LoRa-based Wireless
Communication System

The current wildfire prediction systems in Sri Lanka predominantly rely on traditional methods, which often lack accuracy and adaptability. These systems typically depend on static data and historical fire patterns, which are not sufficient for predicting the dynamic behavior of modern wildfires. This limitation hinders emergency response efforts and compromises public safety by reducing the effectiveness of evacuation procedures and resource allocation.

To address this gap, there is a pressing need for innovative wildfire prediction systems that integrate real-time data from various sources. Emerging technologies such as satellite imagery, IoT sensor networks, and AI-driven analytics can significantly enhance the accuracy of fire behavior models. These tools can monitor environmental conditions, such as temperature, humidity, wind speed, and fuel moisture levels, providing timely and precise predictions of wildfire spread and intensity.

Moreover, machine learning algorithms and AI can analyze vast datasets to identify patterns and correlations that traditional models might overlook. These systems can continuously learn from new data, improving their predictive capabilities over time. Leveraging AI in wildfire management not only enhances prediction accuracy but also enables more efficient decision-making processes in allocating resources and prioritizing areas for evacuation.

Proposed Problem

How to address wildfires escalating in Sri Lanka, driven by climate change, inadequate prediction systems, limited weather data, outdated communication, and security concerns, for sustainable prevention and management?

Sri Lanka faces escalating wildfires due to climate change, inadequate prediction systems, limited weather data, outdated communication, and security concerns. Rising temperatures and dry conditions fuel fires, while poor data and prediction hinder early warnings. Outdated communication and security issues further complicate response efforts, emphasizing the need for comprehensive wildfire prevention and management strategies.

The proposed loRa-Based wireless communication system consists of 4 main components. They are;

1. LoRa-based Wireless Communication
2. Weather Prediction and wildfire Warning System
3. Image Processing Unit for Predicting the Spread of Fire
4. Secure Communication, Robust Mobile App Security, and Obstacle Detection System.

The operation of a comprehensive communication system designed for forest conservation applications is illustrated by the system diagram. The system relies on multiple microcontrollers mounted in forests with high wildfire risk to collect and process various types of data, including geographical coordinates (longitude and latitude) and sensor readings. In the event of an emergency, the microcontroller handles the transmission of emergency messages. The processed data is selectively displayed on the device’s display unit. To ensure secure communication, all data is encrypted before being transmitted via a LoRa (Long Range) module. The encrypted data is then relayed through intermediary LoRa nodes, which serve as communication relays, effectively extending the device's communication range and forming an ad hoc network. Ultimately, the encrypted data reaches a main station, where it provides crucial functionalities such as emergency alerts, real-time weather updates, and the detection of early signs of wildfire. Low Power Wide Area Networks (LP-WAN) are wireless communication networks designed to connect devices efficiently over long ranges while consuming minimal power.