AirQ: A Privacy-Preserving Truth Discovery Framework for Vehicular Air Quality Monitoring

Air pollution has become an important health concern. The recent developments of vehicular networks and crowdsensing systems make it possible to monitor fine-grained air quality with vehicles and road-side units. On account of the different precisions of onboard sensors and malicious behaviors of participants, sensory data usually vary in quality. Thus, truth discovery has been a crucial task which targets at better utilizing the data. However, in urban cities, there is a significant difference in traffic volumes of streets or blocks, which leads to a data sparsity problem for truth discovery. To tackle the challenge, we present a truth discovery algorithm incorporating spatial and temporal correlations. Besides, to protect the privacy of participating vehicles, we develop the algorithm into a privacy-preserving truth discovery framework by adopting the technique of masking. The proposed framework is lightweight than the existing cryptography-based methods. Simulations are conducted to show that the proposed framework has a good performance. Although the framework is presented for air quality monitoring, we fully discuss the possible applications and extensions.