skip to main content
10.1145/3131672.3131676acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

Enabling Reliable, Asynchronous, and Bidirectional Communication in Sensor Networks over White Spaces

Published:06 November 2017Publication History

ABSTRACT

Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.

References

  1. Microsoft 4AFRIKA. 2017. (2017). http://www.microsoft.com/africa/4afrika/.Google ScholarGoogle Scholar
  2. Ferran Adelantado, Xavier Vilajosana, Pere Tuset-Peiro, Borja Martinez, Joan Melia-Segui, and Thomas Watteyne. 2017. Understanding the Limits of LoRaWAN. IEEE Communications Magazine (January 2017).Google ScholarGoogle Scholar
  3. Paramvir Bahl, Ranveer Chandra, Thomas Moscibroda, Rohan Murty, and Matt Welsh. 2009. White space networking with wi-fi like connectivity. ACM SIGCOMM Computer Communication Review 39, 4 (2009), 27--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Raamkumar Balamurthi, Harshit Joshi, Cong Nguyen, Ahmed K Sadek, Stephen J Shellhammer, and Cong Shen. 2011. A TV white space spectrum sensing prototype. In New frontiers in dynamic spectrum access networks (DySPAN), 2011 IEEE symposium on. IEEE, 297--307.Google ScholarGoogle Scholar
  5. Bluetooth {n. d.}. ({n. d.}). http://www.bluetooth.com.Google ScholarGoogle Scholar
  6. Martin Bor, Utz Roedig, Thiemo Voigt, and Juan Alonso. 2016. Do LoRa low-power wide-area networks scale? (2016).Google ScholarGoogle Scholar
  7. CC1070 {n. d.}. ({n. d.}). http://www.ti.com/product/CC1070.Google ScholarGoogle Scholar
  8. Ranveer Chandra, Ratul Mahajan, Thomas Moscibroda, Ramya Raghavendra, and Paramvir Bahl. 2008. A Case for Adapting Channel Width in Wireless Networks. In SIGCOMM '08. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li. 2009. Mining spectrum usage data: a large-scale spectrum measurement study. In MobiCom '09. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Prabal Dutta, Stephen Dawson-Haggerty, Yin Chen, Chieh-Jan Mike Liang, and Andreas Terzis. 2010. Design and evaluation of a versatile and efficient receiver-initiated link layer for low-power wireless. In SenSys '10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. X. Feng, J. Zhang, and Q. Zhang. 2011. Database-assisted multi-AP network on TV white spaces: Architecture, spectrum allocation and AP discovery. In DySpan '11.Google ScholarGoogle Scholar
  12. TV White Spaces Africa Forum. 2013. (2013). https://sites.google.com/site/tvwsafrica2013/.Google ScholarGoogle Scholar
  13. GNU Radio {n. d.}. ({n. d.}). http://gnuradio.org.Google ScholarGoogle Scholar
  14. D. Gurney, G. Buchwald, L. Ecklund, S. Kuffner, and J. Grosspietsch. 2008. Geo-location database techniques for incumbent protection in TV. In DySpan '08.Google ScholarGoogle Scholar
  15. IEEE 802.11 {n. d.}. ({n. d.}). http://www.ieee802.org/11.Google ScholarGoogle Scholar
  16. IEEE 802.11af {n. d.}. ({n. d.}). http://www.radio-electronics.com/info/wireless/wi-fi/ieee-802-11af-white-fi-tv-space.php.Google ScholarGoogle Scholar
  17. IEEE 802.15.4 {n. d.}. ({n. d.}). http://standards.ieee.org/about/get/802/802.15.html.Google ScholarGoogle Scholar
  18. IEEE 802.15.4c {n. d.}. ({n. d.}). https://standards.ieee.org/findstds/standard/802.15.4c-2009.html.Google ScholarGoogle Scholar
  19. IEEE 802.19 {n. d.}. ({n. d.}). http://www.ieee802.org/19/.Google ScholarGoogle Scholar
  20. IEEE 802.22 {n. d.}. ({n. d.}). http://www.ieee802.org/22/.Google ScholarGoogle Scholar
  21. Dali Ismail, Mahbubur Rahman, Abusayeed Saifullah, and Sanjay Madria. 2017. RnR: Reverse & Replace Decoding for Collision Recovery in Wireless Sensor Networks. In SECON '17.Google ScholarGoogle Scholar
  22. V.D. Jaap, R. Janne, A. Andreas, and M.Petri. 2011. UHF white space in Europe: a quantitative study into the potential of the 470-790 MHz band. In DySpan '11.Google ScholarGoogle Scholar
  23. H. Kim and K. G. Shin. 2008. Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks. In DySpan '08.Google ScholarGoogle Scholar
