An Advanced Internet of Things Enabled Security System Design for Residential Intrusion Detection

The rapid proliferation of Internet of Things (IoT) technologies has enabled smart and responsive security systems for residential safety. This paper presents the design and evaluation of an advanced IoT-enabled intrusion detection system tailored for smart homes, leveraging a multi-sensor architecture combined with edge intelligence and real-time alert mechanisms. The proposed system incorporates PIR motion sensors, magnetic reed switches, sound and ultrasonic sensors, IR beam-break modules, and controlled by an ESP32 central microcontroller. Wi-Fi, GSM, and LoRa protocols are used to facilitate communication, and mobile alerts are received via mobile notifications, SMS, and email. There is also an ESP32CAM component to record visual evidence in the event of a security breach. The system includes a simple TinyML model and a rule-based event scoring system to improve the detection accuracy and minimize false positives. In real life situations involving residential contexts, experimental tests showed that it was able to detect with 96.7 accuracy with a false positive rate of less than 5%. Latency of alerting was 220 ms to 1450 ms based on the communication medium. Cloud interface is developed with Blynk and provides access to live event tracking, historical logs, and user roles. Its modular and energy-saving design suits the deployment in both urban and rural settings. Our system is much cheaper, less expensive than the current commercial systems, and is very reliable and flexible. This project provides a scalable, flexible and secure approach to residential intrusion detection that could be further enhanced with AI-based behavioral profiling, edge detection and collaboration with law enforcement systems to proactively stop any threat.