aspects: 1) Service entity deployment and UE assignment are highly coupled. 2) Due to the overlap of coverage regions of densely deployed small cells, the allocation mechanism of different base stations has mutual effects on the overall service performance. 3) Considering the limited resources of edge clouds, it is a thorny problem to encourage edge clouds to cache and share service startup images. We devote the following efforts to tackle the problem under these challenges. First, we explore blockchain's decentralized, traceable, and secure characteristics, and propose a scheme to encourage image sharing in mobile edge computing. Second, we formulate the service-oriented edge resource allocation as mixed integer non-linear programming. Third, towards the target of reducing the computational complexity, we decouple UE assignment from service entity deployment and solve it through Gibbs sampling. Moreover, the power of Lyapunov optimization and convex optimization is incorporated to reduce the long-term power consumption and budget. Experiment results demonstrate the superiority of our approach over current notable solutions.

关键词： Time execution   energy consumption   computation offloading   mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a new method to overcome the resource limitations of mobile devices by enabling Computation Offloading (CO) with low latency. This paper proposes a multi-user multi-task effective system to offload com-putations for MEC that guarantees in terms of energy, latency for MEC. To begin, radio and computation resources are integrated to ensure the efficient utilization of shared resources when there are multiple users. The energy consumed is positively correlated with the power of transmission and the local CPU frequency. The values can be adjusted to accommodate multi-tasking in order to minimize the amount of energy consumed. The current methods for offloading aren't appropriate when multiple tasks and multiple users have high computing density. Additionally, this paper proposes a multi-user system that includes multiple tasks and high-density computing that is efficient. Simulations have confirmed the Multi-User Multi-Task Offloading Algorithm (MUMTOD). The results in terms of execution time and energy consumption are extremely positive. This improves the effective-ness of offloading as well as reducing energy consumption.

关键词： IRS   Mobile edge computing   Outage probability   Task uploading  

摘要： This paper studies an intelligent reflect surface (IRS) aided mobile edge computing (MEC) network, where the direct link exists in the network can assist the task transmission for computing with the help of multiple elements in the IRS. We perform the performance evaluation by instigating the impact of direct link on the outage probability. Specifically, Firstly, we analyze the system outage probability (SOP) with a different number of reflecting elements and energy consumption constraints. Moreover, we propose two selection methods for the case of multiple reflecting elements. In particular, Method I maximizes the first-hop reflecting channel while Method II maximizes the dual-hop product channel. In further, for the two different methods, we estimate the outage probability of the system by considering the reflecting channel information and providing the analytic expression of the outage probability, respectively. Finally, the numerical results verify the correctness of our results. The results show that increasing the number of reflecting elements can effectively reduce the SOP. (c) 2022 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   blockchain   offloading decision   adaptive data block size   resource allocation  

摘要： With the rapid growth of smart mobile devices, the limited resources and computing performance of mobile devices might increase the delay of processing tasks, resulting in greater energy and cost consumption by terminal devices. This study aimed to focus on the balance of delay and energy consumption in the mobile edge environment, considering the impact of computationally intensive network interference on system performance under blockchain and proposed a joint optimization algorithm for computing offloading and resource allocation of adaptive data block size in the edge environment. First, a computing offloading model and a communication model were established, and the problem model was optimized on that basis. Second, the incentive mechanism of a virtual currency based on blockchain was introduced, and a coordinate descent method was used to formulate the offloading decision of adaptive data block size. Then, a subchannel allocation was based on the improved Hungarian algorithm and the greedy algorithm while satisfying the user delay constraint. Finally, energy minimization problems were converted into power minimization problems, then into a convex optimization problem, and finally into the user's optimal transmit power. Experimental results showed that compared with similar computing offloading algorithms, the proposed offloading decision could reduce the energy consumption of the system while satisfying different time delay requirements of the users and improving the system performance.