Smart Home – “Aashraya” IoT Automation System
DOI:
https://doi.org/10.15680/IJCTECE.2023.0601007Keywords:
Smart Home, Internet of Things, Automation, gas management, water management, intrusion detection, machine learningAbstract
Smart homes increasingly integrate connected sensors, automated control, and intelligent security functions, but fragmented subsystem management can limit the effectiveness of household resource and safety automation. This paper presents Aashraya, an integrated IoT-based smart-home framework that combines gas-cylinder monitoring and automated refill support, overhead water-tank control, and CNN-based intrusion detection within a unified architecture. The proposed system uses sensor nodes, MQTT-based communication, edge processing on a Raspberry Pi, database services, and a mobile application for monitoring and alert delivery. Gas and water resource-management behaviour was evaluated using the reported household usage scenarios, while the security module was evaluated using a 700-image dataset containing authorized-resident and unknown-visitor images
The reported results show that average gas-outage duration decreased from 2.3 to 0.2 h/month, corresponding to a 91.3% reduction in outage duration, while the automated gas-management process provided a multi-day replacement buffer. For water management, pump on/off errors decreased from 40% under the baseline condition to 0% with the proposed system, and average pump energy consumption decreased from 480 to 384 Wh/day, corresponding to a 20.0% reduction. On the reported 140-image test set, the intrusion classifier achieved 95.0% accuracy, 90.2% precision, 92.5% recall, and a 91.4% F1 score. MQTT sensor-to-database communication exhibited a reported round-trip latency of 50 ± 10 ms under the evaluated multi-sensor configuration
The findings demonstrate the feasibility of integrating household resource automation, intrusion monitoring, and low-latency IoT communication within a unified smart-home platform. However, the evaluation is limited by the controlled test environment, partly simulated resource-usage scenarios, and the relatively modest security dataset. Further validation using longer-duration real-world deployments, larger and more diverse datasets, and broader sensor configurations is required to establish generalizability and large-scale deployment performance
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