AI-based model driven approach for adaptive wireless sensor networks design

The development of IoT systems based WSN denotes a significant issue on providing intelligent capabilities to verify nodes behaviors and battery constraints. Existing AI-based works have been recently emerged for the analysis of dynamic WSN systems. Unfortunately, they failed to capture the design of dynamic intelligent WSN requirements at a high abstraction level. They provide AI solutions which are related to the target system and focus on specific problems without supporting reusability and interoperability. The Model Driven Engineering (MDE) and in particular the UML/MARTE profile become promising solutions for high-level abstraction to ease the design of WSN. We propose an AI-based model driven approach for the analysis and the prediction of WSN nodes behaviors and its interaction. It starts with a high-level specification based on the UML/MARTE profile, which describes the adaptation of WSN nodes and their interaction. Then, Model-to-Text (M2T) transformations are used to generate simulation scripts for analysis of WSN on a target AI-based platform. This later focuses on the prediction of WSN nodes behaviors, network clusters interaction and analysis of battery constraints. The prediction is based on training dataset which are collected from the German Weather Service (DWD) and measured within Measurement and Sensor Technology (MST) professorship, in the Technology University of Chemnitz.