Edge Computing and Sensor Fusion Approaches for Smart Wildlife Surveillance in Protected Areas

Abstract

Smart wildlife surveillance in protected areas is also an intelligent monitoring system, such as one that can provide accurate, real-time observations, with limited communication, energy, and computation resources. This research introduces an edge computing and sensor fusion system combining camera traps, acoustic sensors, thermal imaging cameras and LiDAR sensors for continuous monitoring of wildlife without any latency. At the distributed edge nodes, lightweight deep learning models are applied to process multi-modal data for the purpose of object detection, species classification and localization of the wildlife and send summarized data to the centralized cloud platform. Under different lighting, vegetation density and weather conditions, the robustness of the sensor fusion can be improved, and environmental noise can be reduced to trigger false alarms. The proposed architecture is edge intelligence with adaptive inference, efficient resource utilization and real-time event notification, which enables quick conservation decision making and anti-poaching surveillance. High detection accuracy, reliable species identification, low inference latency and efficient edge resource usage are confirmed by experimental evaluation in a protected ecosystem under various habitat conditions. Edge computing combined with heterogeneous sensing has been found to provide better monitoring coverage, scalability, communication efficiency, and operational reliability than traditional cloud-based ones when compared against them. The proposed framework is scalable, energy efficient and intelligent solution for ecosystems monitoring and management, wildlife conservation and for next generation of protected landscapes in remote areas.