## 1 Introduction  
The Internet of Things (IoT) has experienced rapid development in recent years, transforming industries to Internet-based paradigms by enabling the connection of millions of devices [1]. Owing to advancements in 5G and the upcoming 6G wireless communication technologies, data transmission has become significantly faster and more reliable, laying a solid foundation for IoT and edge intelligence applications [2]. By 2030, it is projected that approximately 500 billion IoT devices will be in use globally [3]. IoT data generation is expected to grow significantly, with estimates suggesting a rise to 73.1 ZB by 2025, a 422% increase from the 17.3 ZB produced in 2019 [4]. IoT devices are increasingly used in smart homes [5], healthcare [6, 7], manufacturing [8, 9], and transportation [10, 11], where they collect vast amounts of data. These data are essential for deriving insights and powering edge intelligent applications through advanced analytics and machine learning [12], driving the convergence of IoT, edge devices, and cloud into a seamless computing continuum [13], which serves as a cornerstone of modern digital ecosystems. With the expansion of IoT and edge intelligence computing, ensuring the security and privacy of the massive amount of data generated and processed by these devices has become a significant challenge [14]. Trusted Execution Environment (TEE) provides a secure, isolated space within a processor where sensitive data and code can be processed, safeguarding them from unauthorized access or tampering [15]. TEE is crucial for protecting the growing volume of sensitive data generated by billions of interconnected devices in IoT. These devices often operate in untrusted environments and are vulnerable to security breaches, making TEE essential for ensuring data integrity and privacy [16]. Various TEE technologies enhance data security, including AMD Secure Encrypted Virtualization (SEV) [17] for virtual machine encryption, Arm TrustZone [18] for embedded devices, and Penglai Enclave [19] on RISC-V platforms. Intel SGX [20], in contrast, focuses on application-level security through enclaves, making it ideal for protecting sensitive data in cloud and edge intelligence applications [21] and is widely used in IoT [22; 23]. However, as edge intelligence becomes an essential component of IoT architectures, the usage of TEE in these scenarios faces several challenges. The resource constraints of edge devices, such as limited processing power and memory, coupled with the high-performance demands of real-time applications, make it difficult for TEE technologies to operate efficiently [24]. Additionally, ensuring data integrity [25; 26], managing I/O operations securely [27], and handling the performance overhead from frequent enclave transitions [28] present significant hurdles, limiting the widespread deployment of TEE-based solutions in edge intelligence. In this paper, we analyze the performance and security challenges of using SGX-based TEE in IoT and edge intelligence scenarios. Through a series of experiments, we evaluate the impact of TEE on system performance, focusing on key aspects such as I/O operations and data handling in resource-constrained environments. We concentrate on the performance of different SGX implementations in real-world scenarios, conducting comprehensive and detailed evaluations on SGX-based TEE through various carefully designed test cases. Our results reveal significant performance degradation and security limitations when using TEE technologies in edge scenarios. This research provides critical technical evaluations and security analyses that offer valuable insights for advancing the development and application of TEE in IoT and edge intelligence scenarios. The contributions of this paper could be summarized as follows:  
1. A comprehensive survey of current TEE technologies in IoT scenarios is conducted, identifying key challenges such as I/O security and performance overhead. 2.