Spatio-Temporal Analytics Tools For IoT
Interpreting data from sensor networks relies heavily on the context of sensed values from devices. The two most common correlation factors are time and space. Most analytic tools today are typically well-tuned towards one vertical application. However, Smart Cities will be built across many verticals, and the correlation of events across these applications will rely on a common transaction and data fusion layer. We intend to explore a general architecture that can be used to support several deployed information systems and allow integration of such systems.
We will design and deploy a set of tools to aid in storage, processing and visualization of data from a variety of live sensor networks. Visualization tools will be developed to aid in operational views (realtime, system health data) and analytical views (information processing). The set of tools will be used to correlate data within and across different sensor network applications.
This project is aimed to support several CROSSMobile efforts, including the Traffic Calming project in collaboration with the City of Palo Alto. The services developed will both serve the backbone and forefront of CROSSMobile research projects in Networks and Smart Cities.
Desired Outcomes for Semester
- A deployed spatio-temporal database for use across different applications
- Web UI for visualizing realtime sensor data on both a timeline and map
- Web UI for displaying processed Traffic Flow analytics
- A query interface for combining readings across applications
- Deployment and support of TrafficDots in the City of Palo Alto
- Experience in web server development
- Experience working with the following is a plus:
- MongoDB, InfluxDB, Neo4J Databases
- Node-RED, MQTT
- Grafana, D3.js, Mapping Frameworks (OSM, Google, MapBox)
- Has taken 15-619 and 18-647 or equivalent
- Test-driven Development
- Version Control using Git
Send an email with your resume to:
- Cef Ramirez - email@example.com