The Smart PM2.5 Monitoring System which combines real-time data from PM2.5 sensors and GPS with various factors from the API to compare PM2.5 data from the sensor and API for visualization.

Primary Data

• Dust Sensor: Measures PM levels.
• GPS: Provides location data.

Secondary Data

• Wind API: Retrieves wind-related information.
• Humidity API: Send humidity percentage data
• PM 2.5 API: Gathers information on fine particulate matter levels.
• (Optional) Traffic API: Accesses real-time traffic data.

What will your API provide to users?

• APIs will provide the information of relations of PM2.5 in different data sources and location on different times of a day.

Sensor device needed

• Dust Sensor: Dust sensor Plantower PMS5003 and PMS7003

Github: PMtoLocation

Presentation Slide: Slide

Our project aims to enhance the garden's conditions, we monitor temperature, air humidity, soil humidity, and light levels while also keeping an eye on potential rain.

Primary Sensors

• Soil Moisture Sensor (1)
• Light Sensor (1)
• Humidity Sensor (1)
• Temperature Sensor (1)

Secondary Sensors

• Openweather API (For Rainfall / Weather forecast)
  • The API can provide weather forecasting, humidity and wind from Openweather API.

Features

• View garden's condition
  • Temperature
  • Humidity
  • Light
  • Soil
• Weather forecast
  • Predict Rain
  • Predict Cloud
  • Predict Temperature
• API Data sharing
• Comparison table between API and Sensors

Guitar Environment Monitoring API

A project all about keeping your guitar safe. It gives tips on how to improve the environment of the guitar storage. The application mainly focuses on the light, humidity, and temperature levels and external rainfall of the guitar storage space.

Tech Stack

• Node-red
• Kidbright
• SQLAlchemy
• strawberry-graphql

Primary Sources

• Temperature Sensor: Measures ambient temperature. (Kidbright)
• Light Sensor: Detects light levels in the environment. (Kidbright)
• Humidity Sensor (DHT11): Monitors moisture levels in the air. (3 unit)

Secondary Sources

• Weather and Rainfall Reports: Gathered from various web sources to complement our data.
• Ideal Environment for Guitar: Gathered from various web sources.

API Features

• Real-Time Environmental Data:

  • Provides values of temperature, humidity, light, and weather at regular intervals.

• Guitar Suitability Prediction:

  • Utilizes collected data to assess if a room or surrounding is suitable for storing a guitar.

• Customized Tips for Guitar Care:

  • Offers advice to improve the environment for optimal guitar storage and maintenance.

• What are your primary and secondary data sources?

Primary: Sensor: - [2 pieces] Temperature and humidity sensor-> Measure room’s current temperature and humidity(1) + Measure food humidity(1) - [1 piece] Ultrasonic (Ultrasonic sensor) -> Measure remaining pet food - [1 piece] Air quality sensor(Dust Sensor) -> Measure room’s air quality - [1 piece] Infrared Distance Sensor -> Detect pet around the pet feeder area (to check that pet is eating)

Secondary: Local Air Quality and Temperature: Use an air quality and temperature monitoring API to obtain real-time air quality data for your area. This can help users decide whether it's safe to open a window or if an air purifier should be activated when the pet eats. (https://aqicn.org/city/bangkok/)

• What will your API provide to users? I.e., what kind of questions your project aims to answer?

In this project, we assume that users will monitor the behavior of their pets by themselves. We aim to show the data visualization and let users make decisions and make their own feeding and caring plans for their pets.

1. Is the pet's environment safe?

2. This aspect combines the air quality sensor data with the API data. It aims to answer whether the current air quality conditions are suitable for pets or not. For example, if the air quality is poor due to pollution or allergens, the system could suggest using an air purifier.

3. How does air quality affect the pet's appetite?

4. By correlating air quality, temperature, and humidity data with pet's feeding patterns, users can determine if there is a relationship between air quality, temperature, and humidity and pet's appetite (measured by Infrared Obstacle Detector). This can help answer questions like whether the environment affects their desire to eat.

5. How does environmental data impact food preservation?

6. Combining temperature and humidity data with Temperature and humidity sensor, users can assess how environmental conditions (temperature and humidity) affect the quality and freshness of the pet food. This can help determine if specific environmental conditions accelerate food spoilage or drying.

7. Are there any relations between food humidity and a pet's appetite?

8. By analyzing the relationship between food humidity with a pet's feeding behavior, users can identify the food conditions in which the pet prefers to eat. For example, users might find that their pet eats more when the food is dry and in good quality.

9. Should pet owners adjust the feeding schedule to optimize pet feeding habits?

10. Using the combined sensor and API data, users can experiment with adjusting the feeding schedule based on air quality and other environmental factors to determine if this leads to improved feeding habits and overall well-being for their pet.

11. Are there any relations between environmental conditions and a pet's health?

12. By tracking environmental conditions and the pet's health over time, users can explore whether certain conditions, such as poor air quality or extreme temperatures, are correlated with changes in the pet's health or behavior.

Area Environmental Monitoring

Project Overview

The Area Environmental Monitoring project serves as a proof of concepts, designed to provide the most recent data on some of the key environmental parameters where we integrated data from multiple data source which are air quality(PM2.5) data, population density, sound levels in a specific location. By addressing these critical aspects, the project aims to contribute significantly to the environment monitoring and measuring, public health, and safety.

Primary and Secondary data sources

Primary Data Sources

We are planning to use, a dust sensor, big sound detector, light sensor, and analog infrared distance sensor connecting to Kidbright (ESP32) as our primary data sources.

Secondary Data Sources

For secondary data, these are uri for APIs we plan to use as listed.

1. https://api.airvisual.com/v2/nearest_city
2. https://aqicn.org/api/
3. https://api.openweathermap.org/data/2.5/air_pollution/forecast

We will use NodeRED and keep data in the MySQL database in PHPMyAdmin at iot.cpe.ku.ac.th for data integration and for our data sharing via our RESTful API where we will write an API specification standard using OpenAPI/Swagger. Lastly, for data visualization, we plan to show data from our data that have been integrated and data from our API including a chart of the sound, dust, and number of people at particular locations and times.

What we provide to users?

1. What is the latest pm2.5 aqi index at a certain location?
2. How many people are in a particular area recently?
3. What is the latest temperature at a certain location?
4. What is the latest sound intensity at a certain location?
5. Measurement of the latest wind at a certain location?
6. Correlation between population and PM2.5, and population and temperature in the area.

Background & Motivation

Our Smart Farming System utilizes IoT devices and external APIs to transform agriculture, addressing challenges like precision, resource optimization, and sustainability. By integrating on-board sensors and external data, our approach offers real-time insights and controls, empowering farmers with actionable information. The user-friendly web API streamlines data access, contributing to the advancement of agriculture by promoting efficiency, productivity, and environmentally conscious practices through smart farming technologies.

What are your primary and secondary data sources?

Primary source

1. LDR from board
2. Temperature from board
3. Soil moisture sensor
4. Temperature and humidity sensor

Secondary source

1. Hourly rain volume from TMD API
2. Hourly weather condition from TMD API

What will your API provide to users?

Our API offers comprehensive environment control for users managing various aspects of our farm surroundings. For plant care, it provides insights on when to water based on real-time weather conditions, soil moisture levels, humidity, and light intensity. Additionally, it will recommend to open or close sunshades by considering ambient light intensity (lux). For roof control, the API factors in weather patterns and rainfall, allowing users to open or close their roofs based on these environment conditions.

what kind of questions your project aims to answer?

1. What condition should we water the plant?
2. What condition should we open/close the sunshade?
3. What condition should we open/close the roof?

Overall Architecture

Data Acquisition Layer

IoT devices, including on-board sensors for light, temperature, soil moisture, and external data from the Thai Meteorological Department.

User-Friendly Dashboard

A user-friendly dashboard provides visualizations on Node-RED.

Our Own API

We use Python-Flask

Link to Github

Overview

This project aims to investigate the variations in air quality at different altitudes within a multi-story building. Specifically, we are collecting data on

Primary:

• Dust sensor: 1 piece
• CO sensor: 1 piece
• Smoke gas sensor: 1 piece

Secondary:

• Dust values from Web API.

The levels that we will measuring are at ground level (7M), 4th floor (12M), and 18th floor(60M). The primary objective is to determine whether air quality is significantly affected by altitude within the building.

The measured altitude is Height above mean sea level.

What our API provide

• Air pollution of interested areas in different Height.

Data Collection

The sensors continuously measure the concentration of PM10, PM2.5, smoke, and CO at the specified height and location. Data is collected at 1-Hour intervals and stored into https://iot.cpe.ku.ac.th/pma for later analysis.

Since we have only 1 sensor each, we have to measure each location one by one.

We visualize our data on Node-RED Dashboard

User Questions and Project Aims: 1. Develop a customized API for extracting exclusive data. 2. Design and implement a website that showcases insights into characters.

Primary: Files (CSV, JSON, XML) Web scraping data(cancelled due to source inconsistency)

Secondary: Questionaires

API provides: Resources usage per level with level.id and insight indicator. Basic Information for every available characters. In-depth statistic of each character's stat value.

