## 7 Conclusions  
This work presented the Smart Rural IoT Lab, a reproducible and cost-effective laboratory specifically designed to support research on IoT communication, cybersecurity, and artificial intelligence in rural environments. Unlike many existing research platforms that focus on urban or industrial settings, the laboratory validates the operational viability of low-power, modular infrastructures based on open-source tools and affordable commercial hardware such as low-cost sensors, gateways, and Arduino boards. The six experimental scenarios demonstrated that effective IoT deployments are feasible, even in environments with limited or no Internet connectivity, supporting use cases such as environmental monitoring, livestock tracking, wildfire detection, and facility access control. A key contribution of this work is the generation of a dataset built from real network traffic, captured through passive monitoring using tools such as Tcpdump and Zeek. This dataset reflects protocol usage and device behavior, making it valuable for future research in AI-based intrusion detection and anomaly analysis. The complete data lifecycle (acquisition,storage, and visualization) is implemented using open-source software and does not rely on cloud infrastructure, enabling fully local operation. Despite these achievements, several limitations remain. Experiments were conducted in a controlled indoor environment, which did not replicate the physical challenges of rural outdoor deployments. Additionally, the dataset currently only includes benign traffic. In future work, the platform will be extended to include the controlled injection of malicious traffic. This will simulate common threat scenarios in IoT environments, such as spoofing, replay, and denial-of-service (DoS) attacks. Tools like hping3, kali linux, scapy, and tcpreplay will be used to generate reproducible adversarial flows. Each trace will be labeled accordingly and integrated into the existing dataset to support supervised machine learning methods and facilitate reproducible intrusion-detection experiments. Another pending enhancement is node-level energy profiling to evaluate energy efficiency, particularly for battery-powered devices. The modular architecture of the laboratory allows for easy reconfiguration, making it suitable for exploring alternative network topologies or new communication protocols. Future plans include expanding the range and diversity of sensors, developing new rural application scenarios, and integrating machine learning models for real-time threat detection. The infrastructure is well suited to support lightweight AI models, including federated learning. In summary, the Smart Rural IoT Lab provides a flexible and realistic foundation for experimentation in digitally underserved environments. It contributes to reducing the gap between urban and rural technology adoption and supports the development of secure, sustainable, and autonomous IoT solutions for Smart Rural environments. Conceptualization, D.S.-I., R.P.-V. and A.R.-G.; methodology, D.S.-I., R.P.-V. and A.R.-G.; software, D.S.-I. and P.V.-B. ; validation, R.P.-V. and A.R.-G.; formal analysis, D.S.-I., R.P.-V, and A.R.-G.; investigation, D.S.-I., R.P.-V. and A.R.-G.; resources, D.S.-I., L.T., R.P.-V., A.R.-G., P.V.-B. and J.S. ; data curation, D.S.-I., L.T., R.P.-V. and A.R.-G.; writing--original draft preparation, D.S.-I. ; writing--review and editing, R.P.-V. and A.R.-G.; visualization, D.S.-I., L.T., R.P.-V. and A.R.-G.; supervision, R.P.-V. and A.R.-G.; project administration, R.P.-V. and A.R.-G.; funding acquisition, R.P.-V. and A.R.-G. All authors have read and agreed to the published version of the manuscript. The Spanish Government (through the Cybersecurity Institute of Spain, INCIBE) with the International Chair \"Smart Rural IoT and Secured Environments\", within the context of the Recovery, Transformation and Resilience Plan financed by the European Union (NextGenerationEU/PRTR). The resulting dataset presented in this work is currently not publicly available.