Mobile Edge Computing Technology Research Articles

A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing

Task offloading in mobile edge computing (MEC) improves the efficacy of mobile devices in terms of computing performance, data storage, and energy consumption by offloading computational tasks to edge servers. Efficient task offloading can leverage MEC technology to reduce task processing latency and energy consumption. By integrating the reasoning ability and machine intelligence of the cognitive computing architecture, such as SOAR and ACT-R, reinforcement learning (RL) algorithms have been applied to resolve the task offloading in MEC. To solve the problem that conventional Deep RL (DRL) algorithms cannot adapt to dynamic environments, this paper proposed a task offloading scheduling strategy which combined multi-agent reinforcement learning and meta-learning. In order to make the two actions of charging time and offloading strategy fully considered at the same time, we implemented a learning network of two agent on a mobile device. To efficiently train the policy network, we proposed a first order approximation method based on clipped surrogate objective. Finally, the experiments are designed with variety of the number of subtasks, transmission rate, and edge server performance, and the results show that the MRL-based strategy has the overwhelming overall performance and can be quickly applied in various environments with good stability and generalization.

Green NOMA based MU-MIMO transmission for MEC in 6G Networks

Mobile edge computing (MEC) is a distributed computing paradigm that brings computing and data storage closer to the network’s edge. This paper considers a multiple-input multiple-output (MIMO) uplink scenario where non-orthogonal multiple access (NOMA) users partially offload their data to a MEC server. The objective of this study is to minimize the total energy consumption during local computing, task offloading, and MEC computing. To this end, the base station optimizes power allocation vectors and task assignment coefficients under time and power constraints. The generalized singular value decomposition-based linear beamforming method integrates NOMA, MIMO, and MEC technologies. Due to the non-convex nature of the problem, a successive convex optimization (SCA) and alternation optimization (AO) based low-complex solution is proposed. The impact of the delay tolerance, the size of the offloaded task, and the distance of the users are investigated concerning energy consumption. The simulation results show that the proposed method achieves better energy performance than the orthogonal multiple access (OMA) schemes. More importantly, the performance gap increases when the data size is large, and the delay requirement is stringent, as 6G networks require.

Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Upon exploiting massive spectrum resources, millimeter-wave (mmW) communication can significantly improve the transmission rate of mobile-edge computing (MEC) offloading, whereas the directional mmW links are constrained by shrunk beam coverage and demand extra phase for beam alignment. To enhance the accessing efficiency, we develop the nonorthogonal multiple access (NOMA) scheme-based mmW MEC mechanism, namely, NOMA-mmW MEC, therefore motivating to minimize the average delay of the MEC offloading, by jointly optimizing the beamwidth, user equipment (UE) scheduling, and transmit power. To tackle the mixed-integer nonlinear programming (MINLP) problem of delay minimization, we develop the alternative optimization (AO) approach-based RA scheme, namely, AO-RA, to obtain the close-optimum solutions. In the AO-RA scheme, we propose the matrix control many-to-one with externality (MC-M2OE) algorithm, to find the best UE scheduling for the NOMA groupings of different types of UEs. Upon the above, we further design the joint beamwidth and transmit power (JBTP) algorithm, which determines the optimal beamwidth and transmit power for the MEC offloading transmissions. Our simulation results show the effectiveness of the proposed AO-RA scheme in minimizing the offloading delay, where our MC-M2OE and JBTP algorithms can significantly outperform the existing approaches. From the simulation results, we may conclude that it needs to carefully address the tradeoff between beam alignment overhead and transmission gain while properly balancing the loading among different NOMA groups, for the practical consideration of NOMA-mmW MEC technology.

Secure Aerial Computing: Convergence of Mobile Edge Computing and Blockchain for UAV Networks

The introduction of mobile edge computing (MEC) technology in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users with or without communication infrastructure coverage. However, mobile users’ privacy may be leaked during the computation offloading process due to the information interaction between UAVs and the migration of computation tasks between mobile users and UAVs. To this end, we propose a secure aerial computing architecture that integrates MEC and blockchain technology for UAV networks to effectively ensure the security and privacy of computation offloading between UAVs and mobile users. Under this architecture, a problem of joint optimization of user association, UAV trajectory, block processor scheduling, and computation resource allocation is formulated to minimize the weighted sum of the energy consumption and the delay in completing computation tasks and blockchain tasks processing. To handle this intractable issue, we first decouple the optimization variables and then separate the original problem into multiple subproblems to be solved alternately. In addition, we design a block coordinate descent (BCD)-based algorithm for user association and computation resource allocation, and a successive convex approximation (SCA)-based algorithm to optimize the trajectories of UAVs. Simulation results show that the proposed algorithm has better performance.

