  Data sensing and gathering is an essential task for various
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 suffer from limited sensing coverage. On the other hand, data can
also be gathered dynamically through crowdsensing contributed by voluntary
users but suffer from its unreliability and the lack of incentives for users'
contributions. In this paper, we explore an integrated paradigm called "hybrid
sensing" that harnesses both IoT-sensing and crowdsensing in a complementary
manner. In hybrid sensing, users are incentivized to provide sensing data not
covered by IoT sensors and provide crowdsourced feedback to assist in
calibrating IoT-sensing. Their contributions will be rewarded with credits that
can be redeemed to retrieve synthesized information from the hybrid system. In
this paper, we develop a hybrid sensing system that supports explicit user
privacy -- IoT sensors are obscured physically to prevent capturing private
user data, and users interact with a crowdsensing server via a
privacy-preserving protocol to preserve their anonymity. A key application of
our system is smart parking, by which users can inquire and find the available
parking spaces in outdoor parking lots. We implemented our hybrid sensing
system for smart parking and conducted extensive empirical evaluations.
Finally, our hybrid sensing system can be potentially applied to other
information-driven services in smart cities.
