In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.

With the growing popularity and usage of smartphone devices, safeguarding it against malware becomes increasingly essential. In this paper, we define and present a strongly non-intrusive observation method that monitors network traffic data of the device to detect the presence of malware. The proposed method is advantageous as it neither requires any modification to the device, nor it needs any explicit connection between the device and the observing tool. We have evaluated the performance of two anomaly detection techniques, namely, changepoint detection and HOG+CNN, on the observed data. We compared the performance of the two detection techniques using both ordinary non-intrusive power signal data and strongly nonintrusive network traffic data. We also ran experiments to detect once-activated simulated malware and real malware. Validation tests confirm the effectiveness of the methodology in detecting the presence of malware.