Multi-access Edge Computing Technology Research Articles

Integration of ICN and MEC in 5G and Beyond Networks: Mutual Benefits, Use Cases, Challenges, Standardization, and Future Research

Multi-access Edge Computing (MEC) is a novel edge computing paradigm that moves cloud-based processing and storage capabilities closer to the mobile users by implementing server resources in the access nodes. MEC helps fulfill the stringent requirements of 5G and beyond networks to offer anytime-anywhere connectivity for many devices with ultra-low delay and huge bandwidths. Information-Centric Networking (ICN) is another prominent network technology that builds on a content-centric network architecture to overcome host-centric routing/operation shortcomings and to realize efficient pervasive and ubiquitous networking. It is envisaged to be employed in Future Internet including Beyond 5G (B5G) networks. The consolidation of ICN with MEC technology offers new opportunities to realize that vision and serve advanced use cases. However, various integration challenges are yet to be addressed to enable the wide-scale co-deployment of ICN with MEC in future networks. In this paper, we discuss and elaborate on ICN MEC integration to provide a comprehensive survey with a forward-looking perspective for Beyond 5G networks. In that regard, we deduce lessons learned from related works (for both 5G and Beyond 5G networks). We present ongoing standardization activities to highlight practical implications of such efforts. Moreover, we render key B5G use cases and highlight the role for ICN MEC integration for addressing their requirements. Finally, we layout research challenges and identify potential research directions. For this last contribution, we also provide a mapping of the latter to ICN integration challenges and use cases.

UAV-Assisted Multi-Access Edge Computing: Technologies and Challenges

As the number of end users served by wireless networks continues to grow, modern wireless communication systems have put forward higher requirements for the diversity of wireless network forms. The unmanned aerial vehicle (UAV) wireless network communication system will occupy an important position in the future wireless communication system. With the explosive growth of 5G data traffic and Internet of Things (IoT) devices, computing-intensive, communication-intensive, and delay-sensitive applications have put tremendous pressure on UAV communication systems. Unlike terrestrial communication networks, the inherent characteristics of UAVs make it more troublesome to deal with the above-mentioned difficulties. Multi-access edge computing (MEC) can help mobile devices improve computing and communication capabilities, and its combination with UAVs is also an open subject. This article mainly summarizes the related knowledge and research of MEC and UAV wireless communication systems. We combine the UAV system with MEC technology and try to propose a UAV-assisted MEC wireless communication system. We analyze the key methods to realize this technology and point out a series of possible challenges. This article aims to provide new ideas for the proposed new architecture of aerial computing.

Mobility-aware dynamic offloading strategy for C-V2X under multi-access edge computing

Multi-access edge computing (MEC) technology is envisioned as a promising paradigm to achieve the user needs of low-latency applications. Complex computation tasks are offloaded from resource-constrained devices to resource-rich devices to reduce task completion delays. However, given the high-speed mobility of vehicles, the traditional MEC network architecture is insufficient for the Internet of Vehicle (IoV) computing scenarios. Besides, many resource-rich idle vehicles can be used as mobile servers to perform computing tasks, extending the choices for vehicle users. To better integrate MEC and IoV technologies, we introduce a Cellular-V2X-based MEC offloading scenario, enabling MEC servers and vehicle nodes in the 5G cellular network environment to perform offloading cooperatively. For a typical delay-sensitive task, taking the Augmented Reality (AR) application as an example, we divide the task into multiple subtasks with linear correlation. A mobility-aware dynamic offloading algorithm (MADO) is proposed to minimize the impact of vehicular mobility on offloading. The algorithm is used to find an optimal resource allocation, which reduces the overhead of the entire offloading process. It continuously updates the resource selection and the offloading strategy with the vehicular location changes. Simulation results corroborate that our proposed MADO algorithm can effectively reduce task completion time, improve task completion success rate and adapt to a dynamic environment.

Base Station switching and edge caching optimisation in high energy-efficiency wireless access network

The improvement of the energy efficiency and the reduction of the latency are two of the main goals of the next generation of Radio Access Networks (RANs). In order to achieve the latter, Multi-access Edge Computing (MEC) is emerging as a promising solution: it consists of the placement of computing and storage servers, directly at each Base Station (BS) of these networks. For the RAN energy efficiency, the dynamic activation of the BSs is considered an effective approach. In this paper, the caching feature of the MEC paradigm is considered in a portion of an heterogeneous RAN, powered by a renewable energy generator system, energy batteries and the power grid, where micro cell BSs are deactivated in case of renewable energy shortage. The performance of the caching in the RAN is analysed through simulations for different traffic characteristics, as well as for different capacity of the caches and different spread of it. New user association policies are proposed, in order to totally exploit the MEC technology and reduce the network energy consumption. Simulation results reveal that, thanks to this technology and the proposed methodologies, the experienced delay and the energy consumption drop, respectively, up to 60% and 40%.

