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Estimating rainfall intensity by using vehicles as sensors

Abstract:

Vehicles are key elements in the envisioned Smart Cities, not only providing more efficient mobility, but also becoming mobile network elements able to perform many useful tasks. Environment sensing is a good example where the combination of data coming from vehicles allows achieving insight only comparable to the deployment of hundreds or thousands of sensors in a city. Obtaining rainfall estimations with a high spatial granularity is an example of a task where relying on traditional methods would become too expensive due to the high number of data sources required. Vehicular networking has a great potential to address such challenge by converting every vehicle in a rain sensor. In this paper we carry out a simulation study to estimate the rainfall intensity in a specific area using a vehicular network as data source. To this purpose, we model a rainfall pattern taking real values as reference, and we devise a simulation scenario where the rainfall pattern is deployed. Experimental results using the OMNeT++ simulator show that, even with a low density of vehicles contributing to the proposed monitoring system, rainfall intensity can still be pbkp_redicted with a high accuracy and granularity, thereby validating the proposed approach.