Table of contents
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Introduction
Overview
The commercial equipment is mostly expensive and hardly accessible to the common user. Similarly, the software and hardware of these equipment are not modifiable. Lack of essential tools lead to shortage of necessary data to devise location specific designs. Most builders around the world do not consider environmental analysis and disregard the correlation between the buildings and ecosystems.
sustainability = (i.e. development of economy, social development, and environmental protection) [www.environmentalscience.org/sustainability]
contrived. Conversely,
The verification process included graphs to compare the quality of data and sensor responses at distinctive intervals of time. This study is foundation for designing and testing the performance such a tool with similar commercial equipment. evolving with exploration of advancements in open-source technologies.
Environmental data loggers are widely used assets for the climate monitoring and urban planning. But the industrial data loggers are expensive and are not easily accessible to the commoners. The lack of necessary tools lead to lack of knowledge. Therefore, most builders around the world do not consider environmental analysis and disregard the correlation between the buildings and ecosystems. To promote ecofriendly approaches, an alternative environmental measurement tool can be devised to help Architects, Environmental scientist, Civil Engineers, Urban-planners and Clima-designers. This study is foundation for such a tool that keeps evolving with exploration of advancements in open-source technologies.
To begin with, the rising demand for agriculture and industrialization is creating an imbalance in the global ecosystem. More than half of the ecosystem services like wood, drinking water, and air are not being utilized sustainably [1]. Human-beings influence both their immediate and remote environments, and consequently, the remaining life on Earth [2]. Climate change and global warming are the products of human activities that unveil countless threats to the entire world [3]. Metropolises are particularly susceptible to the impacts of climate change due to their abrupt expansion owing to dense populations. The climate change scenarios imply that the average annual temperature will rise by 1ºC to 5 ºC after 2080. The cities experience higher temperatures with respect to their surroundings, an effect known as the urban heat island (UHI), indicating that the urban microclimates will be exposed to harsher environmental fluctuations compared to the rural areas. The severity of UHI over time depends on meteorological location and urban characteristics [4].
The urban microclimates directly affect the quality of life in cities. The pedestrians and cyclists are in constant connection with nature. Therefore, outdoor thermal comfort stimulates the usage of public spaces and promotes sustainable means of daily commute [5]. To raise the urban livability standards, the outdoor spaces must be planned in reference to thermal comfort and urban geometry [6]. Designing the communities with the knowledge of local microclimates promotes business and tourism, ensuring positive amendments in economy, ecology, health, and wellbeing [7].
So what causes thermal discomfort? The source of thermal discomfort is the inequality of heat exchange between human body and its surroundings. The heat exchange mechanism considers addition of heat (by metabolism and absorbed radiation) opposite to the loss of heat (by convection, evaporation and emitted radiation). The human body precisely maintains its core temperature between 36.5-37.5 ºC by the process of thermoregulation. Prolonged heat stress or cold stress may force thermoregulatory failure and lead to hyperthermia (elevated body temperature) or hypothermia (reduced body temperature). The consequences can vary from fatigue and nausea to permanent disabilities and death [8,9,10]. (FIGURE ARTICLE.PDF) WRITE MORE ABOUT THIS
The thermal comfort in indoor environments has been researched intensively because of the possibility to control it with current technologies. The most commonly used method for indoor comfort evaluation is Fanger’s predicted mean vote (PMV). The outdoor environmental conditions are usually beyond control and the human tolerance against these conditions is respectively higher. Therefore, the thermal acceptability ranges are much wider. The universal thermal comfort index (UTCI) is formulized to translate human comfort in outdoor spaces as precisely as possible. UTCI is the air temperature of the reference environment producing the same dynamic physiological response as actual environments. The expression includes the meteorological input, the physiological model, and the clothing model to get a dynamic response model. The main factors are air temperature, relative humidity, wind speed, mean radiant temperature, solar irradiance, metabolic rate, and clothing factor. Some supplementary information (i.e. reference to the climate zone, personal background, and physiological data) will enhance the understanding of an individual’s perception of comfort in different environmental situations [11,12].
