Multi-access Edge Computing Applications Research Articles

Integrating Multi-Access Edge Computing (MEC) into Open 5G Core

Multi-Access Edge Computing (MEC) represents the central concept that enables the creation of new applications and services that bring the benefits of edge computing to networks and users. By implementing applications and services at the edge, close to users and their devices, it becomes possible to take advantage of extremely low latency, substantial bandwidth, and optimized resource usage. However, enabling this approach requires careful integration between the MEC framework and the open 5G core. This work is dedicated to designing a new service that extends the functionality of the Multi-Access Traffic Steering (MTS) API, acting as a strategic bridge between the realms of MEC and the 5G core. To accomplish this objective, we utilize free5GC (open-source project for 5G core) as our 5G core, deployed on the Kubernetes cluster. The proposed service is validated using this framework, involving scenarios of high user density. To conclude whether the discussed solution is valid, KPIs of 5G MEC applications described in the scientific community were sought to use as a comparison parameter. The results indicate that the service effectively addresses the described issues while demonstrating its feasibility in various use cases such as e-Health, Paramedic Support, Smart Home, and Smart Farms.

Privacy-Preserving Proxy Re-Encryption With Decentralized Trust Management for MEC-Empowered VANETs

Multi-access edge computing (MEC) technology is widely deployed at the edge of Vehicular Ad hoc Networks (VANETs) to enhance their communication and computational capabilities. However, existing security and privacy preservation solutions for MEC applications in VANETs face several challenges, such as the risk of privacy exposure of vehicle authentication, increased overhead due to cryptographic algorithms, as well as resource occupation and malicious attacks on edge servers. In this paper, we propose an aggregated security solution for the confidential, efficient, and trustworthy sharing of data while safeguarding the privacy of vehicle identities. Firstly, we present a broadcast proxy re-encryption scheme based on cubic spline interpolation that ensures the security of the VANET system and the identity privacy of large-scale vehicles. The re-encryption system is designed to prioritize the reduction of re-encryption computation time rather than focusing on the sizes of re-encryption keys and ciphertexts. We further model the aggregation overhead in terms of communication and computation. Additionally, we propose an efficient protocol based on Practical Byzantine Fault Tolerant (PBFT) consensus to facilitate decentralized trust management for edge proxy servers. Our security analysis demonstrates that the proposed scheme satisfies the security and confidentiality requirements for data sharing in VANETs. Finally, we provide extensive simulations that reveal the performance and effectiveness of our solution.

Design of a 5G Multimedia Broadcast Application Function Supporting Adaptive Error Recovery

The demand for mobile multimedia streaming services has been steadily growing in recent years. Mobile multimedia broadcasting addresses the shortage of radio resources but introduces a network error recovery problem. Retransmitting multimedia segments that are not correctly broadcast can cause service disruptions and increased service latency, affecting the quality of experience perceived by end users. With the advent of networking paradigms based on virtualization technologies, mobile networks have been enabled with more flexibility and agility to deploy innovative services that improve the utilization of available network resources. This paper discusses how mobile multimedia broadcast services can be designed to prevent service degradation by using the computing capabilities provided by multiaccess edge computing (MEC) platforms in the context of a 5G network architecture. An experimental platform has been developed to evaluate the feasibility of a MEC application to provide adaptive error recovery for multimedia broadcast services. The results of the experiments carried out show that the proposal provides a flexible mechanism that can be deployed at the network edge to lower the impact of transmission errors on latency and service disruptions.

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. We consider how to provide secure and privacy-preserving communication channels between a mobile user and a MEC application in the non-roaming case. It includes protocols for registration of the user to the main server of the MEC application, renewal of the shared key, and usage of the MEC application in the MEC host when the user is stationary or mobile. For these protocols, we designed a privacy-enhanced version of the 5G authentication and key management for applications (AKMA) service. We formally verified the current specification of AKMA using ProVerif and found a new spoofing attack as well as other security and privacy vulnerabilities. Then we propose a fix against the spoofing attack. The privacy-enhanced AKMA is designed considering these shortcomings. We formally verified the privacy-enhanced AKMA and adapted it to our solution.

Rapid prototyping and performance evaluation of ETSI MEC-based applications

The Multi-access Edge Computing (MEC) standard of the European Telecommunications Standards Institute (ETSI) will enable context-aware services for users of mobile 4G/5G networks. ETSI MEC application developers need tools to aid the design and the performance evaluation of their apps. During the early stages of deployment, they should be able to evaluate the performance impact of design choices - e.g., what round-trip delay can be expected due to the interplay of computation, communication and service consumption. When a prototype of the app exists, it needs to be tested live, under controllable conditions, to measure key performance indicators. In this paper, we present an open-source framework that allows developers to do all the above. Our framework is based on Simu5G, the OMNeT++ based simulator of 5G (NewRadio) and 4G (LTE) mobile networks. It includes models of ETSI MEC entities (i.e., MEC orchestrator, MEC host, etc.) and provides a standard-compliant RESTful interface towards application endpoints. Moreover, it can interface with external applications, and can also run in real time. Therefore, one can use it as a cradle to run a MEC app live, having underneath both 4G/5G data packet transport and MEC services based on information generated by the underlying emulated radio access network. We describe our framework and present a use-case of an emulated MEC-enabled 5G scenario.

