Mobile Edge Computing Server Research Articles

Optimizing Task Completion Time in Disaster-Affected Regions with the WMDDPG-GSA Algorithm for UAV-Assisted MEC Systems

In this paper, we investigate a UAV-assisted Mobile Edge Computing (MEC) system that is deployed with multiple UAVs to provide timely data processing services to disaster-stricken areas. In our model, since base stations are unavailable in disaster-affected areas, we solely employ UAVs as MEC servers, as well as enable real-time data transmission during UAV flights by estimating and compensating for the Doppler Frequency Shift (DFS). Subsequently, an optimization problem is formulated to jointly optimize the trajectories and offloading strategies of multiple UAVs to minimize the task completion time. We enhance the performance of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm by using a weighted strategy algorithm, and we thus propose the Weighted-Strategy-Based Multi-Agent Deep Deterministic Policy Gradient (WMDDPG) algorithm for optimizing UAV trajectories. We employ the Greedy-Based Simulated Annealing (GSA) algorithm to overcome the limitations of the greedy algorithm and to obtain the best offloading strategy. The results demonstrate the effectiveness of the proposed WMDDPG-GSA algorithm, as it outperforms benchmark algorithms.

A game incentive mechanism for energy efficient federated learning in computing power networks

Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

A Comprehensive Physical Layer Security Mechanism for Mobile Edge Computing

Mobile edge computing (MEC) is becoming popular in many civilian applications. However, due to the broadcast nature of wireless connections, traditional MEC is open to eavesdropping attacks that endanger mobile users’ information confidentiality. Friendly jamming (FJ), as one of the physical layer security techniques, can efficiently protect users from such threats by degrading attackers’ wiretap channel capacities. In this paper, we propose an FJ-based physical layer security (PLS) mechanism to safeguard the confidentiality of data uploading in MEC services. We design a comprehensive security scheme that uses FJ from both base station (BS) and nearby mobile users to cover upload transmission. Accordingly, the eavesdropping risk zone (ERZ) is formulated as an area under high secrecy outage probability (SOP). To promote FJ from users, we further introduce Device-to-device (D2D) communication-based FJ, which is inspired by non-orthogonal multiple access (NOMA) techniques. Then, we formulate, simplify, and solve the problem by optimizing the MEC secrecy performance under the required risk, with efficiencies in data uploading, energy consumption, and incentive delivery. Furthermore, we tackle the challenge of efficient incentive design under information asymmetry by introducing Contract Theory and providing differentiated rewards. By simulations, we evaluate the mechanism in terms of secrecy performance and incentive efficiency under information asymmetry, and the results accordingly show the effectiveness of the proposed security mechanism.

Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL

With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed as an effective paradigm by the academic community. Users can offload tasks to MEC servers, greatly reducing the computing latency and energy consumption when using services. However, traditional single access point edge computing networks cannot meet the usage requirements of a large number of users. In addition, the privacy leakage issues arising from the offloading process are also easily overlooked. In this paper, we propose a privacy-preserving offloading scheme based on stochastic game theory considering multiple access points. We first construct a multi-access point offloading framework and quality of service (QoS) model. In terms of privacy, the privacy risks caused by the offloading preferences of different edge nodes are studied, and the privacy entropy is used to evaluate the privacy protection level. We comprehensively consider the energy consumption, latency requirements, user experience, and privacy protection of the system and formulate the problem as a Markov Decision Process (MDP). Finally, a joint optimal DRL algorithm with privacy preservation (JODRL-PP) is proposed to achieve the optimal offloading scheme of the system. Simulation results verify the effectiveness of our model and algorithm.

Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework.

Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and between vehicles and a network intelligence server. Both low and high-level support is needed for such an operation, meaning that various access technologies and other architectural elements also need to incorporate features enabling the effective use of V2X applications with strict requirements. The new 5G core architecture promises even more supporting technologies, like Multi-access Edge Computing (MEC). To test the performance of these technologies, an integrated framework for V2X simulations with 5G network elements is proposed in the form of combining Simu5G, a standalone 5G implementation, with the go-to V2X-simulator, Artery. As a first step toward a fully functional MEC-assisted CP Service, an extension to Simu5G's edge implementation is introduced. The edge application is responsible for dispatching the Collective Perception Messages generated by the vehicles via the 5G connectivity so that a MEC server provided by the network can process incoming data. Simulation results prove the operability of the proposed integrated system and edge computing's potential in assisting V2X scenarios.