Mobile Edge Computing in Space-Air-Ground Integrated Networks: Architectures, Key Technologies and Challenges

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work.

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.

Edge Computing Resource Allocation and Optimization Method and Its Application in Internet of Vehicles Environment

With the emergence of new vehicle-mounted services, such as autonomous driving and augmented reality, the computing capability at the edge of the Internet of vehicles (IoV) has become increasingly demanding, and the edge computing of the IoV has also emerged. With the rise of the IoV, new vehicle-mounted applications keep emerging. Vehicle-mounted applications need to process the sensor data of themselves and other vehicles around them. The amount of data is huge, and processing these sensor data requires powerful computing power. But typical vehicles have limited computing power, so such computation-intensive tasks need to be uploaded to a data center for processing. The MEC (Mobile Edge Computing) technology is a good choice to solve this problem. This paper advocates reasonable scheduling of parked vehicles to cooperate with edge servers for collaborative edge computing and studies the resource scheduling scheme under the target of user relative satisfaction. This paper describes the main entities and related functions in the network and proposes a secure and reliable protocol for interaction between entities. The serviceability of different parked vehicles in collaborative edge computing was evaluated to select stable task performers and solve the resource scheduling optimization problem between parked vehicles and edge servers. The simulation results show that the proposed scheme has better customer satisfaction and maximum overhead than the existing scheme that only considers using edge server to perform tasks. Therefore, the method of this paper has important theoretical significance and potential practical value.

Resource Optimization in MEC-Assisted Multirobot Cooperation Systems

With prevalent utilization of multirobot cooperation (MRC) systems, people pay more attention to improve the system performance. Among them, the energy consumption and implementation latency of MRC systems are major concerns, and mobile edge computing (MEC) provides a potential way to solve these problems. Therefore, how to leverage MEC to get the balance between computing and communication consumption in MRC systems needs to be investigated urgently. In this paper, a MRC system deployed to accomplish multiple time-critical tasks by MEC technology is studied. The proposed MRC system includes a powerful master robot (MR) and several slave robots (SRs). As a scheduler, MR is responsible for allocating tasks to SRs and has more computing power. SRs are robots with sensors that interact with the environment. In this paper, we propose a strategy for task allocation and resource management in MRC systems. The results show that the proposed scheme can effectively reduce the total energy consumption in SRs.

Time-critical tasks implementation in MEC based multi-robot cooperation systems

Mobile edge computing (MEC) deployment in a multi-robot cooperation (MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot (MR) acts as an edge server to provide services to multiple slave robots (SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs' energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.

The Innovative Development Path of Financial Media Based on Mobile Edge Computing Technology from the Perspective of Rural Revitalization

Rural Revitalization is a systematic and complex strategic project that must keep up with the times while also taking into account the times’ characteristics and social development. The media should be at the forefront of rural revitalization efforts. The interaction of various “forces” is required for the successful implementation of the rural revitalization strategy. The county-level financial media center, as a “force of the media,” will encourage mainstream media to overcome their own development challenges, become an important mainstream public opinion publicity platform, and aid in the realization of the great cause of rural revitalization. For rural comprehensive revitalization, big data provides new means and tools. It is not only a powerful support for rural comprehensive revitalization, but also an important means of realizing the integrated development of the digital economy and rural revitalization, as well as improving the quality and level of rural revitalization. We need to accelerate the realization of agricultural and rural modernization to implement the rural revitalization strategy, and the construction of smart countryside is an important embodiment of agricultural and rural modernization. Mobile edge computing technology has become ingrained in many aspects of social production and daily life. With the power of MEC, urban radio and television must accelerate the pace of multiscreen and multifrequency interaction, as well as online and offline integration; create an all-media instant communication and interactive platform; make new media products suitable for rural audiences; realize integrated development of urban and rural areas; and improve the influence and credibility of new media on agricultural communication. From the perspective of rural revitalization, this paper will investigate the development path of financial media under the influence of data mining (DM) and mobile edge computing technology.

