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 ESP32-CAM 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.
Keywords—IoT, Intrusion Detection, ESP32, Smart Home
Security, PIR Sensor, LoRa, GSM, TinyML, Multi-Sensor
Fusion, Real-Time Alert