AI and IoT-Based Smart Surveillance for Wildlife Protection
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901691025063936 |
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| author | Anand Kumar |
| author_facet | Anand Kumar |
| contents | <p>Wildlife poaching and habitat destruction pose significant threats to biodiversity, leading to the rapid decline<br>of endangered species. This paper presents a sustainable, AI-driven Internet of Things (IoT) surveillance<br>framework for real-time wildlife monitoring and poaching prevention. The proposed system integrates thermal<br>imaging, acoustic sensors, and unmanned aerial vehicles (UAVs) with convolutional neural networks (CNN)<br>for automated detection and classification of potential threats. Data is transmitted via low-power wide-area<br>networks (LPWAN) to cloud servers for centralized analytics, alert dissemination, and decision-making. The<br>approach focuses on energy efficiency, cost-effectiveness, and scalability to remote forest reserves. Simulation<br>and prototype evaluations demonstrate a detection accuracy of over 94% for human intrusions and an average<br>latency of under 2 seconds for alert transmission, making it a viable solution for large-scale wildlife protection.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16845599 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI and IoT-Based Smart Surveillance for Wildlife Protection Anand Kumar Artificial Intelligence, Internet of Things, Wildlife Protection, Smart Surveillance, Thermal Imaging, Poaching Detection, Convolutional Neural Network, Sustainability. <p>Wildlife poaching and habitat destruction pose significant threats to biodiversity, leading to the rapid decline<br>of endangered species. This paper presents a sustainable, AI-driven Internet of Things (IoT) surveillance<br>framework for real-time wildlife monitoring and poaching prevention. The proposed system integrates thermal<br>imaging, acoustic sensors, and unmanned aerial vehicles (UAVs) with convolutional neural networks (CNN)<br>for automated detection and classification of potential threats. Data is transmitted via low-power wide-area<br>networks (LPWAN) to cloud servers for centralized analytics, alert dissemination, and decision-making. The<br>approach focuses on energy efficiency, cost-effectiveness, and scalability to remote forest reserves. Simulation<br>and prototype evaluations demonstrate a detection accuracy of over 94% for human intrusions and an average<br>latency of under 2 seconds for alert transmission, making it a viable solution for large-scale wildlife protection.</p> |
| title | AI and IoT-Based Smart Surveillance for Wildlife Protection |
| topic | Artificial Intelligence, Internet of Things, Wildlife Protection, Smart Surveillance, Thermal Imaging, Poaching Detection, Convolutional Neural Network, Sustainability. |
| url | https://doi.org/10.5281/zenodo.16845599 |