The urban strategies for designing public spaces and bioclimatic buildings are based on the most prevailing climatic patterns [13]. Therefore, every source of data is critical for the analysis and observation of ecological variations and their consequences [14]. Contemporary analysis tools such as, global integrated surface dataset (ISD) along with geographic information system (GIS), computer-aided design (CAD), computer-aided engineering (CAE) and computational fluid dynamics (CFD) help to understand the climatic conditions and designing the cities. Many complex problems such as, the urban street canyons and UHI are estimated with three-dimensional simulations. Occasionally, uninterrupted data acquisition tools are used to retrieved data at microclimate level, especially for thermal comfort analysis. The acquired data may influence analytical decisions while planning, reshaping and reactivating the urban infrastructure. The climatic data can also be beneficial to environmental researches.
Meanwhile open-source technologies are continuously expanding. Technology enthusiasts and hobbyists can easily access Arduino and Raspberry-Pi prototyping platforms to build countless projects. The source codes and schematics, contributed by programmers and engineers, keep evolving. Many researchers have used this platform to build environmental dataloggers intending to obtain particular datasets that interests their studies. It would be sensible to test the available opensource technologies to acquire environmental data focusing on outdoor thermal comfort parameters.
Statement of the Problem
Environmental science is an emerging field that requires special monitoring equipment. Current researches in this field are in high demand but they are not as frequently deployed due to their expenses. These environmental researches often involve hefty data acquisition tools that come in privately-owned programming languages. These devices mostly support sensors from the same brand. They can cost multiple times compared to their common counterparts [15]. To promote extensive environmental researches, data procurement tools should be effortlessly available to masses. This petition stresses on exploration and acknowledgement of open-source technologies. Whilst the industrial dataloggers are highly reliable in terms of accuracy and speed, the users need easily modifiable, low cost, and multifunctional devices.
Hypothesis
If the environmental data could be acquired in real time by using open-source technologies, then the logged data could accurately evaluate outdoor thermal comfort.
Research Questions
This research focuses on these main questions:
What existing technologies are used by researchers to measure outdoor thermal comfort?
Which hardware and software alternative could be used to measure outdoor thermal comfort?
How to combine all the elements to build a testable device?
How accurate is the acquired data?
How to improve the device?
What are the potentials for future work?
Objectives/ Motivation
The aim of this thesis is to build a portable data procurement instrument connected with the relevant environmental sensors for the analysis of outdoor thermal comfort. The study concentrates on Arduino prototyping and accessible environmental sensors. The programming platform will be the Arduino integrated development environment (IDE) that uses C and C++ languages. The logged data will be compared with that of branded environmental dataloggers. Subsequently, the tool will be used to collect georeferenced data in Maxvorstadt, Munich. For evaluation, the data will be mapped using Grasshopper3D to visualize distinctive microclimates and investigate human thermal comfort.
The incentive is to equip the architects and urban planners with a tool that could be easily assembled and modified according to user’s needs. In future, multiple hubs can be established within urban communities to retrieve sensible data. The device can also be expended as a weather station e.g., the outcast regions of Afghanistan and other devastated countries.
Limitation
This thesis is a limited overview of the steps taken to build a portable weather station. It does not show every problem faced during the process. It is not meant to be a step-by-step tutorial, but a display of the wirings and schematics and slight discussions about how they function.
The device’s limitations are the accuracy of the acquired data and the reaction time of the sensors. There are numerous environmental sensors available in the market, but their precision is still an issue. An intensive understanding of the sensors, their calibration, and the Arduino microcontrollers are needed to reinvent code for the project’s specific goals. Instead of reinvention, this study modifies the existing codes and uses them in combination. Although the device keeps evolving, the study focuses on setting a foundation for future work. It discusses the results extracted from the device and proposes additional steps to enhance its performance in future.
Review of existing concepts
Introduction to data acquisition
Data acquisition means to sample the observed physical phenomena and to convert the sample into numerical values that are compatible for computer analysis [17B]. The real-world conditions are measured with fluctuations in electrical signals of certain material effected by those conditions. These analog signals are amplified and converted into digital signals. The digital signals are packets of 0s and 1s that are converted into decimal system by an interpreter. The processed data is stored in the R/W memory of the computer. A simple data acquisition system (DAS) is outlined in (figure 1a).
Figure 1a
History
The concept of meteorology traces back to 3000 B.C, India [17].MORE DATA COMES HERE
Methodologies and equipment
Studies based on commercial equipment
But the relevance to this study originates from Spognolo & Dear’s field study in 2002, Sydney. In their study they selected four environmental sensors for air temperature (T_AIR, humidity RH_%, mean radiant temperature (MRT), and wind speed (v) to determine outdoor thermal comfort. The sensors, in combination with an industrial datalogger, were mounted on a tripod. Two radiation sensors were used facing up and down to calculate the total radiation fluxes. For calculation of MRT the following formula was used [18]:
MRT= ∜(T_DIR^4+T_DIF^4+ T_REF^4+ T_IR^4 ) , (2.1)
where T_(DIR,) T_(DIF,) T_REF and T_IR reffer to the proportions of MRT attributed correspondingly to direct radiation, diffuse radiation, reflected short-wave radiation, and infrared fluxes. To evaluate outdoor comfort, a number of thermal indices were used, including operative temperature (TOP), effective temperature (ET), outdoor standard effective temperature (OUT_SET), perceived temperature (PT), and physiological equivalent temperature (PET). Surveys were part of the data collection. The paper concludes with an emphasis on the development of UTCI. The equipment listed in Table 1, was named as, “TROJAN” [19].
Table 1
Logger Parameter Sensor model
Campbell Scientific Instruments 21X Air Temperature OMEGA 44032 Linear Composite Thermistor
Relative Humidity HYCAL IH-3605B Solid State Hygrometer
Global Shortwave Radiation LI-COR LI-200SA Silicon Pyranometer
Diffuse Shortwave Radiation LI-COR LI-200SA Silicon Pyranometer
Longwave Radiation EKO MS-201 Pyrgeometer
Wind Speed [Air Speed <= 2.5〖 ms〗^(-1)] TSI 8475-150 Omni-directional Heated-Sphere Anemometer
Wind Speed [2.5〖 ms〗^(-1)< Air Speed< 10〖 ms〗^(-1)] MINI-RIMCO 3-Cup Photo-Chopper Anemometer
The UTCI was introduced in 2013 nevertheless, some outdoor comfort studies compared PET with surveys of each subject’s thermal sensational vote (TSV). Weather-proof boxes with shoulder straps were used to conveniently carry the dataloggers and sensors instead of tripods. A temperature sensor inside a blackened table-tennis ball could measure the globe temperature for MRT. For the analysis of the sky condition, a fish-eye camera lens was used. The following equation was utilized to estimate the MRT [20]:
MRT =[ 〖〖(T〗_g+273.15)〗^4+(1.10×〖10〗^8×v^0.6 )/(ε×D^0.4 ) ×(T_g-T_(a ) )]^(1⁄4)-273.15 , (2.2)
where T_g refers to globe temperature, T_a refers to air temperature, v refers to air velocity, refers to emissivity of globe and D refers to globe diameter. The equipment used are listed in Table 2 [21].
