Explainable AI in IoT: A Survey of Challenges, Advancements, and Pathways to Trustworthy Automation

Abstract

The integration of artificial intelligence (AI) into the Internet of Things (IoT) has revolutionized industries, enabling smarter decision-making through real-time data analysis. However, the inherent complexity and opacity of many AI models pose significant challenges to trust, accountability, and safety in critical applications such as healthcare, industrial automation, and cybersecurity. Explainable AI (XAI) addresses these challenges by making AI-driven decisions transparent and interpretable, empowering users to understand, validate, and act on algorithmic outputs. This paper examines the pivotal role of XAI in IoT development, synthesizing advancements, challenges, and opportunities across domains. Key issues include the computational demands of XAI methods on resource-constrained IoT devices, the diversity of data types requiring adaptable explanation frameworks, and vulnerabilities to adversarial attacks that exploit transparency. By looking at healthcare IoT, predictive maintenance, and smart homes, we can see how XAI bridges the gap between complex algorithms and human-centric usability—for instance, clarifying medical diagnoses or justifying equipment failure alerts. We discuss multiple XAI implementations with IOT, such as lightweight XAI for edge devices and hybrid models combining rule-based logic with deep learning. This paper advocates for XAI as a cornerstone of trustworthy IoT ecosystems, ensuring transparency without compromising efficiency. As IoT continues to shape industries and daily life, XAI will remain essential to fostering accountability, safety, and public confidence in automated systems.