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SNOW: Sensor Network over White Spaces

Published: 14 November 2016 Publication History

Abstract

Wireless sensor networks (WSNs) face significant scalability challenges due to the proliferation of wide-area wireless monitoring and control systems that require thousands of sensors to be connected over long distances. Due to their short communication range, existing WSN technologies such as those based on IEEE 802.15.4 form many-hop mesh networks complicating the protocol design and network deployment. To address this limitation, we propose a scalable sensor network architecture - called Sensor Network Over White Spaces (SNOW) - by exploiting the TV white spaces. Many WSN applications need low data rate, low power operation, and scalability in terms of geographic areas and the number of nodes. The long communication range of white space radios significantly increases the chances of packet collision at the base station. We achieve scalability and energy efficiency by splitting channels into narrowband orthogonal subcarriers and enabling packet receptions on the subcarriers in parallel with a single radio. The physical layer of SNOW is designed through a distributed implementation of OFDM that enables distinct orthogonal signals from distributed nodes. Its MAC protocol handles subcarrier allocation among the nodes and transmission scheduling. We implement SNOW in GNU radio using USRP devices. Experiments demonstrate that it can correctly decode in less than 0.1ms multiple packets received in parallel at different subcarriers, thus drastically enhancing the scalability of WSN.

Supplementary Material

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References

[1]
http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1043474.pdf.
[2]
FCC, ET Docket No FCC 08-260, November 2008.
[3]
FCC, Second Memorandum Opinion and Order, ET Docket No FCC 10-174, September 2010.
[4]
http://www.radio-electronics.com/info/wireless/wi-fi/ieee-802-11af-white-fi-tv-space.php.
[5]
http://www.link-labs.com/what-is-sigfox/.
[6]
https://github.com/Lora-net/LoRaMac-node/tree/v3.2.
[7]
https://github.com/Lora-net/LoRaMac-node/wiki/LoRaMAC-node-Wiki.
[8]
Bluetooth. http://www.bluetooth.com.
[9]
CC1070 chip. http://www.ti.com/product/CC1070.
[10]
CC2420 chip. http://www.ti.com/product/cc2420.
[11]
Ettus Research. http://www.ettus.com/product/details/UB210-KIT.
[12]
GNU Radio. http://gnuradio.org.
[13]
IEEE 802.11. http://www.ieee802.org/11.
[14]
IEEE 802.15.4. http://standards.ieee.org/about/get/802/802.15.html.
[15]
IEEE 802.15.4c. https://standards.ieee.org/findstds/standard/802.15.4c-2009.html.
[16]
IEEE 802.19. http://www.ieee802.org/19/.
[17]
IEEE 802.22. http://www.ieee802.org/22/.
[18]
LoRa modem design guide. http://www.semtech.com/images/datasheet/LoraDesignGuide STD.pdf.
[19]
LoRaWAN. https://www.lora-alliance.org.
[20]
Microsoft 4Afrika. http://www.microsoft.com/africa/4afrika/.
[21]
NB-IoT. https://www.u-blox.com/en/narrowband-iot-nb-iot.
[22]
ngmn. http://www.ngmn.org.
[23]
PetroCloud. http://petrocloud.com/solutions/oilfield-monitoring/.
[24]
QualNet. http://web.scalable-networks.com/content/qualnet.
[25]
SIGFOX. http://sigfox.com.
[26]
Spectrum Policy Task Force Reports. http://www.fcc.gov/sptf/reports.html.
[27]
TelosB datasheet. http://www.xbow.com/Products/Product\_pdf\_files/Wireless\_pdf/TelosB\_Datasheet.pdf.
[28]
TinyOS Community Forum. http://www.tinyos.net.
[29]
TV White Spaces Africa Forum 2013. https://sites.google.com/site/tvwsafrica2013/.
[30]
WirelessHART Specification. http://www.hartcomm2.org.
[31]
WirelessHART System Engineering Guide. http://www2.emersonprocess.com/siteadmincenter/PM%20Central%20Web%20Documents/EMRWirelessHART_SysEngGuide.pdf.
[32]
Understanding FFTs and windowing, 2015. http://www.ni.com/white-paper/4844/en/.
[33]
P. Bahl, R. Chandra, T. Moscibroda, R. Murty, and M. Welsh. White space networking with wi-fi like connectivity. In SIGCOMM '09.
[34]
R. Balamurthi, H. Joshi, C. