Edge Computing for Autonomous Vehicles

2020 ◽  
pp. 171-181
Keyword(s):  
Autonomous Vehicles ◽  
Edge Computing

scholarly journals A Survey on Secure Deployment of Mobile Services in Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mengmeng Cui ◽  
Yiming Fei ◽  
Yin Liu
Keyword(s):  
Autonomous Vehicles ◽  
Mobile Network ◽  
Edge Computing ◽  
Internet Of Vehicles ◽  
It Service ◽  
Computing Services ◽  
Service Deployment ◽  
Cloud Computing Services ◽  
Complete Survey ◽  
Research Studies

Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

scholarly journals Edge-Oriented Computing: A Survey on Research and Use Cases

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 452
Author(s):  
Nour Alhuda Sulieman ◽  
Lorenzo Ricciardi Celsi ◽  
Wei Li ◽  
Albert Zomaya ◽  
Massimo Villari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Autonomous Vehicles ◽  
Research Field ◽  
Edge Computing ◽  
Use Cases ◽  
Comprehensive Overview ◽  
System Architectures ◽  
Computing Paradigm ◽  
Edge Networks

Edge computing is a distributed computing paradigm such that client data are processed at the periphery of the network, as close as possible to the originating source. Since the 21st century has come to be known as the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors and Internet of Things devices operating in real time from remote locations and inhospitable operating environments almost anywhere in the world is a relevant emerging need. Indeed, edge computing is reshaping information technology and business computing. In this respect, the paper is aimed at providing a comprehensive overview of what edge computing is as well as the most relevant edge use cases, tradeoffs, and implementation considerations. In particular, this review article is focused on highlighting (i) the most recent trends relative to edge computing emerging in the research field and (ii) the main businesses that are taking operations at the edge as well as the most used edge computing platforms (both proprietary and open source). First, the paper summarizes the concept of edge computing and compares it with cloud computing. After that, we discuss the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks. Finally, we explain the edge computing applications to 5G/6G networks and industrial internet of things. Several studies review a set of attractive edge features, system architectures, and edge application platforms that impact different industry sectors. The experimental results achieved in the cited works are reported in order to prove how edge computing improves the efficiency of Internet of Things networks. On the other hand, the work highlights possible vulnerabilities and open issues emerging in the context of edge computing architectures, thus proposing future directions to be investigated.

Deep Learning With Analytics on Edge

2021 ◽  
pp. 111-133
Author(s):  
Kavita Srivastava
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Autonomous Systems ◽  
Edge Computing ◽  
Computing Paradigm ◽  
Network Training ◽  
Storage Overhead ◽  
Cloud Server ◽  
Learning Frameworks

The steep rise in autonomous systems and the internet of things in recent years has influenced the way in which computation has performed. With built-in AI (artificial intelligence) in IoT and cyber-physical systems, the need for high-performance computing has emerged. Cloud computing is no longer sufficient for the sensor-driven systems which continuously keep on collecting data from the environment. The sensor-based systems such as autonomous vehicles require analysis of data and predictions in real-time which is not possible only with the centralized cloud. This scenario has given rise to a new computing paradigm called edge computing. Edge computing requires the storage of data, analysis, and prediction performed on the network edge as opposed to a cloud server thereby enabling quick response and less storage overhead. The intelligence at the edge can be obtained through deep learning. This chapter contains information about various deep learning frameworks, hardware, and systems for edge computing and examples of deep neural network training using the Caffe 2 framework.

scholarly journals Service Migration in Edge Computing Environments for Connected Autonomous Vehicles

2020 ◽  
Author(s):  
Lucas Sousa Pacheco ◽  
Denis Lima Rosário ◽  
Eduardo Coelho Cerqueira ◽  
Leandro Aparecido Villas
Keyword(s):  
Autonomous Vehicles ◽  
High Mobility ◽  
Future Generation ◽  
Edge Computing ◽  
Quality Level ◽  
Mobility Prediction ◽  
Processing Capacity ◽  
Prediction Scheme ◽  
Service Migration ◽  
Computing Environments

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

scholarly journals Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

2021 ◽  
Vol 2 ◽  
Author(s):  
Ovidiu Vermesan ◽  
Reiner John ◽  
Patrick Pype ◽  
Gerardo Daalderop ◽  
Kai Kriegel ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
The Internet ◽  
High Definition ◽  
Data Infrastructure ◽  
Traffic Lights ◽  
Digital Infrastructure

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

Edge Computing Resource Allocation Orchestration System for Autonomous Vehicles

2020 ◽  
Author(s):  
Abdukodir Khakimov ◽  
Aleksandr Loborchuk ◽  
Ibodulaev Ibodullokhodzha ◽  
Dmitry Poluektov ◽  
Ibrahim A. Elgendy ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Autonomous Vehicles ◽  
Edge Computing ◽  
Computing Resource

scholarly journals Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications: A Case Study of Tollgate Selection for Autonomous Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Junhee Lee ◽  
Sungjoo Kang ◽  
Jaeho Jeon ◽  
Ingeol Chun
Keyword(s):  
Medical Devices ◽  
Mobile Applications ◽  
Autonomous Vehicles ◽  
Base Station ◽  
Edge Computing ◽  
Computation Offloading ◽  
Cloud Infrastructure ◽  
5G Network ◽  
Selection For

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.

scholarly journals Evolution of V2X Communication and Integration of Blockchain for Security Enhancements

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1338 ◽  
Author(s):  
Rakesh Shrestha ◽  
Seung Yeob Nam ◽  
Rojeena Bajracharya ◽  
Shiho Kim
Keyword(s):  
Autonomous Vehicles ◽  
Vehicular Networks ◽  
High Reliability ◽  
Rapid Evolution ◽  
Edge Computing ◽  
Cloud Services ◽  
Driving Safety ◽  
Future Research ◽  
Traffic Efficiency ◽  
Multi Access

With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.

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