Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring
Abstract
:1. Introduction
2. State of the Art of Dynamic Acoustic Urban Sensing
2.1. Static Acoustic Urban Sensing
2.2. Mobile Acoustic Urban Sensing
2.3. Participative Urban Sensing
2.4. Hybrid Urban Sensing
3. Mobile Measure Platforms and Their Connectivity
3.1. Hardware Platforms
- Cortex-A: basically used by embedded systems that need a high level of operational system computing capabilities as low-cost handsets to smartphones or tablets.
- Cortex-R: this series is the smallest ARM processor and is commonly used in automotive, networking and data storage applications.
- Cortex-M: being the most popular of the ARM family, this series is being used for all types of low-cost and low consumption applications, from real-time signal processing to industrial control.
- mbed: this is a platform that provides free libraries, hardware designs and online tools for rapid prototyping of 32-bit ARM-based microcontroller products. This framework includes a standards-based C/C++ SDK, a microcontroller HDK and supported development boards, an online compiler and online developer collaboration tools [71,72].
- MATLAB: MathWorks offers the Embedded Coder Support Package for the Freescale FRDM-KL25Z Board to run the Simulink® model on an FRDM-KL25Z board. The support package includes a library of Simulink blocks for configuring and accessing Freescale FRDM-KL25Z peripherals and communication interfaces. Then, it is quite simple to build applications using the block-based interface of Simulink®, which generates also the code for the Freescale FRDM-KL25Z board and runs the generated code on the board [73].
- Signal conditioning module, in charge of accommodating the analog signal captured by the microphone to the next block; that is, the Analog to Digital Converter (ADC). This conditioning stage adjusts the analog signal levels of voltage and current to the ADC input. Besides, this module also matches the output impedance of the microphone to the input impedance of the ADC.
- Analog to digital audio converter. Obviously, this block converts the analog signal captured by the microphone to a digital signal that can be later processed. To select the ADC, the main characteristics that should be analyzed are: the number of bits used in the conversion (as many bits are used, a bigger resolution will be obtained in the conversion, but a longer time of conversion), the speed of the conversion, distortion performance, sensitivity and errors in the conversion. The ADC can be implemented inside the System on Chip (SoC) on which the embedded system is based or also it can be placed externally, and it is controlled via an I2C or SPI bus.
- CODEC block: Although this block is optional, it can be used to reduce the number of bits needed to save the digitalized signal in the memory of the embedded system to its post-process. A huge variety of codecs can be used, but all of them are focused on compressing the audio stream with the maximum fidelity and quality.
- Memory: after the CODEC, data are dumped into a memory block from which the data can be retrieved for further processing by the application program.
- Directivity: as it is desired to monitor the noise generated in all directions, the microphone must be omnidirectional; that is, it must be able to capture noise from all directions, and then, it must be omnidirectional.
- Sensitivity: understood as the ratio of the analog output voltage or digital output value to the input noise pressure. This value, combined with the signal to noise ratio, is quite important to obtain a high quality monitoring system in order to record the sound with the maximum fidelity.
- Signal to noise ratio: it specifies the ratio of a reference signal to the noise level of the microphone output. Measured in decibels, it is the difference between the noise level and a standard 1-kHz, 94-dB SPL (Sound Pressure Level) reference signal. This specification is typically specified as an A-weighted value (dBA), which means that it includes a correction factor that corresponds to the human ear’s sensitivity to sound at different frequencies. Combined with the sensitivity, these factors will be important to be able to discriminate background noises during the monitoring of the acoustic environment.
- Operating frequency: this is the range of frequencies that can be collected by the microphone. As the application under design is to control the noise in an urban scenario and analyze its impact on humans, the frequency range should be from 20 to 20 kHz; that is, the dynamic range in with human ears work.
3.2. Connectivity of the Platforms
- WiFi ESP8266: this is a very low price and consumption WiFi module that implements a complete TCP/IP protocol stack. It provides a set of instructions and functionalities that make it very easy to control and start to work without any complicated configuration.
- Adafruit FONA 808: this is and all-in-one mobile communication interface plus a GPS module. Although it works with a 2 G SIM card, it is quite enough to allow remote control and data download, reducing the price of the whole system. It communicates with the controller through a serial port, making the deployment of applications easy and fast.
4. Mobile Bus Acoustic Measurement
4.1. Signal Processing Challenges
4.1.1. Reliability of the Measure
4.1.2. Mobile Vehicle Noise Contribution
4.1.3. Classification of Road Traffic Vehicles
4.2. Challenges in Terms of Noise Mapping
4.2.1. Mobile Trajectories Design and Data Collection
4.2.2. Noise Mapping Real-Time Update
4.3. Challenges in Terms of Hardware Platform Selection
- Low price: the main goal of the application being the collection of data about the noise in the city to generate a dynamic noise map, the more sensors can be placed, the better. Then, the use of a inexpensive hardware platforms is recommended, which can be easily deployed, and this should include all of the elements needed to achieve the objectives of the applications.
