Air quality based optimal path search model for spatio-temporal data set

The proliferation of GIS and GPS technology provide huge amount of geo-data in the form of spatio-temporal dataset. With the increased use of mobile devices, end users tend to avail location based services in various contexts. This requires frequent updation of the database and also the development of fast search and optimization techniques. The increasing vehicular traffic in urban sectors leads to the emission of toxic gases thereby increasing air pollution levels. Ambient air quality can be monitored dynamically using vehicular network called VANET, which senses the ambient air pollutant levels with reference to GPS points and stores them in the central GIS database server. Our proposed approach returns the optimal and the safest route when enquired for the route between a source and a destination considering acceptable pollution level limits enroute. The existing models for pointal queries provide the shortest path between source and destination consuming more disk space, construction time and execution time. They support only distance based optimization, whereas our proposed optimal path search model is based on the ambient air quality. The investigation shows that our proposed model reduces searching and execution time.