A Lightweight Explainable AI-Enabled IOT Framework for Real-Time Smart Environment Monitoring using Intelligent Image Analytics

Chouhan, Ajay; Singh, Srikant; Parihar, Ashwin

doi:https://doi.org/10.55041/ijsmt.v2i5.198

Plagiarism Passed

Peer reviewed

Open Access

A LIGHTWEIGHT EXPLAINABLE AI-ENABLED IOT FRAMEWORK FOR REAL-TIME SMART ENVIRONMENT MONITORING USING INTELLIGENT IMAGE ANALYTICS

AUTHORS:

Ajay Chouhan

Srikant Singh

Ashwin Parihar

Mentor

Affiliation

DOI: 10.55041/ijsmt.v2i5.198

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DOWNLOAD ARTICLE

REVIEW REPORT

CITE THIS ARTICLE

Abstract

The rapid growth of smart environments has increased the demand for intelligent and energy-efficient IoT monitoring systems capable of real-time image analysis. This paper proposes a Lightweight Explainable AI-Enabled IoT Framework for Real-Time Smart Environment Monitoring Using Intelligent Image Analytics. The proposed framework integrates lightweight deep learning, edge computing, and Explainable Artificial Intelligence (XAI) to enable accurate and transparent environmental monitoring with reduced computational overhead and latency.

The system utilizes optimized convolutional neural networks for anomaly detection, object recognition, and environmental event classification using real-time visual and sensor data. The framework was evaluated using CIFAR-10, PASCAL VOC 2012, and a custom IoT environmental monitoring dataset. Experimental results achieved an accuracy of 98.4%, precision of 97.8%, recall of 97.2%, and reduced inference latency by 41% compared with conventional approaches. Additionally, the XAI module improved interpretability through real-time visual explanation of predictions.The proposed framework offers a scalable, low-cost, and reliable solution for smart city surveillance, industrial safety, and intelligent environmental monitoring applications.

Keywords

Article Information

Article Metrics

Article Views

PDF Downloads

HOW TO CITE

References

Ethics and Compliance

✓ All ethical standards met

This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

Indexed In