Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring

Abstract

Mobile devices present several features which make them attractive as enabling technology for crowdsensing systems. In particular, their spectrum of sensing capabilities, together with consolidated diffusion and ease of use contribute to an increasing adoption in different mobility-based sensing scenarios. On the other hand, the availability of massive volumes of geospatial data provided by large-scale distributed sensing systems prompts the need for innovative approaches to efficient data gathering and processing. Data reduction strategies are often necessary in order to cope with challenges posed by these volumes, for instance when dealing with real-time visualization of query results. In this paper we present a data reduction and aggregation approach for mitigating the impact of data size in a vehicular sensing application aimed at monitoring the roughness of road surfaces. Data collected by smartphones on board of vehicles is progressively thinned at different levels of the proposed architecture through sampling and spatial/temporal aggregation. Preliminary results show that the proposed methodology provides substantial benefits in terms of reduced impact of data while, at the same time, it enables full exploitation of statistical error compensation.