An IoT Based Smart Home System for Fault Detection

This paper includes an IoT-based smart house system to detect faults in real time with the help of embedded platforms including. It incorporates the environmental, electrical, and operation sensors to detect abnormalities and failures of the home facilities and equipment. NODE to NODE communication is facilitated with BLE, Zigbee, Z-Wave and Wi-Fi that is optimized to support different power and bandwidth demands. Firmware is run on microcontrollers, which have a capability to interface low-level sensors and do edge processing in C/C++, or Python. Advanced diagnostics are done on complex components based on embedded Linux and either Python or JavaScript. The cloud services such as AWS IoT offer access at a distance, data recordings, and fault classification based on machine learning. Scalable and modular system architecture ensures powerful and effective fault detection in a smart home setting. Isolation Forest and LSTM neural networks for sensor problem diagnosis and predictive maintenance. Network adaptability is increased by local fallback strategies, watchdog timers, and secure firmware updates. This research supports the development of smart home systems which are scalable, safe, and intelligent.