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@article{167285,
        author = {V.Mahila and Dr. T. Thenmozhi},
        title = {Real-time landslide detection through edge AI solution},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1273-1279},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167285},
        abstract = {Outlines an innovative approach that harnesses the power of edge computing and AI to enhance the efficiency and accuracy of landslide detection. This system deploys a network of edge devices equipped with accelerometer sensor to continuously monitor environmental conditions such as soil moisture, seismic activity, and atmospheric parameters in real-time. By processing data locally on these edge devices, the system significantly reduces the latency typically associated with cloud-based solutions, enabling immediate analysis and rapid response to potential landslide threats. Advanced AI algorithms are utilized to analyzed data and identify early warning signs of landslides and providing timely alerts that can facilitate preventive measures and emergency response. This approach not only ensures a high level of data security and reduces bandwidth. The integration of Edge AI in landslide detection represents a significant advancement in natural disaster monitoring, offering a scalable and effective solution to mitigate the impacts of landslides and enhance community safety.},
        keywords = {Edge AI computing, STM32CubeIDE, Nano Edge AI, Anamoly classification, Motion detection.},
        month = {August},
        }