Identification of Deployment Environments Based on Link Quality Fluctuation Patterns
Conference
Dargie, W, Farrokhi, S, Tasissa, A et al. (2025). Identification of Deployment Environments Based on Link Quality Fluctuation Patterns
. 10.1109/ICCCN65249.2025.11133893
Dargie, W, Farrokhi, S, Tasissa, A et al. (2025). Identification of Deployment Environments Based on Link Quality Fluctuation Patterns
. 10.1109/ICCCN65249.2025.11133893
Low power sensing networks often operate in open and potentially hostile environments. Ensuring that only legitimate devices communicate with the network is paramount. Authentication serves as the first line of defense in securing communication and data integrity, thereby protecting the network and devices from unauthorized access and data breaches. However, the ease of access to devices and sensors in the Internet-Of-Things (IoT) makes it easier for an attacker to replace legitimate devices and implant rogue ones in their stead; or physically tamper with devices (e.g., by moving them to another location). In this paper, we propose a resilient machine learning approach to uniquely identify deployment environments based on the link quality footprints of the devices that transmit from these environments. Our approach complements device identification based on unique RF transmission footprints. To the best of our knowledge, this is the first approach that attempts to uniquely identify the deployment environment despite considerable variations in both external and internal factors that affect signal propagation. We employ two different machine learning models, one based on a Convolutional Neural Network (CNN) and the other based on a Residual Network (ResNet). Through independent experiments involving actual deployments in five different environments in Miami, Florida (land, lake, Biscayne Bay, South Beach, and Crandon Beach), we attest that both models were able to uniquely identify the deployment environments with an average accuracy exceeding 99%. Furthermore, our models were able to distinguish between specific deployment configurations. In general, the ResNet model correctly identified the type of prototypes used with 100% accuracy (and CNN, with 98% accuracy). Both models were able to identify the types of radio used for transmission with 99% accuracy.
