Embedded Sensor-Data Compression Frameworks for Connected Lighting Systems: Poster.

We present compression algorithms for analog responses of Passive Infra-Red (PIR) sensors and a corresponding benchmarking framework based on ARM Cortex-M4 micro-controller. Compression ratio, reconstruction accuracy, memory footprint, and running times for a compression algorithm based on Discrete Cosine Transform (DCT) are presented. Analog responses can be compressed by up to 90% and recovered with less than 10% error. Our framework presents a first step in overcoming the computational limitations of the edge nodes in connected lighting systems to collect fine-grained occupancy patterns and enable beyond-lighting applications, such as Space Optimization and Heating Ventilation and Air Conditioning (HVAC) controls.