Mobile edge computing servers deployment with improved genetic algorithm in cellular Internet of Things

Mobile edge computing (MEC) provides services to devices and reduces latency in cellular internet of things (IoT) networks. However, the challenging problem is how to deploy MEC servers economically and efficiently. This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm (GA) scheme. We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations. We then select base stations (BSs) based on clustering center coordinates as the deployment locations set for potential MEC servers. We further select BSs using a combined simulated annealing (SA) algorithm and GA to minimize the deployment cost. The simulation results show that the improved GA deploys MEC servers effectively. In addition, the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.

Joint Resource Allocation and Trajectory Optimization in UAV-Enabled Wirelessly Powered MEC for Large Area

This paper investigates a wirelessly-powered mobile edge computing (MEC) framework with the cooperation between an unmanned aerial vehicle (UAV) and a center Cloud. In this system, we minimize the waiting delay of user equipments (UEs) under controllable energy consumption for a UAV serving a large area, where the UAV is equipped with a MEC server and an energy transmitter (ET). Thus, it can provide energy and computing services for UEs and transmit intractable tasks to the Cloud for further execution. To make the UAV serve all UEs, we propose the method by using long and short time slots mixed mechanism, and handle the offloading decision and flying area selection by a mixed-strategy with the Stackelberg game architecture during long time slots, while the resource allocation and trajectory optimization issues are settled within short time slots. Specifically, considering limited battery capacity and severe energy propagation loss of the wireless power transfer (WPT), energy consumption can be controlled through queue optimization in short time slots. We decompose the original nonconvex problem into a series of deterministic optimization sub-problems based on the Lyapunov optimization theory. Furthermore, using Lagrangian duality theory, alternative optimization and successive convex approximation (SCA) technique, we can obtain the closed-form solutions to the original problem. Performance analysis and simulation results demonstrate the convergence and the effectiveness of our proposed algorithm.

Joint Terminal Pairing and Multi-Dimensional Resource Allocation for Cooperative Computation in a WP-MEC System

The combination of multi-access edge computing (MEC) and wireless power transfer (WPT) can potentially improve system performance and prolong terminals’ battery lifetime. In this paper, we study cooperative computation in a wireless powered multi-access edge computing (WP-MEC) system. In this system, there are an access point (AP) deployed with an MEC server and multiple cellular terminals that are powered by harvested energy from a power station. The computational task of each terminal is partially completed locally and partially offloaded to the AP and an nearby idle terminal. In order to minimize energy consumption in the system, we jointly optimize terminal pairing and multi-dimensional resource (i.e., computing power and communication resource) allocation. A non-convex mixed-integer non-linear programming (MINLP) problem is formulated in this paper. To solve this challenging problem, we propose a bilevel optimization approach, in which the multi-dimensional resource allocation scheme is designed under given terminal pairing relationships in the inner level, and then the pairing relationships between terminals are obtained in the outer level based on the obtained multi-dimensional resource allocation scheme. Extensive numerical results show that the proposed bilevel optimization scheme can reduce energy consumption considerably compared to some baseline schemes.

Collaborative Satellite-Terrestrial Edge Computing Network for Everyone-Centric Customized Services

The collaborative Satellite-Terrestrial network plays an important role in the future 6G mobile communication network, which can realize the global ubiquitous connection and support everyonecentric customized services. Taking into account the multi-layer heterogeneity of the Satellite-Terrestrial network, Mobile Edge Computing (MEC) technology can provide users with support for various data business computing services. Most of the existing satellite edge computing regards satellites as relay networks and neglects the deployment of MEC servers on satellites. We first propose a collaborative Satellite-Terrestrial edge computing network that deploys edge servers on LEO satellites. Then, we introduce the collaborative Satellite-Terrestrial network architecture and design a central-edge multiagent collaboration model. Furthermore, it integrates SDN and NFV to achieve optimal control for the collaborative network. Finally, extensive simulations show that the proposed strategy that jointly tasks offloading and resource allocation can achieve higher performance with low complexity.

Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

MEC Network Resource Allocation Strategy Based on Improved PSO in 5G Communication Network

Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.

