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AcuTe: acoustic thermometer empowered by a single smartphone

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Published:16 November 2020Publication History

ABSTRACT

Though measuring ambient temperature is often deemed as an easy job, collecting large-scale temperature readings in real-time is still a formidable task. The recent boom of network-ready (mobile) devices and the subsequent mobile crowdsourcing applications do offer an opportunity to accomplish this task, yet equipping commodity devices with ambient temperature sensing capability is highly non-trivial and hence has never been achieved. In this paper, we propose Acoustic Thermometer (AcuTe) as the first ambient temperature sensor empowered by a single commodity smartphone. AcuTe utilizes on-board dual microphones to estimate air-borne sound propagation speed, thereby deriving ambient temperature. To accurately estimate sound propagation speed, we leverage the phase of chirp signals to circumvent the low sample rate on commodity hardware. In addition, we propose to use both structure-borne and air-borne propagations to address the multipath problem. Furthermore, to prevent disruptive audible transmissions, we convert chirp signals into white noises and propose a pipeline of signal processing algorithms to denoise received samples. As a mobile, economical, highly accurate sensor, AcuTe may potentially enable many relevant applications, in particular large-scale indoor/outdoor temperature monitoring in real-time. We conduct extensive experiments on AcuTe; the results demonstrate a robust performance, a median accuracy of 0.3° C even at a varying humidity level, and the ability to conduct distributed temperature sensing in real-time.

