Chaac: Real-Time and Fine-Grained Rain Detection and Measurement Using Smartphones

Rain observations with fine spatio-temporal granularity are significant for professional researches, decision-making, and our daily lives. However, the existing rain gauges can only cover less than 1% of the earth surface, and its amount is still decreasing. Even with the help of several other limited and immature supplementary techniques, rain observations today are still not precise enough. In such context, crowdsourcing paves the avenues toward a fault-tolerant rain observation network with unprecedented resolution and coverage, based on an alternative, nowadays omnipresent source, smartphones, which are integrated with abundant advanced sensors and are becoming more and more ubiquitous around us. In this paper, we propose Chaac, a novel system that exploits opportunistically crowdsourced audio clips from smartphone users to achieve precise detection and intensity measurement of rain. The evaluation results of performing Chaac on 1-s long audio segments demonstrate that it can detect and measure rain with 92.0% and 93.9% true positive rates, respectively.

[1]  Xiaohui Liang,et al.  Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense , 2016, IEEE Transactions on Dependable and Secure Computing.

[2]  Mo Li,et al.  A Participatory Urban Traffic Monitoring System: The Power of Bus Riders , 2017, IEEE Transactions on Intelligent Transportation Systems.

[3]  Yunhao Liu,et al.  Sensor Network Navigation without Locations , 2009, INFOCOM.

[4]  Fawzi Nashashibi,et al.  Detection of unfocused raindrops on a windscreen using low level image processing , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[5]  Daren C. Brabham.  MOVING THE CROWD AT THREADLESS , 2010 .

[6]  Andreas Pitsillides,et al.  Mobile Phone Computing and the Internet of Things: A Survey , 2016, IEEE Internet of Things Journal.

[7]  Alex X. Liu,et al.  Fast and Accurate Tracking of Population Dynamics in RFID Systems , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[8]  U. Hadar,et al.  Comparison of two methodologies for long term rainfall monitoring using a commercial microwave communication system , 2012 .

[9]  Z. Zivkovic.  Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[10]  A. Bárdossy,et al.  Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting , 2008 .

[11]  H. Messer,et al.  Frontal Rainfall Observation by a Commercial Microwave Communication Network , 2009 .

[12]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[13]  Hidde Leijnse,et al.  Rainfall measurement using radio links from cellular communication networks , 2007 .

[14]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[15]  Alex X. Liu,et al.  Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it , 2013, MobiCom.

[16]  P. Joe,et al.  So, how much of the Earth's surface is covered by rain gauges? , 2014, Bulletin of the American Meteorological Society.

[17]  Francisco J. Tapiador,et al.  An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers , 2010 .

[18]  A. Kitoh,et al.  APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges , 2012 .

[19]  Shaojie Tang,et al.  VADS: Visual attention detection with a smartphone , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[20]  Wei Xi,et al.  Verifiable Smart Packaging with Passive RFID , 2016, IEEE Transactions on Mobile Computing.

[21]  Monika Sester,et al.  Areal rainfall estimation using moving cars as rain gauges – a modelling study , 2009 .

[22]  D. Grimes,et al.  Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts , 2003 .

[23]  Shaojie Tang,et al.  Tefnut: An Accurate Smartphone Based Rain Detection System in Vehicles , 2016, WASA.

[24]  Jie Wu,et al.  Multi-task assignment for crowdsensing in mobile social networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[25]  Mo Li,et al.  iType: Using eye gaze to enhance typing privacy , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[26]  Xiang-Yang Li,et al.  PPS: Privacy-Preserving Strategyproof Social-Efficient Spectrum Auction Mechanisms , 2013, IEEE Transactions on Parallel and Distributed Systems.

[27]  Francesco Laio,et al.  Toward the camera rain gauge , 2015 .

[28]  Andreas Geiger,et al.  Video-based raindrop detection for improved image registration , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[29]  Guoliang Xing,et al.  iSleep: unobtrusive sleep quality monitoring using smartphones , 2013, SenSys '13.

[30]  Jie Wu,et al.  Tell me what i see: recognize RFID tagged objects in augmented reality systems , 2016, UbiComp.

[31]  Anton Kummert,et al.  Vision-based rain sensing with an in-vehicle camera , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[32]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .

[33]  BhuiyanMd Zakirul Alam,et al.  Sensor Placement with Multiple Objectives for Structural Health Monitoring , 2014 .

[34]  Dana Chandler,et al.  Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets , 2012, ArXiv.

[35]  Di Ma,et al.  Demographic Information Inference through Meta-Data Analysis of Wi-Fi Traffic , 2018, IEEE Transactions on Mobile Computing.

[36]  Yi Li,et al.  Privacy-Preserving Location Proof for Securing Large-Scale Database-Driven Cognitive Radio Networks , 2016, IEEE Internet of Things Journal.

[37]  Mo Li,et al.  IODetector: a generic service for indoor outdoor detection , 2012, SenSys '12.

[38]  Yunhao Liu,et al.  Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System , 2017, IEEE/ACM Transactions on Networking.

[39]  Daniel J. Veit,et al.  More than fun and money. Worker Motivation in Crowdsourcing - A Study on Mechanical Turk , 2011, AMCIS.

[40]  Yunhao Liu,et al.  STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization , 2017, IEEE/ACM Transactions on Networking.

[41]  Ning Liu,et al.  Bathroom Activity Monitoring Based on Sound , 2005, Pervasive.

[42]  Yunhuai Liu,et al.  LIPS: A Light Intensity Based Positioning System For Indoor Environments , 2014, ACM Trans. Sens. Networks.