Edge Computing Platform Research Articles

Cloud-Native Network Slicing Using Software Defined Networking Based Multi-Access Edge Computing: A Survey

Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

QoS Guaranteed Edge Cloud Resource Provisioning for Vehicle Fleets

Nowadays vehicle fleets are launched to perform business or scientific tasks, with new features supported by the emerging multi-access edge computing (MEC) platform. In the presence of high vehicle mobility, however, it is challenging to precisely provision resources among distributed edge clouds so that i) the QoS of vehicular service is guaranteed and meanwhile ii) the provisioning cost is minimized. We systematically investigate the QoS guaranteed optimal resource provisioning problem for the connected vehicle fleet in the MEC environment. Based on stochastic traffic analysis, we propose an optimization framework to minimize the cost of resource provisioning, while the service blocking probability is guaranteed to be smaller than a predefined threshold. We then present a lightweight two-phase algorithm based on bracketing and binary searching to solve the problem efficiently. To evaluate our method, we use two large real-world datasets collected by an online taxi service platform and validate the QoS with our resource provisioning strategy. The results demonstrate that our method can save the total provision cost up to 40%, compared with the naive resource provisioning strategy, and meanwhile can provide reliable QoS guarantee, compared with the mobility estimation-based approach.

Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.

Edgence: A blockchain-enabled edge-computing platform for intelligent IoT-based dApps

Nowadays scalable IoT management is a bottleneck of IoT development due to the geographically dispersed distribution, fragmented ownerships, and ever-growing population of IoT devices. To intelligently manage massive decentralized applications (dApps) in IoT usecases, Edgence (EDGe + intelligENCE) is proposed to use edge clouds to access IoT devices and users, and then use its in-built blockchain to realize self-governing and self-supervision of the edge clouds. Edgence proposes to use masternode technology to introduce IoT devices and users into a closed blockchain system, which can extend the range of blockchain to IoT-based dApps. Further, masternodes do good to scalability by raising the TPS (transactions per second) of the blockchain network. To support various dApps, a three-tier validation is proposed, namely script validation, smartcontract validation, and masternode validation. To avoid energy consumption resulted by blockchain consensus, Edgence proposes a random but verifiable way to elect a masternode to generate each new block. The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training.

Social-viewport adaptive caching scheme with clustering for virtual reality streaming in an edge computing platform

This paper proposes a novel social-viewport adaptive caching scheme (SACS) for virtual reality (VR) streaming in an edge-computing platform. In VR contents with 360 degree views where only a part of the entire view (i.e., the viewport) is shown and the remaining parts are decoded but not shown, we collect and record multiple clients’ viewports of the same VR contents in local proximity on the edge-computing platform. We extract a social-viewport map, which represents where most of the local clients are directing their attention. By utilizing the social-viewport map, under our proposed scheme, k-means and mean-shift clustering algorithms are adopted to partition 360 degree views into multiple clusters with the nearest mean of hit-ratios from multiple clients. Accordingly, in order to save cache storage while maintaining a high-quality VR streaming service, we adaptively assign different encoding rates with various levels to multiple viewports. We implement the proposed scheme using a commercial EdgeX foundry edge-computing platform. A measurement-based experiment reveals that the proposed scheme achieves a maximum storage reduction of almost 74%, with a 92% hit-ratio to the highest encoded viewports.

An Edge-Computing Paradigm for Internet of Things over Power Line Communication Networks

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency.

