Smart environments are environments where digital devices are connected to
each other over the Internet and operate in sync. Security is of paramount
importance in such environments. This paper addresses aspects of authorized
access and intruder detection for smart environments. Proposed is PiBase, an
Internet of Things (IoT)-based app that aids in detecting intruders and
providing security. The hardware for the application consists of a Raspberry
Pi, a PIR motion sensor to detect motion from infrared radiation in the
environment, an Android mobile phone and a camera. The software for the
application is written in Java, Python and NodeJS. The PIR sensor and Pi camera
module connected to the Raspberry Pi aid in detecting human intrusion. Machine
learning algorithms, namely Haar-feature based cascade classifiers and Linear
Binary Pattern Histograms (LBPH), are used for face detection and face
recognition, respectively. The app lets the user create a list of non-intruders
and anyone that is not on the list is identified as an intruder. The app alerts
the user only in the event of an intrusion by using the Google Firebase Cloud
Messaging service to trigger a notification to the app. The user may choose to
add the detected intruder to the list of non-intruders through the app to avoid
further detections as intruder. Face detection by the Haar Cascade algorithm
yields a recall of 94.6%. Thus, the system is both highly effective and
relatively low cost.

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