IoT-Based Home Automation System with Energy Monitoring and Optimization

Abstract

Ordinary households are turning into intelligent, connected places because of the evolution of Internet of Things (IoT) technology. Still integrated energy monitoring, real-time safety features, and decision-making skills are absent in most of existing home automation systems.In this paper the design, development and implementation of an IoT-based home automation system that includes layered safety features, rule-based artificial intelligence (AI), and real-time energy monitoring into a single framework is presented.The DHT22 temperature-humidity sensor, PIR motion sensor, LDR light sensor, MQ-2 gas sensor, and four ACS712 current sensors for per-appliance energy monitoring are all interfaced with an ESP32 microcontroller. The Blynk IoT platform allows cloud connectivity and user interaction remotely.In the system three automation rules are used:fan regulation based on temperature, light regulation based on ambient light, and appliance control based on occupancy. When there is unoccupancy condition, all appliances turn off automatically, which results in significant energy savings. Safety measures include prevention of overcurrent by cutoff of each appliance relay if current consumption of appliance goes above 3.00 A and automatic turning on of exhaust fan for ventilation, also immediate shutdown of appliances after gas detection to avoid ignition risk. Quick reporting of critical events was done through blynk notifications and automated email alerts. The proposed system was evaluated between the period of January 2026 to April 2026 and the results showed constant automation accuracy, accurate current readings for all the four appliances, and reliable emergency response. So according to the results the proposed system offers a practical, scalable, and cost-effective solution for modern smart homes. So the presented system successfully addresses user convenience, operational safety, and energy efficiency.

Introduction

The paper presents an IoT-based smart home automation system that integrates energy monitoring, safety features, and rule-based automation using an ESP32 microcontroller connected to sensors (temperature, light, motion, gas, and current) and cloud control via the Blynk platform. Unlike basic home automation systems that only allow remote switching, this design adds real-time energy tracking, safety mechanisms (gas leakage detection and overcurrent protection), and context-aware automation such as lighting and fan control based on occupancy, temperature, and ambient light.

The system addresses limitations found in existing research, such as lack of energy feedback, weak safety integration, scalability issues, and reliance on complex machine learning models. It uses a simple rule-based AI approach instead of heavy predictive algorithms to keep the system low-cost and efficient. Cloud connectivity enables remote monitoring, alerts, and control, though it depends on stable internet access.

Overall, the proposed system demonstrates improved practicality by combining automation, safety, and energy efficiency in a single integrated IoT framework, while acknowledging trade-offs such as limited real-time precision and reduced flexibility due to rule-based logic.

Conclusion

This paper presented the design and implementation of an Internet of Things (IoT) based home automation system that includes real-time energy monitoring, rule-based decision logic, and integrated safety features into a single structure. This ESP32 based system uses the Blynk IoT platform for cloud connectivity, embedded control and environmental sensing. The system seems to be more advanced than many basic automation systems because the components function collectively rather than as independent components. Based on the observations made throughout the three month evaluation time period it is observed that he automation seems to function effectively under different situations. The rule-based AI manages switching of the appliances based on occupancy condition, switches light in response to surrounding light conditions and manages fan operation based on temperature. By responding to only its own events, each rule helps to an overall decrease in energy consumption. Additionally, the system prevents unnecessary intervention, which improves user comfort. The Energy monitoring with the help of ACS712 sensors provide appliance-level data rather than average estimations. Thus, it is possible to observe consumption patterns over time and make a direct correlation with automation behavior. Along with the confirmation operation, the data collected gives confirmation of how and when energy savings take happen. Safety performance is another area where the proposed system shows consistent behavior. When the MQ-2 gas sensor detects gas leakage, the unneeded loads are immediately turned off and the exhaust fan gets switched on for ventilation. While the overcurrent protection mechanism further ensures that each appliance functions within defined limits. Redundancy is implemented by notifications given over the cloud platform using email and in-app alerts, which can reduce the probability of missing warnings. Thus, the system shows features that suggest applicability in real-world scenarios. It handles energy consumption, safety, and usability in an appropriate way while being cost-effective and scalable. However, an extension is still possible. Predictive models, particularly using lightweight machine learning, along with supporting more devices and environmental factors, could be explored in the future studies. Such improvements could improve adaptability but they would also need to be balanced against increasing system complexity.

References

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Copyright

Copyright © 2026 Prajakta Khenat, Sweety Kamble , Chandrakant Bhange. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.