关键词： Mobile edge computing   Federated learning   Energy-efficient  

摘要： With the booming development of fifth-generation network technology and Internet of Things,the number of end-user devices(EDs)and diverse applications is surging,resulting in massive data generated at the edge of *** process these data eficiently,the innovative mobile edge computing(MEC)framework has emerged to guarantee low latency and enable eficient computing close to the user ***,federated learning(FL)has demonstrated its empirical success in edge computing due to its privacy-preserving ***,it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks,which are the major workloads in ***,EDs are typically powered by batteries with limited capacity,which brings challenges when performing energy-intensive FL *** address these challenges,many strategies have been proposed to save energy in *** the absence of a survey that thoroughly summarizes and classifies these strategies,in this paper,we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in ***,we first introduce the system model and energy consumption models in FL,in terms of computation and *** we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives:learning-based,resource allocation,and client *** conduct a detailed analysis of these strategies,comparing their advantages and ***,we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental ***,several potential future research directions for energy-efficient FL are discussed.

关键词： Mobile edge computing  

摘要： With the rapid advancements in the Internet of Things (IoT), edge computing has a significant role in many eHealth applications. However, security and data privacy are major challenges due to the widespread popularity of the digital health domain. The network and data storage should withstand adversarial entities and allow access to only legitimate users. Medical users and servers must be registered with a trusted third-party authority to obtain permissions to authenticate remaining users. Millions of smart medical devices connect online to collect critical patient information, analyze reports, and perform meaningful decisions without human interaction. In this scenario, standard security is essential to safeguard eHealth applications. This research provides a secured, lightweight mobile edge computing framework to address these issues. The empirical results show that our framework mitigates computational overheads. The informal and formal analysis shows that our framework withstands potential attacks. © 2018 IEEE.

关键词： Servers   Mobile applications   Mobile computing   Fans   Wireless communication   Urban areas   Predictive models   Mobile edge computing   predictive service provisioning   spatio-temporal data mining   submodular optimization  

摘要： Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus on statical service provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users' demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, i.e., how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the q-independence system constraint, where q is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a 1/(1+q)approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

关键词： Task analysis   Servers   Energy consumption   Carbon dioxide   Energy efficiency   Wireless communication   Resource management   Carbon emission   energy efficiency   intelligent reflective surface   mobile edge computing   wireless power transfer  

摘要： A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This article studies the Wireless Power Transfer-based Mobile Edge Computing (WPT-MEC) network system assisted by Intelligent Reflective Surface (IRS) to enhance communication performance while improving the battery life of MD. In order to maximize the Computation Energy Efficiency (CEE) of the system and reduce the carbon footprint of the MEC server, we jointly optimize the CPU frequencies of MDs and MEC server, the transmit power of Power Beacon (PB), the processing time of MEC server, the offloading time and the energy harvesting time of MDs, the local processing time and the offloading power of MD and the phase shift coefficient matrix of Intelligent Reflecting Surface (IRS). Moreover, we transform this joint optimization problem into a fractional programming problem. We then propose the Dinkelbach Iterative Algorithm with Gradient Updates (DIA-GU) to solve this problem effectively. With the help of convex optimization theory, we can obtain closed-form solutions, revealing the correlation between different variables. Compared to other algorithms, the DIA-GU algorithm not only exhibits superior performance in enhancing the system's CEE but also demonstrates significant reductions in carbon emissions.

关键词： Mobile edge computing   Reconfigurable intelligent surface   Task priority   Deep reinforcement learning  

摘要： In this work, we investigate a reconfigurable intelligent surface (RIS) assisted mobile edge computing (MEC) network, where multiple users offload tasks to edge servers (ES) via RIS for accelerating computation. However, in practical MEC networks, computational tasks often exhibit diverse priorities, resulting in varying degrees of computational utility. This variability poses significant challenges to system optimization. In response to this challenge, we propose a deep reinforcement learning (DRL)-based approach to improve the performance of the RIS-assisted MEC network, where the task priority and resource allocations are jointly considered. Specifically, we exploit functions to evaluate the computational benefit of different tasks. Then, we devise a joint resource allocation scheme, which jointly considers RISs' phase shifts, user-RIS allocation, and task offloading, to maximize the system utility. To solve the problem under the complicated environment of fading channels and various task priorities, we employ the DRL approach to obtain effective resource allocation strategies to improve the system performance. Simulations are finally conducted to validate the effectiveness and superiority of the proposed schemes in this work.

关键词： Digital twin placement and migration   Digital twin synchronization   Cost modeling of digital twin placement   Digital twin service cost minimization   Mobile Edge Computing  

摘要： In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behaviour simulations, and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this article, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

关键词： Mobile edge computing   unmanned aerial vehicles   service caching and task offloading   online algorithm   machine learning  

摘要： Unmanned Aerial Vehicles (UAVs) have gained increasing attention by both academic and industrial communities, due to their flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this article, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow time, where the flow time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrate that the proposed algorithms outperform their comparison counterparts, by reducing the flow time no less than 19% on average.

关键词： Resource management   Stochastic processes   Heuristic algorithms   Task analysis   Servers   Optimization   Mathematical models   Mobile edge computing   stochastic resource allocation   multi-task offloading   whale optimization algorithm  

摘要： In this paper, we present a novel coexistence uplink-downlink stochastic mobile edge computing (MEC) system that considers the dynamic characteristics of both small cell base stations (SBSs) and user equipments (UEs). To devise an efficient radio resource management strategy encompassing user association, channel allocation, and power allocation, we formulate an optimization problem that considers time, energy, and achievable rate in the utility function. The formulated problem is a Mixed Integer Nonlinear Program (MINLP) and has been proven to be NP-hard. To address this complexity, we propose a unified nature-inspired optimization framework, which can be deployed for subproblems in various settings and can be integrated with the Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA), and Particle Swarm Optimization (PSO). Through our rigorous mathematical and numerical analysis, the proposed algorithms show that they can converge to a near-optimal solution while keeping negligible optimality gaps. Our numerical results show the advantages and drawbacks of the proposed algorithms, highlighting their potential for effective resource management in MEC systems. The results also show the performance evaluation of stochastic characteristics on the performance of MEC.

关键词： Mobile edge computing   DDoS attacks   container   queue networks   scheduling  

摘要： Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%.

关键词： Task analysis   Servers   Delays   Resource management   Energy consumption   NOMA   Minimization   Mobile edge computing   non-orthogonal multiple access   user association   resource allocation  

摘要： This paper designs a helper-assisted offloading strategy in non-orthogonal multiple access enabled mobile edge computing systems, in order to guarantee the quality of service of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a performance metric called energy-delay tradeoff. Aiming at the maximal energy-delay tradeoff minimization, the joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a non-convex problem with coupled continuous and 0-1 variables. To tackle this challenging problem, we decompose it as a two-level problem. For the inner-level problem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the inner-level problem, we model the outer-level problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the IPCA algorithm can converge to a stationary Karush-Kuhn-Tucker (KKT) point and the FS-URHSM algorithm is guaranteed to converge to a stable matching with polynomial complexity. Simulation results demonstrate the superior performance of proposed algorithms in terms of the energy consumption and the delay.