Website and API for Synopsys Science Fair 22-23 Project

Robust, Lora-based Mesh Network - Solution for Wildfire Detection and Growth Forecast through Custom Prediction Algorithm

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Web Client:

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IoT Sensor Module:

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Developer's Notes:

API routes in routes/api.js API implementation & documentation in controllers/[module].js Databse schematics in models/[schema].js Web Client source in client/src/

API hosted on port 8000 (change via .env file), Client hosted on port 3000

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Motivations

The ever more frequent natural disasters are harmful tragedies that develop from an unfortunate environmental coincidence. Some disasters have quantifiable signs that can be compiled into a measure of risk. Though the sensor technologies exist, there lacks a system to connect a large network of sensors to survey the environment. The project aims to develop a software framework for IoT sensor networks to be used in environmental surveillance. It presents a networking protocol capable of maintaining a mesh topology, allowing for a greater covered area.

Past IoT solutions lacked an extensible topology and a long-ranged radio, often only used to cover small areas. Most IoT are not designed to span very large areas – their architectures typically only implement a hub-and-spoke topology, limiting the network diameter by the radio range. Past radio modules typically have a range-to-power tradeoff, and as such most IoT solutions do not use modules with great peer-to-peer range. Contemporary techniques in the detection of wildfires – such as aerial, visual, or infrared – require the fire to have already grown considerably.

Materials & Methods:

• Arduino Nano: small, low power, extensible microcontroller
• ESP-8266: similar to the nano, but with WiFi capabilities.
• Ra-02 LoRa Module: SPI interface, long-range, low-power module with interrupts.
• MongoDB & Mongoose: Development database and its Node.js interfacing library.
• Express.js: A web server framework for Node.js.
• React.js: JS library for reactive web pages.
• Google Maps API: API to display interactive maps.
• Pandas: Python library for handling mass data.

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Setup:

Installation:

• Clone repo and install neccesary packages with npm install for both the root and client folders.

Database Setup (Mongo DB)

• Sign up for a MongoDB account, create a data cluster, and copy your connection key into .env under the DATABASE_URI section.

Hardware Setup:

• Configure all nodes to run on the same channel.
• Write Network credentials into the gateways' EEPROM.
• Set correct server address in gateways' source file.

Google Maps API:

• Project employs Google Maps API to visualize geographical data to impose node markers upon. In order to set this up, follow this post from Google: https://support.google.com/looker-studio/answer/109z8075?hl=en
• After ensuring all has been set up, enter your API key on line 24 of src/pages/components/Map.js.

Hosting the project:

• API and client are run on different ports. If using VS, split terminal into two seperate processes, and run the following commands in the root dir:
  

1. npm run start //starts server
2. npm run client //starts client

Hardware Source Files:

• Repository for LoRaNet Library: repo
• Repository for LoRaNet Nodes, Gateways & Utilities: repo
• Repository for MQ2 Sensor Driver: repo

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Engineering Notebook: