Long-term Habit Tracking from Lightbulbs

Today, the Internet of Things (IoT) permeates our lives, from working environments adjusting the temperature depending on occupation, to our ovens and dishwashers connected to our phones. Similarly, Philips Hue is a popular lighting system that builds on the ZigBee communication protocol [1]. 

Over the years, researchers have found a variety of vulnerabilities in the system [1,2,3] that allow users to easily eavesdrop on network communication, thus allowing them to extract information on the owner's usage of the system. With [3]'s approach, it became possible to extract ZigBee command usage from encrypted data traffic, thus allowing the unhindered profiling of users. 

This thesis proposes the use of the tool developed in [3] to expand simple user profiling to long-term habit tracking, to potentially track year round user habit differences between winter and summer, or even the potential linking of lighting habits with mood changes, excitement, or potentially depression. 

References:

1. https://essay.utwente.nl/89274/
2. https://arxiv.org/abs/2408.14613
3. https://ieeexplore.ieee.org/abstract/document/10814609

• Philips Hue Lighting System at Home