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Wireless Acoustic Low-cost Sensor Network for Urban Noise Monitoring
Simona Domazetovska, Maja Anachkova, Viktor Gavriloski, Zlatko Petreski
To cite this version:
Simona Domazetovska, Maja Anachkova, Viktor Gavriloski, Zlatko Petreski. Wireless Acoustic Low- cost Sensor Network for Urban Noise Monitoring. Forum Acusticum, Dec 2020, Lyon, France. pp.677- 682, �10.48465/fa.2020.0595�. �hal-03233740�
WIRELESS ACOUSTIC LOW-COST SENSOR NETWORK FOR URBAN NOISE MONITORING
Simona Domazetovska Maja Anachkova Viktor Gavriloski Zlatko Petreski
Faculty of Mechanical Engineering in Skopje, University Ss Cyril and Methodius in Skopje, North Macedonia simona.domazetovska@mf.edu.mk
ABSTRACT
The concept of low-cost sensor network based on Internet of Things (IoT) technology for monitoring environmental parameters is trendy area of research that has attracted a lot of scientific attention recently. Amongst the several pollutant that have to be faced on a daily basis, the noise pollution is one of the most widely known emerging and challenging problem in all large metropolitan areas, affecting the health of citizens in multiple ways. Recent technologies of low-cost and low-power devices allowed researchers to develop monitoring Wireless Sensor Units (WSUs) with wireless network for providing higher granularity than the traditional hand-held analyzers, allowing massive noise monitoring. This paper presents prototyping of low-cost WSUs based on chosen sensor with different network platforms for noise monitoring and analysis. The units aim to measure the equivalent noise level in dB(A) and provide continuous and autonomous data acquisition. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer through laboratory and outdoor evaluation test. This evaluation has shown the devices as powerful and affordable low-cost units able to publish results online that could help in increasing the population awareness for the noise.
KEYWORDS: Urban Noise, Wireless Sensor Units, Low-cost Sensor Network, Noise Monitoring
1. INTRODUCTION
According to [1], more than 55% of the population currently live in urban areas, which is about to reach 68%
by 2050. The concentrated population growth, the increase of traffic, demographic expansion and a lot of industries development lead into creating new challenges in order to effectively manage resources and maintain a high quality of the living environment [2]. This rapid urbanization requires monitoring cities for identifying the environmental parameters that influence the health and comfort of the population, such as air pollution, floods, noise exposure and other variables [3].
Environmental noise is one of the main concerns that has to be addressed, due to its negative impact on the health of people. Exposure to noise in urban areas across Europe affects 27.6 million people, who are alarmed by the noise caused by road traffic, railways, air traffic and industry, of which 12.8 million suffers from serious anxiety [4]. Noise pollution is a global problem all
around the world, mainly caused by urban traffic [5], considering the health problem in the urban cities. Noise is a significant public health problem that leads to hearing loss, sleep distribution, reduced productivity, increased drug use and traffic accidents [6].
Based on END [7], the European Union requires noise maps of big cities to check the noise level and create action plans for lowering and eliminating the noise exposure. The maps are updated every five years, so in the process of updating and creating new maps, a lot of measurements have to be done, which are cost and time- consuming. To be able the notice the short-term changes that can cause major changes in the noise conditions, real-time observation is of abundant importance [8]. To this aim, trendy and novel technologies based on Internet of Things within smart cities lead into designing and development of wireless acoustic sensor networks (WASNs) consisted of low-powered wireless sensors that are valid as infrastructure for deployment that could serve for a longer time [9]. The application of smart city and IoT could help in improving the urban environment, including the effect on the citizens, transport and services. The final goal is to have low-cost units that can be deployed in any environment and with the advanced sensing and computation capabilities, data gathered and evaluated in real time to extract the information into usable knowledge [10]. However, this sensor units require high energy-efficient sensor nodes able to communicate across long distance. This motivates the development of many Low-Power Wide Area Networks (LPWAN) technologies, such as LoRa, to fulfill these requirements [11]. Also, the use of Wi-Fi networks for WSUs for smart city concepts, enable wireless communication.
In the smart city development, when designing the sensor node, a small-size, low-cost and resource- limited unit is required. Integrating and modeling of sensors needs to be applied in the cities in order to improve the human health and environment [12], allowing higher granularity to better understand the noise pollution. In [13], a sensor function design and implementation of a wireless sensor network for measuring environmental acoustic noise is built. The researchers in [14, 15] have presented a new approach to monitor noise pollution, enabling citizens to measure the exposure to noise based on crowd sensing in their everyday environment, helping into better urban planning of the city. Low-cost system to measure and monitor noise based on Raspberry Pi in the industrial, as well the urban environment has been presented in [3, 16]. In [17], a customized noise level meter was designed and tested,
while simultaneously minimizing overall power consumption across the network. In [18], the researchers used wireless sensors to monitor five minutes of traffic noise, using a self-developed CiNet platform.
