21,226 research outputs found
Software Defined Networking-based Vehicular Adhoc Network with Fog Computing
Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios. We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture
The Digital Foundation Platform -- A Multi-layered SOA Architecture for Intelligent Connected Vehicle Operating System
Legacy AD/ADAS development from OEMs centers around developing functions on
ECUs using services provided by AUTOSAR Classic Platform (CP) to meet
automotive-grade and mass-production requirements. The AUTOSAR CP couples
hardware and software components statically and encounters challenges to
provide sufficient capacities for the processing of high-level intelligent
driving functions, whereas the new platform, AUTOSAR Adaptive Platform (AP) is
designed to support dynamically communication and provide richer services and
function abstractions for those resource-intensive (memory, CPU) applications.
Yet for both platforms, application development and the supporting system
software are still closely coupled together, and this makes application
development and the enhancement less scalable and flexible, resulting in longer
development cycles and slower time-to-market. This paper presents a
multi-layered, service-oriented intelligent driving operating system foundation
(we named it as Digital Foundation Platform) that provides abstractions for
easier adoption of heterogeneous computing hardware. It features a multi-layer
SOA software architecture with each layer providing adaptive service API at
north-bound for application developers. The proposed Digital Foundation
Platform (DFP) has significant advantages of decoupling hardware, operating
system core, middle-ware, functional software and application software
development. It provides SOA at multiple layers and enables application
developers from OEMs, to customize and develop new applications or enhance
existing applications with new features, either in autonomous domain or
intelligent cockpit domain, with great agility, and less code through
re-usability, and thus reduce the time-to-market.Comment: WCX SAE World Congress Experience 202
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned
Large network multi-level control for CAV and Smart Infrastructure: AI-based Fog-Cloud collaboration
Supporting Big Data at the Vehicular Edge
Vehicular networks are commonplace, and many applications have been developed to utilize their sensor and computing resources. This is a great utilization of these resources as long as they are mobile. The question to ask is whether these resources could be put to use when the vehicle is not mobile. If the vehicle is parked, the resources are simply dormant and waiting for use. If the vehicle has a connection to a larger computing infrastructure, then it can put its resources towards that infrastructure. With enough vehicles interconnected, there exists a computing environment that could handle many cloud-based application services. If these vehicles were electric, then they could in return receive electrical charging services.
This Thesis will develop a simple vehicle datacenter solution based upon Smart Vehicles in a parking lot. While previous work has developed similar models based upon the idea of migration of jobs due to residency of the vehicles, this model will assume that residency times cannot be predicted and therefore no migration is utilized. In order to offset the migration of jobs, a divide-and-conquer approach is created. This uses a MapReduce process to divide the job into numerous sub-jobs and process the subtask in parallel. Finally, a checkpoint will be used between the Map and Reduce phase to avoid loss of intermediate data. This will serve as a means to test the practicality of the model and create a baseline for comparison with future research
5G Mobile Transport and Computing Platform for verticals
The support of 5G verticals service requires todesign an efficient Mobile Transport and Computing Platformwhere transport, mobile and MEC must interact effectively. Inthis paper, a novel architecture is proposed providing itsmapping on ETSI NFV. Two relevant use cases, such asautomotive and cloud robotics are presented to assess the novelarchitecture.This work has been partially funded by the EU H2020 5G-Transformer Project (grant no. 761536)
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