Mobile Edge Computing Architecture Research Articles

Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. Firstly, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

Task offloading and trajectory scheduling for UAV-enabled MEC networks: An MADRL algorithm with prioritized experience replay

As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost.

A3C-Based and Dependency-Aware Computation Offloading and Service Caching in Digital Twin Edge Networks

The combination of Mobile Edge Computing (MEC) and Digital Twin (DT) is anticipated to enhance the quality of mobile application services in the 6G era. However, current research often overlooks service caching and task dependency, which may deteriorate system performance. Moreover, Edge Servers (ESs) have limited computing resources and caching capacities, which require collaboration to meet user demands. To address these challenges, we propose a DT-empowered MEC architecture that supports Mobile Users (MUs) offloading dependency-aware tasks, while considering service caching and edge collaboration. The objective is to jointly optimize computation offloading and resource allocation to minimize the system’s energy consumption. Hence, this problem can be formulated as a Mixed Integer Non-linear Programming (MINLP) problem and addressed by utilizing the Asynchronous Advantage Actor-Critic (A3C)-based method. Extensive simulation results demonstrate that our approach outperforms other benchmark algorithms under various scenarios, significantly reducing energy consumption.

SDN-Assisted Mobile Edge Computing for Collaborative Computation Offloading in Industrial Internet of Things

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

Collaborative computation offloading and resource allocation based on dynamic pricing in mobile edge computing

The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.

Computation Offloading for Partitionable Applications in Dense Networks: An Evolutionary Game Approach

Mobile-edge computing (MEC) is a burgeoning paradigm that performs computation close to the network edge. In MEC, computation offloading is an essential mechanism to alleviate the limitations of user equipment (UEs) by offloading computation-intensive and latency-sensitive tasks to edge servers deployed in base stations (BSs). For the partitionable task model, multi-BS offloading makes the parallel execution of tasks possible, which can further reduce the execution delay. In this article, we consider a MEC architecture in a dense network with multiple BSs and multiple UEs and formulate a computation offloading problem for partitionable tasks with the goal of minimizing the task completion time. We jointly optimize the computation and radio resources considering the heterogeneity of UEs’ geographic locations and task types. Specifically, we construct a task offloading game using the evolutionary game theory (EGT). We apply replicator dynamics to model the offloading decision process and obtain the evolutionary equilibrium, which is proved to be an evolutionary stable strategy. Based on the theoretical analysis, we propose an EGT-based offloading mechanism to achieve a suboptimal solution in the case of limited numbers of subtasks. The extensive evaluation results demonstrate the effectiveness of our proposed mechanism compared with the centralized optimal solution.

Satellite MEC with Federated Learning: Architectures, Technologies and Challenges

Satellite communication has made great progress in recent years since it is characterized by wide information coverage and can support diverse types of users, which beneficially fulfills the demand of beyond 5G communications. Besides, mobile edge computing (MEC) technologies energize the edge devices with computational abilities to deal with the majority of training tasks without having to upload to the cloud server, which substantially enhances a system's efficiency. In satellite MEC, the raw data of edge users vested in different owners cannot be allowed to be shared, considering data privacy requirements. To address this, federated learning (FL) architecture can be applied to satellite MEC where only parameters and model updates can be transmitted, which avoids the interaction of raw data from diverse sources. In this article, we construct a FL-based satellite MEC architecture, followed by introducing its key techniques in the aspects of resource management and multi-modal data fusion. Furthermore, we study the data privacy and security protection on the FL-aided satellite MEC relying on a blockchain framework. Finally, we portray the challenges of FL-aided satellite MEC systems.

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

Resource Allocation on Blockchain Enabled Mobile Edge Computing System

Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.

MEC Computation Offloading-Based Learning Strategy in Ultra-Dense Networks

Mobile edge computing (MEC) has the potential to realize intensive applications in 5G networks. Through migrating intensive tasks to edge servers, MEC can expand the computing power of wireless networks. Fifth generation networks need to meet service requirements, such as wide coverage, high capacity, low latency and low power consumption. Therefore, the network architecture of MEC combined with ultra-dense networks (UDNs) will become a typical model in the future. This paper designs a MEC architecture in a UDN, which is our research background. First, the system model is established in the UDN, and the optimization problems is proposed. Second, the action classification (AC) algorithm is utilized to filter the effective action in Q-learning. Then, the optimal computation offloading strategy and resource allocation scheme are obtained using a deep reinforcement learning-based AC algorithm, which is known as the DQN-AC algorithm. Finally, the simulation experiments show that the proposed DQN-AC algorithm can effectively reduce the system weighted cost compared with the full local computation algorithm, full offloading computation algorithm and Q-learning algorithm.

Double-Layer Mobile Edge Computing-Enabled Power Line Inspection in Smart Grid Networks

Unmanned aerial vehicles (UAVs) have the advantages of flexibility, low cost, and good communication channel gain. In recent years, the inspection of power grid systems supported by UAVs has been widely used. However, due to the constraint of wireless channels and UAVs, it is impossible to transmit video content to the surveillance center. To solve this problem, in this study, a new mobile edge computing (MEC) technology-enabled power line inspection scheme for smart grid networks was designed. Within this scheme, a double-layer MEC architecture is proposed. In the upper-level layer, several UAVs installed with MEC equipment can locally process inspection videos with higher propriety. At the same time, the remaining tasks with lower priority are performed by the terrestrial base stations. In addition, a cost minimization problem is proposed and solved by the alternating optimization algorithm. The simulation results show that the proposed algorithm can significantly reduce the energy consumption of the system.