The twenty-first century noise exposure is a major public health problem in the general living environment, requiring effective noise measures for public health protection. The researchers need to design mechanisms and strategies to reduce the noise exposure by combining the scientific research and the technological innovations into existing public health infrastructure [19].
In this paper, a low-cost sensor units using different wireless networks for will be presented, which have been constructed as a part of a students project. The results are validated and compared using 1st class hand-held analyzer in laboratory and outdoor conditions.
2. ARCHITECTURE OF THE WIRELESS ACOUSTIC SENSOR NETWORK
The noise levels are traditionally measured using hand- held devices (1st and 2nd class sound level meters) that require manual operation by a trained user. Following the standards for noise measuring, the results lead in creating fewer data points over a short periods of time. However, using this methodology could not capture the full spectrum of noise for longer time period. These models are insufficient for today’s dynamic world.
To better understand the impact of noise, a higher level of granularity is required, followed by continuous monitoring system that is offered through wireless acoustic sensor networks. WASNs have been used in numerous smart city and IoT applications all around the world, trying to resolve the challenges and capabilities related to this technology.
In order to use these novel technologies, wireless sensor units have to be designed and developed. The design and the creation of the prototype with the implementation and the cloud connections needs to be well established in order to generate low-budget, effective and reliable device. In order to design affordable and suitable unit, the device, i.e. the wireless sensor unit should fulfill these requirements:
• The device has to be built with low-cost components to create affordable sensor network consisted of several devices.
• The quality of the sensor must be provided in terms of long-term measurements enough for advanced calculation. It should contain a battery for achieving sustainability and individuality.
• The device should be connected to cloud for software analysis and sharing results. The network between the sensor units has to be able to communicate wireless.
• The final device has to be protected from outdoor conditions (e.g. water, wind).
The main component of the device is the processing device, which is used for programming, data acquisition and connectivity. To establish this, two noise monitoring devices, using the same sensor, but different board computers with integrated wireless modules (LoRa
and Wi-Fi) were chosen. Figure 1 shows block diagram of the concept for the two noise monitoring systems.
Figure 1. Block diagram of the noise monitoring systems The two sensor units analyzed in Figure 1 use the Gravity Analog Sound sensor for measuring the equivalent noise level (ܮ). The first sensor unit is connected to Wi-Fi module, which connects to the ThingSpeak platform for analyzing and graphical visualization of the results in real-time. The second sensor unit is connected to microcontroller which has integrated LoRa module, allowing connecting to low- power technology (LoRaWAN) for data transfer. Using The Things Network platform, real-time results with the ability to be analyzed are transferred into the cloud connection on the ThingSpeak platform. Both of noise monitoring systems enable data storage and visualization of the results.
This section explains the general concept of the noise monitoring system. When extending WASN deployments from short-term to long-term, monitoring additional challenges are encountered, such as data management and network longevity. The proposed noise monitoring system includes sensor units which are connected through wireless uplink to the cloud service.
Based on the Internet of Things paradigm and the smart city concepts, wireless acoustic low-cost sensor units able to communicate wireless and maintain sustainability are presented.
3. DESIGN AND PROTOTYPING 3.1 Hardware implementation
The wireless sensor unit is consisted of three main hardware components: microcontroller with integrated wireless module, sensor and external battery. Table 1 shows the parts that were used for prototyping of the wireless sensing units and the price per unit which indicates ͳͷͷ ൊ ͳͲ € per unit to construct the low- budget sensor.
Part Type Price Main board ESP32 microcontroller
/ Arduino MKR WAN 1310
10€ / 25€
Sensor Gravity Analog Sound
Sensor V.1 40€
Box Outdoor protection 10€
Battery 20000 mAh 30 €
Components Cables/Antenna 5 € Assembly
cost 4 h, 15 €/h 60 €
Total price
per unit 155€ / 170€
Table 1. Parts for the wireless sensor unit The sensor used for the two WSUs is Gravity Analog Sound Level meter, designed to measure any type of noise, especially the urban noise. The sensor has many advantages: it can detect sound values from 30݀ܤሺܣሻ to 130 ݀ܤሺܣሻ at frequency range from 31.5 Hz to 8.5 kHz, has factory-integrated A-weighting curve and low price.
The constructed units measure A-weighted 15 seconds equivalent sound pressure level ܮ values are calculated continuously, and the most high noise value is detected within the measurement time segment. The prescribed sensor error is േʹ ݀ܤሺܣሻ. To alleviate the privacy issues concerning the continuous audio capturing and storage, the most of the analysis and processing is done already in the sensor and only analyzed data is transferred and stored in the default setting. This approach lowers the amount of transferred data from a sensor to the cloud service, and enables placing sensors to areas with lower quality of wireless uplinks.
The main component of the device is the processing unit, which is also used for the data acquisition and the connectivity. For achieving the established requirements, the design of the noise monitoring devices was based on two different microcontrollers, mainly chosen for the purpose of different wireless connectivity. For the 1st WSU it was used ESP32 microcontroller, which is a low cost, low power system with integrated Wi-Fi module. For the 2nd WSU, Arduino MKR 1310 microcontroller was used because of the integrated LoRa module, which allows connecting to the low-power energy network (LoRAWAN). The basic information and the comparison for the both of the microcontrollers are shown on Table 2.
Table 2. Comparison between the used microcontrollers
For the assembly of all the components chosen for the final device, an isolated box was selected, and the different parts were all connected and placed inside the protective enclosure, so that they can be deployed outside, as shown on Figure 2.
Figure 2. Final finish of the wireless sensor units As it can be seen on Figure 2, all the components for the final device were assembled, connected and placed in isolated boxes. The box fits all of the used components and enables protection from outdoor conditions.
3.2 Energy consumption
To measure the power consumption of the WSUs, the units together with multimeter should form a serial circuit, as shown on Figure 3. Since the components in the device are connected, the power for each of the unit is measured. For the 1st WSU that use Wi-Fi, 140 mA are consumed, while for the 2nd WSU, only 46.5 mA power is consumed.
Figure 3. Measurements for battery consumption For energy consumption, a typical B-type connector battery, i.e. power bank with capacity of 20000mAh was chosen.
To measure how many hours will each of the WSU will spent to operate continuously, the following eqn. 1 was used:
ሺሻ
ሺሻൌ ሺሻ (1) From this equation, it was measured 142.8 hours, i.e. almost 6 days for the 1st WSU, and 430 hours, i.e. almost 18 days for the 2nd WSU for continuously monitoring without requiring human intervention or a new power source.
Actually, by controlling the network, the sensor units can be put into sleep mode, and wake up on demand. By this use, the battery life time can be significantly increased.
3.3 Data acquisition and transmission
The 1st WSU prototype has integrated wireless connectivity through a Wi-Fi connection, which has been exploited to store and show the results of the extracted acoustics parameters. This task has been carried out using an online platform called ThingSpeak. It is an open source IoT application and Application Programming Interface (API) to store and retrieve data from the devices using the Hypertext Transfer Protocol over the Internet.
Moreover, the platform enables the creation of sensor logging applications with status updates. In MATLAB, the connection with the device and ThingSpeak is performed using the API key, the number or variables to send and the updating address. In ThingSpeak, graphs indicating the noise levels have been created showing real time data extracted directly from the sensor, which is calculated and sent to the cloud. Once the data is gathered, the channels can be set up as public or private.
Data can be extracted in different formats for offline tests, backups or analysis of the data. The results are analyzing the equivalent sound pressure level ܮ and the maximum value in each measurement segment.
For the 2nd WSU, an integrated wireless connectivity through a LoRaWAN connection was used. The LoRaWAN protocol is based on LoRa, which is a long range low power wireless technology platform that uses 868 MHz radio spectrum in the industrial, scientific, and medical radio band (ISM band). LoRa aims to eliminate repeaters, reduce device cost, increase battery lifetime on devices, improve network capacity, and support a large number of devices used for long range communication.
The Things Network is an open and global network of IoT infrastructure that uses LoRaWAN as the core technology. The Things Network enables long-range devices to use long-distance gateways to connect to an open-source decentralized network for exchanging and storing data through applications. Once the data is uploaded on The Things Network platform, it is stored and connected to the ThingSpeak for data storage and visualization.
Using the two wireless conectivity, LoRa and Wi-Fi, data transmision is achieved, followed graphical visualization of the results that can be publshed public using the mentioned online platforms.
4. RESULTS 4.1 Laboratory evaluation
Indoor verification of the accuracy of the sensor unit was made in laboratory by comparing the results with hand- held analyzer 2250 from Brüel & Kjær 1st class, where the levels were compared and adjusted. The tests were performed comparing the sound level meter with omnidirectional microphone and the WSUs, by using speaker and audio amplifier connected to Agilent function generator, as shown on Figure 4.
Figure 4. Indoor test configuration for comparing the WSU measurements against the hand-held SPL device
For the first validation test, a sine waves on different frequencies were output from the function generator, passed through an audio amplifier, and played on a speaker, while the hand-held SPL device and both the WSUs recorded the resulting ܮ in decibels ݀ܤሺܣሻ.
The sensor units were very close to the hand-held sound level meter, all of them 40 cm away from the sound source. The test duration was repeated three times, and it was consisted of 9 samples with each configuration of 15 seconds, as shown on Figure 4. With the signal generator and the speaker, signals with nine different frequencies from 250 Hz to 6000 Hz and same amplitude were emitted. The results from the three repeated measurements were averaged and then compared, as shown on Figure 5. By comparing the results, it can be concluded that up to 2000 Hz, the curves follow the trend, while at 3000 Hz, an error occurs in the values of the low-budget WSU. At 4000 hertz, the 1st WSU overlaps with the value from the hand-held analyzer, while the 2nd WSU has a slight deviation. The maximum value error happens for the both units at frequency of 5000 Hz. For the frequency of 6000 hertz, there is an overlap with the second unit, while the first one has a large error.
Figure 5. Results from the laboratory evaluation at different frequencies between the hand-held analyzed
B&K 2250 and the two low-budget WSU Table 3 shows the calculated average error for the two WSUs. For the most frequencies, the units have the allowed error of േʹ ݀ܤሺܣሻ for the sensor. Large errors in the measurements happened at frequency of 3000 and 5000 Hz, where high errors values are occurred, from 3.3 to 7.7 ݀ܤሺܣሻ.
Table 3. Calculated average error for the two WSU (in dBA) compared to the B&K hand-held analyzer 2250 For the second validation test, one tone on the same frequency was played on different loudness, from
Ͳ ൊ ͻͲ ݀ܤሺܣሻ for 15 seconds. Firstly, the validation test was repeated three times, and afterwards, the results have been averaged and compared, as shown on Figure 6. The results show that the noise sensors met acceptance േʹ
݀ܤሺܣሻ error at sound levels between 60 ݀ܤሺܣሻ and 70
݀ܤሺܣሻ, whereas for higher sound levels, the error is between ͲǤ͵ ൊ ͲǤͻ ݀ܤሺܣሻ. This result indicates that the noise sensor was in better agreement with the SLM at sound pressure levels greater than 75 ݀ܤሺܣሻ. The correlation between the sensor and SLM was slightly stronger from ͷ ൊ ͻͲ ݀ܤሺܣሻ compared to the Ͳ ൊ ͷ
݀ܤሺܣሻ test range.
Figure 6. Laboratory evaluation for different loudness (on the vertical axis is shown the measured dB(A) from the
B&K 2250, while on the horizontal axis the measured dB(A) from the two WSU and B&K 2250)
The sensor units were also tested by playing music from radio for 1 minute and 15 minutes. The results show that the measured sound level has smaller error for the 15 minutes measurement with an occurred error from ͲǤͶ to ͳǤʹ ݀ܤሺܣሻǤ
4.2 Outdoor evaluation
For the outdoor validation, the two WSUs and the hand- held analyzer were set at the university campus at 2.5 m height and street with low amount of traffic, as shown on Figure 7. By monitoring the noise level for 15 minutes, it could be concluded that the error was between ͳǤ͵ ൊ ͳǤ
݀ܤሺܣሻ.
Figure 7. Photography of the outdoor evaluation Afterwards, a pilot test was carried out during 24 hour validation with the installation of the acoustic device outside. In this deployment, the evaluation of these devices through long-term measurements was carried out, obtaining several acoustic parameters in real time for its broadcasting and study. The test has shown the units as a powerful tool for monitoring and analyzing the environmental urban noise. The WSU with integrated Wi-Fi module operated successfully without any interruptions, while the WSU with integrated LoRa module had difficulties when connecting and remaining sustainability of the network. The measurement cluster operated successfully without any interruptions and data available real-time. Figure 8 shows both of the diagrams of the 24 hours measurements for the two WSUs, from where it can noticed that the measurements follow the same trend line.
Figure 8. Diagrams for the two WSU for the 24-hour outdoor evaluation
From the outdoor evaluation, it can be concluded that the WSU with integrated Wi-Fi module achieves better sustainability in places with decent Wi-Fi connection for long-term measurement because of the consistency of the Wi-Fi network, but has less battery lifetime. For the WSU with integrated LoRa module, the difficulties for remaining sustainability appeared, but the battery lifetime and the independence for the WSU settlement is better. Both of the units can be used for urban noise monitoring, but improvements in terms of the measurement accuracy and more tests for sustainability of the network are needed.
