Wearable Environmental Sensors and Infrastructure for Mobile Large-Scale Urban Deployment

Abstract

We present a platform to allow up to 50000 students to simultaneously collect and learn from their personal activity, transportation, and environmental data. The main goals that we met during the design of our sensor platform were to: 1) be low cost; 2) remain powered for the duration of the data collection campaign; 3) robustly sense a wide range of environmental parameters; and 4) be packaged in a form factor conducive to wide-spread adoption and ease of use. We describe and generalize the design methods we applied on the hardware and firmware. Our sensors employ Wi-Fi communication to move data as well as to localize themselves using a radio-map of Singapore. Our system uses embedded as well as server-based machine learning algorithms to perform on-sensor transportation mode identification and state inference. The testing and validation methods that we applied ensured that over 98% of the deployed sensors successfully met all of their design goals. In addition, we summarize the results of a large-scale deployment of our system for a nation-wide experiment in Singapore in 2015, and describe three sample applications of the collected data. We publish sample data sets and algorithm code for researchers to analyze.