### Abstract Landslides pose a severe and recurring threat to life, infrastructure, and economy in the Himalayan region, particularly in and around Shimla, Himachal Pradesh. Traditional landslide monitoring systems often suffer from high latency, dependency on continuous cloud connectivity, and prohibitive deployment costs in remote mountainous terrain. This independent research presents the design of a **cost-effective IoT-Edge AI framework** for real-time landslide prediction. The proposed system integrates low-cost IoT sensors for collecting critical environmental parameters (soil moisture, rainfall intensity, slope movement, vibration, etc.) with lightweight machine learning models deployed directly at the edge. By performing inference on resource-constrained edge devices, the framework significantly reduces response time, minimizes bandwidth usage, and lowers overall system cost while maintaining acceptable prediction accuracy. Key contributions include:- Architecture design of an IoT-Edge computing pipeline optimized for Himalayan conditions- Selection and optimization of lightweight AI models suitable for edge deployment- Sensor fusion techniques for improved prediction reliability- Cost analysis demonstrating affordability for large-scale deployment in vulnerable areas- Discussion on integration with existing early warning systems This work is conducted as a **self-initiated, unfunded personal research project** and is not affiliated with or supported by any organization. The framework aims to support local disaster management authorities and communities in building proactive, real-time landslide risk mitigation capabilities in Shimla and similar landslide-prone regions of India. **Keywords:** IoT, Edge AI, Landslide Prediction, Real-time Monitoring, Disaster Early Warning, Himachal Pradesh, Himalayan Region, Sensor Fusion, Cost-effective AI **Note:** This is a preprint version of the independent research article. Feedback and collaboration opportunities from NGOs, government agencies, and researchers working in disaster risk reduction are welcome.
Rahul Williams (Thu,) studied this question.