Author Contributions
Conceptualization, C.L. and N.L.; methodology, C.L.; software, C.L. and L.H.; validation, L.H., Z.S. and C.W.; formal analysis, C.L.; investigation, L.H. and Z.S.; data curation, Z.S.; writing—original draft preparation, C.L.; writing—review and editing, C.L. and N.L.; visualization, C.L.; supervision, M.J.; project administration, N.L.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.
Figure 1.
Street built environment data platform based on sensors and IoT technology to realize urban sensing and data acquisition; organize and obtain a variety of data, such as human mobility data, social media data, etc.; and provide services for the healthy development of the city through urban data management and urban data analysis.
Figure 1.
Street built environment data platform based on sensors and IoT technology to realize urban sensing and data acquisition; organize and obtain a variety of data, such as human mobility data, social media data, etc.; and provide services for the healthy development of the city through urban data management and urban data analysis.
Figure 2.
Electronic circuit of the SBEDAD. The Arduino built-in UART enables two devices to communicate at a time, so SBEDAD has the GPS and PM sensors connected to the serial port. The DHT22 data are collected at the digital pin, while the BH1750 and noise sensor data are collected at the analog pins. The availability of each component is confirmed in the overall function realization.
Figure 2.
Electronic circuit of the SBEDAD. The Arduino built-in UART enables two devices to communicate at a time, so SBEDAD has the GPS and PM sensors connected to the serial port. The DHT22 data are collected at the digital pin, while the BH1750 and noise sensor data are collected at the analog pins. The availability of each component is confirmed in the overall function realization.
Figure 3.
Flow chart of SBEDAD.
Figure 3.
Flow chart of SBEDAD.
Figure 4.
(a) The 1st version SBEDAD, half-shelled, with TEMT6000 as the light intensity sensor; the screen is printing the sensing data to ensure the users get the running status of the equipment. (b) SBEDAD with PLA shell (2nd version) using BH1750 as the light intensity sensor.
Figure 4.
(a) The 1st version SBEDAD, half-shelled, with TEMT6000 as the light intensity sensor; the screen is printing the sensing data to ensure the users get the running status of the equipment. (b) SBEDAD with PLA shell (2nd version) using BH1750 as the light intensity sensor.
Figure 5.
Evaluation calibrations of SBEDAD using the digital oscilloscope FNIRSI-1013D.
Figure 5.
Evaluation calibrations of SBEDAD using the digital oscilloscope FNIRSI-1013D.
Figure 6.
(a) Arduino IDE reads GPS/BDS data via a serial port. (b) Measurement of the real frequency of GPS/BDS sensor.
Figure 6.
(a) Arduino IDE reads GPS/BDS data via a serial port. (b) Measurement of the real frequency of GPS/BDS sensor.
Figure 7.
Field test of GPS/BDS sensor in Beijing (a–c) and Sanya (d–f). By inverting the geographic location by latitude and longitude on the Baidu map (upper left column), the result (the red icons) showed that ATGMH-5N has high accuracy and meets the urban spatial data requirements.
Figure 7.
Field test of GPS/BDS sensor in Beijing (a–c) and Sanya (d–f). By inverting the geographic location by latitude and longitude on the Baidu map (upper left column), the result (the red icons) showed that ATGMH-5N has high accuracy and meets the urban spatial data requirements.
Figure 8.
Lab test of the temperature and humidity sensor. (a) Temperature and humidity data for UT332+ through the host computer application. (b) Oscillograph of the DHT22. (c) Testing the sensor’s ability to respond quickly to changes in temperature and humidity using a hairdryer.
Figure 8.
Lab test of the temperature and humidity sensor. (a) Temperature and humidity data for UT332+ through the host computer application. (b) Oscillograph of the DHT22. (c) Testing the sensor’s ability to respond quickly to changes in temperature and humidity using a hairdryer.
Figure 9.
(a) The 600-set data comparison of DHT11, DHT22, and UT332+ in different modes. (b) Statistical analyses of the error rate between the sensors and professional tester data for the 600 sets of data.
Figure 9.
(a) The 600-set data comparison of DHT11, DHT22, and UT332+ in different modes. (b) Statistical analyses of the error rate between the sensors and professional tester data for the 600 sets of data.
Figure 10.
(a) Measurement of the real frequency of Plantower G7003s. (b) The lab test with the Plantower G7003S using sandpaper to rub nails, plastics, and chalks.
Figure 10.
(a) Measurement of the real frequency of Plantower G7003s. (b) The lab test with the Plantower G7003S using sandpaper to rub nails, plastics, and chalks.
