Hanlin Cai

University of Cambridge | Cam · Department of Engineering (IoE Group)

Bluetooth Low Energy (BLE) serves as a critical protocol for lowenergy communication, playing a vital role in various sectors including industry, healthcare, and home automation. Despite its widespread adoption, inherent security limitations and firmware vulnerabilities expose BLE to significant risks, notably from spoofing attacks that threaten de...

As the most popular low-power communication protocol, cy-bersecurity research on Bluetooth Low Energy (BLE) has garnered significant attention. Due to BLE's inherent security limitations and firmware vulnerabilities, spoofing attacks can easily compromise BLE devices and tamper with privacy data. In this paper, we proposed BLEGuard, a hybrid detect...

This paper utilises image pre-processing techniques and deep residual neural networks to enhance the traffic sign detection system. A novel Analytic Hierarchy Process (AHP) model for performance evaluation has been proposed and utilised to determine the optimal parameter configuration of the learning models. Four evaluation metrics, namely accuracy...

This paper established three deep residual neural network models with different architectures for traffic sign detection. Also, a new systematic analytic hierarchy process method for model performance evaluation has been proposed, which was utilized to determine the configuration of the deep learning model. In this paper, four evaluation metrics we...

Nowadays, with the increasing output of municipal waste, the pressure on municipal waste treatment is increasing. In this case, utilizing low-cost and low-power IoT technology to improve urban waste management has become a popular trend. This paper proposes an intelligent garbage management system for urban communities: Garbage Manager. The Garbage...