Deep Reinforcement Learning-Based Dynamic Offloading Management in UAV-Assisted MEC System

Unmanned aerial vehicles (UAVs) have been envisioned as a promising technique to provide relaying and mobile edge computing (MEC) services for ground user equipment (UE). In this paper, we propose a UAV-assisted MEC architecture in dynamic environment, where a UAV flies with a fixed trajectory and may act as a MEC server to process the tasks offloaded from the UE or act as a relay to help the UE to offload their tasks to the ground base station (BS). The objective of this work is to maximize the long-term number of completed tasks of the UE. An optimization problem is formulated to optimize the task offloading decisions of the UE. Considering the random demands of the UE, a deep reinforcement learning- (DRL-) based algorithm is proposed to solve the formulated nonconvex optimization problem. Simulation results verify the effectiveness and correctness of the proposed algorithm.

Federated Deep Reinforcement Learning for Online Task Offloading and Resource Allocation in WPC-MEC Networks

Mobile edge computing (MEC) is considered a more effective technological solution for developing the Internet of Things (IoT) by providing cloud-like capabilities for mobile users. This article combines wireless powered communication (WPC) technology with an MEC network, where a base station (BS) can transfer wireless energy to edge users (EUs) and execute computation-intensive tasks through task offloading. Traditional numerical optimization methods are time-consuming approaches for solving this problem in time-varying wireless channels, and centralized deep reinforcement learning (DRL) is not stable in large-scale dynamic IoT networks. Therefore, we propose a federated DRL-based online task offloading and resource allocation (FDOR) algorithm. In this algorithm, DRL is executed in EUs, and federated learning (FL) uses the distributed architecture of MEC to aggregate and update the parameters. To further solve the problem of the non-IID data of mobile EUs, we devise an adaptive method that automatically adjusts the FDOR algorithm’s learning rate. Simulation results demonstrate that the proposed FDOR algorithm is superior to the traditional numerical optimization method and the existing DRL algorithm in four aspects: convergence speed, execution delay, overall calculation rate and stability in large-scale and dynamic IoT.

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and non-collaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers’ workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ES_{s} $ </tex-math></inline-formula> . We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost.

Literature Review and Novel Trends of Mobile Edge Computing for 5G and Beyond

Because of the rapid evolution of communications technologies, such as the Internet of Things (IoT) and fifth generation (5G) systems and beyond, the latest developments have seen a fundamental change in mobile computing. Mobile computing is moved from central mobile cloud computing to mobile edge computing (MEC). Therefore, MEC is considered an essential technology for 5G technology and beyond. The MEC technology permits user equipment (UEs) to execute numerous high-computational operations by creating computing capabilities at the edge networks and inside access networks. Consequently, in this paper, we extensively address the role of MEC in 5G networks and beyond. Accordingly, we first investigate the MEC architecture, the characteristics of edge computing, and the MEC challenges. Then, the paper discusses the MEC use cases and service scenarios. Further, computations offloading is explored. Lastly, we propose upcoming research difficulties in incorporating MEC with the 5G system and beyond.

UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.

Fairness-Aware Task Scheduling and Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV&#x2019;s energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) trajectory optimization <inline-formula> <tex-math notation="LaTeX">$\mathbf {P_{T}}$ </tex-math></inline-formula>; 2) task decision and bits scheduling <inline-formula> <tex-math notation="LaTeX">$\mathbf {P_{S}}$ </tex-math></inline-formula>; and 3) resource allocation <inline-formula> <tex-math notation="LaTeX">$\mathbf {P_{R}}$ </tex-math></inline-formula>. Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty method-based algorithm to reduce computation complexity when the branch-and-bound (B&#x0026;B) algorithm incurs a high complexity to solve <inline-formula> <tex-math notation="LaTeX">$\mathbf {P_{S}}$ </tex-math></inline-formula>. Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7&#x0025; &#x2013; 54.6&#x0025; and 78.9&#x0025; &#x2013; 91.9&#x0025; energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88&#x0025; running time and achieves relatively satisfactory performance compared with B&#x0026;B.

A survey of mobile edge computing in developing countries: challenges and prospects

Mobile Edge Computing (MEC) is a technology for mobile services. MEC integrates the cloud computing into the mobile environment and overcomes obstacles related to distance and the performance such as energy efficiency, storage, bandwidth, scalability and privacy in mobile computing. With the growing adoption of 5G technology, MEC offers a lot of technological and economic advantages to the developing economies amidst challenges associated with its implementation. This paper gives a survey of MEC, which helps every reader to have an overview of the concept and architecture of mobile edge computing. The challenges and prospects of its implementation in developing economies and the future research directions of MEC are presented.

Converged Reconfigurable Intelligent Surface and Mobile Edge Computing for Space Information Networks

Space information networks (SINs) are facing an ever increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SINs is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SINs and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area.

Optimizing the Operation Cost for UAV-Aided Mobile Edge Computing

Mobile edge computing (MEC) is leveraged to reduce the latency for the computation-intensive and latency-critical tasks offloaded from wireless devices and Internet of Things Devices (IoTDs). Unmanned aerial vehicles (UAVs) have attracted much attention from both academia and industry attributed to high mobility, high flexibility, and high maneuverability of UAVs. In this article, a novel UAV-assisted MEC architecture is proposed to provision services to IoTDs, where a UAV provides both communication and computing services or works as a relay node. We then formulate the joint computation offloading, spectrum resource allocation, computation resource allocation, and UAV placement (Joint-CAP) problem in the UAV-MEC network to minimize the operation cost of provisioning IoTDs. Since the Joint-CAP problem is a mixed integer non-linear programming problem and NP-hard, we decompose it into two sub-problems and solve the sub-problems sequentially. Then, we propose a (1+ε)-approximation algorithm, named AA-CAP, to solve the Joint-CAP problem, and the performance of the AA-CAP algorithm is demonstrated to be superior to the baseline algorithms via simulations.