Mobile Edge Computing Server Research Articles

A Dueling DQN-Based Computational Offloading Method in MEC-Enabled IIoT Network

Abstract Industrial Internet of Things (IIoT) is a promising mechanism of Industry 4.0. Mobile edge computing (MEC) is an emerging mechanism that is an enabler for IoT applications, which applies computation offloading mechanism to offload workload from IoT devices to MEC servers to enhance computing quality. This paper addresses the computation offloading problem under a heterogeneously loaded MEC-enabled IIoT network, which specifically applies one-hop offloading for devices and task queue for devices and servers. We first formulate our computation offloading problem as a Markov Decision Process, then design a dueling deep Q-network-based computation offloading method for better optimizing the value function for specific state and advantage function for specific action. Experimental results verify that our scheme reduces more energy consumption (30%), latency (50%) and task dropped rate (50%) of IIoT devices, compared with other popular methods.

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.

Towards Energy-And Cost-Efficient Sustainable MEC-Assisted Healthcare Systems

To meet the demands of new healthcare applications with high computation complexity, multi-access edge computing (MEC) is emerging as a key component of modern healthcare systems which provides rich computing services to the users. With the increase in the number of wireless body area network (WBAN) users requesting computing services, the computational load on the MEC servers increases. A major issue related to the operation of these MEC servers is their sustainability in terms of energy consumption and heavy carbon emission. Therefore, in this work, we propose a resource management scheme, which minimizes the energy consumption of the MEC server without compromising on the quality-of-experience (QoE) of the WBAN users. For that, we propose a cooperative framework between the MEC server and WBAN users, where the MEC server motivates WBAN users to opt for partial offloading instead of full offloading of the computing services. More specifically, the MEC server bargains with each WBAN user for the amount of the task it compute locally and the corresponding reimbursement. We model this economic interaction between the MEC server and all the participating WBAN users using the Nash bargaining theory. Thereafter, we derive the closed-form Nash bargaining solutions (NBS) for two different bargaining protocols. Finally, numerical results show the proposed bargaining scheme is capable of improving the MEC server payoff by <inline-formula><tex-math notation="LaTeX">$ 44.3\%$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$ 51.4\%$</tex-math></inline-formula> , and <inline-formula><tex-math notation="LaTeX">$ 56.1\%$</tex-math></inline-formula> , respectively, compared to the state-of-the art schemes.

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users’ task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users’ energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users’ activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things (IoT) is accelerating the emergence and growth of new IoT services and applications, which will result in massive amounts of data being generated, transmitted and processed in wireless communication networks. Mobile Edge Computing (MEC) is a desired paradigm to timely process the data from IoT for value maximization. In MEC, a number of computing-capable devices are deployed at the network edge near data sources to support edge computing, such that the long network transmission delay in cloud computing paradigm could be avoided. Since an edge device might not always have sufficient resources to process the massive amount of data, computation offloading is significantly important considering the cooperation among edge devices. However, the dynamic traffic characteristics and heterogeneous computing capabilities of edge devices challenge the offloading. In addition, different scheduling schemes might provide different computation delays to the offloaded tasks. Thus, offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay. This paper seeks to guarantee low delay for computation intensive applications by jointly optimizing the offloading and scheduling in such an MEC system. We propose a Delay-Greedy Computation Offloading (DGCO) algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices. A Reinforcement Learning-based Parallel Scheduling (RLPS) algorithm is further designed to schedule offloaded tasks in the multi-core MEC server. With an offloading delay broadcast mechanism, the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization. Finally, the simulation results show that our proposal can bound the end-to-end delay of various tasks. Even under slightly heavy task load, the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%, while that given by benchmarked algorithms is reduced to intolerable value. The simulation results are demonstrated the effectiveness of DGCO-RLPS for delay guarantee in MEC.

User Association and Resource Allocation for MEC-Enabled IoT Networks

This paper studies a mobile edge computing (MEC) network to support emerging applications for Internet-of-things, where multiple access points, each attached with an MEC server, need to collect data from multiple sensors, process them, and then send computation results to the paired actuators for control. Specifically, we consider a three-phase operation protocol for data uploading, edge computing, and result downloading, where the frequency-division multiple access is implemented to accommodate communications of multiple sensors/actuators. Under this setup, we minimize the end-to-end (E2E) latency of the sensing-communication-computation-actuation loop by properly designing the user association and resource allocation policy, subject to the communication and computation resource constraints. The formulated problem, however, is a mixed-integer non-linear program that is difficult to be solved. Despite this fact, we obtain the optimal solution via using the brute-force search for user association, and convex optimization for resource allocation under given user associations. Next, to reduce the computation complexity from the brute-force search, we propose an alternative algorithm, where the user association and resource allocation are optimized iteratively in an alternating manner, via concave-convex procedure and convex optimization, respectively. Finally, numerical results show that the proposed designs significantly reduce the E2E latency, compared with conventional separate designs.

