REAL-TIME HUMAN DETECTION IN LANDSLIDE ZONES USING 5G AND ADVANCED SENSOR NETWORKS
Landslides are catastrophic natural events that can lead to significant loss of life, as people often become trapped under debris or buried beneath the surface, making traditional rescue efforts extremely challenging. This project introduces a comprehensive real-time human detection system specifically engineered for landslide-affected areas, utilizing the latest advancements in 5G networks and sensor technology. The system integrates a variety of advanced sensors, including motion detectors, infrared cameras, mobile signal detectors, and Ground Penetrating Radar (GPR). These sensors work together to continuously monitor the environment, identifying signs of human presence such as body movement, heat signatures, and active communication signals. GPR further aids in pinpointing the locations of individuals who may be buried deep underground, providing a detailed subsurface map for the rescue teams. The high-speed and low-latency data transmission capabilities of 5G networks enable the system to process and analyze large volumes of sensor data in real time, significantly enhancing the speed and accuracy of victim detection. By leveraging 5G technology, the proposed solution can quickly relay critical information to emergency responders, facilitating prompt and informed decisionmaking. The system is designed to be scalable, making it deployable across a variety of landslide-prone regions. Its versatility and reliability position it as a valuable tool for emergency management agencies to improve rescue response times and minimize casualties during landslide incidents
Authors
- R. Mani Murali (ORCID: https://orcid.org/0000-0002-0539-8076)
- P Bhavya
- V. Abhijit
- Dr.A. Balamurugan
- R. Sandhya (ORCID: https://orcid.org/0000-0001-8737-837X)
- Mohammed Raeed
Institutions
- Indian Institute of Technology Palakkad (IN)
Publication Details
- Journal
- International Journal of Engineering Applied Sciences and Technology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.33564/ijeast.2024.v09i06.012
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00