Verticals in 5G MEC-Use Cases and Security Challenges

5G is the fifth-generation cellular network satisfying the requirements IMT-2020 (International Mobile Telecommunications-2020) of the International Telecommunication Union. Mobile network operators started using it worldwide in 2019. Generally, 5G achieves exceptionally high values of performance parameters of access and transmission. The application of edge servers has been proposed to facilitate implementing such requirements of 5G, resulting in 5G MEC (Multi-access Edge Computing) technology. Moreover, to optimize services for specific business applications, the concept of 5G vertical industries has been proposed. In this article, we study how the application of the MEC technology affects the functioning of 5G MEC-based services. We consider twelve representative vertical industries of 5G MEC by presenting their essential characteristics, threats, vulnerabilities, and known attacks. Furthermore, we analyze their functional properties, give efficiency patterns and identify the effect of applying the MEC technology in 5G on the resultant network's quality parameters to determine the expected security requirements. As a result of the research, we identify the impact of classified threats on the 5G empowered vertical industries and identify the most sensitive cases to focus on their protection against network attacks in the first place.

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

Modeling and performance analysis of smart map application in the Multi-access Edge Computing paradigm

Multi-access Edge Computing (MEC) recognized as an emerging technology that provides cloud computing services in the proximity of Radio Access Network (RAN). Mobile subscribers could offload their computation-intensive or memory-intensive tasks to the nearest Base Stations (BS) and deliver the cloud services from MEC servers. This technology can meet the requirements of low latency and location awareness. Smart map applications are an indispensable part of our digital life in large cities. In this paper, we introduce how the map application could gain from MEC technology. We concentrate on performance modeling and analysis of direction-finding in the map application. We study the impact of variation of workload, the number of servers, queue length, and connection failure on the task rejection probability and mean sojourn time. We model each phase of direction-finding service by the Markov Reward Model (MRM). The numerical results presented by applying models in the SHARPE software package. Furthermore, inter-dependencies between sub-models resolved by the fixed-point iteration technique. Moreover, the analytical results verified by the Discrete-Event Simulation (DES), which conducted in MATLAB. The result could help the MEC providers to assess their platform performance.

On the integration of NFV and MEC technologies: architecture analysis and benefits for edge robotics

Forthcoming networks will need to accommodate a large variety of services over a common shared infrastructure. To achieve the necessary flexibility and cost savings, these networks will need to leverage two promising technologies: Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). While the benefits of NFV and MEC have been largely studied as independent domains, the benefits of an harmonized system comprising these two technologies remains largely unexplored. In this article we first identify a set of reference use cases that would benefit from a joint use of MEC and NFV. Then, we analyze the current state-of-the-art on MEC and NFV integration and we identify several issues that prevent a seamless integration. Next, we consider a reference use case, namely Edge Robotics, to exemplify and characterize these issues in terms of the overall service life cycle: from the initial development, to deployment and termination.

Multi-Access Edge Computing: The Driver Behind the Wheel of 5G-Connected Cars

The automotive and telco industries have taken an investment bet on the connected car market, pushing for the digital transformation of the sector by exploiting recent information and communications technology (ICT) progress. As ICT developments continue, it is expected that the technology advancements will be able to fulfill the sophisticated requirements for vehicular use cases, such as: low latency and reliable communications for safety; increased computing power to process large amounts of sensed data; and increased bandwidth for onboard infotainment. The aforementioned requirements have received significant focus during the ongoing definition of the 3GPP 5G mobile standards, where there has been a drive to facilitate vertical segments such as automotive, in addition to providing the core aspects of the communication infrastructure. Among the technology enablers for 5G, multi-access edge computing (MEC) can be considered essential. That is, a cloud environment located at the edge of the network, in proximity of the end users and coupled with the service provider's network infrastructure. Even before 5G is rolled out, current mobile networks can already target support for these challenging use cases using MEC technology. This is because MEC is able to fulfill low latency and high bandwidth requirements, and, in addition, lends itself to be deployed at vertical industrial sector premises such as road infrastructure, air/seaports, and smart factories, thus bringing computing power where it is needed most. This work showcases the automotive use cases that are relevant for MEC, providing insights into the technologies specified and investigated by the ETSI Industry Specification Group MEC, who were the pioneers in creating a standardized computing platform for advanced mobile networks with regard to network edge related use cases.

Evolving IoT networks by the confluence of MEC and LP-WAN paradigms

Novel technologies are emerging as promising enablers for future IoT networks. From the architecture organization perspective, a new trend of pushing both processing and storage capabilities closer to the end-devices is gaining momentum. Following this strategy, the Multi-access Edge Computing (MEC) paradigm proposes to place intelligence at the Radio Access Network (RAN) edge, i.e., in between the RAN and the cloud. This approach permits reducing operational latencies, as well as the possibility of accessing useful information from the RAN in real-time. Another technology that is receiving great attention during the last times is Low Power — Wide Area Network (LP-WAN). Solutions based on this paradigm promise long coverage range of about 10 km and improved power efficiency that permits end-device’s lifetimes of about 10 years. Thus, in this work, both paradigms are deeply explored and its potential convergence is discussed. Furthermore, the results of experimental use-cases are presented. Concretely, a MEC node is integrated within an LP-WAN system with the aim of hosting several services: real-time physical layer management, IPv6 integration, storage offloading and data caching, and quick access to context-aware information from the end-nodes. The attained results show the validity of the proposed architecture, which gathers both novel paradigms: MEC technologies and LP-WAN networks.

Survey on Multi-Access Edge Computing for Internet of Things Realization

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.