References

  1. Analog Devices (ADI). Temperature Measurement Theory and Practical Techniques. https://www.analog.com/media/en/technical-documentation/application-notes/an_892.pdf, 2020.Google ScholarGoogle Scholar
  2. Anderson, M., Norman, J., Diak, G., Kustas, W., and Mecikalski, J. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sensing of Environment 60, 2 (1997), 195 -- 216.Google ScholarGoogle ScholarCross RefCross Ref
  3. BankMyCell. How Many Smartphones Are in the World? https://www.bankmycell.com/blog/how-many-phones-are-in-the-world, 2020.Google ScholarGoogle Scholar
  4. Barbato, A., Borsani, L., Capone, A., and Melzi, S. Home Energy Saving through a User Profiling System Based on Wireless Sensors. In Proc. of ACM Workshop on BuildSys (2009), pp. 49--54.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bi, C., and Xing, G. Real-Time Attitude and Motion Tracking for Mobile Device in Moving Vehicle. In Proc. of the 16th ACM Sensys (2018), pp. 357--358.Google ScholarGoogle Scholar
  6. Bies, D., Hansen, C., and Howard, C. Engineering Noise Control, Fifth Edition. CRC Press, 11 2017.Google ScholarGoogle ScholarCross RefCross Ref
  7. Cai, C. Acoustic Thermometer Empowered by a Single Smartphone. https://caichao.github.io/proj_dirs/acute.html, 2020.Google ScholarGoogle Scholar
  8. Cai, C., Pu, H., Hu, M., Zheng, R., and Luo, J. SST: Software Sonic Thermometer on Acoustic-enabled IoT Devices. IEEE Transactions on Mobile Computing (2020), 1--14.Google ScholarGoogle Scholar
  9. Campbell, S.D., and Diebold, F. X. Weather Forecasting for Weather Derivatives. Journal of the American Statistical Association 100, 469 (2005), 6--16.Google ScholarGoogle ScholarCross RefCross Ref
  10. Casey, J. World Climate Maps. https://www.climate-charts.com/World-Climate-Maps.html, 2020.Google ScholarGoogle Scholar
  11. Celik, M., Dadaser-Celik, F., and Dokuz, A. S. Anomaly Detection in Temperature Data using DBSCAN Algorithm. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (2011), pp. 91--95.Google ScholarGoogle ScholarCross RefCross Ref
  12. Chauhan, J., Hu, Y., Seneviratne, S., Misra, A., Seneviratne, A., and Lee, Y. BreathPrint: Breathing Acoustics-based User Authentication. In Proc. of the 15th ACM MobiSys (2017), pp. 278--291.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Chen, J., Tan, R., Wang, Y., Xing, G., Wang, X., Wang, X., Punch, B., and Colbry, D. A Sensor System for High-Fidelity Temperature Distribution Forecasting in Data Centers. ACM Trans. Sen. Netw. 11, 2 (Dec. 2014), 30:1--25.Google ScholarGoogle Scholar
  14. Claasen, T., and Mecklenbräuker, W. The Wigner Distribution---A tool for Time---Frequency Signal Analysis---Part II: Discrete Time Signals. Philips Research 35 (Jan 1980), 276--300.Google ScholarGoogle Scholar
  15. Ding, S., Chen, Z., Zheng, T., and Luo, J. RF-Net: A Unified Meta-Learning Framework for RF-enabled One-Shot Human Activity Recognition. In Proc. of the 18th ACM SenSys (2020), pp. 1--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. El-Sayed, N., Stefanovici, I. A., Amvrosiadis, G., Hwang, A. A., and Schroeder, B. Temperature Management in Data Centers: Why Some (Might) like It Hot. In Proc. of the 12th ACM SIGMETRICS (2012), pp. 163--174.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Fisher, R. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. In Biometrika (1915), vol. 10, pp. 507 -- 521.Google ScholarGoogle ScholarCross RefCross Ref
  18. Gao, H., Matters-Kamrnerer, M. K., Harpe, P., Milosevic, D., Johannsen, U., van Roermund, A., and Baltus, P. A 71GHz RF Energy Harvesting Tag with 8% Efficiency for Wireless Temperature Sensors in 65nm CMOS. In Proc. of 2013 IEEE Radio Frequency Integrated Circuits Symposium (RFIC) (June 2013), pp. 403--406.Google ScholarGoogle ScholarCross RefCross Ref
  19. Gayen, A. The Frequency Distribution of the Product Moment Correlation Coefficient in Random Samples of Any Size Draw from Non-Normal Universes. In Biometrika (1951), vol. 38, pp. 219 -- 247.Google ScholarGoogle Scholar
  20. Google Play. Sound Meter. https://play.google.com/store/apps/details?id=com.gamebasic.decibel&hl=en_SG, 2020.Google ScholarGoogle Scholar
  21. Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., and Zhou, X. Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm. ACM Comput. Surv. 48, 1 (2015).Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Gupta, V., Mittal, S., Bhaumik, S., and Roy, R. Assisting Humans to Achieve Optimal Sleep by Changing Ambient Temperature. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016), pp. 841--845.Google ScholarGoogle ScholarCross RefCross Ref
  23. Han, K., Zhang, C., and Luo, J. Taming the Uncertainty: Budget Limited Robust Crowdsensing through Online Learning. IEEE/ACM Trans. on Networking 24, 3 (2016), 1462--1475.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Han, K., Zhang, C., Luo, J., Hu, M., and Veeravalli, B. Truthful Scheduling Mechanisms for Powering Mobile Crowdsensing. IEEE Trans. on Computers 65, 1 (2016), 294--307.Google ScholarGoogle Scholar
