IaaSI: a device based interoperability as a service for IoMT devices

Interoperability is a crucial aspect of the effective functioning of Internet of Things (IoT) devices, particularly in the healthcare industry. Although the use of IoT devices in healthcare has brought numerous benefits, such as remote sensing, monitoring, and data analysis, it has also introduced new challenges, notably in the area of interoperability. The lack of semantic and syntactic interoperability has hindered the ability of these devices to communicate and share data, leading to inefficiencies and limitations in their use. To address these challenges, this study proposes a solution that employs natural language processing (NLP) techniques to enhance the efficiency and effectiveness of healthcare IoT. Specifically, the solution utilizes Bidirectional Encoder Representations from Transformers (BERT) based string matching and Fuzzy Inference System (FIS) to facilitate data correlation with an existing vocabulary and a parser. The proposed approach was evaluated with real-world data from healthcare IoT devices, yielding an accuracy of 85.71% and an average processing delay of 0.46 s, thus demonstrating the potential of natural language processing techniques to enhance the interoperability of healthcare IoT devices.