Advanced analytics for the Internet of Things

Description

There has been a lot of talk about the Internet of Things lately: intelligent households, smart cities, wearable technology, manufacturing sensors, and more.

The Internet of Things poses a great challenge for data analysts, on the one hand because of the very large amounts of data created over time; and on the other because of the algorithms that make the sensor-equipped object (house or city) capable of learning, and therefore smarter.

We decided to take up this challenge and put KNIME to work to:

• Collect the large amount of data generated by Internet of Things sensors
• Enrich the original data with responses from external RESTful services
• Transform the data into a more meaningful set of input features
• Apply time series analysis to add some intelligence to the system
• Optimize the smarter system to get the best performances with the leanest feature set
• Integrate a number of different visualization tools, from R Graphics libraries to OpenStreetMap and network graphs, into our KNIME workflow.

This project focuses on the data from a bike share system in Washington, DC, called Capital Bikeshare. Each Capital Bikeshare’s bike carries a sensor, which sends the current real-time bike location to a central repository. All historical data is public and has been downloaded and used for this study.

The downloaded data has also been enriched with topology, elevation, local weather, holiday schedules, traffic situations, business locations, tourist attractions, and other types of information widely available on the internet via web or REST services. After transformation and the application of machine learning algorithms for time series prediction, an optimized alarm system can give us a warning when bike restocking is necessary at a given bike station in the next hour.