关键词： blockchain   mobile edge computing   mobile services   resource management  

摘要： In recent decades, newly emerging mobile applications and services are becoming increasingly resource hungry and computation intensive. Portable size mobile devices fall short in providing such services. Cloud computing has been a core computation technology to provide visualized resources in a scalable way for mobile services. However, the unpredictable network transmission latency makes cloud computing not efficient enough for time-sensitive mobile services, which requires major changes in underlying computing platform. Mobile edge computing is widely known as one of the novel technologies has emerged in recent years to address this issue. For being impractical to construct huge edge cloud in network edge, edge clouds can still be overloaded in rush time. Offloading to near-user facilities using device-to-device (D2D) or other technologies becomes an augmentation approach. However, how to manage the facilities/resources effectively become a new issue. In this paper, we proposed a resource management scheme named BCEdge based on blockchain in D2D-assisted mobile edge computing. BCEdge is a reliable scheme that operates in a distributed way to relieve the load of edge clouds. We illustrate the advantages and technical details of BCEdge using flow charts and interaction charts. The experiment results validate the effectiveness of our scheme. Finally, we discuss the possible future extensions.

关键词： Task analysis   Games   Wireless communication   Uplink   Resource management   Optimization   Ultra-dense networks   Computation offloading   mobile edge computing   ultra-dense network   mean field game  

摘要： In ultra-dense networks, the increasing popularity of computation intensive applications imposes challenges to the resource-constrained smart mobile devices (SMDs), which may be solved by offloading these computation tasks to the nearby mobile edge computing centers. However, when massive SMDs offload computation tasks in a dynamic wireless environment simultaneously, the joint optimization of their offloading decisions becomes prohibitively complex. In this paper, we firstly model the joint optimization problem as a multi-user non-cooperative dynamic stochastic game, then propose a mean field game based algorithm to solve it with a drastically reduced complexity. We derive the two partial differential equations ruling the optimal strategies of the mean field game, namely the Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations, which are solved in an iterative manner in our proposed algorithm. Numerical results demonstrate that the proposed mean field game-based offloading algorithm requires a lower cumulated cost than the conventional strategies under the latency constraints of computation tasks, with perfect prediction of future channel states. It also appears that the performance of the mean field game-based offloading strategy depends on the accuracy of the future channel knowledge provided to the system, as the uncertainty may compromise its cumulated cost performance.

关键词： Mobile edge computing   Evaluation index   Factor analysis   Cluster analysis   Index weight   Judgment matrix   Online and offline hybrid teaching  

摘要： In order to enhance the hybrid teaching of physical education online and offline, a quality evaluation method for online and offline hybrid teaching based on mobile edge computing is proposed. Through the evaluation target, index, weight of index and evaluation standard, this paper first constructs the quality evaluation index system of hybrid physical education online and offline. Then, factor and cluster analysis on the importance evaluation grade data of each index to simplify the index items are carried out. By combined with the mobile edge calculation, the weights of quality evaluation indexes for hybrid teaching are calculated respectively, and the fuzzy comprehensive evaluation model is used to effectively realize the quality evaluation of hybrid physical education online and offline. The simulation results show that the proposed method can effectively reduce the cost and error of quality evaluation for hybrid teaching online and offline, improve the evaluation efficiency, and enhance the quality of online and offline hybrid teaching.

关键词： Mobile web augmented reality   Mobile edge computing   Model data computing service   Collaborative computing  

摘要： Mobile web-based augmented reality (MWAR) provides users with quick richer information interaction forms due to its convenience and universality. However, the 3D model data service for MWAR is a type of centralized file-based data service, which cannot simultaneously meet diverse user and response delay requirements in large-scale and complex applications. This affects the application and promotion of large-scale and complex MWAR application. To this end, this paper proposes a collaborative model data computing service framework (CMCSF) for MWAR between Mobile Edge Servers (MES), Cloud Servers (CS), and mobile devices. The main contributions of this paper include: (1) The CMCSF converts the file-based 3D model data service in MWAR to a computing-based and interfaced data service to meet the diverse service requirements of users;(2) The CMCSF establishes a collaborative computing model data service between the MES and the CS, and it computes a control and deployment strategy for the collaborative computing model data service in order to reduce the response delay of 3D model data;and (3) The CMCSF optimizes the loading method of the WebGL engine (for example, ***) and applies the asynchronous loading method of the JSON interface data on the http protocol for browsers to get persistent model data services. An experimental evaluation shows the CMCSF improves the response efficiency of the 3D model data file Significantly when compared with the original centralized file-based 3D model file service.

关键词： Mobile edge computing   5G network   Cache prefetching   Cache replacement   Dynamic popularity  

摘要： Recently, with the rapid development of the fifth-generation (5G) communication technology, various new services such as virtual reality (VR), augmented reality (AR), and video conferencing, etc. have emerged. To achieve optimized quality of service (QoS), high load and low latency, we propose an effective mobile edge caching strategy. Specifically: first, a cache prefetching algorithm is proposed to improve cache hit rate. second, the load balancing algorithm based on the maximum distribution weighted entropy is designed to improve bandwidth utilization and avoid mobile network congestion;third, the optimized cache replacement policy is built based on the content future popularity to reduce access latency and backhaul link pressure. With these proposed cache prefetching and replacement algorithm, the access latency can be reduced significantly, the load balance can be maintained and cache hit ratio can be improved obviously in 5G campus networks. This mobile edge caching policy is an effective improvement of the existing strategy, offering a strong support for the full realization of the 5G network potential. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   Stackelberg game   Computation offloading   Utility maximization  

摘要： Aiming at the problem of offloading optimization of transmission power and edge cloud pricing in mobile edge computing systems, this paper proposes an Offloading Strategy-and Price Control(OSPC) algorithm based on Stackelberg game. First, the Stackelberg game is used to establish the edge cloud pricing and transmission power decision model, which realizes the optimization of the utility of edge cloud and each user under the task delay constraint and the edge cloud capacity constraint;Then, a relationship model between the optimal transmission power and the amount of offloading data is established, which simplifies the user's optimal transmission power offloading decision;Finally, an OSPC algorithm is studied, which achieves the optimal pricing of edge cloud utility maximization and the optimal offloading strategy for each user to maximize their own utility under this pricing. The simulation results show that the method proposed in this paper not only guarantees the utility of the edge cloud, but also maximizes the utility of users, and the algorithm has good convergence and scalability.

关键词： service-oriented production   cyber-physical systems   mobile edge computing   network performance   real time  

摘要： Symmetry is one of the most important notions in the digital twins-driven manufacturing cyber-physical system (CPS). Real-time acquisition of production data and rapid response to changes in the external environment are the keys to ensuring the symmetry of the CPS. In the service-oriented production process, in order to solve the problem of the service response delay of the production nodes in a smart job shop, a CPS based on mobile edge computing (MEC) middleware is proposed. First, the CPS and MEC for a service-oriented production process are analyzed. Secondly, based on MEC middleware, a CPS architecture model of a smart job shop is established. Then, the implementation of MEC middleware and application layer function modules are introduced in detail. By designing an MEC middleware model and embedding function modules such as data cache management, redundant data filtering, and data preprocessing, the ability of data processing is sunk from the data center to the data source. Based on that, the network performances, such as network bandwidth, packet loss rate, and delay, are improved. Finally, an experiment platform of the smart job shop is used to verify different data processing modes by comparing the network performance data such as bandwidth, packet loss rate, and response delay.

关键词： 5G communications   Massive MIMO   Beam-forming   mmWave communications   Mobile edge computing   Small cell stations   NOMA   network slicing  

摘要： The fifth-generation (5G) of mobile communications networks is emerging as a revolutionary technology that will accelerate the development of smart cities and the realization of the information society. This paper aims to provide an introduction to 5G for non-specialists, and a survey of this new technology for those already familiar with mobile communications, covering the conceptualization and the core technologies underpinning 5G networks. The paper also discusses the status of the commercial roll-out of 5G until 2020 from a worldwide perspective and gives a future view of mobile communications beyond 5G.

关键词： Task analysis   Processor scheduling   Dynamic scheduling   Servers   Heuristic algorithms   Energy consumption   Optimization   Mobile edge computing   computation offloading and scheduling   parallel transmission and execution   interdependent tasks  

摘要： Mobile edge computing (MEC) has recently emerged as an effective paradigm to enhance the computing capability of capability-limited mobile devices (MDs). In this article, we consider an MEC system consisting of a number of MEC servers and one MD which generates a series of tasks characterized by their dependency relationships. We study computation scheduling and offloading problem of the tasks. To improve the task processing performance, we first propose a parallel transmission and execution (PTE) scheme, based on which we design a computation scheduling and offloading algorithm. Considering the fairness among tasks in terms of task transmission and execution time, we formulate the computation scheduling and offloading problem as a constrained worst-case latency optimization problem which minimizes the maximum completion time of all the tasks. As the original optimization problem cannot be solved conveniently, we first categorize the tasks into high priority tasks (HPTs), medium priority tasks (MPTs) and low priority tasks (LPTs) based on their task execution status and causal relationship. Then, a dynamic priority-based computation scheduling and offloading algorithm is proposed, which designs computation scheduling and offloading strategy for dynamically-changed HPTs and MPTs, respectively. In particular, for HPTs, a multiple knapsack-based heuristic algorithm is proposed, and a task weight and data size-based computation scheduling and offloading algorithm is further proposed for MPTs. Numerical results demonstrate the effectiveness of the proposed scheme.

关键词： Task analysis   Energy harvesting   Resource management   Batteries   Optimization   Wireless communication   Servers   Internet of Things   mobile edge computing   energy harvesting   game theory   perturbed Lyapunov optimization  

摘要： With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.