Scalable Generation of Synthetic IoT Network Datasets: A Case Study with Cooja

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks.