The Engaged Internet of Things (EngagedIoT) project team was created with the support from the Shen Fund for Social Impact. EngagedIoT aims to address social, economic and environmental challenges that communities are facing by integrating technological development with community engagement. The project team is also supported by an interdisciplinary research group at Cornell designing a statewide public IoT network led by Prof. Max Zhang. Undergraduate students have the opportunity to work closely with faculty members, graduate and professional students to learn more about impact-driven engineering designs to benefit communities.
Current Projects:
Electricity Metering: Network of Buildings
Cornell University’s Energy and the Environment Research Lab is working together to provide building managers with a system to visualize their building’s energy and environmental trends. Low-cost Long Range Low-Power Wide Area Network (LPWAN) based hardware is being developed in-house capable of monitoring power consumption, air quality, water consumption and other energy and environmental trends. Building managers can watch trends from multiple devices in real-time and are provided with personal recommendations outlining suggested actions for improving energy and environmental performance. Building performance data is shared amongst building managers in a friendly network environment, where multiple users can share ideas and work together to further improve building performance.
- Hardware: Collect aggregate and submeter power readings from Cornell Cooperative Extension (CCE) offices across New York State. Use current transformers safely to make a LPWAN-enabled power meter.
- Software: Improve dashboard for users to better visualize power consumption data
Electricity Metering: Water pump monitoring
- Hardware: Collect power readings for water pumps supplying water to the Town of Geneva to develop energy and cost savings strategies.
- Software: Develop dashboard page for town Water and Sewer management to visualize power consumption
Road Surface Condition Monitoring
- Hardware: Evaluate hardware used at Game Farm Road weather station. Gather ML data for a mobile system.
- Software: Train TensorFlow lite model to classify road surface condition. Incorporate live data into a quickly updating dashboard.
Solar and Agriculture
- Hardware: Build radiation and microclimate sensors for optimized solar panel placement and crop growth
- Software: Incorporate summary statistics into dashboard
- Software: Build an online radiation tool for designing a dual-use solar facility
Telehealth
- Hardware: Build devices to remotely monitor chronic-disease patient population across Tompkins County
- Software: Present relevant health information coherently to users and health care providers
- Hardware: Build robust hardware that monitors the quantity of goods in a food cabinet. The solution should preserve privacy.
- Software: Integrate LPWAN data to generate updates and alerts based on cabinet stock.
