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Natural disasters, such as landslides and floods, can cause significant loss of life, damage to property, and disruption of essential services. Timely and accurate early warning systems are vital for minimizing the impact of these disasters and enabling proactive emergency response. However, many regions prone to such disasters, particularly in remote areas, often suffer from limited network connectivity, which hampers the deployment and effectiveness of traditional early warning systems. In recent years, the rise of the Internet of Things (IoT) has revolutionized the way we collect, analyze, and utilize data. IoT technologies offer the potential to overcome the challenges posed by low network connectivity and provide real-time monitoring and early warning capabilities even in remote areas. By deploying a network of interconnected sensors, data can be collected and processed locally, reducing reliance on extensive network infrastructure. This paper proposes an IoT-based architecture specifically designed for landslide and flood early warning systems in regions with low network connectivity. The architecture harnesses the power of IoT devices, including wireless sensor networks, data analytics algorithms, and cloud computing, to enable the collection, analysis, and dissemination of critical data in real-time. The key contribution of this research is designing a robust and scalable IoT architecture tailored to the specific requirements of landslide and flood early warning systems. The architecture provided the efficient data collection mechanisms using wireless sensor networks to capture environmental parameters and provide real-time updates, implementing advanced data analytics algorithms to process sensor data and detect potential landslide or flood events. The architecture was designed a cloud-based infrastructure for storage, processing, and analysis of the collected data, considering the limitations of low network connectivity support by a reliable communication framework that enables timely dissemination of warnings and alerts to relevant stakeholders, including local communities and emergency response teams. The outcome of this research is expected to contribute to the advancement of early warning systems in low network connectivity regions prone to landslides and floods. By providing real-time monitoring, accurate detection, and timely warnings, the proposed IoT-based architecture can potentially save lives, minimize damages, and improve disaster response in remote and vulnerable areas.
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