Cost-effective Energy Optimization and Indoor Surveillance

Abstract

A cost-effective surveillance and energy optimization is the primary focus area of modern smart homes and buildings. Pyroelectric Infrared Sensor (PIR) is an excellent device to detect human/animal presence with a small form factor, rugged design, privacy noninvasive, and cost-effective surveillance. Often, a discrete ON/OFF decision of the PIR sensor is used to control lights, electrical appliances, and/or to activate the alarm upon the presence of a human/animal body in a surveillance zone of the sensor. This paper is focusing on decision-making based on the analog pattern obtained from the PIR sensor. An analog pattern and associated with wave shape can reveal valuable information about the direction of movement, approximate distance, and other parameters related to movement in the Field of View (FoV) of a sensor. Machine learning and time-series pattern analysis algorithms can be used to improve estimation reliability. Finally, the sensor module can be connected wirelessly with the master control PC by a low-cost LoRa module to log the movement analytics. The stored information can be useful for optimal logistics and background resource management. The method can be very effective compared with its peer camera-based surveillance that requires more processing power, cost, and more importantly, it is privacy-invasive. A group of Engineering Technology seniors was engaged as a part of their capstone project experience in building PIR circuit design, 3D printed customized sensor module housing design and recording physical movements for backend processing. This adds a valuable inter-disciplinary learning experience of professional project work.