Integrating IoT-sensing and crowdsensing for privacy-preserving parking monitoring
Hanwei Zhu, Chi-Kin Chau · 2021
Data sensing and gathering is essential for diverse information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffering from limited sensing coverage. On the other hand, data can be gathered dynamically by crowdsensing contributed from voluntary users but suffering from its unreliability and the lack of incentives for users' contributions. In this paper, we explore an integrated paradigm called "hybrid sensing" that aims to harness both IoT-sensing and crowdsensing in a complementary and privacy-preserving manner. We implemented our hybrid sensing system and conducted some initial empirical evaluations.