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Smart Detection of Elephant Intrusions Using LoRaWAN-Enabled IoT Sensors

Leveraging IoT and AI to mitigate human-elephant conflict through real-time detection and monitoring.

25-26J-015

Group Research Project

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Current limitations in manual patrols and electric fences necessitate a more robust early-warning solution.

Addressing the Conflict Gap

Problem Context

The Human–Elephant Conflict in Sri Lanka causes significant agricultural loss and safety risks to rural villagers.

Current methods like Electric Fences and Manual Patrols are often reactive and suffer from high maintenance costs.

Existing Camera Traps face limitations due to low visibility in dense foliage and delayed data transmission.

The Research Gap lies in the need for a low-power, long-range system capable of early vibration and motion detection.

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System Framework Overview

A four-layered approach to detection, communication, processing, and real-time monitoring.

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Field Sensor Node

Utilizes Geophone and PIR sensors to capture ground vibrations and physical movement of large animals.

Communication Layer

Deploys LoRaWAN technology for low-power, long-distance data transmission across challenging terrain.

Processing Layer

Applies Detection Logic and ML models to distinguish elephant movement from environmental noise.

Cloud & Monitoring

Provides a Real-time Dashboard and automated alert system for farmers and wildlife authorities.

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Technical Technology Stack

High-performance hardware paired with a scalable cloud backend for data integrity and speed.

Technology Stack

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Hardware Components

ESP32 Microcontroller for low-power processing Geophone SM-24 for seismic vibration sensing PIR Sensor for proximity motion detection LoRa Module (SX1278) for wireless telemetry

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Software & Network

LoRaWAN Gateway for rural internet connectivity Backend Cloud for data storage and analysis Web Dashboard for visual tracking and history SMS/App Alerts for immediate threat notifications

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Implementation Status

Current progress showing the transition from individual sensor testing to full system integration.

Node Prototype

Successfully assembled the Sensor Node with integrated Geophone and PIR modules.

Data Collection

Captured initial baseline Vibration Data for environmental noise filtering calibration.

LoRa Connectivity

Established stable Node-to-Gateway communication over a verified distance.

Dashboard Interface

Developed the Basic Web Dashboard for real-time visualization of sensor triggers.

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Live Demonstration Plan

A step-by-step walkthrough of the intrusion detection and alert sequence.

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Sensor Trigger

Simulated Elephant Movement triggers vibration and motion sensors.

LoRa Transmission

Encrypted sensor packets are sent via LoRa Radio to the gateway.

Cloud Processing

Server analyzes the Signal Patterns to confirm a valid intrusion event.

Dashboard Alert

Visual and Audible Alerts are triggered on the monitoring interface.

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Performance Evaluation Metrics

Quantitative analysis of system reliability and operational efficiency.

Key Metrics

90%

Detection Accuracy

Targeted accuracy for distinguishing large animal movement.

5s

Response Time

Total latency from sensor detection to dashboard alert.

15K

Comm Range

Maximum LoRa communication range in line-of-sight conditions.

6M

Battery Life

Projected operational duration on a single solar-assisted charge.

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Mitigating Sensor Noise from rain or traffic using digital signal processing (DSP) filters.

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Enhancing LoRa Reliability through adaptive data rates and frequency hopping techniques.

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Optimizing Power Consumption via Deep Sleep modes and energy-efficient firmware logic.

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Developing Weatherproof Enclosures to protect sensitive electronics from high humidity and heat.

Challenges & Strategic Solutions

Overcoming environmental and technical hurdles through innovative engineering.

Challenges

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Future Impact & Scalability

Envisioning a nationwide network for human-wildlife coexistence.

Community Safety

Directly reduces human and elephant fatalities through reliable Early Warnings.

ML Model Refinement

Training the system on larger datasets for even higher Detection Precision.

Large Scale Deployment

Expanding the sensor mesh to cover entire National Park boundaries.

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