# Edge Intelligence: Edge Computing for 5G and the Internet of Things
## 2 An Overview of Published Articles
(contribution five) addresses the critical issue of securing Internet Protocol security (IPsec) tunnels against future quantum computing threats by integrating post-quantum cryptography (PQC) algorithms. The authors experimentally demonstrate the first IPsec tunnel secured by PQC algorithms such as Falcon, Dilithium, and Kyber, deploying it in two scenarios: a high-performance data center environment with a 100 Gbit/s line rate throughput and a wireless client-to-cloud connection with a 0.486 Gbit/s throughput. The significance of this work lies in its contribution to the development of quantum-resistant communication protocols and the adoption of PQC in existing network infrastructures, ensuring data security in the post-quantum era. In the sixth article, [PERSON] and [PERSON] (contribution six) tackle the critical issue of determining driver anxiety levels while driving, which is crucial for improving driving safety and reducing the risk of traffic accidents. The authors propose an intelligent system based on fuzzy logic to decide a driver's anxiety level (DAL) by considering various factors, including driving experience, in-car environment conditions, and driver age. They implement and compare two fuzzy-based models (DALM1 and DALM2), with DALM2 incorporating an additional parameter related to accident anxiety state. The simulation results show that the system can effectively evaluate driver anxiety levels, with DALM2 providing more accurate assessments due to its more comprehensive rule base. The study bridges the gap between psychological and technical approaches in assessing driver anxiety, providing a reliable and explainable method for real-time driver state evaluation in intelligent transportation systems. The research from [PERSON] et al. (contribution seven) delves into the problems faced by diagnosing respiratory diseases, particularly asthma, using lung sound recordings. Traditional diagnostic methods, such as the stethoscope, are limited by their reliance on physician expertise, lack of recording functionality, and inability to filter out noise. Cloud-based deep learning solutions, while effective, often face issues related to latency, Internet dependency, and privacy concerns. To overcome these limitations, the authors developed Tiny Machine Learning (TinyML) models that can be deployed on low-power, cost-effective devices like digital stethoscopes to provide the real-time and accurate diagnosis of respiratory conditions. They trained and compared three models--a custom CNN, an Edge Impulse CNN, and a custom LSTM--using a publicly available lung sound dataset. The custom CNN achieved the highest accuracy while maintaining moderate resource usage. This work highlights the potential of TinyML to enhance healthcare by providing accessible, reliable diagnostic tools, especially in remote and underserved areas. [PERSON] and [PERSON]'s study (contribution eight) presents EdgeGuard, a novel decentralized architecture that addresses the challenges of data privacy, security, and resource management in Internet of Medical Things (IoMT) networks. It integrates blockchain technology, federated learning, and edge computing to enable secure and efficient medical resource orchestration. The key contributions include a federated learning algorithm optimized for medical data with differential privacy, a lightweight blockchain consensus mechanism tailored for IoMT devices to ensure data integrity and security, and an adaptive edge resource allocation method to enhance system scalability and efficiency. Additionally, it introduces an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard significantly improves computational performance, data value, and privacy protection compared to existing solutions. This work provides a comprehensive and effective framework for secure and efficient medical data management in IoMT ecosystems. The work from [PERSON] et al.