A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks

Multi-access Edge Computing (MEC) has played an important role in realizing intelligent beyond 5G (B5G) vehicular networks. The computation tasks of intelligent applications can be offloaded to and processed by near-end-user MEC servers to meet strict latency requirements. However, the latency of provided services is dependent on MEC processor scheduling and millimeter wave (mmWave) transmission conditions for the urban B5G vehicular networks. To alleviate the mmWave signal attenuation caused by buildings, Intelligent Reflecting Surface (IRS) has been regarded as efficient and prospective infrastructure. In this article, we study the IRS-aided MEC-served vehicular networks and analyze the relationship between computation resource allocation and offloading policy at an intersection. Considering the vehicle mobility patterns, transmission conditions, and task sizes, we optimize the task scheduling by improving the allocation of limited processors and IRS resource. Moreover, the mutual interference among concurrent transmissions is taken into account. Assuming the moving directions available, a dynamic task scheduling algorithm is proposed which considers both the communications and computations. The simulation results illustrate that our proposal outperforms benchmark methods in terms of task offloading rate, computing rate, and finish rate for the IRS-aided MEC-served vehicular networks.

Binary vs partial offloading in wireless powered mobile edge computing systems with fairness guarantees

Mobile Edge Computing (MEC) has recently emerged as a new communications/computing concept that amends the limited computing of IoT devices by completely or partially offloading the computational tasks to the MEC servers at the network edge (typically co-located with the base stations). Because IoT devices are typically power limited, the potential of the MEC is further enhanced by its integration with wireless power transfer technology, especially for those IoT devices with a high duty cycle that requires frequent battery replacement. This paper develops fairness-aware resource allocation schemes for a WPT-assisted MEC system whose Energy Harvesting Users (EHUs) employ either binary or partial offloading. Specifically, the proposed schemes optimize the computational speeds and the energy harvesting and offloading durations of the EHUs with the aim to maximize the minimum of their computed bits (sum of locally and remotely processed bits of each EHU), subject to the RF energy harvested from the base station. When EHUs are concentrated closer to the base station, remote processing is preferred over local processing, as local processing consumes more energy than the Radio Frequency (RF) power for offloading data to the MEC server, but this effect diminishes for lower values of the computational effort needed for the processing of a single bit. Interestingly, in terms of the sum computation rate, the partial offloading scheme only slightly outperforms the binary offloading scheme, but only when the EHUs are moderately away from the base station.

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers

A Video Cache Replacement Scheme based on Local Video Popularity and Video Size for MEC Servers

Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices

In mobile edge computing (MEC) systems with energy harvesting, edge devices are powered by unstable energy harvested from the environment. To prolong the lifetime of edge devices, some computing tasks should be offloaded to MEC servers. However, computing services offered by MEC servers may be very costly. In this work, we aim to minimize total costs caused by computing services and dropping tasks while avoiding the devices running out energy. With the consideration of the unpredictability of the harvestable energy, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make task execution decisions (i.e., local executing, offloading, or dropping tasks) and develop an online algorithm which could help asymptotically obtain the optimal results for the whole system. The algorithm does not require any knowledge of the harvestable energy and the statistics of task arriving processes and can be easily implemented in a distributed manner. Numerical results corroborate that the proposed algorithm can try its best to push battery energy of edge devices to a preset parameter and effectively reduce the service costs and task drops.

Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach

Making effective use of scarce spectrum resources, along with efficient computational performance, is one of the key challenges for future wireless networks. To tackle this issue, in this paper, we focus on the intelligent dynamic spectrum allocation (DSA) in a mobile edge computing (MEC) enabled cognitive network. And our objective is to optimize the spectrum utilization and load balance among idle channels. Since users can only acquire part of environment information in a decentralized way, we model such a problem as decentralized partially observed Markov decision process (Dec-POMDP) and design the corresponding evaluating metric to encourage users sense and access spectrum properly. Then, we propose a QMIX-based DSA method with centralized training decentralized execution (CTDE) structure to tackle it. In the training phase, the users offload the computational tasks to the MEC server to obtain the optimal distributed DSA strategies, through which the users select the optimal channel locally in the execution phase. Simulation results show that, using the proposed algorithm, users can independently capture spectrum holes, and hence improve the spectrum utilization while balancing the load on available channels.

Efficient Resource Allocation for Security-Aware Task Offloading in MEC System Using DVS

With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.

5GMEC-DP: Differentially private protection of trajectory data based on 5G-based mobile edge computing

Machine learning has become a core technology in areas such as big data, Internet of Things, and cloud computing. Training machine learning models requires a large amount of data, How to protect these data with low cost and high efficiency is an important issue. This paper proposes a trajectory data collection model based on the 5G-based mobile edge computing, and uses the service characteristics of the MEC server to propose a differentially private protection scheme of trajectory data based on 5G-based mobile edge computing (5GMEC-DP). If the encrypted location information does not belong to the service range of the current MEC server, it will be projected to the service edge of the MEC server, thereby limiting the amount of noise of the Geo-Indistinguishability algorithm, making it suitable for the protection of trajectory data, and it also proves that the 5GMEC-DP algorithm still satisfies the definition of ε-Geo-Indistinguishability. Finally, experiments on the real data set prove that 5GMEC-DP can maintain the high practicability of the data when the degree of privacy protection is high. At ε=0.001 and ε=0.0005, Average Qloss of each coordinate in the trajectory decreases by 64% and 81%, respectively, compared with Geo-Indistinguishability algorithm. The overall database availability loss decreases by 64% and 82%, respectively.

Task Offloading Strategy of Vehicular Networks Based on Improved Bald Eagle Search Optimization Algorithm

To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively.

Adaptive VR Video Data Transmission Method Using Mobile Edge Computing Based on AIoT Cloud VR

Aiming at the high requirements of cloud service-based virtual reality in AIoT for data transmission rate and delay sensitivity, a cloud VR system scheme based on MEC (Mobile Edge Computing) is proposed, which mainly incorporates viewpoint-based VR video data processing and hybrid digital-to-analog (HDA) transmission optimization and can be served for AIoT transmission filed. Firstly, a learning-driven multiaccess MEC offloading strategy is designed, in which the VR terminal automatically selects the optimal MEC server for task offloading, thereby effectively improving network efficiency and reducing service delay. Secondly, the progressive transmission of the VR data is realized through viewpoint-aware dynamic streaming based on RoI (region of interest) and the priorities of different objects. The transmission priority of each object in the scene is determined through the ROI layering, which effectively solves the contradiction between the large data volume in the VR scenes and the network bandwidth limitation when applied in AIoT domain, and further improves the real-time performance of the system. Then, the HDA (hybrid digital-analog) technique is introduced to optimize the transmission. Finally, the base station protocol stack is modified on the basis of the LTE (Long-Term Evolution) system, and the MEC technology is integrated to realize a complete cloud VR system in AIoT. The experimental results show that compared with other advanced schemes, the proposed scheme can achieve more robust and efficient data transmission performance and provide better VR user experience.

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to the mobile-edge computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this article, we introduce a vehicular fog-edge computing paradigm and formulate it as a multistage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, and let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, roadside unit (RSU), MEC server, and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on the Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.

BDEdge: Blockchain and Deep-Learning for Secure Edge-Envisioned Green CAVs

Green Connected and Autonomous Vehicles (CAVs) are the future of next-generation Intelligent Transportation Systems (ITS) that will help humans to improve road safety and reduce pollution, energy consumption, and travel delays. To increase the performance of green CAV, the data generated by Autonomous Vehicles (AVs) and associated infrastructure needs to be processed in real-time. Mobile Edge Computing (MEC) is a promising paradigm that can be integrated with green CAV to save energy and to improve the network performance in terms of low latency for data processing. However, MEC servers and other communication entities in green CAV environment cannot be fully trusted and may bring vulnerabilities related to data privacy and security. Motivated by the above challenges, we design a blockchain and Deep-Learning (DL)-enabled secure data processing framework for an edge-envisioned green CAV environment (hereafter referred to as BDEdge). In blockchain-based scheme, all communication entities are registered, verified and thereafter validated using smart contract-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authenticated data is forwarded to DL scheme. In DL-based scheme, an Intrusion Detection System (IDS) based on a hybrid model of Sparse Auto-Encoder-enabled Attention Bidirectional Gated Recurrent Unit (SAE-ABIGRU) is designed by analyzing the link load behaviors of the MEC-enabled Road Side Unit (RSU) server. The security analysis and comparative simulation findings shows that the proposed BDEdge framework can significantly reduce false alarm rate and increase accuracy close to 99%.

Distributed Offloading for Cooperative Intelligent Transportation Under Heterogeneous Networks

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.

HNIO: A Hybrid Nature-Inspired Optimization Algorithm for Energy Minimization in UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.

Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks

As a novel computing paradigm, multiaccess edge computing (MEC) migrates computing and storage capabilities to edge nodes of the network to meet the requirements of executing computationally intensive or delay-sensitive tasks on intelligent vehicles. In addition, MEC fills the gap between cloud computing and terminals in vehicular networks. In the MEC system, to reduce the load on MEC servers with large-scale vehicle deployment and promote the efficient use of network resources, vehicles can also transfer tasks to neighboring resource-rich vehicles using cooperative computation offloading. However, cooperative computation offloading between vehicles faces the challenges of security and insufficient information about the server vehicle. Therefore, this paper proposes using blockchain technology to achieve efficient data sharing between vehicles and service providers (i.e., server vehicles) and ensure the security of computation offloading between vehicles. First, we design a secure data sharing architecture in blockchain-based vehicular edge computing networks. Then, a new consensus mechanism in this architecture is proposed to improve the efficiency of data sharing and prevent malicious attacks. Furthermore, we present a cooperative offloading decision-making method using an offloading game, and the Nash equilibrium of the offloading strategy is achieved using this method. The results of numerical experiments demonstrate the superior performance of the proposed method.