{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:19:55Z","timestamp":1775081995738,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T00:00:00Z","timestamp":1762992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RA Science Committee","award":["22rl-052"],"award-info":[{"award-number":["22rl-052"]}]},{"name":"European Union\u2014Next Generation EU","award":["B53C22003970001"],"award-info":[{"award-number":["B53C22003970001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u201375 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.<\/jats:p>","DOI":"10.3390\/fi17110518","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T12:57:13Z","timestamp":1763038633000},"page":"518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Scalable Generation of Synthetic IoT Network Datasets: A Case Study with Cooja"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1544-5649","authenticated-orcid":false,"given":"Hrant","family":"Khachatrian","sequence":"first","affiliation":[{"name":"Machine Learning Group, Center for Mathematical and Applied Research, Yerevan State University, Yerevan 0025, Armenia"},{"name":"YerevaNN, Yerevan 0025, Armenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aram","family":"Dovlatyan","sequence":"additional","affiliation":[{"name":"YerevaNN, Yerevan 0025, Armenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Greta","family":"Grigoryan","sequence":"additional","affiliation":[{"name":"Machine Learning Group, Center for Mathematical and Applied Research, Yerevan State University, Yerevan 0025, Armenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2906-584X","authenticated-orcid":false,"given":"Theofanis P.","family":"Raptis","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97052","DOI":"10.1109\/ACCESS.2019.2929296","article-title":"Data Management in Industry 4.0: State of the Art and Open Challenges","volume":"7","author":"Raptis","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4468","DOI":"10.1109\/TII.2018.2856884","article-title":"From Best Effort to Deterministic Packet Delivery for Wireless Industrial IoT Networks","volume":"14","author":"Koutsiamanis","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_3","unstructured":"Mekhilef, S., Favorskaya, M., Pandey, R.K., and Shaw, R.N. (2021, January 2\u20133). Performance and Parametric Analysis of IoT\u2019s Motes with Different Network Topologies. Proceedings of the Innovations in Electrical and Electronic Engineering, New Delhi, India."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109456","DOI":"10.1016\/j.comnet.2022.109456","article-title":"IoT and digital circular economy: Principles, applications, and challenges","volume":"219","author":"Voulgaridis","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., and Voigt, T. (2006, January 14\u201316). Cross-level sensor network simulation with cooja. Proceedings of the 2006 31st IEEE Conference on Local Computer Networks, Tampa, FL, USA.","DOI":"10.1109\/LCN.2006.322172"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101089","DOI":"10.1016\/j.softx.2022.101089","article-title":"The Contiki-NG open source operating system for next generation IoT devices","volume":"18","author":"Oikonomou","year":"2022","journal-title":"SoftwareX"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grigoryan, G., Khachatrian, H., and Raptis, T.P. (2024, January 9\u201311). Toward Automating Cooja Experiment Workflows for Dataset Generation. Proceedings of the 2024 11th International Conference on Software Defined Systems (SDS), Gran Canaria, Spain.","DOI":"10.1109\/SDS64317.2024.10883894"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Essop, I., Ribeiro, J.C., Papaioannou, M., Zachos, G., Mantas, G., and Rodriguez, J. (2021). Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks. Sensors, 21.","DOI":"10.3390\/s21041528"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s43586-024-00294-7","article-title":"Graph neural networks","volume":"4","author":"Corso","year":"2024","journal-title":"Nat. Rev. Methods Prim."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.pmcj.2019.04.007","article-title":"Multi-objective surrogate modeling for real-time energy-efficient station grouping in IEEE 802.11ah","volume":"57","author":"Tian","year":"2019","journal-title":"Pervasive Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ngo, D.T., Aouedi, O., Piamrat, K., Hassan, T., and Raipin-Parv\u00e9dy, P. (2023). Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities. Future Internet, 15.","DOI":"10.3390\/fi15120377"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12021","DOI":"10.1109\/JIOT.2021.3075901","article-title":"A Theoretical Discussion and Survey of Network Automation for IoT: Challenges and Opportunity","volume":"8","author":"Arzo","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1109\/JIOT.2017.2786639","article-title":"Internet of Things (IoT): Research, Simulators, and Testbeds","volume":"5","author":"Chernyshev","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Almutairi, R., Bergami, G., and Morgan, G. (2024). Advancements and Challenges in IoT Simulators: A Comprehensive Review. Sensors, 24.","DOI":"10.3390\/s24051511"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1002\/spe.2787","article-title":"IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments","volume":"50","author":"Jha","year":"2020","journal-title":"Softw. Pract. Exp."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111351","DOI":"10.1016\/j.jss.2022.111351","article-title":"iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments","volume":"190","author":"Mahmud","year":"2022","journal-title":"J. Syst. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Levis, P., Lee, N., Welsh, M., and Culler, D. (2003, January 5\u20137). TOSSIM: Accurate and scalable simulation of entire TinyOS applications. Proceedings of the SenSys \u201903: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, CA, USA.","DOI":"10.1145\/958491.958506"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dunkels, A., Osterlind, F., Tsiftes, N., and He, Z. (2007, January 25\u201326). Software-based on-line energy estimation for sensor nodes. Proceedings of the 4th Workshop on Embedded Networked Sensors, Cork, Ireland.","DOI":"10.1145\/1278972.1278979"},{"key":"ref_19","unstructured":"Moteiv Corporation (2025, October 06). Tmote Sky Wireless Sensor Node Datasheet. Available online: http:\/\/www.crew-project.eu\/sites\/default\/files\/tmote-sky-datasheet.pdf."},{"key":"ref_20","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017, January 4\u20139). Inductive representation learning on large graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_21","unstructured":"Rusch, T.K., Bronstein, M.M., and Mishra, S. (2023). A survey on oversmoothing in graph neural networks. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/11\/518\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T13:11:29Z","timestamp":1763039489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/11\/518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":21,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["fi17110518"],"URL":"https:\/\/doi.org\/10.3390\/fi17110518","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]}}}