POLSTM: Poplar optimization-based long short term memory model for resource allocation in cloud environment

Due to the evolution of Internet of Things (IoT) paradigm, the number of devices is growing day by day which arises the stringent requirements for various communications. The standard cloud computing model is not capable of efficiently hosting IoT tasks due to the high latency associated with it. Hence, this work utilizes the mobile edge computing and fog concept to process the input tasks independent of the cloud layer. Generally, the high computation costs and energy consumption resulted in the applications such as power-hungry and computation-intensive, which become massive challenges for an IoT device. Motivated by the previous research, we mainly plan to investigate the resource allocation issues that arise in the MEC-enabled IoT–Fog–cloud architecture. We propose a Poplar Optimization algorithm (POA) based on Attention 1DCNN-LSTM architecture (POA-A1DCNN-LSTM) for improving the total utility of the MEC servers by optimizing the energy consumption and task delay. Initially, the POA algorithm is implemented for cluster head (optimal fog node) selection using the node degree, node distance, and residual energy. Next, the A1DCNN-LSTM architecture is employed for task offloading by selecting the fog node with minimal task length and processing delay. The performance of the proposed method is validated by average latency, user satisfaction, network lifetime, energy consumption, average time delay, and normalized system utility. The experimentation results revealed that the proposed method attained better effectiveness in different metrics by achieving 1.5 ms, 0.65, 60 s, 0.45 j, 385 ms, and 0.98 compared to state-of-the-art methods.The experimentation outcomes demonstrate the effectiveness of the proposed POA-A1DCNN-LSTM architecture to decrease task completion delay and offer effective task scheduling as well as resource allocation performances in the MEC-enabled IoT–Fog–cloud network.

Joint optimization of resource allocation and computation offloading based on game coalition in C-V2X

In order to solve the problem of limited resources in the vehicle, mobile edge computing (MEC) is integrated into the cellular-vehicle-to-everything (C-V2X) system to provide users with low-latency services. Considering the resource limitation of computing, storage and communication of MEC servers, it is necessary to improve the system performance through the collaborative optimization of multi-domain resources. Therefore, for applications with double dependence on time and data in C-V2X scenarios, we design a MEC hierarchical resource management framework to jointly optimize system offloading decision, scheduling decision, and caching decision to minimize the system delay. The optimization problem is decomposed into a resource allocation problem within a single MEC server and a load balancing problem among multiple MEC servers. For the former, we present a task scheduling algorithm based on the latest start time and a caching decision algorithm based on dynamic programming to minimize the average task completion delay; for the latter, we present a load balancing algorithm based on the coalition game to minimize the global system delay. Simulation results show that the proposed scheme can significantly improve the performance in terms of application completion delay, application failure rate and resource utilization compared with the benchmark schemes.

Joint computation offloading and resource allocation in space-air-terrestrial integrated networks for IoT Applications

Internet of Things (IoT) devices can reduce their energy consumption by computation offloading. However, IoT devices located in areas without deployed ground communication facilities face significant challenges in computation offloading. For this reason, we propose the space-air-terrestrial integrated networks (SATINs) and design a three-tier computing framework for providing computing services to IoT devices. In the computing framework, device's task can be computed locally, on mobile edge computing (MEC) servers in the air layer, or on cloud servers in the ground. In this article, we jointly optimize the computation offloading decisions of tasks and computing resource allocation of MEC servers. We aim at minimizing the total cost of executing tasks while satisfying both the time constraints of tasks and capacity constraints of MEC servers. And the total cost includes the energy cost of IoT devices and the usage cost of servers. Since the computation offloading decisions are binary variables, the joint optimization problem is a mixed integer nonlinear programming (MINLP) problem and is NP-hard. To tackle this problem, we use relaxation technique and Majorize-minimize (MM) method to transform the optimization problem into a series of convex problems for solving. Moreover, we propose a distributed algorithm based on Lagrange dual decomposition method with low time complexity. Experimental results demonstrate that our proposed distributed algorithm can effectively reduce the total cost of the system compared with other benchmark algorithms.

Performance analysis and optimization of multiple IIoT devices radio frequency energy harvesting NOMA mobile edge computing networks

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium.

Security architecture for authorized anonymous communication in 5G MEC

The use of services provided in modern mobile networks using edge servers (5G MEC) requires extended security solutions. One of them is an access control system adapted to the different needs and requirements of end users and service providers. Another, equally important, is the protection against the takeover of the service during its lifetime, which leads to the inevitable leakage of information and economic losses. In this paper, we present a new access control architecture for the 5G MEC network, which meets both security requirements in a flexible and lightweight way. First, we specify the architecture scheme and its central element, MEC Enabler, which manages access control security policies and generates access credentials (tokens). Next, we describe the method of securing data packets in the communication process related to the service provided by the MEC server, preventing the session from being intercepted by an unauthorized user. The countermeasure is designed to protect data identifying packets (node addresses, port numbers) and prevent identification of packets’ connection with the protected service. Experiments on the operation of the service with the implemented protection mechanism made in a testbed show that the proposed security solution has a negligible effect on the MEC-hosted application delays.

Multi-UAV-assisted computation offloading in DT-based networks: A distributed deep reinforcement learning approach

In the industrial Internet, Mobile Edge Computing (MEC) can provide the ability to transfer a large number of delay-sensitive and compute-intensive tasks to MEC servers, thus improving Quality of Service (QoS). Considering Unmanned Aerial Vehicles (UAVs) have the advantages of wide communication coverage and low deployment cost, UAVs have great potential to be employed as aerial base stations to provide computation resources for Intelligent Mobile Devices (IMDs). Due to the limited computation resources and energy of IMDs, we designed a multi-UAV-assisted MEC system in ground cells. To minimize the weighted sum of task completion delay and energy consumption, and ensure the QoS requirements of IMDs, we jointly consider the dynamic channel state, renewable energy utilization, UAVs trajectory, and tasks offloading ratio. To solve the non-convexity problem of complex high-dimensional states, we propose a model-free Deep Reinforcement Learning (DRL) offloading scheme based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Moreover, we adopt Federated Learning (FL) to train DRL models to enhance the robustness of the model and the security of IMDs data. Meanwhile, the real environment is modeled as Digital Twin (DT) to monitor network changes and train the local DRL model, and the central cloud server can obtain the local model in real-time to aggregate the global model. Extensive experimental numerical results show that the proposed algorithm improves the system energy efficiency and reduces task completion delay.

Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above problems, in this paper, the task offloading problem is formulated as an optimization problem, and Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared to recent advanced strategies, namely, Ant Colony Optimization (ACO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG), Deep Meta Reinforcement Learning-based Offloading (DRMO), Iterative Proximal Algorithm (IPA), and Parallel Random Forest (PRF).

Intelligent Resource Allocation for Edge-Cloud Collaborative Networks: A Hybrid DDPG-D3QN Approach

Mobile edge computing (MEC) has recently emerged as an effective paradigm for computation-intensive and delay-critical applications supported by Internet of Things (IoT) devices. However, the computational resources at MEC servers are normally much smaller compared with the remote cloud server (CS). To handle the ever-increasing IoT devices and applications, the collaborative edge and cloud computing should be jointly exploited. Further, with finite block length (FBL) utilized in URLLC-supported networks, the coding error probability cannot be ignored. On these grounds, this paper investigates the dynamic offloading of FBL packets in an edge-cloud collaborative MEC system consisting of multi-mobile IoT devices (MIDs) with energy harvesting (EH), multi-edge servers, and one CS in a dynamic environment. The optimization problem is formulated to minimize the average long-term service cost defined as the weighted sum of MID energy consumption and service delay (including the uploading transmission delay, the handover cost, and the execution delay for the partial offloaded part, the local execution delay for the local processing part), with the constraints of the available resource, the energy causality, the allowable service delay, and the maximum decoding error probability. To address the problem involving both discrete and continuous variables, we propose a multi-device hybrid decision-based deep reinforcement learning (DRL) solution, named DDPG-D3QN algorithm, where the deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Specifically, we improve the actor-critic structure of DDPG by combining D3QN. It utilizes the actor part of DDPG to search for the optimal offloading rate and power control of local execution. Meanwhile, it combines the critic part of DDPG with D3QN to select the optimal server for offloading. Simulation results demonstrate the proposed DDPG-D3QN algorithm has better stability and faster convergence while achieving higher rewards than the existing DRL-based methods. Furthermore, the edge-cloud collaboration has been proven to attain improved performance when compared with other offloading schemes with no collaboration between edge and cloud.

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.