Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Abstract

The ever increasing amount of connected mobile devices and users in the Internet of vehicles and the fact bicycles, electric scooters and users' smartphones are also connected poses a real challenge in terms of ensuring quality of service. The constantly changing topology due to the high‐mobility of devices and users greatly impacts its stability and the connectivity of devices. Furthermore, to ensure safety of people in the case of autonomous vehicles it is of paramount importance to ensure excellent reliability. This is why we have developed a solution that is based on machine learning to classify devices according to their mobility profile and that uses a scoring system to select the best candidates to act as mobile relays amongst devices with a suitable mobility profile. The scoring system allows to find critical locations in terms of user density. This solution does not require a dedicated infrastructure such as road side units. Simulations results will show the proposed solution increases the packet delivery ratio by up to 6%, reduces the energy consumption by up to 30% and increases the efficiency of bandwidth usage without sacrificing the end delay of users and devices compared with the state‐of‐the‐art.

Design and Implementation of an Intelligent Online English Learning System Based on Mobile Internet Technology

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Intelligent passenger demand prediction‐based rerouting for comfort perception in public bus transport systems

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“…In this situation, the traffic flow estimation process will stop or be complex to estimate. 13 Forecasting passenger flow concerns can be done using a variety of ways. There are two types of transportation forecasting techniques: Parametric and nonparametric.…”

Development of Electronic Component Life Prediction Model Using Rough Set Theory in Case Study of Relay

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