5. CONCLUSIONS
In this work, two fully functional wireless sensor units had been constructed and tested. In the first part, the wireless sensor units are analyzed, their architecture, design and components. The energy consumption and the data transmission and acquisition for each unit are discussed. Added features of on-board calculations and real-time data presentation online are included. In the second part of the paper, the WSUs were tested under laboratory and outdoor circumstances with a given methodology. Testing the sensor units proved two concepts, validation of the results and the sustainability of the sensor units with possibility of real-time online transfer on an IoT publishing platform.
With the low price of the units, higher granularity can be achieved, and by continuous and autonomous data acquisition the concept of smart city and IoT can be deployed. The deployed WSUs could serve as a tool for increasing the noise awareness, apart from simply a tool for gathering data with research purposes. The overall trends in urban noise can be observed and better understood, thus allowing improve city designs so as to establish the rapid urban progress and maintain the overall quality of life.
6. REFERENCES
[1] Nations, U. (2012). World urbanization prospects:
the 2014 revision. CD-ROM Edition.
[2] Peckens, C., Porter, C., & Rink, T. (2018). Wireless sensor networks for long-term monitoring of urban noise. Sensors, 18(9), 3161.
[3] Noriega-Linares, J., & Navarro Ruiz, J. (2016). On the application of the Raspberry PI as an advanced acoustic sensor network for noise monitoring. Electronics, 5(4), 74.
[4] Noise in Europe 2017: updated assessment
[5] Saadu, A. A., Onyeonwu, R. O., Ayorinde, E. O., &
Ogisi, F. O. (1996). Community attitudinal noise survey and analysis of eight Nigerian cities. Applied Acoustics, 49(1), 49-69.
[6] Goines, L., & Hagler, L. (2007). Noise pollution: a modern plague. SOUTHERN MEDICAL JOURNAL- BIRMINGHAM ALABAMA-, 100(3), 287.
[7] Environmental Noise Directive END 2002/49/EC, 2002
[8] Kivelä, I., & Hakala, I. (2015). Area-based environmental noise measurements with a wireless
sensor network. In Proceedings of the Euronoise (pp. 218-220).
[9] Alías, F., & Alsina-Pagès, R. M. (2019). Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities. Journal of Sensors, 2019.
[10] Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M.
(2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things journal, 1(2), 112-121.
[11] Khutsoane, O., Isong, B., & Abu-Mahfouz, A. M.
(2017, October). IoT devices and applications based on LoRa/LoRaWAN. In IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society (pp. 6107-6112). IEEE.
[12] Maisonneuve, N., Stevens, M., Niessen, M. E., Hanappe, P., & Steels, L. (2009, May). Citizen noise pollution monitoring. In Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (pp. 96-103). Digital Government Society of North America.
[13] Hakala, I., Kivela, I., Ihalainen, J., Luomala, J., &
Gao, C. (2010, July). Design of low-cost noise measurement sensor network: Sensor function design. In 2010 First International Conference on Sensor Device Technologies and Applications (pp.
172-179). IEEE.
[14] Zappatore, M., Longo, A., & Bochicchio, M. A.
(2017). Crowd-sensing our smart cities: A platform for noise monitoring and acoustic urban planning.
[15] Zamora, W., Vera, E., Calafate, C., Cano, J. C., &
Manzoni, P. (2018). GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing. Sensors, 18(8), 2596.
[16] Blasco, N., de Diego, M., Belda, R., de Fez, I., Arce, P., Martínez-Zaldívar, F. J., & González, A. (2017).
Distributed Sensor Network for Noise Monitoring in Industrial Environment with Raspberry Pi. In The Sixth International Conference on Intelligent Systems and Applications (includes InManEnt) IARIA (pp. 51-55).
[17] Filipponi, L.; Santini, S.; Vitaletti, A. Data collection in wireless sensor networks for noise pollution monitoring. In Proceedings of the 4th International Conference on Distributed Computing in Sensor Systems, DCOSS 2008, Santorini Island, Greece, 11–14 June 2008.
[18] Hakala, I.; Kivela, I.; Ihalainen, J.; Luomala, J.; Gao, C. Design of low-cost noise measurement sensor network: Sensor function design. In Proceedings of the 2010 First International Conference on Sensor Device Technologies and Applications, Venice, Italy, 18–25 July 2010; pp. 172–179.
[19] Hammer, M. S., Swinburn, T. K., & Neitzel, R. L.
(2013). Environmental noise pollution in the United States: developing an effective public health response. Environmental health perspectives , 122(2), 115-119.