Table 2
Logger Parameter Sensor model
TESTO480 Air Temperature TESTO Air Temperature Probe
Relative Humidity TESTO Humidity Probe
Globe Temperature Globe Thermometer (D = 38mm, = 0.95)
Wind Speed TESTO Air Flow Probe
LI-COR LI-1400 Solar Radiation LI-COR LI-200SA Silicon Pyranometer
Outdoor comfort was further studied, the UTCI and other factors effecting microclimates were mapped using similar tools including infrared camera to capture skin temperatures of participants at certain points. A fish-eye camera lens was used to determine the sky view factor (SVF). In this experiment, additional factors were realized that effect MRT in outdoor spaces like the presence of direct solar radiation. Therefore, the following formula was used:
MRT^*=[ MRT^4+(f_p×a_(k )× I^*)/(ε_p×) ]^(1⁄4), (2.3)
where I^* refers to the intensity of the solar radiation on a surface perpendicular to the incident radiation direction, _p refers to the emission coefficient of the human body, a_k refers to absorption coefficient of the irradiated body surface area for short wave radiation, f_p refers to the function of the incident radiation direction and body posture, is Stefan-Boltzmann constant. For the above equation the values are:
_(p )=0.97 (standard value)
a_k=0.7 (standard value)
=5.67 × 〖10〗^(-8) W/(m^2 K^4)
f_p=0.308 cos〖[ × (1- ^( 2))/48402 ] 〗, (4)
where refers to elevation of the Sun. The calculation of f_p also considers solar declination, altitude & azimuth of the Sun and local standard time. The equipment used are listed in Table 3 [22].
Table 3
Logger Parameter Sensor model Accuracy
TESTO480 Air Temperature TESTO Air Temperature Probe 06280143 0.5 C
Relative Humidity TESTO Humidity Probe 06369743 1.0 %
Globe Temperature TESTO Globe Thermometer 06020743 (D = 150mm, = 0.95) 1.0 C
Wind Speed TESTO Air Flow Probe 06280143 0.03 m/s
LI-COR LI-1500 Global Solar Radiation LI-COR Pyranometer LI-200R 0.183 〖 W/m〗^2
Global Positioning System (GPS) RADIONOVA Radio Frequency (RF) Antenna m
Comparable open-source projects
There are many Arduino and Raspberry-Pi projects that involve weather sensing and data logging. But they mostly serve as stationary weather stations or objectify a different purpose than the analysis of outdoor thermal comfort. Some of these projects are compared in Table 4.
Table 4
Project Component Description Range Accuracy
1. Arduino Based Weather Monitoring System: A weather monitoring project concentrated on basic parameters such as, Air Temperature, Relative Humidity, and Light Intensity without data logging [23]. Arduino Nano Microcontroller Board
LM35 Temperature Sensor -55 to 150 C 0.5 C [24]
DHT11 Temperature and Humidity Sensor 0 to 50C, 20 to 80% (Relative Humidity) 2.0 C, 5% [25]
Light Dependent Resistor (LDR) Variable Resistor used as a Light Sensor
Liquid Crystal Display (LCD) 1602 A 16X02 Alpha-numerical Display
2. An Arduino-Based Weather Station: A data logging tool focused on three weather parameters [26]. Arduino Pro Mini Microcontroller Board
DHT22 Temperature and Humidity Sensor -40 to 80C, 0 to 100% (Relative Humidity) 0.5 C, 2 to 5% [27]
BME280 Temperature, Humidity, and Pressure Sensor -40 to 85C, 0 to 100% (Relative Humidity), 300-1100 hPa 3% [28]
Real Time Clock (RTC) Timekeeper for Datalogging
Secure Digital (SD) Card Slot Read/Write port for Datalogging
3. Building an Arduino based Weather station and connecting it as a slave to a control system: Weather sensing with three basic parameters and Bluetooth connectivity for wireless communication [29]. Arduino UNO Microcontroller Board
LM35 Temperature Sensor -55 to 150 C 0.5 C
DHT22 Temperature and Humidity Sensor -40 to 80C, 0 to 100% (Relative Humidity) 0.5 C, 2 to 5%
BME280 Temperature, Humidity, and Pressure Sensor -40 to 85C, 0 to 100% (Relative Humidity), 300-1100 hPa 3%
HC06 Bluetooth Module
4. Design, Development and Implementation of a Weather Station Prototype for Renewable Energy Systems: A weather station observing five parameters without datalogging or wireless communication [30]. Arduino Mega 2560 ADK Microcontroller Board
DHT22 Temperature and Humidity -40 to 80C, 0 to 100% (Relative Humidity) 0.5 C, 2 to 5%
BMP085 Barometric Pressure and Temperature Sensor 300-1100 hPa, 0 to 65C, 2.0 hPa, 2.0 C [31]
SSHU005 Water Detection Sensor
PZT LDT0-028 Vibration Sensor