Design Mode of Stage Performing Arts Based on 3D Modeling and Moving Edge Computing Technology

In recent years, the virtual stage background has become increasingly popular in the stage design of large-scale art evenings as well as in dance performances. It deftly blends the form of dance beauty with the content of dance performance, creating an atmosphere that not only sets the scene but also expands the stage space and enhances the artistic expression of dance. This paper focuses on the design of stage performance art using 3D modeling and MEC (mobile edge computing) technology. The data fusion method of multi-Kinect joints is researched and realized using 3DMAX to complete the modeling and virtual design of a 3D virtual stage scene. The performance scheme is created by analyzing music features with a computer, designing lighting actions, and matching music and lighting actions. Finally, it shows how DDLS (distributed deep learning shunting) configuration affects convergence performance as well as how model setting affects the diversion scheme.

Distributed Edge Computing with Blockchain Technology to Enable Ultra-Reliable Low-Latency V2X Communications

Vehicular communication is a promising technology that has been announced as a main use-case of the fifth-generation cellular system (5G). Vehicle-to-everything (V2X) is the vehicular communication paradigm that enables the communications and interactions between vehicles and other network entities, e.g., road-side units (RSUs). This promising technology faces many challenges related to reliability, availability and security of the exchanged data. To this end, this work aims to solve the scientific problem of building a vehicular network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The proposed work provides a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels and an energy-efficient offloading algorithm to manage traffic offloading to the MEC server. The main applications of the blockchain and MEC technology in the developed system are discussed. Furthermore, the developed system, with the introduced sub-systems and algorithms, was evaluated over a reliable environment, for different simulation scenarios, and the obtained results are discussed.

Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing

The purpose is to better develop China's music industry, facilitate users' online music works queries, and encourage the development of excellent music works. The music recommendation system is analysed, and a new music recommendation system is constructed based on the combination of the hybrid Deep Learning (DL) Artificial Neural Network (ANN) and Mobile Edge Computing (MEC) technologies. First, the principle of MEC is analysed. Then, the combination of Deep Learning Artificial Neural Network (DLANN) technology and MEC technology is discussed. Afterward, DLANN and MEC are fused to implement the music recommendation model, and the proposed model is evaluated through experiment. The results show that the combination of DLANN and MEC technologies can increase the DL efficiency of the computer, the storage capacity, and the overall efficiency of the servers. This proves the feasibility of the proposed music recommendation system. The user satisfaction of the proposed music recommendation system exceeds all mainstream music recommendation algorithms on the market. Thus, the research can provide a reference for the improvement of the music recommendation algorithm in the future and is of great significance. The proposed music recommendation system outperforms most of the current music recommendation systems on the market.

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.

Robust, Resilient and Reliable Architecture for V2X Communications

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review

With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices; Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAV-enabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption.In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

A Blockchain-Enabled Multiple Object Tracking for Unmanned System With Deep Hash Appearance Feature

The unmanned system based on mobile-edge computing (MEC) and blockchain can conveniently share computing resources and realize multi-devices collaboration. However, frequent data communication in the framework of object detection brings a heavy computational burden. In this paper, a novel blockchain-based multi-view unmanned equipment fusion architecture using multiple object tracking (MOT) technique is designed. Inspired by the idea of person re-identification and hash representation for image retrieval, a novel MOT technique using HashNet to extract deep hash appearance of objects is proposed. In addition, based on the blockchain and MEC technology, we make some improvements in feature fusion and tracking interrupt recovery. We combine deep hash appearance features with motion features and design a tracking interruption recovery mechanism to solve the problem of object occlusion. The experiment results on the MOT challenge dataset demonstrate that the proposed algorithm can handle object occlusion problem effectively and successfully reduce the number of identity switches. In real application scenes our algorithm performs particularly well, showing that our algorithm is more practical.

NOMA-based energy-efficient task scheduling in vehicular edge computing networks: A self-imitation learning-based approach

Mobile Edge Computing (MEC) is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access (OMA) technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access (NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning (SIL)-based Deep Reinforcement Learning (DRL) has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.

Joint Optimization of Path Planning and Resource Allocation in Mobile Edge Computing

With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity $L$ L -step lookahead branch scheme (referred to as $L$ L -step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed $L$ L -step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed.