Pushing Participatory Sensing Further to the Edge

Zheng Song, Junjie Cheng, Abhishek Chauhan, Eli Tilevich

Abstract: Participatory sensing uses both local devices for data collection and cloud-based servers for processing. However, transferring the collected data to the cloud can lead to draining device battery power and cause network bandwidth bottlenecks, especially for large multimedia files. In this paper, we report on the experiences of designing, implementing, and evaluating a sensing system that constructs indoor maps by recognizing door signs. A distinguishing characteristic of our system is an almost exclusive use of edge-based processing for tasks that include ML-based image recognition, human-assisted data verification, and data model retraining. Our evaluation shows that our system architecture effectively leverages the available edge resources, while greatly reducing network traffic.