6710-WINd02 Anemometer
MQ135 Gas Sensor
HMC5883L 3-Axis Digital Compass
5. An Intelligent Weather Station: A weather station built to observe three atmospheric variables and predict each for 48 steps ahead [32]. Raspberry Pi B+ Mini-computer
SENSIRION SHT75 Temperature and Humidity Sensor -40 to 123.8C, 0 to 100% (Relative Humidity) 0.3 C, 1.8% [33]
APOGEE SP-110 Global Solar Radiation Sensor 360 to 1120 nm 5% [34]
6. Low Cost Weather Monitoring Station Using Raspberry Pi: A stationary weather station with inbuild internet connectivity that focuses on seven atmospheric parameters [35]. Raspberry Pi 3 Model B Microcontroller Board
BME280 Temperature, Humidity, and Pressure Sensor -40 to 85C, 0 to 100% (Relative Humidity), 300-1100 hPa 3%
SEN-08942 Wind Speed, Wind Direction, and Rain Gauge
DYNALAB DWR 8102 Solar Radiation Sensor 350 to 1150 nm
Liquid Crystal Display (LCD) 1602 A 16X02 Alpha-numerical Display
Conclusion
Although each project has a unique combination of atmospheric sensors and data collection technique, yet none of them target outdoor thermal comfort analysis nor they fulfill the demand to replace the industrial tools for this purpose. Therefore, it is imperative to imitate the industrial instruments and implement the combinations used by researchers (specific to outdoor comfort) through open-source platform.
To achieve the goal, a simple prototype has to be built that could be modified with experimentation and testing. The next chapter will focus on explanation and development of such a prototype.
Prototype developement
Introduction
The data acquisition tool (portable weather station) requires both hardware and software development. The hardware segment consists of a collection of environmental sensors connected with the microcontroller board. The microcontroller receives power from a 9 volts (V) direct current (DC) source. The microcontroller’s embedded voltage regulator can supply 3.3V, 5V, and source voltage (input voltage “9V”) for the sensors. The microcontroller is programmed by a computer through the universal serial bus (USB). The sensor data can be logged into an SD card. The same data can be viewed and save on the internet through Wi-Fi module or the IDE terminal via USB interface. An overview of the concept is illustrated in (figure 1).
Figure 1
The environmental sensors include air temperature, relative humidity, globe temperature, wind speed, and global solar radiation. GPS module is used for attaining real-world coordinates. SD card sloth and RTC are connected to the microcontroller for datalogging. The microcontroller’s built-in USB-port is used for primary communication. A Wi-Fi module is connected for effortless user-to-device interaction.
The main challenges for the hardware are accuracy and reaction time of the sensors and precision of the RTC. The secondary challenge is enhancement of functionality relevant to the user’s demand. For this purpose, particulate matter and noise pollution sensing are added. Additional data will be extracted from GPS such as, mobility speed, number of satellites in view, and altitude.
The software side of the project includes microcontroller coding with IDE and online applications. After the programming, real-time commands can be sent to the microcontroller through IDE terminal, web-browsers, and smart-phone apps
List of components
The main components used in the project are listed in (table 5) with short description of their functionalities and limitations Make a table with measuring range and accuracy only and explain the rest in the bottom.
Table 5
Arduino MEGA 2560 The core of the project based on ATmega2560 microcontroller. This board includes 54 input/output pins and 14 pins can be used as pulse width modulation (PWM) outputs. It has 16 analog inputs and 4 universal asynchronous receiver-transmitter (UART) also known as hardware serial ports. It also includes a 256 KB flash memory and a 16 MHz crystal oscillator. The operating voltage is 5V and input voltage limits are 6-20V. It is programmable through the Arduino IDE [36].
Data Logger Shield Data logging shield that consists of an SD card slot and a DS3231 RTC. It is easily stackable on most of the Arduino boards and is used for accurate time keeping and to read/write the sensor data on SD card [37].
