Published May 30, 2026 | Version CC-BY-NC-ND
Journal article Open

AI and IoT Based Anti-Poaching System

Authors/Creators

  • 1. Assistant Professor, Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.

Contributors

Contact person:

  • 1. Assistant Professor, Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.
  • 2. Department of Information Science and Engineering, R. V. College of Engineering, Bengaluru (Karnataka), India.

Description

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.

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Additional details

Identifiers

DOI
10.35940/ijese.C2638.14060526
EISSN
2319-6378

Dates

Accepted
2026-05-15
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

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