Empirical Dataset Generation for AI-Optimized IoT Infrastructure Placement

The strategic placement of nodes in Wireless IoT Networks (WIoTs) is crucial for ensuring optimal coverage, connectivity, and energy efficiency. Traditionally, node placement has relied on heuristic and manual methods, which often result in inefficiencies and suboptimal network performance. In this paper, we focus on optimizing the coverage performance of WIoTs, which play a pivotal role in environmental monitoring and event detection. In particular, we first develop a tool that allows IoT designers to simulate and generate datasets for multiple sensor deployment options. Then, we empirically generate a dataset that can contribute to the growing field of optimized sensor placement strategies by bridging algorithmic simulations with predictive modeling. Finally, we use the generated dataset to train a decision tree model for sensor node placement predictions. The prototype implementation of our tool and the generated datasets are publicly available for exploitation from the research community.