UPM Bachelor Environmental Management: EMG3003 Environmental Monitoring System

Chapter 1: Introduction

This module is created to assist the students regarding the use of IoT technology in managing the environmental data and thus help the decision making process. In the begining, we need to understand the following ideas, related to Big Data Analytics (BDA), IoT and their applications and insight.

Big Data

The big data is the new paradigm where the use of data is importance in making decision. The journey of data is everything with regard to the intelligent development. The basic idea will be DATA > INFORMATION > KNOWLEDGE > INSIGHT > VALUE

IoT

The concept of IOT can be utilized by the use of MCU, SBC and Computer. Its begin with the capability of the computer in managing numbers of operations. Then the evolution of SBC (Raspberry Pi), lead to the development of the advancement of MCU/ PLC and else.

Dashboard

When talking about IoT, we need to visualize our findings and making decision of the data collected before. We need comprehensive real-time dashboard. The Dashbord useful for manipulating data, integrated and mix with outher data and simulate the findings. One of the main function of the interactive dashboard will be the automatic reporting, and forensics applications

Intelligent Environmental Monitoring

When all data are collected at granular level, the level of monitoring become useful for generating predictable insight. The non-representative method of sampling before, become real-time or near real-time in giving the value of the environment. Advance computer modelling and cloud technology enable us to calculate complex machne learning algorithm for decision making process. From a simpler logic, the use of the real-time data together with the visualization technique and continous reporting can help for rapid environmental health risk assessment. This include the more complex element and integration namely as Environmental Monitoring, Surveillance and Analytics (EMSA).

Chapter 2: Fundamental

When talk about IoT, we have to understand there are embedded technology involve. This include (but not limited to) Micro-Controller (MCU). There are different type of MCU available in the market depends on its capabilities and functionalities.

MCU

1. Arduino Uno
2. Arduino WiFi
3. Espressif (ESP8266 & ESP32)
4. Seeeduino (ESP32)
5. STM32

Sensor & Actuator

There are input and output involve in IoT. The easier to understand is the function. When the monitoring take place, and the sensor + MCU used for measuring environment, the unit used mainly the input. When there are mechanism of control and display, we will involve the output.

Software

Software Step 1: IDE Setup

1. Navigate arduino website: https://arduino.cc/software
2. Download the software according to your Operating System (OS)
3. Install the Arduino Software including the related USB Driver
4. Copy the following link (ESP32): https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_index.json
5. Copy the following link (ESP8266): http://arduino.esp8266.com/stable/package_esp8266com_index.json
6. Go to File > Preferences > URL/ Board Manager, paste the above link followed by ","
7. Click OK
8. Then go to Tool Menu > Board > Board Managers
9. In the board manager, type: ESP32
10. Click install on the ESP32 by Espressif
11. Done Installation

Software Step 2:

Installation of Libraries *There are two ways to install the libraries, first using the Arduino Library Manager, second using the folder/ zip file.

Method 1 (Install from Library Manager)

1. Go to Sketch Menu > Include Library > Manage Libraries
2. Install the library as you need.

Method 2 (Install from zip file)

1. Navigate the source
2. Download the Zip file
3. Extract the Zip file
4. Rename the folder, remove the "-master" extension if any
5. Copy the unzip folder into Arduino library folder
6. Paste the folder into Arduino > libraries

Method 2.1 (Install from the Github/ Repo)

1. Navigate to the link
2. Download
3. Extract
4. Rename the folder, remove the "-master" name/ extension (if any)
5. Copy the unzip and rename folder
6. Paste into the Arduino > libraries folder

List of Libraries:

1. MQunifiedsensor Link
2. ClosedCube HDC1080
3. ThingSpeak Mathworks
4. BH1750 Light Sensor Link
5. SD card if not present link: https://github.com/espressif/arduino-esp32

Software Step 3:

1. Identify the urban environmental health analytics issues to be explore
2. Perform secondary data analysis (screening)
3. Priotitize the location of interest and start prototyping
4. Refer following connection []
5. Test each of the sensor using one module at one time
6. Follow Chapter 4

Arduino IDE

1. Install the ESP32 Board Manager (refer to Chapter 1 & 2)
2. Upload Sketch of RTC by Makuna
3. Upload sketch of Step 1:

Cloud Technology

This chapter, you can begin to register the following account:

1. IoT Thingspeak Cloud
2. IFTTT for the notification
3. OpenWheatherMap

Chapter 3: Creating the Unit

In general, there are numbers of ways to connect the sensor and the MCU. This step, please follow the ideas below:

1. 3 Wire - GND, VCC, SIG
2. 4 Wire (I2C) - GND, VCC, SDA, SCL
3. 6 Wire (SPI) - GND, VCC, CS, MOSI, MISO, SCK
4. Transmit and Received - GND, VCC, Rx/ Tx

Chapter 4: Programming

This chapter, you should familiarize with the basic C++ programming and relevant sketch to connect the sensor and MCU.

Programing Step 1: Familiarize The MCU + Sensor

Irsensor

1. Connect IR sensor Pin (4)
2. Upload code: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/irsensor.ino

Irsensor + OLED Display

1. Connect IR Sensor Pin(4)
2. Connect the OLED display (SDA, SCL) - I2C
3. Upload code: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/irsensor_oled.ino

Irsensor + Oled + Additional Sensor (MQ135)

1. Connect IR sensor (4)
2. Conenct Oled
3. Connect MQ135
4. Upload Code: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/irsensor_oled_mq135.ino

IRsensor + oled + MQ + i2c sensor (BMP280)

1. Follow step 1-3 as mention in previous
2. Install additional sensor
3. Upload code (BMP280): https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/ir_oled_mq_tp.ino
4. Upload Code (SHT21): https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/ir_oled_mq_th.ino

Let's Make Datalogger

1. Setup the RTC (Using RTC by MAKUNA)
2. Setup the SD Card (Ensure format the SD Card - FAT32) - name file as data.txt
3. Upload the CODE: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/ir_oled_mq_tp_datalogger.ino
4. Observe the result in SD Card Reader

Let's Make Even Challenging (IOT)

1. Follow steps until particle sensor
2. Register Thingspeak account > create channel > copy ID + API Write Key
3. Ensure to try 2 times, upload with and without comment this LINE: //wifiManager.resetSettings();
4. Upload Code: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/ir_oled_mq_tp_datalogger_pms_iot.ino
5. Upload Code 2 (Cleaner SD Card Output): https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/final_all.ino
6. Connect to internet (Wataverse Technology)
7. Go to link: http://192.168.4.1/
8. Insert your username + password

Final Moment/ Express way

1. Upload RTC by Makuna
2. Edit your Thingspeak ID and API WRITE KEY
3. Upload the Code1 (8 parameters) OR: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/final_all.ino
4. Upload the Code2 (10 parameters - PMS all): https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/final_fullPMS.ino

Latest Code (30/4/2023)

1. Upload RTC by Makuna
2. Edit your Thingspeak ID, API Write Key, Username + Password (WiFi)
3. Upload the Code Final (WiFi HardCode): https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/airqualityFinal.ino

TroubleShoot Problem

1. Error on the COM Port - Download driver from the following Link: https://www.silabs.com/developers/usb-to-uart-bridge-vcp-drivers?tab=downloads
2. Error on the ESP32 Compiling - Uninstall the Arduino IDE, Install Back the IDE, Install the Library
3. Error on the ESP32 Compiling - Update the Arduino IDE board Manager

Checking the Output

1. Check your Thingspeak Channel
2. Check your SD Card
3. Check the SD Card data into the following column: Date | Time | Temperature | Pressure | co2 | pm03 | pm01 | pm05 | pm50 | pm25 | pm10 | ir detect

Chapter 5: Cloud IoT

IoT Cloud Services

Seting Up the Fetch

Chapter 6: Dashboard & Visualization

Database for Urban Analytics (Air Quality Datalogger)

The datalogger is now ready for data collection and you should ensure that the power supply is sufficiently provide for the unit together with the stable internet connection. Now is the part where we will import the thingspeak data from the IOT Analytics into the Google Sheet for further action/ dashboard.

Dashboard Thingspeak + Google Site

Now we will construct normal Google Site for our display of the Thingspeak Chart into the Google Site.

Personalized Dashboard Using Google Appsheet + Google Sheet + Goolge App Script

Now this time we will using the Google Sheet as databases retrive from the IOT Thingspeak server.

1. Setup the Google Sheet (Insert Relevant Headers)
2. Setup the Google App Script
3. Copy coding from the following link: https://raw.githubusercontent.com/ismailsakdo/uitm_emsa/main/GOODimportThingspeakFinal.gs
4. Activate, save and run accordingly

Chapter 7: Intelligent

We can have numbers of machine learning intelligent run continously via cloud or MCU. In today's application, majority of the data was capture and send to cloud, thus the intelligent was performed in cloud. However, some advance features and smaller application can be integrated with the TinyML concept where the inteligent can be embeded into the MCU, thus making prediction of the output after receiving the input from the environmental surroundings. Refer the following examples