Fast Logistics Vehicle Localizing Based on EMVS-MIMO Radar and Edge Computing

This article focus on logistics vehicle active localizing using multiple-input multiple output (MIMO) radar, and a fast localizing architecture that integrates edge computing and cloud platform is proposed. The core of the proposed methodology is to measure the angles of logistics vehicle using bistatic MIMO radars, which are configured with coprime electromagnetic vector sensors (EMVS). Unlike the existing localization systems, the proposed localizing architecture provides two-dimensional (2D) angle estimation, i.e., 2D direction-of-departure (DOD) estimation and 2D direction-of-arrival (DOA) estimation, and it offers additional polarization status of the logistics vehicle. A parallel factor (PARAFAC) estimator is developed. Firstly, it estimate the factor matrices via PARAFAC decomposition. Thereafter, the elevation angle estimation is accomplished via least squares (LS) technique, which are ambiguous due to the coprime property of the EMVS. The azimuth angle estimation are followed via vector cross-product. Besides, the polarization information of the targets can be obtained estimated via LS approach. The above process is accomplished at cloud platform. Finally, the localization of the logistics vehicle is achieved with the estimated 2D angles, which is computationally friendly and achieved via edge computing. Detailed analyses concerning degree-of-freedom, identifiability, complexity as well as Cramér-Rao bound (CRB) are provided. To show the effectiveness of the proposed architecture, numerical simulations have been designed.

Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

In this article, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs), and unmanned aerial vehicles (UAVs), all with the mobile edge cloud installed to enable user equipments (UEs) or Internet of Things (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real time, while considering the dynamic environment. To this end, we propose a hybrid deep-learning-based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LS-FCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Second, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed-integer nonlinear programming (MINLP) problems and generate the sample data sets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Third, a DNN with the scheduling layer is introduced to provide the user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from the traditional DNN predictor, we only input one UE’s information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.

A Decentralized and Trusted Edge Computing Platform for Internet of Things

With the development of Internet of Things (IoT), edge computing becomes more and more prevalent currently. However, edge computing needs to deploy a large number of edge servers to reduce the communication latency, which will bring additional costs to the system. Although there exist some idle computing resources at the edge, the owners distrust each other and lack the incentives to contribute to the system. In this article, we propose a new edge computing platform decentralized and trusted platform for edge computing (DeTEC), which provides a unified interface to users, resolves the user’s requests to the most appropriate edge server through domain name server, and returns the computational results to the IoT user. To build a trustworthy system, DeTEC integrates the blockchain technology with edge computing, such that the contributions of each participant could be accounted and rewarded. We formulate the task allocation problem, taking both node capacity and reward fairness into consideration, and solve it through a heuristic algorithm. Finally, to guarantee the trustworthiness of computational results, we utilize a police patrol model and try to optimize the system overall reward. We implement DeTEC based on an open source project and conduct comprehensive experiments to test its performance. The results show that our DeTEC system works well in the IoT scenario.

Study on Dynamic Service Migration Strategy with Energy Optimization in Mobile Edge Computing

In the mobile edge computing (MEC) platform, tasks that are being performed often change due to mobile device migration. In order to improve the energy utilization of the MEC platform and the migration process of the mobile terminal and to ensure effective and continuous operation of services, dynamic service migration strategy with energy optimization is required. Aiming at the problem of energy consumption optimization of dynamic service migration with the far-near effect in mobile networks, this article proposes a dynamic service migration strategy with energy optimization, which ensures the performance requirements of the service by considering the minimum energy cost of the relevant equipment during the dynamic migration process. First, by analyzing the relationship between migration distance and equipment transmit power, the energy consumption model associated with the migration distance is established. Then, according to the task dynamic service migration scenario, the dynamic service migration energy consumption model is constructed, so as to obtain the reward function for migrating energy consumption. Finally, the dynamic service migration strategy with energy optimization is realized through the optimal migration energy consumption expectation, which is obtained by the optimal stopping theory. The experimental results show that the optimization strategy proposed in this article can effectively reduce the energy consumption of dynamic service migration in different simulation environments and can improve the dynamic migration performance.

Edge and Fog Computing Platform for Data Fusion of Complex Heterogeneous Sensors.

The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.

