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This survey paper reviews hardware-rooted trust mechanisms, including TPMs, PUFs, and hybrid approaches, for securing AI-enabled IoT devices against physical attacks and ensuring AI model integrity. It finds that hardware-rooted solutions, especially PUF-based and hybrid designs, offer stronger protection against tampering and cloning compared to software-only methods, particularly in adversarial environments. The paper concludes by highlighting the trade-offs between security, scalability, cost, and deployment complexity to guide future development of trustworthy AI-enabled IoT platforms.
Software-only security is insufficient for AI-enabled IoT in adversarial settings: hardware-rooted trust mechanisms, like PUFs and hybrid designs, are the only viable path forward.
The rapid integration of artificial intelligence (AI) into Internet of Things (IoT) and edge computing systems has intensified the need for robust, hardware-rooted trust mechanisms capable of ensuring device authenticity and AI model integrity under strict resource and security constraints. This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems. It systematically examines and compares representative trust anchor mechanisms, including Trusted Platform Module (TPM)-based measurement and attestation, silicon and FPGA-based Physical Unclonable Functions (PUFs), hybrid container-aware hardware roots of trust, and software-only security approaches. The analysis highlights how hardware-rooted solutions generally provide stronger protection against physical tampering and device cloning compared to software-only approaches, particularly in adversarial and physically exposed environments, while hybrid designs extend hardware trust into runtime and containerized environments commonly used in modern edge deployments. By evaluating trade-offs among security strength, scalability, cost, and deployment complexity, the study shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems, whereas software-only trust mechanisms remain insufficient in adversarial and physically exposed settings. The presented comparison aims to clarify current design challenges and guide future development of trustworthy AI-enabled IoT platforms.
