Real-Time AI Surveillance Using Acoustic Sensors in Dense Forest Borders

Continuous monitoring in dense forest border regions is exceptionally challenging. Thick vegetation, rugged terrain, and poor visibility significantly degrade the effectiveness of traditional surveillance systems like CCTV cameras and aerial drones. In these dense, obscured landscapes, acoustic sensing offers a far more reliable alternative because sound waves naturally bypass physical and visual obstructions. Sound signatures from illicit activities—such as gunshots, chainsaws, or unauthorized vehicle movement—can act as immediate indicators of border incursions. This paper evaluates recent advancements published between 2022 and 2025 in the fields of acoustic sensor networks, IoT-driven surveillance, and deep learning architectures for environmental sound recognition. Beyond examining existing methodologies, we designed and simulated a functional, real-time acoustic monitoring framework. To minimize processing latency and ensure quick response times, the system leverages an edge-computing architecture that analyzes audio data close to the physical sensors. Audio features are extracted using the Librosa library to compute Mel-Frequency Cepstral Coefficients (MFCCs), which are then classified by a PyTorch-based Convolutional Neural Network (CNN). The broader infrastructure incorporates a FastAPI backend, simulated LoRa communication modules for energy-efficient, long-range data transmission, and an interactive command dashboard that manages live alerts, tracks active sensor nodes, and logs environmental events. The system architecture was evaluated against real-world operational challenges, including ambient environmental noise, unpredictable outdoor conditions, and the strict power limits of remote hardware. Ultimately, this research presents a scalable, energy-conscious surveillance model capable of maintaining a robust security posture where traditional visual systems fail.