Apply Deep Learning to Building-Automation IoT Sensors

by Jonathan Laserson | Electronic Design , Jul. 27, 2016 – 

In building automation, sensors such as motion detectors, photocells, temperature, and CO₂ and smoke detectors are used primarily for energy savings and safety. Next-generation buildings, however, are intended to be significantly more intelligent, with the capability to analyze space utilization, monitor occupants' comfort, and generate business intelligence.

To support such robust features, building-automation infrastructure requires considerably richer information that details what's happening across the building space. Since current sensing solutions are limited in their ability to address this need, a new generation of smart sensors (see figure below) is required to enhance the accuracy, reliability, flexibility, and granularity of the data they provide.

Data Analytics at the Sensor Node
In the new era of the Internet of Things (IoT), there arises the opportunity to introduce a new approach to building automation that decentralizes the architecture and pushes the analytics processing to the edge (the sensor unit) instead of the cloud or a central server. Commonly referred to as edge computing, or fog computing, this approach provides real-time intelligence and enhanced control agility while simultaneously offloading the heavy communications traffic.

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