Figure 11.
Measurement of the real frequencies of TEMT6000 (a) and BH1750 (b). Lab test of TEMT6000, BH1750, and Konica T-10A (c).
Figure 11.
Measurement of the real frequencies of TEMT6000 (a) and BH1750 (b). Lab test of TEMT6000, BH1750, and Konica T-10A (c).
Figure 12.
(a) Lab test of light intensity with TEMT6000, BH1750, and Konica T-10A. The TEMT6000 shows accuracy in limited light environments (green dots), but in environments with high luminous flux, the results deviate greatly from professional measuring instruments (orange dots). (b) Field test of light intensity with BH1750 (b).
Figure 12.
(a) Lab test of light intensity with TEMT6000, BH1750, and Konica T-10A. The TEMT6000 shows accuracy in limited light environments (green dots), but in environments with high luminous flux, the results deviate greatly from professional measuring instruments (orange dots). (b) Field test of light intensity with BH1750 (b).
Figure 13.
Illumination fitting of BH1750. A total of 100 sets of data in a daily light environment were collected (pink dots), and the data from BH1750 were fit to T-10A to perform data calibration for BH1750 in two ways, the fitting curve in brown is the result of Equation (1), while fitting curve in green is the result of Equation (2).
Figure 13.
Illumination fitting of BH1750. A total of 100 sets of data in a daily light environment were collected (pink dots), and the data from BH1750 were fit to T-10A to perform data calibration for BH1750 in two ways, the fitting curve in brown is the result of Equation (1), while fitting curve in green is the result of Equation (2).
Figure 14.
(a) Measurements of the real frequency of the Yahboom sensor. (b) We obtained environmental noise in the lab by playing different types of sounds.
Figure 14.
(a) Measurements of the real frequency of the Yahboom sensor. (b) We obtained environmental noise in the lab by playing different types of sounds.
Figure 15.
Data fitting with the Yahboom sensor and cellphone sensor. We fit with three modes: (a) curves, (b) 5th degree polynomials, and (c) 10th degree polynomials to the data using a Python script.
Figure 15.
Data fitting with the Yahboom sensor and cellphone sensor. We fit with three modes: (a) curves, (b) 5th degree polynomials, and (c) 10th degree polynomials to the data using a Python script.
Figure 16.
Field research with SBEDAD in three major areas in the Beijing core area. (a) Statistical information of field measurements using SBEDAD. (b–d) The streets being measured by three teams.
Figure 16.
Field research with SBEDAD in three major areas in the Beijing core area. (a) Statistical information of field measurements using SBEDAD. (b–d) The streets being measured by three teams.
Figure 17.
Field test with SBEDAD. (a) The first version of SBEDAD. (b) The measurements used both the panoramic camera and SBEDAD.
Figure 17.
Field test with SBEDAD. (a) The first version of SBEDAD. (b) The measurements used both the panoramic camera and SBEDAD.
Figure 18.
Urban spatial data visualization by Urban Design Studio students using the data collected from SBEDAD in conjunction with the D3.js visualization script to visualize and analyze the spatial–temporal features of urban sensing data to deeply reveal the features of human activities in the urban physical built environment.
Figure 18.
Urban spatial data visualization by Urban Design Studio students using the data collected from SBEDAD in conjunction with the D3.js visualization script to visualize and analyze the spatial–temporal features of urban sensing data to deeply reveal the features of human activities in the urban physical built environment.
Figure 19.
Urban spatial data visualization by Urban Design Studio students using the data collected from SBEDAD in conjunction with the Tableau to visualize and analyze the spatial–temporal features of PM concentration.
Figure 19.
Urban spatial data visualization by Urban Design Studio students using the data collected from SBEDAD in conjunction with the Tableau to visualize and analyze the spatial–temporal features of PM concentration.
Table 1.
Technical parameters of the Arduino Mega 25.60 [
40].
Table 1.
Technical parameters of the Arduino Mega 25.60 [
40].
Mega 2560 Specification Parameter |
---|
Operating voltage | 5 V |
Input voltage (recommended) | 7–12 V |
Input voltage (limit) | 6–20 V |
Digital I/O pins | 54 |
Analog I/O pins | 16 |
DC I/O pin | 40 mA |
DC for 3.3 V pin | 50 mA |
Flash memory | 256 KB |
SRAM | 8 KB |
EEPROM | 4 KB |
Clock frequency | 16 MHz |
Table 2.
Configuration of SBEDAD.
Table 2.
