AN IOT BASED SMART LAKE FIRE DETECTION MONITORING AND ALERT SYSTEM

Abstract

The Smart Lake Monitoring System project offers a pragmatic solution to the pressing need for fire prevention and safety measures in lakeside environments. By deploying smoke, temperature, and methane sensors strategically across susceptible lakeside locations, the system continuously monitors real-time environmental conditions, focusing on temperature changes, smoke presence, and methane levels as critical indicators of potential fire outbreaks or hazardous gas leaks. Its primary objective is to provide timely alerts via SMS notifications through a GSM module to designated stakeholders, enabling immediate action in response to potential fire hazards.The system also integrates a cloud-based machine learning (ML) model hosted on AWS SageMaker to analyze sensor data, calculate risk scores, and determine the likelihood of a fire or gas leak. This enhances the system's accuracy and reliability in detecting and responding to potential threats. Upon detecting conditions that exceed predefined thresholds, the system triggers an SMS alert, ensuring a swift and coordinated response. Unlike complex monitoring systems, the project prioritizes simplicity and practicality, emphasizing efficient sensor deployment, cloud-based data processing, and real-time SMS notifications over extensive user interfaces. Through its straightforward approach, the system ensures comprehensive monitoring, timely detection, and proactive response to fire and gas leak risks, thereby enhancing safety and safeguarding communities and ecosystems in lakeside areas cost-effectively. Overall, the Smart Lake Monitoring System project represents a scalable solution for addressing fire and gas leak risks in lakeside environments, emphasizing simplicity, efficiency, and real-time alerting to promote safety effectively.

Specification

Description:The Analysis of Accident Detection and Alert System Based on IoT (100) comprises sensors
and an Arduino controller (101) that detects environmental hazards. The system includes a
smoke sensor (102) to detect smoke, a temperature sensor (103) to identify sudden temperature
rises, and a methane sensor (104) to monitor methane gas levels. The Arduino controller (101)
decodes the sensor data and transmits it to the AWS IoT cloud (105) via a WiFi module for
further analysis using a machine learning model in AWS SageMaker (106). The ML model
processes the data, calculates an average risk score, and determines if the risk exceeds a pre-
defined threshold. If the risk score is above the threshold, the system triggers an SMS alert to the
nearest fire station using the GSM module (107), ensuring a timely response to potential fire
hazards. , C , Claims:1. Analysis of Accident Detection, Alert System Based on IoT (100) comprise ; sensors
Arduino (101).
2. Analysis of Accident Detection, Alert System Based on IoT (100) where smoke sensor(102)
detects the smoke in the surroundings , temperature sensor(103) detects the sudden rise in
temperature and smoke sensor(104) detects the methane gas in the surroundings.
3. Analysis of Accident Detection, Alert System Based on IoT(100) wherein; wherein the
Arduino controller (101) decodes the sensor data and transmits this data to the AWS IoT
cloud(105) via a WiFi module for further analysis using a machine learning model in AWS
SageMaker(106) .
4. Analysis of Accident Detection and Alert System Based on IoT (100); wherein the machine
learning model processes the incoming data, calculates an average risk score, and determines
if the risk exceeds a pre-defined threshold .
5. Analysis of Accident Detection and Alert System Based on IoT (100); wherein upon
detection of a risk score above the threshold, the system triggers an SMS alert to the nearest
fire station using the GSM module (107), ensuring timely response to potential fire hazards.

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