Progress Update: Hosted working local databases. Set-up and Connect Redash to available data sources. Set up queries and visualise and dashboard.

Keeping plants healthy can be a challenge, which is why we're developing a tool to determine when to water them with the use of sensors and weather prediction.

We gather our weather prediction data from Thai Meteorological Department (TMD). We also find the accuracy of this prediction data with actual weather data from TMD and our kidbright sensors data.

Primary data sources:

• Soil moisture sensor (ZX-SOIL) (1 ea)
• Light sensor (Kidbright)
• Temperature sensor (Kidbright)
• Humidity sensor (Temperature and humidity sensor KY-015) (1 ea)

Secondary data sources:

• TMD API Forecast Daily
• Classification of amount of light intensity in lux (low, medium, high)

API

• Show collected data from both primary and secondary sources.
• For light intensity, show light level (low, medium, or, high) from lux.
• Show the percentage of accuracy of temperature and humidity comparing between TMD API Forecast Hourly with TMD actual weather and data that we collect by ourselves.
• Tell users if they should water their plant considering the soil moisture and weather forecast.

Primary data sources:

4 Kidbright/IoT devices to collect:

• Room temperature at home (temperature sensor)
• Light at home (LDR, on the desk)

4 smartphones to collect:

• Total walking steps
• Total walking distance
• Sleep time
• Screen time (phone)

Secondary data sources:

• Walking Steps Thai: https://www.nationthailand.com/in-focus/30320672
• Walking Steps Ger: https://www.statista.com/statistics/747380/daily-step-averages-select-countries-worldwide/
• Phone & Sleep Ger: https://www.nature.com/articles/s41598-023-42039-8
• Sleep Thai: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889964/
• Phone Thai:https://www.mdpi.com/1660-4601/16/8/1315

What will your API provide to users? I.e. what kind of questions your project aims to answer

Our project aims to compare the lifestyle of of Thai and German students, specifically exchange students in Thailand. The API will provide users with access to the following data: Historical temperature data from the IoT sensors Historical data about movement, rest and smartphone usage via smartphones Aggregated questionnaire, showcasing temperature preferences, smartphone usage and movement of exchange and Thai students Thus we aim to find the differences and similarities in Thai and German lifestyles of university students despite living in the same environment (here in Bangkok) and how different factors impact the preferences and daily routines of these two groups. We will identify and summarize patterns and correlations in the data that can provide insights into the experiences of these students. Our project may provide insight into how foreigners living in Thailand cope with the rapid change in environment and whether they adapt the “Thai lifestyle” after a short period of time.

Progress Update 22.11.2023:

• Start gathering data
• First draft for UI Template
• First draft API
• Looking for secondary data source

Our project collects 3 main datas: PM2.5, Traffic Index in Bangkok and CO value.
Then we find the relationship among those values and visualize it.

Primary data

• CO sensor (MQ-9)

Secondary data

• PM 2.5 from https://aqicn.org
• And Traffic Index from https://traffic.longdo.com/trafficindex

API

• Values of PM2.5 , CO(Carbon Monoxide) and Traffic Index

Primary Data

• Our primary data is questionnaire that ask about exercise which is in google form.

Secondary data

Secondary data are exercises data that provide information of exercise for each body part, food data that provide nutrition and calorie of food, reps/set data that provide information about how many reps you should play or how many set and rest between set following each condition, nutrition data that following the goal that user needed for example if user need to increase muscle you should eat high-protein food

• Exercises: from here
• Foods: from here
• Reps/set: from here
• Nutrition: from here

API

• Our api about the exercise and nutrition. It aim to answer those question:
  • What exercises and foods are recommended for building muscle and losing weight?
  • Which exercise is most effective for targeting specific areas or achieving body composition goals, such as muscle building or weight loss?
  • How many calories per day should I consume? So, Our api aim to answer about exercise and nutrition information.

This project can be use to count people coming in and out of a room using laser emitter and light sensor and can be use to measure rain with rain guage (made by 1 close-sided straw). To check whether it's rainning or not, if it's rainning, the rain guage will be lifted up and the covered part of the straw will be lifted up, so that the light sensor value will be increasing. Otherwise, it's not rainning. And call Openweathermap api to get rain forecast.

Primary data

• kidbright with 2 Infrared emitters and 2 Infrared receivers
  or (kidbright with 2 PIR)
  
• kidbright with rainguage
  

Secondary data

• rain forecast from openweathermap
  

API

1. How many people are at the club
   
2. is it going to rain today(forecast)
   
3. is it raining now at location (ทำ rainguageจากหลอดปิดรู1ข้าง + tilt switch)

rainguage design