DS18B20 One-wire digital thermometer used for measurement of globe temperature in the project. The sensor is water-proof and its voltage rating is -0.5V to +6V DC. The operating temperature ranges from -55C to +125C with 2C accuracy. For maximum accuracy of 0.5C, it must be operated from -10C to +85C. Its response time depends on the read command form the microcontroller [38].
SHT20 Humidity and temperature probe based on SHT20 sensor chip. It is enclosed in weather-proof case for convenient outdoor usage. The communication is based on Inter-integrated circuit (I²C) protocol. The voltage rating is +2.1V to +3.6V DC and operating range 0-100% RH. The accuracy is 3%, response time is 8 seconds with hysteresis of 1% for relative humidity. Temperature ranges are -40C to +125C with an accuracy of 0.3C and response time of 5 to 30 seconds [39].
TSL2561 Luminosity sensor for calculations ranging 0.1-40,000 Lux. Light measurement includes infrared and full spectrum. The voltage range is 2.7V to 3.6V. The working temperature range is -30C to +70C and it uses I²C interface for communication [39b].
TCS34725 RGB sensor developed for color and ambient light sensing with infrared radiation (IR) blockage. The voltage rating is 2.7V to 3.6V DC. The working temperature rating is from -30C to +70C .It uses (I²C) protocol for data transfer to microcontroller [40].
SEN0170 Anemometer to sense wind speed. It consists of three wind cups and a weather protection shell enclosing the circuit module. The supply voltage rating is 9V-24V DC. The wind speed measurement ranges 0 m/s to 30 m/s at 3% accuracy. The starting wind speed is 0.4 m/s -0.8 m/s. The working temperature rating is -40C to +80C and working humidity rating is 35% to 85%. The mode of output is analog signal [41].
Ultimate GPS Breakout GPS module built around MTK3339 chipset. Includes 66 channels and can track up to 22 satellites with -165dBm sensitivity. The positioning accuracy is less than 3 m and velocity accuracy of 0.1 m/s. The Input voltage rating is 3V to 5V. The warm/cold start takes 34 seconds. The outputs include location, time, altitude, speed of motion, and number of satellites. Communication with microcontroller is done through UART. Includes (u.FL) connector for external antenna [42a].
SEN-14262 Sound detector based on LMV3XX voltage feedback amplifiers family. The operating supply voltage rating is 2.5V to 5.5V and operating temperature range is -65C to +150C [42]. The signal gain can be adjusted by adding a resistor R17 or removing the built-in resistor R3. Data can be transferred through audio, envelope, and gate pins [43].
PMS5003 Particulate matter (PM) sensor based on laser light scattered by particles in a measuring cavity. It has three ranges of measurement i.e. 0.3 m ~1.0 m , 1.0 m ~2.5 m , and 2.5 m ~10 m. The counting efficiency is 50% at 0.3 m and 98% at 0.5 m and above. The maximum range at PM2.5 is above 1000 g/m^3 , featuring total response-time ≤ 10 seconds [44].
ESP-01 ESP8266 Wi-Fi module based on Tensilica L106 microcontroller. Its frequency range is 2.4GHz to 2.5GHz. Includes 80MHz clock speed and 1MB external serial peripheral interface (SPI) flash for programming. The peripheral bus supports UART, I²C, PWM, inter-integrated circuit Sound(I²S), hardware SPI (HSPI) and general-purpose input output (GPIO). The operating voltage rating is 3.0V~3.6V and operating temperature ranges -40C to +125C [45].
Adafruit Feather HUZZAH ESP8266 Wi-Fi development board with 3.3V logic, 80 MHz clock speed, and 4MB of flash It is the board version of ESP-01, based on Tensilica chip core, and can be programmed using Arduino IDE. It has external battery support through micro USB and Lithium polymer battery connector. The code can be uploaded at 921600 baudrate. There are 9 GPIO pins that can be used as SPI and I²C. It can receive 1 analog input and is used in substitution to Arduino board and ESP-01 [46].
DHT11 Temperature and humidity sensor that can measure 20% to 80% RH and 0C to 50C temperature. The accuracy ranges for relative humidity and temperature are 5% and 2.0C respectively. The response time ranges from 6 seconds to 15 seconds. This module is used in the beginning for the demonstration of the project idea [47].
