Building security systems are commonly deployed to detect intrusion and burglary in home and business structures. Such systems can accurately detect door open/close events, but their high-cost of installation and maintenance makes them unsuitable for certain building monitoring applications, such as times of high/low entrance traffic, estimating building occupancy, etc. In this paper, we show that barometer sensors found in latest smartphones can directly detect the building door open/close events anywhere inside an insulated building. The sudden pressure change observed by barometers is sufficient to detect events even in presence of user mobility (e.g. climbing stairs). We study various characteristics of the pressure variation due to door events, and demonstrate that door open/close events can be recognized with an accuracy range of 99.34% -- 99.81% based on the data collected from 3 different buildings. Such a low-cost ubiquitous solution of door event detection enables many monitoring applications without any infrastructure integration, and it can also work as an augmentation to the existing expensive security systems.

Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user?s voice input through the microphone. Such applications utilize hotwords such as "Okay Google" or "Hi Galaxy" allowing them to distinguish user?s voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices.

Wearable smart devices such as smart-glasses, smart-watches and life-logging devices are becoming increasingly popular, and majority of them are being equipped with first-person cameras. Such first-person cameras on smart-glasses or lifeloggers capture photos/videos from user's point of view, allowing them to record and share user's everyday events. However, these wearable devices with first-person cameras raise serious privacy concerns because they can also capture extremely private moments and sensitive information of the user. Currently, such devices lack the intelligence to understand user's preferences about certain scenarios being sensitive/private. To address this problem, we present PriFir, a scheme that enables Privacy-preserving First-person cameras. PriFir is based on the idea that low-power sensors (e.g. accelerometer, light sensor, etc.) embedded in smartphones and smart-watches can be leveraged to identify sensitive scenarios. Learning from user's preferences, PriFir employs a cascade of classifiers that tags a scenario to be sensitive simply based on the characteristics of the low-power sensor data. We evaluate PriFir using real sensor traces spanning over multiple days and show that it performs highly accurate classification at a low energy cost.