Latency Analysis for Mobile Cellular Network uRLLC Services

The fifth generation (5G) mobile network technologies include ultra-Reliable Low Latency Communications (uRLLC) capability. To fully exploit uRLLC, distributed Multi- access Edge Computing (MEC) is being developed and introduced at the network edge with an architecture that supports applications and services. Some of the MEC applications will benefit from uRLLC, including virtual reality, augmented reality, education, health, online gaming, automatic manufacturing and Vehicle-to-everything. However, unique challenges and opportunities exist for 5G cellular networks and MEC due to a range of factors, including end-user device mobility and the implementation of the network Control Plane (CP) and User Plane (UP). In this regard, there is a need to optimize protocols and network architecture. This paper investigates latency and related network elements in the next generation mobile cellular network. We also analyze the 5G network latency in the CP and UP. Finally, the paper identifies protocol optimization considerations for MEC integration with 5G to achieve low end-to-end latency.

MEC/Cloud Orchestrator to Facilitate Private/Local Beyond 5G with MEC and Proof-of-Concept Implementation.

The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal’s feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ōokayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system’s feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services.

Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection

The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.

S-PoDL: A two-stage computational-efficient consensus mechanism for blockchain-enabled multi-access edge computing

More recently, there has been a growing interest in the blockchain technique for emerging multi-access edge computing (MEC) applications in relation to security and privacy, considering the benefits of using it in edge computing. In some blockchain-enabled MEC using the best-known consensus algorithm proof-of-work (PoW), the computational efforts increase dramatically with the number of transactions, since a large amount of computational tasks should be conducted by miners with the limited computing resources in edge devices. Hence, improving the computational performance as an important but challenging issue in the design of blockchain system for MEC applications, has attracted intensive attention within last years. To further improve the performance of PoW algorithm, we present a novel implementation mechanism in this paper. Here, motivated by proof-of-deep-learning (PoDL) method in which the deep learning algorithm is used to maintain blockchain, through the design of a two-stage model to achieving computational tasks in PoDL-based blockchain systems, a novel computational-efficient consensus mechanism, named separate-proof-of-deep-learning (S-PoDL), is accordingly proposed. Thus, an energy-efficient blockchain-enabled MEC could be developed with our proposed S-PoDL, which arranges miners to carry out a two-stage-based computation on the basis of accounting_queue technique, while presenting the achieved models as proofs in MEC network. The comparative experiments are conducted between S-PoDL and PoDL, and the experimental results verify the feasibility and efficiency of our consensus mechanism S-PoDL for some blockchain-enabled MEC applications in relation to PoW-based cryptocurrencies, while effectively reducing computing burden of edge devices.

Virtual IoT Service Slice Functions for Multiaccess Edge Computing Platform

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

Multiparty Call Control at the Network Edge

Network programmability is a key feature of fifth generation (5G) system which, in combination with cloud-based services, can support many use cases, including mission critical and healthcare communications. Programmability enables flexibility in customization of service connectivity. Multi-access Edge Computing (MEC) services and applications are enablers for network programmability. In this paper, MEC capabilities for programmability of multiparty multimedia call control at the network edge are studied. Multiparty video calls are one of the key applications of 5G, and are efficient way to exchange ideas, knowledge, expertise, information, and so on. The paper presents an approach to design MEC Application Programming Interfaces (APIs) which enable third party applications to create multiparty multimedia sessions and dynamically manage session participations. The API functionality is described by required information and message flows. The paper specifies the proposed MEC API with data model. Feasibility study includes modelling and formal validation of multiparty session state models supported by the network and mobile edge application. The latency injected by the API is evaluated by emulation.

Multi-access edge computing enabled internet of things: advances and novel applications

Multi-access edge computing (MEC) is emerging as a new and promising methodology with the development of Internet of Things (IoT) and smart devices, which enables a large class of applications and has emerged with a significance to change our life, and also is raising the challenging research issues due to the requirements of low latency, deployment independence, location awareness and mobility support. However, recent advances on distributed artificial intelligence (AI), big data, and edge/terminal cooperation schemes show that IoT still struggles with fundamental, long-standing problems, such as computational models, resource allocation and scheduling, power efficient data processing and cooperative decision-making mechanisms in edge. How to enhance IoT with MEC, distributed AI, cooperation and novel applications is worth exploring. The objective of this special issue is to report high-quality state-of-the-art research contributions that address these key aspects of MEC-enabled IoT and novel applications. By presenting a selection of papers on various topics related to MEC-enabled IoT, we hope to shed light on the multiple aspects of this emerging paradigm. The papers included in this issue propose solutions for resource management and allocation, multimedia processing, as well as novel applications.

