Vehicular Knowledge Networking and Mobility-Aware Smart Knowledge Placement
Seyhan Uçar, Takamasa Higuchi, Chang‐Heng Wang, Duncan Deveaux, Onur Altintas, Jérôme Härri · 2022
It is estimated that the data volume between connected vehicles and edge/cloud server(s) will be about 100 petabytes per month by 2025. The networking framework we have, on the other hand, is the existing cellular network in which the most connected vehicles function today. However, such a network suffers from several issues and may not work under this predicted data demand. To address such a dilemma, a new paradigm, Vehicular Knowledge Networking (VKN), is recently introduced. In VKN, the data is transformed into knowledge and it is distributed with various lifetimes/relevance. To benefit from the knowledge, on the other hand, it should be placed intelligently such that a high number of vehicles can access and consume it. In this paper, we tackle this issue and propose mobility-aware smart knowledge placement. In the proposed method, vehicle mobility is analyzed to measure the centrality degree of a region. The computed centrality degrees are then further analyzed to identify the most central zones. The knowledge is placed on these zones to increase availability. We demonstrate the benefits of the proposed method through a simulation. Our preliminary result has shown that the mobility-aware smart knowledge placement makes knowledge accessible from vehicles over short range communication. Through such short-range availability of knowledge, vehicles can use the free spectrum to download it which decreases the cellular communication cost significantly.