Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities
Hanwei Zhu, Sid Chi-Kin Chau, Gladhi Guarddin, Weifa Liang

TL;DR
This paper presents a privacy-preserving hybrid sensing system combining IoT sensors and crowdsensing for smart city applications, specifically smart parking, with incentives and privacy protections.
Contribution
It introduces a novel hybrid sensing framework that integrates IoT and crowdsensing with privacy-preserving protocols and incentive mechanisms.
Findings
System supports explicit user privacy and anonymity.
Effective in smart parking with accurate space detection.
Extensible to other smart city services.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