A review report on challenges and opportunities of edge, fog and cloud computing by employing IoT technology

In the future, various information and things will be connected to the network. People can now live more convenient and comfortable life where the things and information coordinated together. A world where things are connected to network is referred as IOT (Internet of Things). A huge amount of incomplete data is generated by IOT need to process and responded to very short time. This pose challenge of dealing with big data from many geometrically distributed data sources which are to be managed and processed. To achieve this objective, cloud computing is a treated as one of the popular choice due to its scalability, storage, computational and other capabilities. However current cloud models are not intended to handle the essentials of IOT– volume, variety, and velocity of data. Moreover, as the physical distance between cloud and user increases, transmission latency increases with it, increasing response time and stressing of the user. In addition to that, the processing speed in this environment is largely dependent on the performance of user device. The viable solution to these problems is identified as Edge Computing. The Edge Computing platform works by allowing some application processing to be performed by a small edge server position between the cloud and user, and crucially in a location physically closed to the user. This paper comprehensively presents various research trends that are available in Edge, Fog computing along with a comparison is made among Cloud. Particularly the architecture, characteristics, key technologies, potential applications, security issues and challenges of Edge, Fog and Cloud Computing are discussed and summarized.

Mobile Edge Computing Platform with Container-Based Virtualization Technology for IoT Applications

The next generation of fifth generation (5G) network, implementing mobile edge computing (MEC), network function virtualization (NFV) and software defined networking technologies, establishes a flexible and resilient network in line with various internet of things (IoT) devices. While NFV adds flexibility scale in or out networks by allowing network functions to be dynamically deployed and inter-connected, MEC provide intelligence at the edge of a mobile network; reduces latency, and increases capacity. With the diverse development of networking applications, the proposed MEC with container-based virtualization technology (CVT) as IoT gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively enhance the quality of service. In this work, the proposed IoT gateway will be analyzed to elucidate the combined effect of simultaneously deploying virtual network functions and MEC applications on the same network infrastructure. Low latency, high bandwidth and high agility, supporting the connection of large-scale devices, and the efficient combination of resources from network edge and cluster clouds, account for real-time network conditions, reducing the IoT applications and services to indicate that a number is the average of 30% of the latency, that could get more suitable service quality to develop such as both augmented reality and virtual reality application intelligence in coming 5G network.

CDS-MEC: NFV/SDN-based Application Management for MEC in 5G Systems

This paper presents and evaluates the first open-source Network Function Virtualization (NFV)/Software Defined Networking (SDN)-based Mobile Edge Computing (MEC) platform. Our platform solves the Mobile Edge (ME) management issues with respect to Application (App) provisioning and traffic management. First, the ME Apps are managed as Virtual Network Functions (VNFs) on top of the virtual environment through the Juju VNF Manager (VNFM). Second, we develop an SDN controller to manage traffic on the ME System. Third, unlike other relevant architectures of ME systems, we use the control plane (i.e., S1 interface) to derive appropriate states for traffic management. Finally, we evaluate our solution in two use-cases: ME caching and Information Centric (ICN)/Delay Tolerant (DTN) Public Safety communication (PS). The MEC caching framework displays improved user Quality of Experience, e.g., latency, in comparison to direct communication, while the PS solution provides a residual mean of communication for rescue teams, when the network core (EPC) and a Public Data Network (PDN) are unavailable.

Centralized Connectivity for Multiwireless Edge Computing and Cellular Platform: A Smart Vehicle Parking System

This study takes an intuitive step to develop the user‐convenient smart vehicle parking system (SVPS), a smart system able to manage the massive crowd of vehicles during parking searching and do the better jobs of parking reservation and management, with the shorter‐path processing tactics. For that, this study inclusively employed the mapping strategy, where the system parking points are prevalent, to assist the users to get the parking information fast and conveniently. This study is comprised of the several parking points systematically spread over the several locations and traceable over the available graphical map, and the overall information is easily accessible using smart devices. For parking information, a smart web application which is another important module of this study is designed, with which the SVPS system’s registered users are able to access all the services provided for smart vehicle parking searching and reservation in efficient and reliable ways. An integrated network approach, RFID (radio frequency identification) and wireless sensors network (WSN), called RF‐WSN, is employed to retrieve the real‐time information from the installed and configured sensor devices in RFID‐WSN network.