关键词： federated learning   optimization   mobile edge computing  

摘要： Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges;these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction;however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.

关键词： Mobile edge computing   Trust evaluation system   Reputation data   Credibility  

摘要： In recent days, Mobile Edge Computing (MEC) emerges as a new computing paradigm that pushes computing resources towards the edge of the Internet and close to end users and more scholars begin to carry out their different kinds of research under the framework of edge computing. Unlike cloud computing however, edge computing often lacks a centralized security mechanism, which increases the security risks of resource consumers. Therefore, to address security issues for facilitating MEC well, we focus on the establishment of an effective trust evaluation system in this paper. We presented a Reputation-based Trust Evaluation and Management (RTEM) system for a secure and efficient MEC network, wherein the edge servers are adopted to execute reputation data management for mobile edge nodes. In this system, a three-tier trust evaluation framework (identity trust, capability trust, behavior trust) was proposed to ensure the edge nodes that join the network system for service interaction are qualified, capable, and reliable. In order to update and calculate reputation data more accurately, we designed a reputation-based trust evaluation model and evaluated the effectiveness and robustness of our designed model on the OMNeT++ platform. The results show that our proposed mechanism can increase the transaction success rate of the edge network by more than two times compared with the network without trust evaluation mechanism, and perform better than the traditional subject logic (TSL) trust evalaution mechanism.

关键词： mobile edge computing   multi-cloud multi-edge cloud   formal concept analysis   skyline  

摘要： The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service-provider lattice and provider-cloud lattice, and select the candidate cloud composition that satisfies the user's requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.

关键词： credit scheme   geo data sharing   location-based service   mobile edge computing   proximity service  

摘要： This paper proposed a mobile edge computing (MEC)-based point of interests (POIs) downloading and proximate sharing system for a group of people, who belong to the same mobile social networks in proximity (MSN-P) and whose handheld devices are connected in a tree-like structure. Using the proposed system, the root handheld device plays the role of an MEC server and is charge of downloading POIs' contents from the remote cloud server using 4G/5G cellular network and then forwarding POIs' contents to other group members' handheld devices using device-to-device (D2D) communication. Since the number of connected handheld devices in each handheld device is limited using D2D communication, this work proposed a control scheme called k-Connection-Limited and n-Hop (kCL-nH) tree topology's construction scheme to achieve the proximate sharing of POIs' contents. Additionally, since the root handheld device consumes more battery power than others, this work proposed a control scheme that has each group member's handheld device to be the root handheld device alternatively. A credit scheme was thus proposed to denote each handheld device's contribution, which is referred for the selecting of the next root handheld device, to achieve the fairness concern. Then, the control scheme, which is executed in the corresponding MEC server, considering both remaining battery power and credit for the switching of the root handheld device was designed. The proposed method was developed using Wi-Fi Direct over the iOS system. Experimental results have shown some interested results among power consumption, downloaded data volume and fairness among all handheld devices.

关键词： Deep learning   Multitask parallel processing architecture   Mobile edge computing   Network model   Loading technology  

摘要： As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5-4, while the time consumed by the traditional algorithm is 4.5-8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.

关键词： Internet of Things (IoT)   Mobile edge computing   Task scheduling   Resource management   Revenue-optimal  

摘要： With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

关键词： game theory   mobile edge computing   offloading   systematic review  

摘要： In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.

关键词： learning (artificial intelligence)   resource allocation   probability   5G mobile communication   quality of service   Internet   cloud computing   promising computing paradigm   mobile edge computing   MEC   hierarchical IoV system   service-enabled resource allocation   account different delay tolerances   different task types   QoS-enabled resource allocation algorithm   mobile sensing   wireless communication   traditional centralised cloud-based IoV network   high-mobility   low-latency services  

摘要： Along with the development of 5G technology in mobile sensing and wireless communication, the internet of vehicles (IoV) has drawn much attention from the research community. The traditional centralised cloud-based IoV network has become a bottleneck in providing computation-intensive, high-mobility and low-latency services. As a promising computing paradigm, mobile edge computing (MEC) addresses such challenges. In this study, the authors propose a hierarchical IoV system, combined with MEC. They then focus on the problem of quality of service (QoS)-enabled resource allocation for computing tasks in the system. However, the existing studies often fail to take into account different delay tolerances between different task types. In order to optimise the completion delay, they design an approach to classify tasks into different priorities according to their delay tolerances and then reorder tasks. After reordering, they use a reinforcement learning algorithm to allocate resources automatically and intelligently. Simulation results confirm that the proposed scheme is feasible and effective in the aspects of time efficiency and outage probability.

关键词： Mobile edge computing  

摘要： Edge offloading, including offloading to edge base stations (BS) via cellular links and to idle mobile users (MUs) via device-to-device (D2D) links, has played a vital role in achieving ultra-low latency characteristics in 5G wireless networks. This paper studies an offloading method of parallel communication and computation to minimize the delay in multi-user systems. Three different scenarios are explored, i.e., full offloading, partial offloading, and D2D-enabled partial offloading. In the full offloading scenario, we find a serving order for the MUs. Then, we jointly optimize the serving order and task segment in the partial offloading scenario. For the D2D-enabled partial offloading scenario, we decompose the problem into two subproblems and then find the optimal solution based on the results of the two subproblems. Finally, the simulation results demonstrate that the offloading method of parallel communication and computing can significantly reduce the system delay, and the D2D-enabled partial offloading can further reduce the latency. © 2020, CC BY.

关键词： Mobile edge computing  

摘要： With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.