Explainable Artificial Intelligence for Smart IoT Systems: Techniques, Applications, and Challenges

Registration ID: IJNRD_326690

Published ID: IJNRD2606428

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Prof. D. B. Parase, Prof. P. S. Pandhare, Prof. P. I. Swami, Dr. J. I. Nandalwar, Prof. U. S. Bhandari

Explainable Artificial Intelligence, Internet of Things, Smart IoT Systems, XAI, AIoT, Trustworthy AI, Edge Computing, Machine Learning Interpretability, Deep Learning Transparency, Intelligent Systems.

The rapid expansion of the Internet of Things (IoT) has created highly interconnected intelligent environments where billions of devices continuously generate massive volumes of heterogeneous real-time data. Artificial Intelligence (AI), particularly machine learning and deep learning models, has become a fundamental component for enabling automated decision-making, predictive analytics, anomaly detection, and autonomous control within modern IoT systems. However, despite remarkable predictive performance, advanced AI models frequently operate as opaque black-box systems whose internal reasoning processes remain difficult for humans to interpret. This lack of transparency introduces serious concerns regarding trust, accountability, reliability, fairness, security, and regulatory compliance, particularly in safety-critical domains such as healthcare monitoring, industrial automation, autonomous transportation, smart city infrastructure, and cyber-physical systems. Explainable Artificial Intelligence (XAI) has emerged as a promising research paradigm designed to make AI-driven decisions understandable, interpretable, and trustworthy by revealing the reasoning mechanisms behind model predictions. This review paper presents a comprehensive and systematic analysis of Explainable Artificial Intelligence techniques for smart IoT systems. The study investigates the convergence of AI and IoT architectures, categorizes major XAI techniques including intrinsic and post-hoc explainability approaches, and examines practical applications across healthcare, smart cities, Industrial IoT, agriculture, cybersecurity, and autonomous systems. Furthermore, the paper critically evaluates current challenges including resource-constrained deployment, scalability limitations, privacy preservation, explanation fidelity, computational overhead, adversarial vulnerability, and lack of standardized evaluation frameworks. Finally, emerging research directions including federated explainable learning, edge-native XAI, self-explainable neural architectures, digital twin integration, explainable reinforcement learning, and trustworthy autonomous IoT ecosystems are discussed. The study provides researchers with a consolidated understanding of the evolving role of XAI in building transparent, secure, and human-centered intelligent IoT systems.

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Prof. D. B. Parase, Prof. P. S. Pandhare, Prof. P. I. Swami, Dr. J. I. Nandalwar, & Prof. U. S. Bhandari (June-2026). Explainable Artificial Intelligence for Smart IoT Systems: Techniques, Applications, and Challenges. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(6), e233-e247. https://ijnrd.org/papers/IJNRD2606428.pdf

Volume 11 Issue 6, June-2026

Pages : e233-e247

Paper Reg. ID: IJNRD_326690

Published Paper Id: IJNRD2606428

Research Area: Other area not in list

Author Type: Indian Author

Country: Solapur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2606428.pdf

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