Abstract
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.
Published Version
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