Mobile Crowd Sensing-based Noise Monitoring as a Way to Improve Learning quality on Acoustics

Transcription

1 Mobile Crowd Sensing-based Noise Monitoring as a Way to Improve Learning quality on Acoustics Salonicco, 19,11.15 Marco Zappatore Antonella Longo, Mario A. Bochicchio Daniele Zappatore Alessandro A. Morrone Gianluca De Mitri mario.bochicchio@unisalento.it

2 Mobile Crowd Sensing: towards MOOL 2

3 Interpolated Measurement Visualization 3

4 Contents Introduction and research purposes Monitoring sound levels via mobiles: our proposal Mobile Learning and Mobile Crowd-Sensing (MCS Mobile Crowd-Sensing (MCS) Developed apps & research outcomes Conclusions and Future Developments 4

5 Research Purposes 1) Performing preliminary, large-scale urban noise-monitoring campaigns thanks to Mobile Crowd Sensing (MCS) 2) Offering complementary and sufficiently accurate noise monitoring capabilities via mobile apps 3) Directly involving students in sensing activities and suggesting them contextual learning support 4) Collecting users comments (psychoacoustic measurements) 5

6 New Units per year Scenario: Mobile Device Pervasivity 3000 M 2500 M Device Types Mobile Devices (2014, worldwide): 6,572 billion 2000 M 1500 M 1000 M 2016 Estimations SMARTPHONES 500 M PERSONAL COMPUTERS

7 Our Vision Mobile Crowd Sensing for multiple environmental parameters Mobile Learning and laboratorial experiences Improvement in learning quality and positive behavioral change NOISE AIR QUALITY WATER QUALITY WEATHER EM FIELDS MOBILITY LIVE SENSING OF MULTIPLE ENVIRONMENTAL PARAMETERS 7

8 Scenario: Urban Noise & Public Concerns EU Urban Mobility Report 2013: of Europeans believe that noise represent the 5 th most significant city problem The situation in Italy ( ): Cities that carried out noise monitoring campaigns Noise monitoring campaigns requested by citizens Monitoring campaigns showing at least one threshold exceeded Thresholds exceeded due to high vehicular traffic Municipalities that still not comply with acoustic classification plans 8

9 Scenario: Urban Noise & Health Effects Wrongly deemed as different from other urban pollutants (chemical, radiological) Few municipalities implement health risk-related noise control policies Evidence of correlation between noise and different health effects ACUTE CHRONIC LONG-TERM o Sleep reduction o Sleep fragmentation o Stress, distraction o Noise-Induced Hearing Loss (NIHL) o Noise annoyance (socio-behavioural changes, overall discontent, increased number of accidents) o o Hypertension o Reduced learning effectiveness o Concentration deficit o Reduced productivity o Disruption of endocrine system o Diabetes o... o Heart diseases o Cardiovascular issues o Permanent NIHL o... 9

10 Mobile Crowd Sensing (MCS) paradigm ANTENNAS - 3G/4G (EDGE, UMTS, LTE) - Short-range (Wi-Fi, Bluetooth) PROXIMITY ACCELEROMETERS GYROSCOPE GPS ENVIRONMENTAL (external sensors) LIGHT MICROPHONES Embedded / External 10

11 MCS Monitoring Applications TRAFFIC & PARKING LARGE-SCALE EVENTS AIR POLLUTION NATURAL DISASTERS Several MCS-based initiatives Typical application in urban environments Up to thousands of users Adoption of external sensors (i.e., additional accelerometers for earthquake early alerts) NOISE EMISSIONS Few systems, only for personal noise assessment registration of surrounding sounds No systems with didactic purposes 11

12 Our proposal MCS-based platform named City Soundscape Mobile app for collecting measurement (participatory & opportunistic modes) Web application for visualizing interpolated measurements (space & time) and for suggesting noise abatement interventions to city managers Data Warehouse (DWH) approach for data management Extract-Transform-Load (ETL) pipeline for outlier detection & data aggregation Partly developed by Alba Project s.r.l. in the framework of the Smart Planning for Urban Mobility project (grant agreement n , sub-grant agreement n. 021), within the frontiercities entrepreneurial accelerator The frontiercities accelerator exploits FIWARE, a set of open APIs easing the development of smart applications in multiple vertical sectors 12

13 Platform Architecture Android 4.2 (API Lev. 17) CartoDB + proprietary app Orion GE Publish/Subscribe Context broker Pentaho CE + proprietary app Mongo DB (No- SQL document DBMS (both nonpersistent and persistent) MySQL relational DBMS 13

14 Dimensional Fact Model (DFM) descriptive attribute fact measures dimension dimensional attribute 14

15 Platform Prototype Tests First test scenario: City: Lecce (95k inhabitants), Apulia administrative Region Tester: 20 students from our Computer Science course at the Engineering School Monitored areas: high-traffic hotspots Monitored platform components: all Measurement validation: EUT (equipment under test): 3 different Android-based smartphones RE (reference equipment): professional class-1 SLM (DeltaOhm HD9019) Test type: repeated comparison against a known source Location: indoor Noise source: steady, mid-level, broadband, fixed time duration Achieved accuracy: ±5dB bias 15

16 Mobile App User Interface (1) Participatory measurement The user has to start the measurement session and she/he can add comments Opportunistic measurement Noise measurements are automatically performed by the smartphone Noise Exposure Numerical Quantifiers Real-Time Noise Measurement Graph (Time-Domain Analysis) Comment and send measurement data or just send them Perform a new measurement 16

17 Mobile App User Interface (2) Popup reminding students how to perform noise measurements correctly Link to didactic material about noise monitoring guidelines 17

18 Mobile App User Interface (3) Two different repositories for noise monitoring regulations Static chart with different thresholds for acoustic classes with the superposition of the current L EQ level Noise limit typologies List of available noise regulation guidelines and norms 18

19 DPCM 14/11/1997 Ex. of earning materials: Italian noise regulations SPL Sound Pressure Level [db(a)] Effective pressure of a sound wave w.r.t. reference value (human hearing threshold) L EQ(T) Equiv. Continuous Sound Lev. [db(a)] Time-averaged sound exposure level during a given time interval Italian noise regulations: 6 acoustic classes Precautionary approach: limits lower than potentially harmful values Thresholds that cannot be trespassed Min target values after acoustic redevelopment Values representing potential health risks Acoustic Class Emission Values: sensor far from the noise source Limit [db(a)] Quality [db(a)] Attention [db(a)] day* night** day night day night C1. Protected (schools, hospitals) C2. Residential C3. Mixed (SOHO, suburban) C4. Intense human activities C5. Mainly industrial C6. Exclusively industrial *day: 06:00 22:00 **night: 22:00 06:00 19

20 Interpolated Measurement Visualization WEB APP Georeferentiation on OpenStreetMap via CartoDB Interpolated measurements 20

21 Conclusions and Future Developments Low-cost & easy-to-use system for measuring and assessing urban noise Pervasive mobiles used as sensing stations (Mobile Crowd-Sensing, MCS) Students provided with a mobile app mimicking the User Interface of common Sound Level Meters and supported with contextual learning materials about acoustic and noise monitoring guidelines and regulations Platform prototype developed and tested to support our vision: mobile app (for gathering measurements) and Web app (for exposing aggregated data) The system will be deployed also in the city of Brindisi (90k inhabitants, Southern Italy) that hosts multiple noise sources (airport, harbor, railway station, highways) This research activity has been partly developed in the framework of the frontiercities entrepreneurial accelerator programme 21

22 Visit our Website and Collaborate with us! EU research funding programmes (H2020) foster research activities having societal impacts Research activities should address actual needs from citizenships by leveraging technological advancements We aim at establishing scientific collaborations and developing research activities capable of providing effective benefits to people from several EU countries 22

23 Thanks for your attention! 23

24 Committente Requisiti Vincoli: C, Q, T Propgettista Modello 24