## 1 Introduction  
The deployment of Internet of Things (IoT) technologies has grown rapidly in multiple areas, particularly in Smart City initiatives, where dense digital infrastructure, stable connectivity, and centralized or cloud-based services enable advanced automation and real-time data analysis. Inspired by these models, the Smart Village [1; 2] concept was proposed to adapt Smart City principles to rural areas. However, these approaches typically focus on populated areas, ignoring uninhabited or low-population areas, such as remote agricultural lands, cultural heritage sites, and decentralized energy systems, where AI-assisted monitoring, anomaly detection, and asset protection are equally crucial. To expand the scope of rural innovation, the concept of Smart Rural Development has emerged. This emphasizes the integration of smart technologies into all types of rural environments, with or without inhabitants, with the goal of achieving inclusive, sustainable, and secure development [3]. Despite their potential in fields such as agriculture, heritage conservation, and environmental monitoring, rural areas often suffer from poor Internet coverage, limited access to skilled personnel, limited energy supplies, and poor infrastructure. Therefore, unlike smart cities, Smart Rural deployments must operate under more restrictive conditions. These constraints impact the choice of communication protocols and challenge the applicability of existing tools and datasets for performance testing and cybersecurity evaluation. Cybersecurity is especially critical in rural contexts, due to the increasing digitization of essential services such as irrigation, heritage conservation, wildfire detection, energy microgrids, and livestock monitoring. Security breaches can impact these critical services, causing significant economic losses, environmental damage, and even risks to human safety. These systems are often unmonitored and usually lack on-site technical staff, making it difficult to detect and respond to cyber threats. Various risks and vulnerabilities in smart farming environments have already been identified [4], including attacks that can lead to crop losses or equipment damage. This was further illustrated by [5], who showed how Denial of Service (DoS) attacks can disrupt smart agriculture by targeting ground sensors and autonomous vehicles, reinforcing the need for cybersecurity awareness in these ecosystems. Therefore, improving the responsiveness of IoT networks through context-aware intrusion-detection mechanisms and robust security architectures is not only relevant but essential for smart and sustainable rural development. Table 1 summarizes key differences between typical urban and rural IoT deployments, showing why distinct approaches are required to ensure effective design, testing, and security in rural deployments. The Smart Rural IoT Lab addresses this gap by providing a low-cost, modular, and autonomous platform capable of simulating realistic rural IoT deployments and generating real-world network traffic datasets. Additionally, the laboratory facilitates the exploration of use cases such as remote environmental monitoring, livestock and agricultural machinery tracking, control of access to facilities, and the protection of cultural heritage sites, offering a practical foundation for future developments in rural IoT applications and cybersecurity research. The primary objective of the Smart Rural IoT Lab is to provide a reproducible research infrastructure for experimenting with IoT technologies in Smart Rural environments. The platform enables the evaluation of communication protocols and security mechanisms, and