Pollution Context-Aware Representation in Vehicular Internet of Things for Smart Cities

Abstract

The present era is associated with remarkable urban developments that have attracted migrants from rural to urban areas for various reasons, hence overpopulated cities. This leads to the increased congestion, air pollution, and other high population density related problems that could threaten the lives of urban commuters. With the current materialization of the Internet of Things, and smart city development, context-aware and pervasive computing are deemed to gain paramount consideration through sensing and actuating technologies. In this research work, we introduce a vehicular IoT pollution context-aware representation system. Firstly, In-vehicle pollution context-aware system is suggested that targets two key pollutants, i.e Carbon-dioxide $CO_{2}$ and Particulate Matter $PM_{2.5}$. Most importantly vehicles having many passengers on-board are monitored for these two pollutants of concern. Once their levels exceed the allowable limits, end-users that are truly concerned should be alerted and mitigation measures are taken. Secondly, vehicular entities are observed in the area of interest, and their gaseous emissions are thought to be the major sources of air pollution. This explains why keeping a sharp eye on each vehicle’s level of pollutants in its emissions is equally important. Drivers and environmental monitoring personnel could be informed of the abnormal levels of some key pollutants such as $NO_{x}$, $NH_{3}$, $CO_{2}$, and so forth. While the $CO_{2}$ and $PM_{2.5}$ monitoring is conducted using on-shelf sensors, an intra-vehicular pollution context-aware system is designed and developed that automatically operates an electric fan that could be deployed to control the level of temperature and pollutant’s level in vehicular environments.

Supported by ACEIoT.