Multi-Access Edge Computing: A Comparative Analysis of 5G System Deployments and Service Consumption Locality Variants

Multi-access edge computing (MEC) is a key technology for 5G systems and offers cloud computing capabilities to application developers and content providers along with an information technology service environment at the edge of the network. In terms of system deployment, MEC infrastructure may, among other options, exist either at the edge of the radio access network (RAN), or at a central office (CO). Moreover, various MEC service consumption variants can be considered, depending on the degree of proximity between service consumers (i.e., MEC applications) and service producers (i.e., running on MEC platforms or exposed by service-producing MEC applications). Consequently, different system scenarios are expected to achieve different performance, e.g., in terms of the round-trip time (RTT) experienced at the user side. In this article, we present a first MEC performance case study to focus on different system deployments and service consumption localities for a number of workloads. It is observed that in all cases, the RTT is highly impacted by the MEC application workload; higher RTT is observed when a MEC application consumes remote MEC services; whereas low RTT gain is observed when moving from smart CO to RAN edge deployments, regardless of the considered workload.

A Game-Based Computation Offloading Method in Vehicular Multiaccess Edge Computing Networks

Multiaccess edge computing (MEC) is a new paradigm to meet the requirements for low latency and high reliability of applications in vehicular networking. More computation-intensive and delay-sensitive applications can be realized through computation offloading of vehicles in vehicular MEC networks. However, the resources of a MEC server are not unlimited. Vehicles need to determine their task offloading strategies in real time under a dynamic-network environment to achieve optimal performance. In this article, we propose a multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources. Moreover, we construct a distributed best response algorithm based on the computation offloading game model to maximize the utility of each vehicle and demonstrate that the strategy in this algorithm can converge to a unique and stable equilibrium under certain conditions. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithms. The fast convergence and the improved performance of this algorithm are verified by numerical results.

Transparent AAA Security Design for Low-Latency MEC-Integrated Cellular Networks

Multi-access edge computing (MEC) is a key enabler for low-latency services in the cellular network. It enables service requests to be served at the edge without reaching the Internet. However, this service model allows data traffic to bypass conventional security functions deployed at the core network, and may pose security threats. To examine its security impact, we analyze current security functions that span authentication, authorization, accounting (AAA), and access control, and then identify two major issues. First, conventional user authentication methods prevent MEC applications from achieving low-latency service offering. Second, current cellular authorization, accounting, and access control mechanisms hardly secure MEC traffic. We thus propose a transparent security design called MECsec to secure the MEC with low latency in the cellular network. It contains three main components: cellular-based OpenID Connect (OIDC) authentication, bitmap-based authorization/accounting, and two-tier hash-based access control. Especially, its transparent design does not need any changes on current cellular operations, and is standard-compliant. We implement and evaluate the MECsec prototype on an MEC-integrated LTE network architecture developed based on the OpenAirInterface (OAI) cellular platform. Our results show that the cellular-based OIDC can reduce delays of current authentication methods by up to 88.3%, and the other components can successfully defend against possible threats with negligible overhead.

Intelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework

Multi-access edge computing (MEC), which is deployed in the proximity area of the mobile user side as a supplement to the traditional remote cloud center, has been regarded as a promising technique for 5G heterogeneous networks. With the assistance of MEC, mobile users can access computing resource effectively. Also, congestion in the core network can be alleviated by offloading. To adapt in stochastic and constantly varying environments, augmented intelligence (AI) is introduced in MEC for intelligent decision making. For this reason, several recent works have focused on intelligent offloading in MEC to harvest its potential benefits. Therefore, machine learning (ML)-based approaches, including reinforcement learning, supervised/unsupervised learning, deep learning, as well as deep reinforcement learning for AI in MEC have become hot topics. However, many technical challenges still remain to be addressed for AI in MEC. In this article, the basic concept of MEC and main applications are introduced, and existing fundamental works using various ML-based approaches are reviewed. Furthermore, some potential issues of AI in MEC for future work are discussed.

Multi-Access Edge Computing Enhanced Video Streaming: Proof-of-Concept Implementation and Prediction/QoE Models

ETSI multi-access edge computing (MEC) provides an IT service environment and cloud-computing capabilities at the edge of the mobile network, enabling application and content providers to deploy new use cases, such as intelligent video acceleration, with low latency and high bandwidth. Specifically, ETSI MEC introduces an MEC server that implements the edge-cloud platform to host partial server-side service logics in the form of MEC applications (MEC Apps). In this paper, we aim to implement the first proof-of-concept (PoC) in the literature for the MEC-enhanced mobile video streaming service. Our PoC consists of Android User Apps, an MEC App, and the YouTube server. The MEC App implements two main functions: popular video caching and radio analytics/video quality adaptation. The User App provides general functions of a YouTube video streaming app and can access the videos from the cache server or the YouTube server under the MEC server's guidance. In addition to the PoC implementation, this paper further develops two machine learning models to be incorporated into the MEC App for popular video prediction and radio channel quality prediction, which allows to consider the effect of non-negligible round-trip times and adjust the video quality more accurately. The experimental results justify that our models, together with other advantages from MEC, can guarantee good performance for the mobile video streaming service. Finally, we model and investigate the effectiveness of the MEC architecture for improving the quality of experience of video-streaming users.