Understanding the Potential of Edge-Based Participatory Sensing: an Experimental Study

Breno Dantas Cruz, Junjie Cheng, Zheng Song, 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 investigate how the processing resources at the edge of the network can be leveraged to enable efficient participatory sensing that avoids heavy network traffic. In particular, 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, data model retraining, and administrative data flow aggregation. Our evaluation shows that our system architecture effectively leverages the available edge resources, while greatly reducing network traffic. Based on our experiences of implementing and evaluating our system prototype, we identify several open research directions for further advancing edge-based participatory sensing.