Ear Phone An End To End Participatory Urban Noise Mapping-Books Pdf

Ear Phone An End to End Participatory Urban Noise Mapping
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A noise map facilitates monitoring of environmental noise pollution in urban. areas It can raise citizen awareness of noise pollution levels and aid in the. development of mitigation strategies to cope with the adverse effects However. state of the art techniques for rendering noise maps in urban areas are expensive. and rarely updated months or even years as they rely on population and traffic. models rather than on real data Participatory urban sensing can be leveraged. to create an open and inexpensive platform for rendering up to date noise maps. In this paper we present the design implementation and performance eval. uation of an end to end participatory urban noise mapping system called Ear. Phone Ear Phone for the first time leverages Compressive Sensing to ad. dress the fundamental problem of recovering the noise map from incomplete. and random samples obtained by crowdsourcing data collection Ear Phone. implemented on Nokia N95 and HP iPAQ mobile devices also addresses the. challenge of collecting accurate noise pollution readings at a mobile device We. evaluate Ear Phone with extensive simulations and outdoor experiments that. demonstrate that it is a feasible platform to assess noise pollution with reason. able system resource consumption at mobile devices and high reconstruction. accuracy of the noise map,1 Introduction, At present a large number of people around the world are exposed to high. level of noise pollution which can cause serious illness ranging from hearing. impairment to negatively influencing productivity and social behavior 12 As. an abatement strategy a number of countries such as the United Kingdom 9. and Germany 10 have started monitoring noise pollution They typically use. a noise map a visual representation of the noise level of an area to assess noise. pollution levels The noise map is computed using simulations based on inputs. such as traffic flow data road or rail type and vehicle type data Since the. collection of such input data is highly expensive these maps can be updated. only after a long period of time e g 5 years for UK 9 To alleviate this. problem a recent study 19 proposes the deployment of wireless sensor networks. to monitor noise pollution Wireless sensor networks can certainly eliminate the. requirements of sending acoustic engineers for taking real measurements but the. deployment cost of a dedicated sensor network in a large urban space will also. be prohibitively expensive, In this paper we instead propose an urban sensing approach also known as. participatory sensing 6 people centric sensing 11 or community sensing 15 in. the literature for monitoring environmental noise especially roadside ambient. noise The key idea in participatory sensing is to crowdsource the collection of. environmental data in urban spaces to people who carry smart phones equipped. with sensors and location providing Global Positioning System GPS receivers. The vision of participatory sensing is inspired by the success of other online. participatory systems such as Wikipedia online reputation systems and human. computation systems such as the Google Image Labeler Due to the ubiquity of. mobile phones the proposed approach can offer a large spatial temporal sensing. coverage at a small cost Therefore a noise map based on participatory urban. sensing can be updated with a very small latency hours or days compared to. months or years which makes information provided by noise map significantly. closer to current noise status than that provided by traditional approaches. It is non trivial to build a noise pollution monitoring system based on mo. bile phones Mobile phones are intended for communication rather than for. acoustic signal processing 1 To be credible noise pollution data collected on. mobile phones should be comparable in accuracy to commercial sound level me. ters used to measure noise pollution Since a participatory noise monitoring. system relies on volunteers contributing noise pollution measurements these. measurements can only come from the place and time where the volunteers are. present Furthermore volunteers may prioritize the use of the microphone on. their mobile phones for conversation They may also choose to collect data only. when the phone has sufficient energy Consequently samples collected from. mobile phones are typically randomly distributed in space and time and are. incomplete In order to develop a useful noise pollution monitoring application. we need to recover the noise map from the random and incomplete samples ob. tained via crowdsourcing In this paper we address these challenges Our main. contributions can be summarized as, 1 We present the design and implementation of an end to end noise mapping. 1 For example devices such as the Nokia N96 or HP iPAQ do not support floating point. arithmetic which must be emulated with fixed point operations. system called Ear Phone to generate the noise map of an area using. participatory urban sensing EarPhone consists of mobile phones and a. central server It encompasses signal processing software to measure noise. pollution at the mobile phone as well as signal reconstruction software. at the central server This new noise mapping system is expected to cost. significantly less than traditional noise monitoring systems. 2 We address the problem of incomplete or missing samples that are ob. tained via crowdsourcing by using compressive sensing focusing on road. side noise pollution 2 To the best of our knowledge this is the first appli. cation of compressive sensing to environmental noise data collection. 3 We evaluated Ear Phone with extensive simulations and real world out. door experiments The results show that Ear Phone has reasonable ac. curacy and resource requirement in terms of CPU load and energy con. The rest of the paper is organized as follows In the next section we describe. the Ear Phone architecture followed by the system design in Section 3 Then. we evaluate Ear Phone with both outdoor experiments Section 4 and extensive. simulations Section 5 We present related work in Section 6 and conclude with. future directions in Section 7,2 Ear Phone Architecture. Figure 2 1 Ear Phone Architecture, In this section we describe the high level view of how Ear Phone works and.
