Privacy Concerns Regarding Occupant Tracking in Smart Buildings

Abstract

Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can track people outside of and inside buildings; including GPS, visual-based tracking using surveillance cameras, and vibration based tracking using sensors such as accelerometers. In this work, those main systems for tracking occupants are compared and contrasted for the levels of detail they give about where occupants are, as well as their respective privacy concerns and how identifiable the tracking information collected is to a specific person. We discuss a case study using vibrations sensors mounted in Virginia Tech's Goodwin Hall in Blacksburg, VA that was recently conducted, demonstrating that similar levels of accuracy in occupant localization can be achieved to current methods, and highlighting the amount of identifying information in the vibration signals dataset. Finally, a method of transforming the vibration data to preserve occupant privacy was proposed and tested on the dataset. The results indicate that our proposed method has successfully resulted in anonymizing the occupant's gender information which was previously identifiable from the vibration data, while minimally impacting the localization accuracy achieved without anonymization.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis

“…These technologies are mainly based on imaging, infrared, ultrasound, Wi-Fi, radio frequency identification (RFID), and Bluetooth. Figure 2 (adapted from [24]) depicts these technologies in terms of accuracy and coverage [24]. cations to provide additional functionalities, such as the following.…”

A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments

“…To begin with, the presence of furniture and appliances in indoor environments decreases the ability to distinguish between occupants and objects [ 4 , 5 ], creating an ambiguity problem. Moreover, people may not want their movements to be tracked, especially in their own homes, and ensuring privacy while still accurately localising occupants poses a significant challenge [ 6 ]. With regards to the former, the most common methods used to deal with the ambiguity problem in localising occupants in indoor environments are: Multi-Sensor Fusion approaches [ 5 , 6 , 7 , 8 ]: They integrate data from multiple sensors such as cameras [ 9 , 10 , 11 , 12 ], microphones [ 13 ], passive infrared motion sensors (PIR), LiDAR, and ultrasonic sensors to improve the accuracy of the localisation system.…”

“…Moreover, people may not want their movements to be tracked, especially in their own homes, and ensuring privacy while still accurately localising occupants poses a significant challenge [ 6 ]. With regards to the former, the most common methods used to deal with the ambiguity problem in localising occupants in indoor environments are: Multi-Sensor Fusion approaches [ 5 , 6 , 7 , 8 ]: They integrate data from multiple sensors such as cameras [ 9 , 10 , 11 , 12 ], microphones [ 13 ], passive infrared motion sensors (PIR), LiDAR, and ultrasonic sensors to improve the accuracy of the localisation system. By using data from multiple sensors, the system can reduce the ambiguity in location estimation and provide more reliable and accurate results.…”

“…Multi-Sensor Fusion approaches [ 5 , 6 , 7 , 8 ]: They integrate data from multiple sensors such as cameras [ 9 , 10 , 11 , 12 ], microphones [ 13 ], passive infrared motion sensors (PIR), LiDAR, and ultrasonic sensors to improve the accuracy of the localisation system. By using data from multiple sensors, the system can reduce the ambiguity in location estimation and provide more reliable and accurate results.…”

“…To begin with, the presence of furniture and appliances in indoor environments decreases the ability to distinguish between occupants and objects [4,5], creating an ambiguity problem. Moreover, people may not want their movements to be tracked, especially in their own homes, and ensuring privacy while still accurately localising occupants poses a significant challenge [6]. With regards to the former, the most common methods used to deal with the ambiguity problem in localising occupants in indoor environments are:…”