1602 LCD Liquid crystal display (LCD) that can show 32 alpha-numerical digits. It runs on 5V input power supply. The display board consists of 16 pins, 8 pins (D0-D7) are data bus line. The contrast pin is connected with a potentiometer to control the contrast settings [48].
Proof of concept
The development started with a mini project to display the initial idea. The demonstration was subdivided into two stages. The first stage included a simple display of data on the LCD and the second stage communicates the data using Wi-Fi.
Stage One
At this stage, a temperature-humidity sensor (DHT11), an LCD (1602) screen, and a potentiometer (10 k) were attached to the Arduino microcontroller unit (MCU). The connections are illustrated with breadboard wiring diagram in (figure 2). The IDE code is shown in Appendix And for further explanation of the connections, the schematic diagram is illustrated in (figure 3). The data entailed are relative humidity, temperature in Celsius degrees and Fahrenheit, and the heat index. The monitored output from LCD and IDE terminal are illustrated in (figure 4 and figure 5).
Figure 2
Figure 3
Figure 4
Figure 5
Stage Two
In this step, the DHT11 sensor was attached to an ESP8266 Huzzah board to use its built-in Wi-Fi sensor. The data was successfully monitored through a smart-phone. The breadboard wiring and schematic diagrams are illustrated in (figures 6 and 7) respectively. For user interface, Internet of Things (IoT) application Blynk was utilized [49]. The data can be viewed in real-time and also logged for future reviews using the application. A snapshot of the output is shown in (figure 8). The IDE code is shown in Appendix
Figure 6
Figure 7
Figure 8
These steps show the fundamental concept of the project. Henceforward, it is focused to apply this concept with relevant sensors to acquire adequate data collection for outdoor comfort analysis.
Prototype
Breadboard connections, schematics and coding
After successful the display of conceptual model, the prototyping evolved step by step with one component at a time. The process started with each sensor separately being tested for relevant outputs owing to connections with the MCU and the available IDE codes. After passing the first test each sensor and the related code was added in the program. The results were monitored, the code was further modified to focus on important data and filter out unnecessary yields. This process was repeated in a loop format (figure 9) until the results were fulfilling the project’s scope.
Figure 9
The Arduino MEGA 2560 MCU was specifically selected because of the wide collection of pins and compatibility with most of the sensors. The Data Logger was mounted on top of the MCU, for the I²C connection, the serial data (SDA) and serial clock (SCL) pins from the MCU were connected to analog pins A4 and A5. The code and libraries were also slightly modified. The I²C connection and code modifications are specific to the project’s data logger and can be skipped in the latest versions. The RTC is powered by a CR1220 coin cell to avoid redundant programming for current-time [50]. The IDE code is shown in Appendix
The DS18B20 temperature sensor’s VDD, ground (GND) and data pins were connected to 5V, GND, and digital pin D2 of the MCU respectively. A 4.7 k pull-up resistor was connected in parallel between the data and VDD lines. As a one-wire sensor, it has a unique 64-Bit serial number, therefore, a vast number of one-wire sensors can share the same bus. The code includes sensor temperature request in the loop and calls the sensor by index, 0 refers to the first sensor on the bus [51]. The IDE code is shown in Appendix
The SHT20 humidity and temperature sensor is controlled by I²C protocol. The connections from the sensor to MCU is completed in the following order; SCK to SCL, data to SDA, VCC to 5V, and GND to GND. The sensor is initiated in the void setup and its specific library is included in the beginning of the code [52]. The IDE code is shown in Appendix
The TSL2561 luminosity digital sensor’s input voltage (VIN) and GND pins were connected in parallel to the MCU’s 5V and GND pins since it has a 3.6V voltage regulator. This terminates the logic level issue with I²C connections. The I²C lines SDA and SCL are joined parallel to SHT20 sensor’s data and clock bus. The address change pin (ADDR) can be used to avoid I²C address conflict, but it’s left idle in our circuit including the interrupt output pin (INT) which is used for configuration of sensor with change in level of light [53]. The IDE code is shown in Appendix
