AI and IoT Based Anti-Poaching System
Vivaan Hooda1, Samarjeet Bhonsle2, Narendra Kumar S3
1Dr. Narendra Kumar, Assistant Professor, Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.
2Vivaan Hooda, Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.
3Samarjeet Bhonsle, Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.
Manuscript received on 09 January 2026 | First Revised Manuscript received on 02 April 2026 | Second Revised Manuscript received on 17 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 16-22 | Volume-14 Issue-6, May 2026 | Retrieval Number: 100.1/ijese.C263814030226 | DOI: 10.35940/ijese.C2638.14060526
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poaching of wildlife and deforestation are constant threats to ecological balance and biodiversity across the globe, particularly in nations such as India with extensive forest cover and diverse fauna richness. Conventional manual patrolling is limited in scale, coverage, and speed, making it impossible to prevent wildlife offences in remote forest areas. This paper presents the design and proposed deployment of an IoT- and AIenabled Anti-Poaching System for real-time detection of human intrusion and gunfire in safeguarded forest reserves. The solution includes an array of multi-sensor pods with cameras, microphones, and GPS modules. Lightweight AI models analyse sensed information, developed and evaluated on benchmark datasets, for human and animal identification, sound categorisation, and prompt alert generation. The AI system achieves high detection accuracy and low inference latency, as demonstrated by experimental evaluation, making it feasible for future integration into IoT hardware. This work highlights the value of integrating embedded systems, AI, and IoT technologies to develop cost-effective, scalable, and energy-efficient antipoaching solutions tailored to remote, resource-constrained forest environments.
Keywords: Acoustic Monitoring, Artificial Intelligence, Forest Conservation, IoT-based Surveillance, Machine Learning, Wildlife Protection.
Scope of the Article: Computer Science and Engineering