- Non-intrusive: that is, as the platform will be deployed on a public means of transport, such as urban buses, it is recommended not to require any special restrictions regarding its installation. Then, it must be auto-powered, small in size and easily integrated on the buses. Moreover, during the performance of the system, it has to have no interference with the electronic and communication systems of the vehicle.
- Low energy consumption: as the hardware platform cannot be connected to the energy system of the vehicle, it must have a power system itself. Then, the complete system should be low consumption and implemented with any software functionality able to control the waste of energy during the operation, for example disabling some subsystems if they are not needed.
- Communication interfaces: one of the main tasks of the platform is to send the recorded data to a central system. Then, different communication interfaces can be deployed on board, according to the characteristics of the environment. That is, if a WiFi network is available, then a free link can be established with the server, but in the case of being out of the WiFi coverage, a mobile communication link should be available to allow the data upload and also the remote control of the platform in case a remote reconfiguration or maintenance is necessary. At the same time, the platform must perform a geolocation interface in order to associate noise-data to location references.
- Communications management sub-system: as the platform needs different interfaces, a management software is needed to control the communications to the server. This functionality will be in charge of determining which interface can be used in each situation in order to save energy and to save money. For example, when free WiFi is available, this functionality will upload all of the saved data to the server automatically.
- Storage capacity: as the platform must save data about the noise in different locations, it must be able to save as much information as possible in order to keep it in memory until being in a free WiFi coverage area to send it to the server or just before overloading of the memory, through a mobile link.
- Real-time data processing capability: the processor provided on the embedded system must be able to process the noise captured by the microphone in real time to obtain the noise frequency and level. Then, this information is saved on the memory joined to the location provided by the GPS. Later, these data are sent to the central server. During this signal processing stage, the processor also must be able to discriminate the noise of the environment from the noise generated by the vehicle in which the hardware will be deployed. Moreover, the resolution of the processing must be enough to allow the classification and characterization of the traffic vehicles.
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
ADC | Analog to Digital Converter |
ANED | Anomalous Noise Event Detection |
ARM | Advanced RISC Machine |
ASIC | Application-Specific Integrated Circuits |
CPU | Central Processing Unit |
EC | European Commission |
END | Environmental Noise Directive |
EU | European Union |
DSP | Digital Signal Processor |
FLD | Fisher’s Linear Discriminant |
FPGA | Field Programmable Gate Array |
GPS | Global Positioning System |
GTCC | Gammatone Cepstrum Coefficients |
HMM | Hidden Markov Models |
ICT | Information and Communication Technology |
KNN | K-Nearest Neighbor |
LMS | Least Mean Squares Filter |
MFCC | Mel-Frequency Cepstrum Coefficients |
MSU | Mobile Sensing Unit |
PLC | Programmable Logic Device |
RAM | Random Access Memory |
RLS | Recursive Least Squares Filter |
SOA | Service-Oriented Architecture |
SoC | System on Chip |
SOM | Self-Organizing Maps |
SPL | reference signal |
SVM | Support Vector Machine |
WASN | Wireless Acoustic Sensor Network |
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Embedded System | Processor Core | Price |
---|---|---|
The chipKIT™ MX3 | Microchip® PIC32MX320F128H Microcontroller (80-MHz 32-bit MIPS 128 KB Flash, 16 KB SRAM) | 44.99$ |
STM32VLDiscovery | ARM® Cortex-M3 (24-MHz 32-bit 128 KB Flash memory, 8 KB RAM) | 9.90$ |
FRDM-KL25Z | ARM® Cortex®-M0+ (48-MHz 32-bit MIPS 128 KB Flash 16 KB SRAM) | 13.25$ |
BeagleBone Black | Sitara™ ARM® Cortex-A8 (2x PRU 32-bit microcontrollers, 512 MB DDR3 RAM) | 51.15$ |
Raspberry Pi 3 Model B | 1.2-GHz Quad-Core ARM Cortex-A53 | 37.00$ |
CYPRESS PSoC® 4 CY8C4245AXI | 32-bit ARM® Cortex™-M0 48-MHz CPU | 24.31$ |
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Alsina-Pagès, R.M.; Hernandez-Jayo, U.; Alías, F.; Angulo, I. Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring. Sensors 2017, 17, 57. https://doi.org/10.3390/s17010057
Alsina-Pagès RM, Hernandez-Jayo U, Alías F, Angulo I. Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring. Sensors. 2017; 17(1):57. https://doi.org/10.3390/s17010057
Chicago/Turabian StyleAlsina-Pagès, Rosa Ma, Unai Hernandez-Jayo, Francesc Alías, and Ignacio Angulo. 2017. "Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring" Sensors 17, no. 1: 57. https://doi.org/10.3390/s17010057
APA StyleAlsina-Pagès, R. M., Hernandez-Jayo, U., Alías, F., & Angulo, I. (2017). Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring. Sensors, 17(1), 57. https://doi.org/10.3390/s17010057