In this paper we present a case study of IoT cloud participatory sensing where a user sends a query to the cloud about the air quality of a location at a particular time to decide if it should enter the location based on its susceptibility to the air quality detected. All IoT devices (e.g., smart phones carried by humans or smart cars driven by humans) capable of detecting air quality can act as participants and submit sensing reports to the cloud for sensing result aggregation. The major challenge is the selection of trustworthy participants because not all IoT devices will be trustworthy. We leverage a “Trust as a Service” (TaaS) cloud utility to address the issue of selecting trustworthy participants. Using real traces of ozone (O3) levels and mobility traces of users in the O3 community of interest (O3COI) group in the city of Houston, we demonstrate that TaaS outperforms contemporary IoT trust protocols in selecting trustworthy participants. We compare the performance of the TaaS cloud utility with two contemporary IoT trust protocols for supporting trust-based IoT participatory sensing applications. With the help of the TaaS cloud utility, a user in this O3COI group is able to obtain O3 readings very close to the ground truth O3 level despite 30% participants are untrustworthy.
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This work is supported in part by the U.S. AFOSR under grant number FA2386-17-1-4076.
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Guo, J., Chen, I., Wang, D. et al. Trust-Based IoT Cloud Participatory Sensing of Air Quality. Wireless Pers Commun 105, 1461–1474 (2019). https://doi.org/10.1007/s11277-019-06154-y
- Internet of things
- Participatory sensing
- Cloud computing