@inproceedings{950,
  author = {Mohammed Elbadry and Mengjing Liu and Yindong Hua and Zongxing Xie and Fan Ye},
  title = {Poster: Towards Robust, Extensible, and Scalable Home Sensing Data Collection},
  abstract = {Home-based health monitoring systems are important to many conditions (e.g., aging, chronic diseases). The absence of suitable data collection infrastructure is a fundamental barrier to the development of related algorithms and systems. In this poster, we present Proteus, a robust, extensible and scalable data collection infrastructure, to enable small research teams to manage large deployments. We identify the desired features and achieve them by combining mature technologies and new components: i) extensibility with new, diverse sensor types and data formats with a few lines of coding (LOC) efforts; ii) scalability in managing sensor/edge devices to automate many deployment, management tasks; iii) resilience to system failures and network outage. Experiments on a prototype show zero data loss or system error for one sensor node running 10 days, and 99.95% of data received for 32 emulated sensors sending data at 200 Mbps, 20 and 100 fold reductions in node setup efforts and LOC for new sensor types. The preliminary results show Proteus is promising for large-scale longitudinal deployment of home-based health monitoring.},
  year = {2023},
  journal = {ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies},
  month = {01},
  url = {https://par.nsf.gov/biblio/10439726},
}