## 6 Discussion  
The experimental results presented in this work demonstrate that the Smart Rural IoT Lab provides a functional, reproducible, and low-cost environment for research on rural IoT deployments. The six implemented scenarios validated the laboratory's ability to simulate representative applications such as environmental monitoring, livestock management, facility access control, and infrastructure monitoring, use cases that are highly relevant in Smart Rural contexts. By using low-cost hardware, open-source software, and both long-range (LoRaWAN) and short-range (WiFi) communication protocols, the laboratory replicates realistic configurations commonly found in rural environments. The results obtained in the six experimental scenarios highlight the adaptability of IoT communication protocols to rural environments and underscore the importance of selecting an appropriate approach based on local constraints and operational objectives. One of the most relevant findings is that there is no universally optimal protocol for rural IoT systems. Each technology tested in the laboratory showed distinct advantages and disadvantages. LoRaWAN, for example, offered long-range coverage with minimal power consumption, ideal for dispersed deployments. However, the use of The Things Network introduced a dependency on external cloud infrastructure, which may be problematic in areas with unreliable connectivity. The alternative ABP-based LoRaWAN configuration, which operated fully offline, proved more suitable for autonomous deployments without Internet access. In WiFi-based scenarios, the results demonstrate the effectiveness and flexibility of using local wireless networks for short-range IoT communication in rural environments. WiFi enables efficient data exchange between devices when localized access points are available, particularly in centralized infrastructures such as barns, machinery sheds, or rural processing facilities. An important contribution of this work is the demonstration of a complete, modular architecture supporting the full IoT data lifecycle, from acquisition to processing, storage, and visualization, without reliance on commercial services. Furthermore, a dataset of real network traffic was generated using tools such as Tcpdump and Zeek. Unlike simulation-based alternatives, this dataset reflects actual protocol usage and device behavior, including metadata such as payload sizes, protocol  
Figure 14: Most common SSH protocol versions. specific fields (e.g., MQTT topics, CoAP methods), and connection anomalies, making it valuable for cybersecurity and AI-based research. Several limitations must be acknowledged. All experiments were conducted in a controlled indoor environment. While this facilitated reproducibility, it limits the ecological validity of the results with respect to outdoor conditions such as terrain variability, weather effects, or long-distance signal degradation, but the captured traffic still exhibited temporal fluctuations and behaviors that are commonly observed in outdoor wireless deployments. Specifically, the analysis of network throughput over time (Figure 9) revealed patterns of activity that do not follow strict periodicity or uniformity. Furthermore, a high percentage of flows (76.48%) were classified with Zeek's _conn_state_ = 50, indicating either unresponsive endpoints or unacknowledged UDP-based exchanges. This suggests eventual loss or one-way communications, both typical in rural IoT networks. Future work will focus on extending the testbed to real outdoor, rural locations and capturing the physical-layer dynamics of such environments under various topological and climatic conditions. Future research will involve the injection of synthetic adversarial traffic into the testbed to simulate typical cyberattack scenarios encountered in rural IoT environments. This includes spoofing, replay attacks, and denial-of-service (DoS) conditions, which are common threats to resource-constrained deployments.