Configuration of SBEDAD.
Purpose | Type | Model | Response Time |
---|
Printed circuit board | Arduino | MEGA2560 | -- |
Longitude and latitude | GPS and BDS | ATGM336H-5N | 0.1 ms |
Temperature and humidity | Humidity and temperature | DHT22 | 10 ms |
Air pollution | PM | Plantower G7003S | 100 ms |
Light | Light sensor | TEMT6000 | 20 ms |
Light sensor | BH1750 | 5 ms |
Noise | Voice sensor | Croco-Sound | 20 ms |
Air flow | Air flow sensor | Air flow sensor | 10 ms |
Status display | LCD | HX8357-B | |
Data storage | SD | OV7670 | |
Video record | Panoramic camera | Pilot One | |
Table 3.
Price and features of SBEDAD.
Table 3.
Price and features of SBEDAD.
Model | Nature | Commercial | Price |
---|
MEGA2560 | Obligatory | YES | 35.28 |
ATGM336H-5N | Obligatory | YES | 10.93 |
DHT22 | Obligatory | YES | 1.08 |
Plantower G7003S | Obligatory | YES | 12.18 |
TEMT6000 | Obligatory | YES | 0.90 |
BH1750 | Obligatory | YES | 1.1 |
Croco Sound | Obligatory | YES | 1.37 |
Air flow sensor | Optional | YES | 6.64 |
HX8357-B | Obligatory | YES | 1.38 |
OV7670 | Obligatory | YES | 0.48 |
PTQS1005 | Optional | YES | 69.16 |
Heart beat rate sensor | Optional | YES | 1.51 |
3D-printed shell | Optional | No | 5 |
Batteries | Obligatory | YES | 2.77 |
Cable and welding | Obligatory | No | 1 |
Switching button | Optional | YES | 1.11 |
Resistor | Obligatory | YES | 0.1 |
Table 4.
Accuracy of data acquired by each sensor of SBEDAD.
Table 4.
Accuracy of data acquired by each sensor of SBEDAD.
Model | Range | Error Precision | SBEDAD Accuracy |
---|
Temperature | 18–25 °C [44,45] | ±1 °C | ±0.5 °C |
Humidity | 40–70% [44,45] | >1% | ±1% |
PM | 0–500 μg/m3 [46] | ±25% | ±10–100 μg/m3 |
Light | 10,000–18,000 Lux [47] | ±20% | >10 lux |
Sound | 40–120 db [48] | ±5 db | 0–40 m/s |
GPS | -- | -- | 2 m |
Table 5.
GPS and BDS model.
Table 5.
GPS and BDS model.
Model | Nature |
---|
Manufacturer | Yahboom |
Cold-start acquisition sensitivity | −148 dBm |
Hot-start acquisition sensitivity | −156 dBm |
Recapture sensitivity | −160 dBm |
Tracking sensitivity | −162 dBm |
Positioning accuracy | 2.5 m(CEP50) |
Typical power consumption | 25 mA @3.3 V |
Agreement | NMEA0183 |
Maximum height | 8000 m |
Maximum speed | 515 m/s |
Operating temperature | −40 °C to + 85 °C |
Size and weight | 10.1 mm × 9.7 mm × 2.4 mm, 0.6 g |
Table 6.
Temperature and humidity model.
Table 7.
Plantower G7003S model.
Table 7.
Plantower G7003S model.
Parameter | Indicator | Unit |
---|
Principle | Laser Scattering | --- |
Measuring range of particles | 0.3~1.0; 1.0~2.5; 2.5~10 | µm |
Effective range of concentration | 0~500 | µg/m3 |
Maximum mass | ≥1000 | µg/m3 |
Mass concentration resolution | 1 | µg/m3 |
DC voltage | 4.5–5.5 | V |
Working current | ≤100 | mA |
Standby current | ≤200 | µA |
operating temperature range | −10~+60 | °C |
Operating humidity range | 0~99% | V |
Table 8.
Light intensity model.
Table 9.
Environmental noise model.
Table 9.
Environmental noise model.
| Yahboom | Noise App |
---|
| | |
Intense range | 0–1023 | 0–140 dB |
Deviation | ±1% | 0.1 dB |
Operating voltage | 3.3–5 V DC | 0.8–1 V DC |
Operating temperature | −40–100 °C | −10–85 °C |
Current supply | 20 mA | 80 mA |
Sampling period | 20 ms | 1000 ms |
Power dissipation | 100 mw | 18–26 W |
Price | USD 0.9 | USD 12 |