70 research outputs found

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

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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

    ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•œ V2X ๊ธฐ๋ฐ˜ ์ฐจ๋Ÿ‰ CDN ์„ค๊ณ„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณตํ•™์ „๋ฌธ๋Œ€ํ•™์› ์‘์šฉ๊ณตํ•™๊ณผ, 2021. 2. ๊น€์„ฑ์šฐ.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.์ตœ๊ทผ๋“ค์–ด ๊ธฐ์ˆ ์˜ ํ˜์‹ ์€ ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ๋ฐœ์ „์„ ๊ฐ€์†ํ™” ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณด๋‹ค ๋†’์€ ์ˆ˜์ค€์˜ ์ž์œจ ์ฃผํ–‰์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด์„œ, ์ฐจ๋Ÿ‰์€ ๋„คํŠธ์›Œํฌ๋ฅผ ํ†ตํ•ด ์„œ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์–ด์•ผ ํ•˜๊ณ  ์ฐจ๋Ÿ‰์˜ ์•ˆ์ „๊ณผ ํŽธ์˜์„ฑ์„ ํ–ฅ์ƒ ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ์ •๋ณด๋ฅผ ๊ณต์œ  ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค. ํ‘œ์ค€ํ™” ๋‹จ์ฒด์ธ IEEE์™€ 3GPP๋Š” ์ฐจ๋Ÿ‰ ํ†ต์‹  ์š”๊ตฌ์‚ฌํ•ญ, ์•„ํ‚คํ…์ฒ˜๋ฅผ ์—ฐ๊ตฌํ•˜๊ณ  ๊ฐœ์ •ํ•ด์™”๋‹ค. IEEE๊ฐ€ ์ „์šฉ ์ฑ„๋„์„ ํ†ตํ•œ ๊ทผ์ ‘ ์ง€์—ญ ํ†ต์‹ ์— ์ดˆ์ ์„ ๋งž์ถ”๋Š” ๋ฐ˜๋ฉด์—, 3GPP์˜ New Radio V2X๋Š” Sidelink ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ Uplink ํ†ต์‹ ์„ ๋™์‹œ์— ์ง€์›ํ•œ๋‹ค. 5G ํ†ต์‹ ์„ ์ง€์›ํ•˜๋Š” 3GPP Release 16์€ Network Slice, NFV, SDN๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ํ†ต์‹  ๊ธฐ๋Šฅ๋“ค์„ ์ œ๊ณตํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒˆ๋กญ๊ฒŒ ์ •์˜๋œ 5G Core Network Architecture๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฐจ๋Ÿ‰ ๋„คํŠธ์›Œํฌ๋ฅผ ์ •์˜ํ•˜๊ณ  ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ์˜ ์ธก์œ„๋ฅผ ์œ„ํ•ด์„œ, ๊ณ ํ•ด์ƒ๋„ ์ง€๋„๋Š” ๊ฐ ๊ตฌ์„ฑ์š”์†Œ๋“ค์˜ ์˜๋ฏธ์™€ ์†์„ฑ์„ ์ž์„ธํ•˜๊ฒŒ ํฌํ•จํ•˜๊ณ  ์žˆ์–ด์•ผ ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ์—ฐ๊ตฌ์—์„œ V2X ๋„คํŠธ์›Œํฌ ์ƒ์— HD map์„ ์ค‘๊ณ„ํ•  ์ˆ˜ ์žˆ๋Š” Edge Server๋ฅผ ์ œ์•ˆ ํ•จ์œผ๋กœ์จ, ์ค‘์•™์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ณ‘๋ชฉํ˜„์ƒ์„ ์ค„์ด๊ณ  ์ „์†ก Delay๋ฅผ ์ตœ์†Œํ™”ํ•œ๋‹ค. ๋˜ํ•œ Edge์˜ ์ปจํ…์ธ ๋ฅผ ๋“ฑ๋กํ•˜๊ณ  ์‚ญ์ œํ•˜๋Š” ์ •์ฑ…์œผ๋กœ ๊ธฐ์กด์˜ LRU, LFU๊ฐ€ ์•„๋‹Œ ์ƒˆ๋กœ์šด ์ปจํ…์ธ  ๊ต์ฒด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‹ค์ œ ์ฃผํ–‰ ์‹œํ—˜๊ณผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์ „์†ก ํ’ˆ์งˆ์„ ํ–ฅ์ƒ์‹œ์ผฐ์œผ๋ฉฐ, Edge ์ปจํ…์ธ ์˜ ํ™œ์šฉ๋„๋ฅผ ๋†’์˜€๋‹ค.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

    Get PDF
    Al giorno dโ€™oggi, lโ€™intelligenza artificiale รจ uno dei campi di ricerca che sta ricevendo sempre piรน 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 dellโ€™Intelligenza Artificiale. Tra tutti i campi dellโ€™Intelligenza Artificiale, quella che sta attualmente suscitando maggiore interesse รจ la guida autonoma. Tantissime case automobilistiche e i piรน illustri college americani stanno investendo sempre piรน risorse su questa tecnologia. La ricerca e la descrizione dellโ€™ampio spettro delle tecnologie disponibili per la guida autonoma รจ parte del confronto svolto in questo elaborato. Il caso di studio si incentra su unโ€™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 รจ arrivati ad utilizzare algoritmi come A3C per descrivere tutte lโ€™ampio spettro di possibilitร . Le tecnologie selezionate verranno confrontate in due esperimenti. Il primo รจ 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 รจ identificare quale algoritmo ha performance migliori elaborando solo immagini. Il secondo รจ 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 sarร  capire se queste tecniche sono in grado di muoversi in autonomia utilizzando i dati forniti dal simulatore. Il confronto mira ad identificare le criticitร  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
    • โ€ฆ
    corecore