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Due to the rapid development of 5G and Internet-of-Things (IoT), various emerging applications have been catalyzed, ranging from face recognition, virtual reality to autonomous driving, demanding ubiquitous computation services beyond the capacity of mobile users (MUs). Mobile cloud computing (MCC) enables MUs to offload their tasks to the remote central cloud with substantial computation and storage, at the expense of long propagation latency. To solve the latency issue, mobile edge computing (MEC) pushes its servers to the edge of the network much closer to the MUs. It jointly considers the communication and computation to optimize network performance by satisfying quality-of-service (QoS) and quality-of-experience (QoE) requirements. However, MEC usually faces a complex combinatorial optimization problem with the complexity of exponential scale. Moreover, many important parameters might be unknown a-priori due to the dynamic nature of the offloading environment and network topology. In this paper, to deal with the above issues, we introduce bandit learning (BL), which enables each agent (MU/server) to make a sequential selection from a set of arms (servers/MUs) and then receive some numerical rewards. BL brings extra benefits to the joint consideration of offloading decision and resource allocation in MEC, including the matched mechanism, situation awareness through learning, and adaptability. We present a brief tutorial on BL of different variations, covering the mathematical formulations and corresponding solutions. Furthermore, we provide several applications of BL in MEC, including system models, problem formulations, proposed algorithms and simulation results. At last, we introduce several challenges and directions in the future research of BL in 5G MEC.

AbstractSixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on‐demand edge cloud services in three‐dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low‐orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space‐borne MEC, enabling intelligent, personalized, and distributed on‐demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non‐terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence‐based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non‐terrestrial nodes.

Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on‐demand edge cloud services in three‐dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low‐orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space‐borne MEC, enabling intelligent, personalized, and distributed on‐demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non‐terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence‐based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non‐terrestrial nodes. [ABSTRACT FROM AUTHOR]

The present study has examined mobile edge computing global research output, as indexed in Scopus database during 2001-18 on a series of Bibliometrics measures, such as publications growth rate, global publications share, citation impact, the share of international collaborative papers, distribution of publications by broad subjects. In addition, the study discusses the citation profile of top organizations and authors in fog computing, and the preferred media for research communication and characteristics of highly cited papers. The global research output (2716 papers) registered 42.48% annual growth rate and averaged to 6.77 citations impact per paper in the subject during the period. 80 countries participated in mobile edge computing research, of which the top 10 countries accounted for 89.51% global publication share and more than 100% of global citation share during 2001-18. China and USA tops the list of top 10 most productive countries in mobile edge computing research with 26.69% and 20.840% global publication share, followed by U.K. (7.29% share), Italy and Canada (6.44% and 6.11%), France (5.01%) and other 4 countries (from 3.94% to 4.86%) during 2001-18. USA tops the list (15.0 and 2.22), followed by Italy (10.73 and 1.59), Canada (9.59 and 1.42), Germany (8.89 and 1.31), U.K. (7.86 and 1.16), etc. in terms of citation impact per paper and relative citation index. 519 organizations and 561 authors participated in global mobile edge computing research, of which the top 15 organizations and authors contributed 21.80% and 8.21% global publication share and 22.54% and 9.71% global citations share during 2001-18. The world contributed 49.47% share of output in mobile edge computing research in top 15 most productive journals, and 28 of its papers have been rated as highly cited papers each with 101 to 844 citations per paper, averaging 222.61 citations per paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of computation offloading in a high-mobility Internet of Vehicles (IoVs) environment. The goal is to address the challenges related to latency, energy consumption, and payment cost requirements. The approach considers both moving and parked vehicles as fog nodes, which can assist in offloading computational tasks. However, as the number of vehicles increases, the action space for each agent grows exponentially, posing a challenge for decentralised decision-making. The dynamic nature of vehicular mobility further complicates the network dynamics, requiring joint cooperative behaviour from the learning agents to achieve convergence. The traditional deep reinforcement learning (DRL) approach for offloading in IoVs treats each agent as an independent learner. It ignores the actions of other agents during the training process. This paper utilises a cooperative three-layer decentralised architecture called Vehicle-Assisted Multi-Access Edge Computing (VMEC) to overcome this limitation. The VMEC network consists of three layers: the fog, cloudlet, and cloud layers. In the fog layer, vehicles within associated Roadside Units (RSUs) and neighbouring RSUs participate as fog nodes. The middle layer comprises Mobile Edge Computing (MEC) servers, while the top layer represents the cloud infrastructure. To address the dynamic task offloading problem in VMEC, the paper proposes using a Decentralized Framework of Task and Computational Offloading (DFTCO), which utilises the strength of MADRL and NOMA techniques. This approach considers multiple agents making offloading decisions simultaneously and aims to find the optimal matching between tasks and available resources. [ABSTRACT FROM AUTHOR]