a detailed description of the system components will be presented in Section 3. Fig 2 1 presents the overall architecture of Ear Phone The Ear Phone archi. tecture consists of a mobile phone component and a central server component. Noise level is assessed on the mobile phones before being transmitted to the. central server The central server reconstructs the noise map based on the noise. measurements Note that reconstruction is required because the urban sensing. 2 We focus on roads because typically noise pollution is most severe on busy roads. framework cannot guarantee noise measurements are available at all the time. and locations, Let us begin with a mobile phone user who is walking along a street We. call a mobile phone with Ear Phone application a MobSLM where SLM stands. for sound level meter which is the instrument used by acoustic engineers. to measure environmental noise level The signal processing module on the. MobSLM computes the equivalent noise level LAeq T over a time interval T. from the raw acoustic samples collected by the microphone over the same time. interval The computed noise level is further attested with the GPS coordinates. which will be denoted by lat lon and system time before being stored in the. phone memory The stored records h time lat lon LAeq T i are uploaded to the. central server when the mobile phone detects an open access point 3G services. on mobile phones can also be used to upload data, The communication manager at the central server waits for transmissions. from the users When there is a transmission it converts the GPS coordinates. of a record to a Military Grid Reference System MGRS see Section 3 2 for the. detailed description grid index and stores the information h time grid index. LAeq T i in a data repository Reconstruction is conducted periodically at. predefined intervals e g minutes or hours when triggered the reconstruction. module is invoked to reconstruct the missing data The reconstructed data is. then stored in the data repository, A query from an end user e g what is the noise level on Oxford Street at. 5pm on 28 October 2009 is processed by a query manager at the central. server The location information e g Oxford Street of the query is first re. solved into grid indices and the reconstructed data associated with those grid. indices are fetched from the data repository Then the grid indices are con. verted back to GPS coordinates and the related noise levels are overlaid on an. Internet map e g Google map before being displayed to the end user. 3 System Components, In this section we describe the major components of Ear Phone in detail. 3 1 Mobile Phone Components,Signal Processing Module.
The aim of the signal processing module is to quantitatively assess the environ. mental noise Noise level or loudness is typically measured as the A weighted. equivalent continuous sound level or LAeq T A weighting is the commonly used. frequency weighting that reflects the loudness perceived by human being 14. Measured in decibel dBA LAeq T captures the A weighted sound pressure. level of a constant noise source over the time interval T which has the same. acoustic energy as the actual varying sound pressure level over the same inter. val Note that sound pressure level is captured by a microphone as an induced. voltage The A weighted equivalent sound level LAeq T in time interval T is. thus given by,LAeq T 10 log10 vA t 2 dt,z offset 3 1. where vA t is the result of passing induced voltage v t through an A weighting. filter and the constant offset is determined by calibrating the microphone against. a standard sound level meter, In order to compute v A T we design a tenth order digital filter whose. frequency response matches with that of A weighting over 0 8kHz since the. acoustic standard IEC651 Type 2 SLM 14 requires to measure the environ. mental noises between 0 and 8 kHz Based on the coefficients of the digital filter. al bl where l 1 10 we then calculate v A T using the following algorithm. Algorithm Compute v A T, 1 Initialize Q Fs T 1 Fs Sampling Frequency Sampling Period Ts. Input Voltage samples v kTs for k 0 1 2 Q 1 over duration 0 T. Output v A T, 2 Based on al bl and initial condition vA kTs 0 for k 0 9 recur. sively compute,vA kTs a vA k Ts,b v k Ts for k 10 3 2.
v A T vA kTs 2 3 3,3 2 Central Server Components, Computing Long term Equivalent Noise Level LAeq LT. In order to compute the long term equivalent noise level LAeq LT over duration. LT where L is an integer bigger than 1 from the equivalent noise levels LAeq T. measured over shorter time duration T we use the following standard formula. LAeq LT 10 log10 100 1LAeq Ti 3 4, where N is the number reference time intervals and LAeq Ti is the time average. A weighted sound pressure level in the i th reference time interval The above. formula can be readily derived by noting that equivalent noise level is defined. as the logarithm of average noise power see equation 3 1. GPS MGRS conversions, Reasons for approximating GPS to square areas are two fold Firstly comput. ing the LAeq T for every possible GPS coordinates is impractical because there. are infinitive GPS coordinates Secondly the acoustic standards for monitor. ing noise pollution suggest to measure the pollution in square areas Section. 5 3 1 a in 1 assuming the noise level is constant over that area In order to. approximate GPS into grids we use MGRS which can divide the earth surface. into a square area of such as 100 m 100 m 10 m 10 m or 1 m 1 m etc. We followed the Australian acoustic standard to determine an appropriate. grid size This standard restricts the noise level difference between two adjacent. grids to be no more than 5 dB Section 5 3 2 in 1 Therefore we conducted a. number of experiments where we put a MobSLM at a static position and put. another MobSLM at difference distances from the first MobSLM and recorded. the difference of LAeq 1s readings for each distance We found that for the. grid size of 10 10 20 20 30 30 40 40 and 50 50 square meters. the corresponding noise level differences between adjacent grids are 2 26 06. 3 82 05 3 86 03 4 11 02 and 4 97 03 dB respectively We could therefore. use square grids which are less than or equal to 50 meters in each dimension. We choose to use grid size of 30m 30m because it takes approximately 30. seconds for a Nokia N95 to acquire a GPS position and a person can travel 30. meters in 30 seconds in normal walking speed 1 m s Furthermore GPS has. an accuracy of 10 meters in outdoor environment therefore a 30 30 grid could. help us to cope with the GPS accuracy We use formulations in 17 to convert. between GPS and MGRS,Signal Reconstruction Module, In this section we will describe two sensing strategies namely projection method. and raw data method and how the central server performs reconstruction using. the information collected by these two different sensing strategies For recon. struction we use the recently developed theory of compressive sensing 7 For. ease of explanation we will explain the two sensing strategy with an example. Ear Phone An End to End Participatory Urban Noise Mapping System Rajib Kumar Rana1 Chun Tung Chou1 Salill Kanhere1 Nirupama Bulusu2 Wen Hu3 1 University of New South Wales Australia rajibr ctchou salilk cse unsw edu au 2 Portland State University USA nbulusu cs pdx edu 3 CSIRO ICT Centre Australia wen hu csiro au Technical Report UNSW CSE

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