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International Journal of Science, Strategic Management and Technology

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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.
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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.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 12 2026
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Chouhan, A., Singh, S. & Parihar, A. (2026). A Lightweight Explainable AI-Enabled IOT Framework for Real-Time Smart Environment Monitoring using Intelligent Image Analytics. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.198

Chouhan, Ajay, et al.. "A Lightweight Explainable AI-Enabled IOT Framework for Real-Time Smart Environment Monitoring using Intelligent Image Analytics." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.198.

Chouhan, Ajay,Srikant Singh, and Ashwin Parihar. "A Lightweight Explainable AI-Enabled IOT Framework for Real-Time Smart Environment Monitoring using Intelligent Image Analytics." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.198.

References
[1] Singh, S., & Sharma, A. (2021). The novel architecture for monitoring and prediction of rice plant diseases. International Journal of Advanced Research in Engineering and Technology, 12(3), 576–582. https://doi.org/10.34218/IJARET.12.3.2021

[2] Dewangan, D., & Singh, S. (2015). Analysis of flooding and directed diffusion protocol. International Journal of Science and Research, 1(1), 167–172.

[3] Mishra, S. K., & Singh, S. (2018). Survey of advanced palmprint recognition systems. International Journal of Innovative Research in Computer and Communication Engineering, 6(8), 7229–7236.

[4] Singh, S. (2018). Big data and its privacy and security concerns. International Journal of Engineering Science Invention (IJESI), 7(8), 53–56.

[5] Mishra, S. K., & Singh, S. (2018). Palm-print authentication using fuzzy logic approach. International Journal of Innovative Research in Computer and Communication Engineering, 8(1), 8140.

[6] Singh, S., & Shrivas, A. K. (2017). The analysis of the privacy issues in big data: A review. International Journal of Recent Trends in Engineering & Research, 3(4), 298–305.

[7] Srivastava, K. T., Patel, H., Kumar, S., Singh, S., Sahoo, S., Mohanta, S. C., Suman, S. K., & Sanjeev. (2024). AI based humanoid device for objects identification (Indian Patent No. 431745-001).

[8] Awasthi, R. K., & Singh, S. (2023). An overview of machine learning methods for the detection of diseases in rice plants in agricultural research. International Journal of Scientific Research in Science and Technology, 10(3), 837–846. https://doi.org/10.32628/IJSRST523103150

[9] Chauhan, A., Parihar, A., & Singh, S. (2025). From leaves to lab: Innovative methods in plant disease diagnosis. International Journal of Engineering in Computer Science, 7(1), 219–226. https://doi.org/10.33545/26633582.2025.v7.i1c.184

[10] Mehta, H., Singh, S., & Awasthi, R. K. (2025). A review of IoT-based technologies for identification and monitoring of rice crop diseases. International Journal of Latest Technology in Engineering, Management & Applied Science, 14(5), 418–422. https://doi.org/10.51583/IJLTEMAS.2025.140500042
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.
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