Learning-driven ubiquitous mobile edge computing: Network management challenges for future generation Internet of Things Keywords: artificial intelligence; Internet of Things; machine learning; mobile edge computing; network Management EN artificial intelligence Internet of Things machine learning mobile edge computing network Management 1 4 4 09/13/23 20230901 NES 230901 Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. Fifth, I Jafar et al. i proposes "A blockchain-enabled security management framework for mobile edge computing", which guarantees secure data storage and includes blockchain features like immutability and traceability. [Extracted from the article]

In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) and Internet technology, the product of the combination of the two, the Industrial Internet, has also received extensive attention and there are more and more research achievements related to the Industrial Internet. In the industrial Internet system, the communication network system composed of sensors, communication nodes, controllers and other intelligent devices can realize efficient and convenient data interaction between people and machines, providing an important infrastructure and technical support for industrial big data analysis and intelligent production. However, in the current industrial Internet system, industrial equipment users generally have the problem of low computing energy efficiency, and the collected industrial data has a high-security risk in the transmission, processing and other processes. At the same time, the size and scale of the industrial Internet equipment group is huge, and the lack of rational resource allocation leads to excessive waste of computing resources in the system, which is also a prominent problem of the current industrial Internet system. In response to the above questions, this paper, on the basis of reading a large number of documents, integrates the improved DRL algorithm, End-Edge-Cloud architecture and blockchain to form a new industrial Internet architecture. The architecture realizes computing offload through the three-tier structure of terminal layer, edge layer and cloud layer, and guarantees the security of industrial data through the decentralized feature of blockchain, ultimately achieving the goal of reducing energy consumption, computing overhead and trusted computing. In the architecture proposed in this paper, the dynamic unloading of industrial data and computing tasks is achieved through a three-tier architecture. The MDP is used to build an optimization problem model, and the improved DRL algorithm is used to iteratively solve the optimal computing resource scheduling strategy. The main research contents of this paper include (1) Using MDP to model optimization problems; (2) Propose an industrial Internet system architecture that integrates and improves DRL, "end edge cloud" and blockchain; (3) The MDP problem is solved iteratively based on deep reinforcement learning. The simulation results show that the proposed architecture has more advantages than the existing six architectures in terms of computing cost, equipment energy consumption and total working time. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising approach. Dynamic service migration is a key technology in MEC. In order to maintain the continuity of services in a dynamic environment, mobile users need to migrate tasks between multiple servers in real time. Due to the uncertainty of movement, frequent migration will increase delays and costs and non-migration will lead to service interruption. Therefore, it is very challenging to design an optimal migration strategy. In this paper, we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration cost. In order to optimize the service delay and migration cost, we propose an adaptive weight deep deterministic policy gradient (AWDDPG) algorithm. And distributed execution and centralized training are adopted to solve the high-dimensional problem. Experiments show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms. [ABSTRACT FROM AUTHOR]

With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper. [ABSTRACT FROM AUTHOR]

Online detection devices, powered by artificial intelligence technologies, enable the comprehensive and continuous detection of high-speed railways (HSRs). However, the computation-intensive and latency-sensitive nature of these detection tasks often exceeds local processing capabilities. Mobile Edge Computing (MEC) emerges as a key solution in the railway Internet of Things (IoT) scenario to address these challenges. Nevertheless, the rapidly varying channel conditions in HSR scenarios pose significant challenges for efficient resource allocation. In this paper, a computation offloading system model for detection tasks in the railway IoT scenario is proposed. This system includes direct and relay transmission models, incorporating Non-Orthogonal Multiple Access (NOMA) technology. This paper focuses on the offloading strategy for subcarrier assignment, mode selection, relay power allocation, and computing resource management within this system to minimize the average delay ratio (the ratio of delay to the maximum tolerable delay). However, this optimization problem is a complex Mixed-Integer Non-Linear Programming (MINLP) problem. To address this, we present a low-complexity subcarrier allocation algorithm to reduce the dimensionality of decision-making actions. Furthermore, we propose an improved Deep Deterministic Policy Gradient (DDPG) algorithm that represents discrete variables using selection probabilities to handle the hybrid action space problem. Our results indicate that the proposed system model adapts well to the offloading issues of detection tasks in HSR scenarios, and the improved DDPG algorithm efficiently identifies optimal computation offloading strategies. [ABSTRACT FROM AUTHOR]

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme's comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively. [ABSTRACT FROM AUTHOR]

Users andedge servers arenot fullymutuallytrusted inmobile edge computing(MEC), andhenceblockchaincanbe introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in bothMEC and blockchain, processing users' tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users' offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the MEC is crucial for the processing delay of blockchain-based MEC. Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system. In this paper, we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aim to minimize the total system processing delay. For the problem, we propose a computing resource Allocation algorithm for Blockchain-based MEC (ABM) which utilizes the Slater's condition, Karush-Kuhn-Tucker (KKT) conditions, partial derivatives of the Lagrangian function and subgradient projectionmethod to obtain the solution. Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC. [ABSTRACT FROM AUTHOR]