The pinouts for TCS34725 include LED pin, INT pin, SDA, SCL, 3v3 pin ( supply from voltage regulator), GND, and VIN. The connection is exactly the same as that of TSL2561. The LED-pin is connected to GND to turn off the built-in LED (4150K neutral) which is generally used to illuminate an object for sensing [54]. The IDE code is shown in Appendix
The SEN0170 anemometer’s VDD line is connected to a 9.6V DC supply and the GND is connected to Arduino GND. The same battery powers the MCU when detached from other supply source. The data line is attached to the analog A1 pin of the MCU [55]. The IDE code is shown in Appendix
The PMS5003 PM2.5 air quality sensor is attached to the MCU with VCC to 5V, GND to GND, and the UART connection transmitter (TX) to receiver (RX1). The rest of the sensor pins are not used in our project [56]. The IDE code is shown in Appendix
The Sound detector SEN-1462 sensor is also powered from the MCU 5V and common GND. The sensor’s gate pin is connected to MCU digital D5 pin and the envelope pin is connected to analog A0 pin. The audio pin remains unused in our project [57]. The IDE code is shown in Appendix
The GPS module is powered by the VIN and GND pins attached to MCU’s 5V supply and GND. The UART pins are connected as; GPS TX to Arduino RX3, and GPS RX to Arduino TX3 [58]. The IDE code is shown in Appendix
The ESP-01 module connection includes several steps to flush the old firmware and install new firmware through an FTDI serial adaptor. In the absence of an FTDI adaptor a voltage divider is used since the Wi-Fi module is a 3V device. It is a good idea to power the module from an external source to supply up to 300mA current. A voltage divider is used between RX pin of the ESP and TX pin of the Arduino since the 5V logic could fry the Wi-Fi module. The connection is done as follows; Arduino TX2 pin to voltage divider and then to RX pin of the ESP, TX pin of the ESP to RX2 of the Arduino, GND to GND, ESP-01 chip-select (CH_PD) to a 3.3V supply through a 10 k resistor. VCC and GND to 3.3V and GND respectively. The IDE code is shown in Appendix
The connections are further explained through breadboard diagram (figure 10) and schematics diagram (figure 11). WRITE ABOUT PINOUTS OF EACH MODULE
Figure 11
Figure 10
Prototype casing and setting
A simple casing is used to protect the circuitry and for ease of conveyance. The temporary casing is subject to replacement in future versions of the prototype.
The Arduino MCU, battery, datalogger, Wi-Fi module and the wires are enclosed within a plastic box by stacking A4 paper-trays and fixing them with screws. The The GPS, RGB, luminosity, wind speed and globe temperature sensors are elevated from the box by hollow metallic rods (approx. 50 cm from the box). There is a gap (approx. 8 cm) between the rods to avoid shadows of one sensor on the other. The air temperature plus humidity probe is fastened at the top edge of the casing for data procurement at human shoulder level. The PM2.5 and sound detector sensors are engraved on the top-right and top-left side of the box respectively. Metallic plates are used to fix the rods on the plastic box casing. The box can be placed inside a bag to be carried conveniently by the user. The globe temperature sensor (DS18B20) in enclosed in a blackened table-tennis ball (D= 40 mm and = 0.95). Correspondingly, the height and width of the model is 92 cm and 29 cm. The positioning of the components and the dimensions of the prototype is further explained in (figure 12).
Figure 12
Data procurement and manipulation
The set of data from the prototype is monitored through IDE terminal and saved in the SD card for future assessment. The connected sensors are called one after the other in the following order according to the prototype’s programming code;
Globe temperature sensor (T_g)
Air temperature (T_AIR), and relative humidity (RH_%) sensor
Anemometer
RGB and illuminance sensors
GPS
Particulate matter (PM_2.5) sensor
Sound detector
The data received from the (T_g), (T_AIR), and (RH_%) sensors are converted and saved in international system of units (SI). Although additional