Project Overview
The Smart Urban Monitoring Network (SUMN) is a large-scale research project aimed at deploying and managing a city-wide network of environmental sensors. The project combines cutting-edge IoT hardware with advanced machine learning algorithms to provide real-time insights into urban environmental conditions.
Objectives
- Deploy 1,000+ environmental sensors across San Francisco
- Develop adaptive sensing algorithms to optimize data collection
- Create predictive models for air quality and noise pollution
- Provide open data access to researchers and city planners
Key Results
- Successfully deployed 500 sensors in pilot phase
- Achieved 99.5% uptime with solar-powered nodes
- Reduced false alarm rate by 75% using ML-based anomaly detection
- Published 5 peer-reviewed papers from project findings
Team Members
- PI: Dr. Jane Smith (Example University)
- Co-PI: Dr. Robert Chen (Stanford University)
- PhD Students: Maria Garcia, Tom Wilson
- Industry Partner: City of San Francisco Environmental Division
Publications
- Smith et al., “Adaptive Sensing for Smart Cities” (SenSys 2023) Best Paper
- Garcia et al., “Energy-Efficient Sensor Networks” (IoTDI 2023)