  24. H. Kim and K. G. Shin. 2008. In-band Spectrum Sensing in Cognitive Radio Networks: Energy Detection or Feature Detection?. In MobiCom '08. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Sukun Kim, Shamim Pakzad, David Culler, James Demmel, Gregory Fenves, Steven Glaser, and Martin Turon. 2007. Health monitoring of civil infrastructures using wireless sensor networks. In IPSN '07. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. K. Langendoen, A. Baggio, and O. Visser. 2006. Murphy loves potatoes: experiences from a pilot sensor network deployment in precision agriculture. In IPDPS '06. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Qinghua Li, Guangjie Li, Wookbong Lee, Moon-il Lee, David Mazzarese, Bruno Clerckx, and Zexian Li. 2010. MIMO techniques in WiMAX and LTE: A feature overview. IEEE Commun. Mag 48, 5 (2010), 86--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Link Labs 2017. (2017). http://www.link-labs.com/what-is-sigfox/.Google ScholarGoogle Scholar
  29. Dongxin Liu, Zhihao Wu, Fan Wu, Yuan Zhang, and Guihai Chen. 2015. FIWEX: Compressive Sensing Based Cost-Efficient Indoor White Space Exploration. In MobiHoc '15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. LoRa iM880B-L {n. d.}. ({n. d.}). http://www.wireless-solutions.de/products/radiomodules/im880b-l.Google ScholarGoogle Scholar
  31. LoRa Modem Design Guide 2013. (2013). http://www.semtech.com/images/datasheet/LoraDesignGuide_STD.pdf.Google ScholarGoogle Scholar
  32. LoRaWAN {n. d.}. ({n. d.}). https://www.lora-alliance.org.Google ScholarGoogle Scholar
  33. LTE Advanced 2017. LTE Advanced Pro. (2017). https://www.qualcomm.com/invention/technologies/lte/advanced-pro.Google ScholarGoogle Scholar
  34. LTE Standard 2014. THE LTE STANDARD. (2014). https://www.qualcomm.com/media/documents/files/the-lte-standard.pdf.Google ScholarGoogle Scholar
  35. Yuan Luo, Lin Gao, and Jianwei Huang. 2015. HySIM: A hybrid spectrum and information market for TV white space networks. In INFOCOM '15.Google ScholarGoogle ScholarCross RefCross Ref
  36. Guoqiang Mao, Bariş Fidan, and Brian D. O. Anderson. 2007. Wireless Sensor Network Localization Techniques. Computer networks 51, 10 (2007), 2529--2553. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Paul Marcelis, Vijay S Rao, and R Venkatesha Prasad. 2017. DaRe: Data Recovery through Application Layer Coding for LoRaWANs. Proc. ACM/IEEE Internet of Things-Design and Implementation (2017). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. E. Meshkova, J. Ansari, D. Denkovski, J. Riihijarvi, J. Nasreddine, M. Pavloski, L. Gavrilovska, and P. Mahonen. 2011. Experimental spectrum sensor testbed for constructing indoor radio environmental maps. In DySpan '11.Google ScholarGoogle Scholar
  39. A. F. Molisch. 2011. Wireless Communications (2nd Ed). John Wiley and Sons Ltd. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. R. Murty, R. Chandra, T. Moscibroda, and P. Bahl. 2011. SenseLess: A database-driven white spaces network. In DySpan '11.Google ScholarGoogle Scholar
  41. R.N. Murty, G. Mainland, I. Rose, A.R. Chowdhury, A. Gosain, J. Bers, and M. Welsh. 2008. CitySense: An Urban-Scale Wireless Sensor Network and Testbed. In HST '08.Google ScholarGoogle Scholar
  42. NBIoT. 2017. (2017). http://www.3gpp.org/news-events/3gpp-news/1785-nb_iot_complete.Google ScholarGoogle Scholar
  43. ngmn {n. d.}. ({n. d.}). http://www.ngmn.org.Google ScholarGoogle Scholar
  44. E. Obregon and J. Zander. 2010. Short range white space utilization in broadcast systems for indoor environment. In DySpan '10.Google ScholarGoogle Scholar
  45. FCC First Order. 2008. (2008). FCC, ET Docket No FCC 08-260, November 2008.Google ScholarGoogle Scholar
  46. FCC Second Order. 2010. (2010). FCC, Second Memorandum Opinion and Order, ET Docket No FCC 10-174, September 2010.Google ScholarGoogle Scholar
  47. PertoCloud 2017. (2017). http://petrocloud.com/solutions/oilfield-monitoring/.Google ScholarGoogle Scholar
  48. Ramjee Prasad and Fernando J Velez. 2010. OFDMA WiMAX physical layer. In WiMAX networks. Springer, 63--135.Google ScholarGoogle Scholar
  49. Ettus Research. 2017. (2017). http://www.ettus.com/product/details/UB210-KIT.Google ScholarGoogle Scholar
  50. Sid Roberts, Paul Garnett, and Ranveer Chandra. {n. d.}. Connecting Africa Using TV White Spaces: From Research to Real World Deployments. In LANMAN '15.Google ScholarGoogle Scholar