  25. He, L., Lee, Y., and Shin, K. G. Mobile Device Batteries as Thermometers. In Proc. of ACM Ubicomp (2020), pp. 1--21.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. He, Y., Liang, J., and Liu, Y. Pervasive Floorplan Generation Based on Only Inertial Sensing: Feasibility, Design, and Implementation. IEEE Journal on Selected Areas in Communications 35, 5 (2017), 1132--1140.Google ScholarGoogle Scholar
  27. Jain, M., Singh, A., and Chandan, V. Portable+: A Ubiquitous And Smart Way Towards Comfortable Energy Savings. Proc. of ACM UbiComp (2017).Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Kaimal, J. C., and Gaynor, J. E. Another Look at Sonic Thermometry. Boundary-Layer Meteorology 56, 4 (Sep 1991), 401--410.Google ScholarGoogle ScholarCross RefCross Ref
  29. Koyamada, Y., Imahama, M., Kubota, K., and Hogari, K. Fiber-Optic Distributed Strain and Temperature Sensing With Very High Measurand Resolution Over Long Range Using Coherent OTDR. J. Lightwave Technol. 27, 9 (May 2009), 1142--1146.Google ScholarGoogle Scholar
  30. Liu, S., and He, T. SmartLight: Light-Weight 3D Indoor Localization Using a Single LED Lamp. In Proc. of the 15th ACM SenSys (2017), pp. 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Lo, C. P., Quattrochi, D. A., and Luvall, J. C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing 18, 2 (1997), 287--304.Google ScholarGoogle ScholarCross RefCross Ref
  32. Lu, C. X., Li, Y., Zhao, P., Chen, C., Xie, L., Wen, H., Tan, R., and Trigoni, N. Simultaneous Localization and Mapping with Power Network Electromagnetic Field. In Proc. of 24th ACM MobiCom (2018), pp. 607--622.Google ScholarGoogle Scholar
  33. Mandal, J., Pal, S., Sun, T., Grattan, K. T. V., Augousti, A. T., and Wade, S. A. Bragg grating-based fiber-optic laser probe for temperature sensing. IEEE Photonics Technology Letters 16, 1 (Jan 2004), 218--220.Google ScholarGoogle ScholarCross RefCross Ref
  34. Mao, W., He, J., Zheng, H., Zhang, Z., and Qiu, L. CAT: High-Precision Acoustic Motion Tracking. In Proc. of the 22th ACM MobiCom (2016), pp. 69--81.Google ScholarGoogle Scholar
  35. Mao, W., Wang, M., and Qiu, L. AIM: Acoustic Imaging on a Mobile. In Proc. of the 16th ACM MobiSys (2018), pp. 468--481.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Mao, W., Zhang, Z., Qiu, L., He, J., Cui, Y., and Yun, S. Indoor Follow Me Drone. In Proc. of the 15th ACM MobiSys (2017), pp. 345--358.Google ScholarGoogle Scholar
  37. Mendelsohn, R., Nordhaus, W. D., and Shaw, D. The Impact of Global Warming on Agriculture: A Ricardian Analysis. The American Economic Review 84, 4 (1994), 753--771.Google ScholarGoogle Scholar
  38. Nandakumar, R., Gollakota, S., and Watson, N. Contactless Sleep Apnea Detection on Smartphones. In Proc. of the 13th ACM MobiSys (2015), pp. 45--57.Google ScholarGoogle Scholar
  39. Parinussa, R. M., Holmes, T. R. H., Yilmaz, M. T., and Crow, W. T. The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations. Journal of Hydrology and Earth System Sciences 15, 10 (2011), 3135 -- 3151.Google ScholarGoogle Scholar
  40. Peacock, G. R. Temperature Sensors: Contact or Noncontact? https://www.sensorsmag.com/components/temperature-sensors-contact-or-noncontact, 2018.Google ScholarGoogle Scholar
  41. Pereira de Souza Neto, E., Custaud, M.-A., Frutoso, J., Somody, L., Gharib, C., and Fortrat, J.-O. Smoothed Ppseudo Wigner-Ville Distribution as an Alternative to Fourier Transform in Rats. Autonomic Neuroscience 87, 2 (2001), 258 -- 267.Google ScholarGoogle Scholar
  42. Robert, P. C. Precision Agriculture: A Challenge for Crop Nutrition Management. In Progress in Plant Nutrition: Plenary Lectures of the XIV International Plant Nutrition Colloquium: Food security and sustainability of agro-ecosystems through basic and applied research, W. J. Horst, A. Bürkert, N. Claassen, H. Flessa, W. B. Frommer, H. Goldbach, W. Merbach, H.-W. Olfs, V. Römheld, B. Sattelmacher, U. Schmidhalter, M. K. Schenk, and N. v. Wirén, Eds. Springer, 2002, pp. 143--149.Google ScholarGoogle Scholar
  43. Roy, N., and Roy Choudhury, R. Listening through a Vibration Motor. In Proc. of the 14th ACM MobiSys (2016), pp. 57--69.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Schumann, A., Ploennigs, J., and Gorman, B. Towards Automating the Deployment of Energy Saving Approaches in Buildings. In Proc. of ACM BuildSys (2014), pp. 164--167.Google ScholarGoogle Scholar
  45. Shaker, G., Tentzeris, M., and Safavi-Naeini, S. Low-cost antennas for mm-Wave sensing applications using inkjet printing of silver nano-particles on liquid crystal polymers. In Proc. of 2010 IEEE Antennas and Propagation Society International Symposium (July 2010), pp. 1--4.Google ScholarGoogle ScholarCross RefCross Ref
  46. Shu, Y., Shin, K. G., He, T., and Chen, J. Last-Mile Navigation Using Smartphones. In Proc. of the 21st ACM MobiCom (2015), pp. 512--524.Google ScholarGoogle Scholar
  47. Song, C., Zhou, X., and Liu, J. Investigation of Human Thermal Comfort in Sleeping Environments Based on the Effects of Bed Climate. Procedia Engineering (2015), 1126--1132.Google ScholarGoogle Scholar
  48. Sozzi, R., and Favaron, M. Sonic Anemometry and Thermometry: Theoretical Basis and Data-Processing Software. Environmental Software 11, 4 (1996), 259 -- 270.Google ScholarGoogle Scholar
  49. Sun, K., Wang, W., Liu, A. X., and Dai, H. Depth Aware Finger Tapping on Virtual Displays. In Proc. of the 16th ACM MobiSys (2018), pp. 283--295.Google ScholarGoogle Scholar
  50. Sun, K., Zhang, T., Wang, W., and Xie, L. VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals. In Proc. of the 24th ACM MobiCom (2018), pp. 591--605.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Texas Instruments (TI). Design Considerations for Measuring Ambient Air Temperature. http://www.ti.com/lit/an/snoa966b/snoa966b.pdf, 2020.Google ScholarGoogle Scholar
  52. Tung, Y.-C., and Shin, K. G. Expansion of Human-Phone Interface By Sensing Structure-Borne Sound Propagation. In Proc. of the 14th ACM MobiSys (2016), pp. 277--289.Google ScholarGoogle Scholar
  53. Wang, A., Sunshine, J. E., and Gollakota, S. Contactless Infant Monitoring Using White Noise. In Proc. of 25th ACM MobiCom (2019), pp. 1--16.Google ScholarGoogle Scholar
  54. Wang, J., Tan, N., Luo, J., and Pan, S. WOLoc: WiFi-only Outdoor Localization Using Crowdsensed Hotspot Labels. In Proc. of the 36th IEEE INFOCOM (2017), pp. 1--9.Google ScholarGoogle Scholar
  55. Wang, Z., Chen, Z., Singh, A., Garcia, L., Luo, J., and Srivastava, M. UWHear: Through-wall Extraction and Separation of Audio Vibrations Using Wireless Signals. In Proc. of the 18th ACM SenSys (2020), pp. 1--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Wu, C., Zhang, F., Fan, Y., and Liu, K. J. R. RF-Based Inertial Measurement. In Proc. of the 31th ACM SIGCOMM (2019), pp. 117--129.Google ScholarGoogle Scholar
  57. Xie, B., Tan, G., and He, T. SpinLight: A High Accuracy and Robust Light Positioning System for Indoor Applications. In Proc. of the 13th ACM Sensys (2015), pp. 211--223.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Xu, C., Firner, B., Zhang, Y., and Howard, R. E. The Case for Efficient and Robust RF-Based Device-Free Localization. IEEE Transactions on Mobile Computing 15, 9 (2016), 2362--2375.Google ScholarGoogle Scholar
  59. Xu, Q., Zheng, R., and Hranilovic, S. IDyLL: Indoor Localization Using Inertial and Light Sensors on Smartphones. In Proc. of ACM Ubicomp (2015), pp. 307--318.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Xu, X., Shen, Y., Yang, J., Xu, C., Shen, G., Chen, G., and Ni, Y. PassiveVLC: Enabling Practical Visible Light Backscatter Communication for Battery-Free IoT Applications. In Proc. of the 23rd ACM MobiCom (2017), pp. 180--192.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Xu, X., Yu, J., Chen, Y., Zhu, Y., Kong, L., and Li, M. BreathListener: Fine-Grained Breathing Monitoring in Driving Environments Utilizing Acoustic Signals. In Proc. of the 17th ACM MobiSys (2019), pp. 54--66.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Yan, T., Marzilli, M., Holmes, R., Ganesan, D., and Corner, M. mCrowd: A Platform for Mobile Crowdsourcing. In Proc. of the 7th ACM SenSys (2009), pp. 347--348.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Yang, Y., Hao, J., Luo, J., and Pan, S. CeilingCast: Energy Efficient and Location-Bound Broadcast Through LED-Camera Communication. In Proc. of the 35th IEEE INFOCOM (2016), pp. 1--9.Google ScholarGoogle Scholar
  64. Zhang, C., Kuppannagari, S. R., Kannan, R., and Prasanna, V. K. Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation. In Proc. of the 6th ACM BuildSys (2019), pp. 287--296.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Zhang, C., Subbu, K., Luo, J., and Wu, J. GROPING: Geomagnetism and cROwd-sensing Powered Indoor NaviGation. IEEE Trans. on Mobile Computing 14, 2 (2015), 387--400.Google ScholarGoogle ScholarCross RefCross Ref
  66. zhen, Q., Heinsch, M. A., Zhao, M., and W. Running, S. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111, 4 (2007), 519 -- 536.Google ScholarGoogle Scholar
  67. Zhou, B., Elbadry, M., Gao, R., and Ye, F. BatTracker: High Precision Infrastructure-Free Mobile Device Tracking in Indoor Environments. In Proc. of the 15th ACM SenSys (2017), pp. 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Zhou, W., Peng, B., Shi, J., Wang, T., Dhital, Y. P., Yao, R., Yu, Y., Lei, Z., and Zhao, R. Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal. Remote Sensing 9, 9 (2017).Google ScholarGoogle Scholar

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      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

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      • Published: 16 November 2020

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