关键词： Mobile edge computing  

摘要： In recent years, mobile clients’ computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients’ resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system’s energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing   Vehicular networks   Task offloading   Performance optimization  

摘要： Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

关键词： Mobile edge computing  

摘要： In this paper, we study a mobile edge computing (MEC) system in which the mobile device is assisted by a base station (BS) and a cooperative node. The mobile device has sequential tasks to complete, whereas the cooperative node assists the mobile device on both task offloading and task computation. In specific, two cases are investigated, which are 1) the cooperative node has no tasks to complete itself, and 2) the cooperative node has tasks to complete itself. Our target is to minimize the total energy consumption of the mobile device and the cooperative node through optimizing the transmit duration in task offloading, CPU frequency in task computing along with the task index to offload in the sequential tasks. In the first case, we decompose the mixed-integer non-convex problem into two levels. In the lower level problem, thanks to the convexity, Karush-Kuhn-Tucker (KKT) conditions are utilized to simplify the problem, which is then solved with bisection search. In the upper level problem, to find solution of the task index to offload, rather than utilizing traversal method, we develop a monotonic condition to simplify the searching process. In the second case, in order to guarantee the successful computation of the mobile device and cooperative node, the uploading transmission is classified into three schemes. Within each scheme, the non-convex problem is decomposed. In the lower level problem, semi-closed solution is found by Lagrangian dual method. In the upper level problem, traversal method is applied to find the optimal offloading index. Copyright © 2021, The Authors. All rights reserved.

关键词： Big Data   Privacy   Recommender systems   Social networking (online)   Context modeling   Prediction algorithms   Differential privacy   Recommender system   differential privacy   online learning   federated Learning   big data   distributed and scalable model   cloud computing   mobile edge computing  

摘要： Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users' preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users' preferences according to the user context and users' history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom. We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.

关键词： Blockchain   Servers   Computational modeling   Games   Stochastic processes   Mobile computing   Edge computing   Blockchain   deep learning   game theory   incomplete information   Markov decision process   mining   mobile edge computing   partially-observable Markov decision process   reinforcement learning   resource management  

摘要： We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.

关键词： utility maximization   lyapunov optimization   task offloading   mobile edge computing  

摘要： In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and *** the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation ***,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high ***,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,***,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

关键词： Mobile edge computing   SDN   Edge caching   Service migration   Deep Q learning  

摘要： In recent years, with the rapid growth in the demand for online streaming services, video streaming platforms are becoming more and more popular, and users' demand for low latency and high-quality services is increasing. Therefore, in order to be able to allocate caching resources reasonably to serve as many user requests as possible, and reduce latency and energy consumption, the edge cooperative caching method based on delay and energy consumption balance in SDN based mobile edge computing is proposed. In the proposed caching method, firstly, the multilayer perceptron neural network is used to predict the video content requested by the mobile user. Secondly, an objective function to minimize delay and energy consumption is established, and an edge cache optimization model is constructed. Finally, the branch-and-bound algorithm is used to obtain the optimal edge cache strategy. Meanwhile, to provide users with seamless service migration to ensure service continuity and high-quality services, the dynamic service migration method based on deep Q learning is proposed. In the proposed service migration method, firstly, the service migration problem is expressed as a Markov decision process. Secondly, the service migration process is analyzed and a service migration reward function is constructed. Finally, deep Q learning is used to obtain the optimal service migration strategy. In the experiment, the proposed edge caching algorithm can effectively improve the cache hit rate, reduce the backhaul traffic load, and control the average access delay and energy cost. Moreover, the proposed service migration algorithm can effectively reduce the number of service migrations and transmission cost, improve the success rate of service migration, and reduce the average traffic consumed by migration.

关键词： Internet of Things   Edge computing   Authentication   Access control   Mobile handsets   Control systems   Computer architecture   Internet of things   mobile edge computing   multi-access authentication   network anomaly detection   software-defined networking  

摘要： Due to the blooming of Internet of Things (IoT), heterogeneous IoT mobile devices emerge to connect the network infrastructure. Traditional mobile access system faces several challenges arising from these IoT devices: 1) centralized controllers are distant from the end devices, 2) inefficient access control of heterogeneous IoT devices, and 3) insufficient authentication and monitoring for IoT devices. In order to tackle the challenges from IoT devices on mobile access control and scalable access monitoring, we present LEDGE, an agile and secured software-defined edge computing system for resilient access management of mobile IoT. In a nutshell, our LEDGE is a synergy of an efficient location authentication method to secure communication between each IoT mobile device and access point (AP) pair, an optimal AP assignment scheme to satisfy IoT flow requests, a Personal AP protocol for scalable access, and a deep learning model for anomaly detection. We prototype our system, and realistic testbed experiments demonstrate that LEDGE could achieve promising results in mobile IoT.

关键词： Cloud computing   Delays   Image edge detection   Base stations   Edge computing   Heuristic algorithms   Microservice   mobile edge computing   coordination   delay   migration  

摘要： As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optima

关键词： Steganography   Mobile Edge Computing   Covert channel   HTTP protocol   D2D communications  

摘要： Recently, due to unexpected conditions introduced by the COVID-19 outbreak, collaborative tools are widely adopted in almost all sectors of our daily lifestyle. Almost all tools rely mainly on the World Wide Web technologies that, in turn, are built upon the HTTP protocol. The HTTP protocol is considered as the "bricks" of all kind of communications among people/devices that exchange messages with different purposes and meanings. Unfortunately, it is widely used to track and monitor people when using the Internet. This paper exploits the HTTP protocol and try to reverse this negative aspect by designing and implementing a way to help users (and devices) to not disclose too much information when collaborating each other even in an unfriendly environment. A novel steganographic protocol is proposed by using the HTTP "control" messages. The proposed protocol can be easily adopted by devices communicating in a MEC (Mobile Edge Computing) environment where it is important to guarantee the integrity and the confidentiality of all communications, especially messages that give "instructions" to devices or in device-to-device communications. The proposed protocol allows to avoid using complex and computationally demanding cryptographic protocols that are very difficult to be used in devices with limited resources.