A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research

Moisture computing-based internet of vehicles (IoV) architecture for smart cities

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches

Conceptualisation of human-on-the-loop haptic teleoperation with fully autonomous self-driving vehicles in the urban environment

The automotive industry aims to deploy commercial level-5 fully autonomous self-driving vehicles (FA-SDVs) in a diverse range of benefit-driven concepts on city roads in the years to come. In all future visions of operating networks of FA-SDVs, humans are expected to intervene with some kind of remote supervisory role. Recent advances in cyber-physical systems (CPS) within the concept of Internet of Everything (IoE) using tactile internet (TI) teleport us to teleoperate remote objects within the cyber-world. Human-on-the-loop (HOTL) haptic teleoperation with an extension of human control and sensing capability by coupling with artificial sensors and actuators with an increased sense of real-time driving in the remote vehicle can help overcome the challenging tasks when the new driver - artificial intelligence (AI) agent - encounters an unorthodox situation that can't be addressed by the autonomous capabilities. This paper analyses HOTL real-time haptic delay-sensitive teleoperation with FA-SDVs, in the aspects of human-vehicle teamwork by establishing two similar remote parallel worlds --- real-world vehicle time-varying environment and cyber-world emulation of this environment, i.e., digital twins (DTs) --- in which a human telesupervisor (HTS), as a biological agent, can be immersed within a reasonable timescale with no cybersickness enabling omnipresence and a bidirectional flow of energy and information. The experiments conducted as a proof of concept of HOTL haptic teleoperation shows promising results and the potential of benefiting from the proposed framework

자율주행을 위한 V2X 기반 차량 CDN 설계

학위논문 (석사) -- 서울대학교 대학원 : 공학전문대학원 응용공학과, 2021. 2. 김성우.Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEEs Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPPs New radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this study, we define and design a vehicular network architecture compliant with 5G core networks. For localization of autonomous driving vehicles, a high-definition map needs to contain the context of trajectory . We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of V2X and of the proposed caching policy via network simulations. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.최근들어 기술의 혁신은 자율주행 자동차의 발전을 가속화 하고 있다. 보다 높은 수준의 자율 주행을 구현하기 위해서, 차량은 네트워크를 통해 서로 연결되어 있어야 하고 차량의 안전과 편의성을 향상 시킬 수 있도록 정보를 공유 할 수 있어야 한다. 표준화 단체인 IEEE와 3GPP는 차량 통신 요구사항, 아키텍처를 연구하고 개정해왔다. IEEE가 전용 채널을 통한 근접 지역 통신에 초점을 맞추는 반면에, 3GPP의 New Radio V2X는 Sidelink 뿐만 아니라 Uplink 통신을 동시에 지원한다. 5G 통신을 지원하는 3GPP Release 16은 Network Slice, NFV, SDN과 같은 새로운 통신 기능들을 제공한다. 이 연구에서는 새롭게 정의된 5G Core Network Architecture를 바탕으로 차량 네트워크를 정의하고 설계하였다. 자율주행 자동차의 측위를 위해서, 고해상도 지도는 각 구성요소들의 의미와 속성을 자세하게 포함하고 있어야 한다. 우리는 이 연구에서 V2X 네트워크 상에 HD map을 중계할 수 있는 Edge Server를 제안 함으로써, 중앙에서 발생할 수 있는 병목현상을 줄이고 전송 Delay를 최소화한다. 또한 Edge의 컨텐츠를 등록하고 삭제하는 정책으로 기존의 LRU, LFU가 아닌 새로운 컨텐츠 교체 알고리즘을 제안하였다. 실제 주행 시험과 시뮬레이션을 통한 실험을 통해 전송 품질을 향상시켰으며, Edge 컨텐츠의 활용도를 높였다.I. Introduction 1 II. Related Works 6 2.1 V2X Standardization 6 2.1.1 IEEE WAVE 6 2.1.2 3GPP C-V2X 9 2.2 Geographic Contents 14 2.3 Vehicular Content Centric Network 17 III. System Modeling 20 3.1 NR-V2X Architecture Analysis 20 3.2 Caching Strategy for HD Map Acquisition 23 IV. Evaluation 30 4.1 Contents Replacement Strategy 30 4.2 V2X Characteristics 36 4.3 Edge Performance in Driving on the Road 38 4.4 Edge Performance on 3D Point Clouds Caching for Localization 44 V. Conclusion 47 Bibliography 49 Abstract 54Maste

A comparison among deep learning techniques in an autonomous driving context

Al giorno d’oggi, l’intelligenza artificiale è uno dei campi di ricerca che sta ricevendo sempre più attenzioni. Il miglioramento della potenza computazionale a disposizione dei ricercatori e sviluppatori sta rinvigorendo tutto il potenziale che era stato espresso a livello teorico agli albori dell’Intelligenza Artificiale. Tra tutti i campi dell’Intelligenza Artificiale, quella che sta attualmente suscitando maggiore interesse è la guida autonoma. Tantissime case automobilistiche e i più illustri college americani stanno investendo sempre più risorse su questa tecnologia. La ricerca e la descrizione dell’ampio spettro delle tecnologie disponibili per la guida autonoma è parte del confronto svolto in questo elaborato. Il caso di studio si incentra su un’azienda che partendo da zero, vorrebbe elaborare un sistema di guida autonoma senza dati, in breve tempo ed utilizzando solo sensori fatti da loro. Partendo da reti neurali e algoritmi classici, si è arrivati ad utilizzare algoritmi come A3C per descrivere tutte l’ampio spettro di possibilità. Le tecnologie selezionate verranno confrontate in due esperimenti. Il primo è un esperimento di pura visione artificiale usando DeepTesla. In questo esperimento verranno confrontate tecnologie quali le tradizionali tecniche di visione artificiale, CNN e CNN combinate con LSTM. Obiettivo è identificare quale algoritmo ha performance migliori elaborando solo immagini. Il secondo è un esperimento su CARLA, un simulatore basato su Unreal Engine. In questo esperimento, i risultati ottenuti in ambiente simulato con CNN combinate con LSTM, verranno confrontati con i risultati ottenuti con A3C. Obiettivo sarà capire se queste tecniche sono in grado di muoversi in autonomia utilizzando i dati forniti dal simulatore. Il confronto mira ad identificare le criticità e i possibili miglioramenti futuri di ciascuno degli algoritmi proposti in modo da poter trovare una soluzione fattibile che porta ottimi risultati in tempi brevi