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Author(s) -
Harriett J. Walton
Publication year - 2018
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201870196
Subject(s) - citation , computer science , information retrieval , world wide web
Autonomous vehicles require new approaches for vehicle-to-everything (V2X) communications to access data and location information. The fifth-generation (5G) networks emerge as a key provider for the stringent connectivity requirements that enable autonomous vehicles to operate safely without any tolerance of service interruptions. Some of the key advantages of 5G networks are deploying ultra-dense small cells and the massive multiple-input multiple-output technology, which allow the connectivity of vehicles all time at any location. This networking system requires high computational power that can process information received from vehicles about their routes and status, to provide feedback messages with minimum delays. In addition, multi-connectivity to autonomous vehicles requires a precise time alignment to maintain online live transmissions. The collected data will not only be used for navigation but also to divert vehicles between charging stations, by employing machine learning-based tools and protocols. The cloud backbone and network functions support massive data processing and storage by employing different connection models to relay data between vehicles and relevant data providers. Relying on peripheral sensors, these technologies communicate all objects locations to vehicles and inform them how to react using the 5G network supporting features.

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