This paper presents an Internet of Things (IoT)-based surveillance framework integrated with Safe and Explainable Artificial Intelligence (XAI) to resolve the transparency and reliability challenges inherent in black-box deep learning systems. Traditional autonomous surveillance models often lack interpretability, leading to high false-alarm rates, vulnerability to adversarial exploits, and delayed human verification. To address these limitations, we design a multi-sensor edge architecture that combines real-time video analytics with environmental telemetry while incorporating model-agnostic XAI frameworks (SHAP and LIME) to provide security operators with real-time, human-interpretable rationales for triggered alarms. To ensure operational safety and cyber-resilience against unauthorized intrusions, the system implements end-to-end AES-256 and SHA-512 cryptographic pipelines alongside model-pruning methodologies to eliminate edge-compute latency bottlenecks. Experimental results demonstrate that the proposed architecture achieves high threat-detection accuracy while significantly reducing Mean Time to Respond (MTTR), offering a scalable, reliable, and legally compliant paradigm for next-generation smart city and industrial security infrastructure.
Internet of Things (IoT), Explainable AI (XAI), Autonomous Surveillance, Deep Learning Transparency, Edge Computing, Cryptographic Security.
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