  51. Abusayeed Saifullah, Dolvara Gunatilaka, Paras Tiwari, Mo Sha, Chenyang Lu, Bo Li, Chengjie Wu, and Yixin Chen. 2015. Schedulability analysis under graph routing for WirelessHART networks. In RTSS '15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Abusayeed Saifullah, Mahbubur Rahman, Dali Ismail, Chenyang Lu, Ranveer Chandra, and Jie Liu. 2016. SNOW: Sensor Network over White Spaces. In SenSys '16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Abusayeed Saifullah, Sriram Sankar, Jie Liu, Chenyang Lu, Bodhi Priyantha, and Ranveer Chandra. 2014. CapNet: A real-Time Wireless Management Network for Data Center Power Capping. In RTSS '14.Google ScholarGoogle ScholarCross RefCross Ref
  54. Scalable Networks {n. d.}. ({n. d.}). http://web.scalable-networks.com/content/qualnet.Google ScholarGoogle Scholar
  55. SemTech. {n. d.}. LoRa Calculator by Semtech. ({n. d.}). http://sx1272-lora-calculator.software.informer.com/.Google ScholarGoogle Scholar
  56. Semtech SX1301 {n. d.}. ({n. d.}). http://www.semtech.com/wireless-rf/rf-transceivers/sx1301/.Google ScholarGoogle Scholar
  57. SIGFOX {n. d.}. ({n. d.}). http://sigfox.com.Google ScholarGoogle Scholar
  58. Spectrum Bridge 2017. (2017). http://spectrumbridge.com/tv-white-space/.Google ScholarGoogle Scholar
  59. Chin-Sean Sum, Ming-Tuo Zhou, Liru Lu, R. Funada, F. Kojima, and H. Harada. 2012. IEEE 802.15.4m: The first low rate wireless personal area networks operating in TV white space. In ICON '12.Google ScholarGoogle Scholar
  60. T. Taher, R. Bacchus, K. Zdunek, and D. Roberson. 2011. Long-term spectral occupancy findings in Chicago. In DySpan '11.Google ScholarGoogle Scholar
  61. TinyOS. {n. d.}. ({n. d.}). http://www.tinyos.net.Google ScholarGoogle Scholar
  62. Understanding FFTs and Windowing 2015. (2015). http://www.ni.com/white-paper/4844/en/.Google ScholarGoogle Scholar
  63. Deepak Vasisht, Zerina Kapetanovic, Jongho Won, Xinxin Jin, Madhusudhan Sudarshan, and Sean Stratman. 2017. FarmBeats: An IoT Platform for Data-Driven Agriculture. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, 515--529. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Thiemo Voigt, Martin Bor, Utz Roedig, and Juan Alonso. 2016. Mitigating Inter-network Interference in LoRa Networks. arXiv preprint arXiv:1611.00688 (2016).Google ScholarGoogle Scholar
  65. WiMAX {n. d.}. WiMAX. ({n. d.}). https://en.wikipedia.org/wiki/WiMAX.Google ScholarGoogle Scholar
  66. WirelessHART Specification {n. d.}. ({n. d.}). http://www.hartcomm2.org.Google ScholarGoogle Scholar
  67. Bei Yin and Joseph R. Cavallaro. 2012. LTE uplink MIMO receiver with low complexity interference cancellation. Analog Integr Circ Sig Process 73 (2012), 5443--450.Google ScholarGoogle ScholarCross RefCross Ref
  68. Xuhang Ying, Jincheng Zhang, Lichao Yan, Guanglin Zhang, Minghua Chen, and Ranveer Chandra. 2013. Exploring Indoor White Spaces in Metropolises. In MobiCom '13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Jincheng Zhang, Wenjie Zhang, Minghua Chen, and Zhi Wang. 2015. WINET: Indoor white space network design. In INFOCOM '15.Google ScholarGoogle ScholarCross RefCross Ref
  70. Tan Zhang, Ning Leng, and Suman Banerjee. 2014. A Vehicle-based Measurement Framework for Enhancing Whitespace Spectrum Databases. In MobiCom '14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Jim Zyren. 2007. Overview of the 3GPP LTE Physical Layer. (2007). http://www.nxp.com/assets/documents/data/en/white-papers/3GPPEVOLUTIONWP.pdf.Google ScholarGoogle Scholar

Index Terms

  1. Enabling Reliable, Asynchronous, and Bidirectional Communication in Sensor Networks over White Spaces

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SenSys '17: Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
        November 2017
        490 pages
        ISBN:9781450354592
        DOI:10.1145/3131672

        Copyright © 2017 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 6 November 2017

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate174of867submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader