[MA] Improving Interoperability of Wearables by Integrating them into the Web of Things

Start: 16.07.2023

Type: Master thesis

Student: Thomas Wehr

Supervisor: In cooperation with the Machine Learning and Data Analytics Lab (MaD Lab)

Abstract:
Integrating Bluetooth Low Energy (BLE)-enabled wearables into the Web of Things (WoT) architecture to improve interoperability is challenging. Existing solutions are often complex to customize and have issues with various BLE devices. This master thesis solves this problem through a comprehensive design that includes a WoT Protocol Binding for the Generic Attribute Profile (GATT) and Generic Access Profile (GAP), with algorithms for deserialization and serialization of bit-fields. This design is then applied to a real-world use case. This thesis aims to develop a versatile solution to improve WoT interoperability with BLE devices. Contributions include a GAP Protocol Binding for the WoT, deserialization and serialization algorithms for bit-fields, and validating the developed solutions in the context of an Android application for the health and fitness domain. The implementation uses WoT standards and semantic annotations to describe the wearables and SoLiD pods for decentralized payload data storage. The evaluation’s results confirm a seamless integration of new wearables with negligible overhead after implementing the WoT architecture and an efficient implementation of the GAP protocol bindings compared to prominent Node
BLE libraries. Additionally, the correctness of the bit-field deserialization and serialization algorithms is proven. Consequently, the approach facilitates the efficient integration of BLE devices and
enables standardized data interpretation within the WoT.