Enabling Seamless Device Association with DevLoc using Light Bulb Networks for Indoor IoT Environments
This addresses the need for seamless and privacy-aware device interactions in IoT settings, though it is incremental as it builds on existing visible light communication methods.
The paper tackles the problem of enabling spontaneous device associations in indoor IoT environments by using light bulb networks for proximity-based grouping, achieving best performance with machine learning-based signal similarity compared to other metrics.
To enable serendipitous interaction for indoor IoT environments, spontaneous device associations are of particular interest so that users set up a connection in an ad-hoc manner. Based on the similarity of light signals, our system named DevLoc takes advantage of ubiquitous light sources around us to perform continuous and seamless device grouping. We provide a configuration framework to control the spatial granularity of user's proximity by managing the lighting infrastructure through customized visible light communication. To realize either proximity-based or location-based services, we support two modes of device associations between different entities: device-to-device and device-to-area. Regarding the best performing method for device grouping, machine learning-based signal similarity performs in general best compared to distance and correlation metrics. Furthermore, we analyze patterns of device associations to improve the data privacy by recognizing semantic device groups, such as personal and stranger's devices, allowing automated data sharing policies.