Urban Pollution Monitoring Based on Mobile Crowd Sensing: An Osmotic Computing Approach
Antonella Longo, Andrea De Matteis, Marco Salvatore Zappatore · 2018
Traditional urban pollution monitoring systems suffer of the sole presence of fixed stations. Data gathered from such devices are precise, thanks to the equipment quality and to established and robust measuring protocols, but these sampled data are located in very limited areas and collected by discontinuous monitoring campaigns. The spread of mobile technologies has fostered the development of new approaches like Mobile Crowd Sensing (MCS), offering the chance of using mobile devices, even personal ones, as sensors of urban data. Nevertheless, one of the open challenges is the management of the integration of heterogeneous data flows, different from types, technical specifications (e.g. diverse transmission protocols) and semantics. Osmotic computing aims at creating an abstract level between the mobile devices and the cloud, enabling opportunistic filtering and the addition of metadata for improving the data processing flow. This work focuses on the design and the development of the middleware which integrates data coming from mobile and IoT devices specifically deployed in urban contexts using the Osmotic Computing paradigm.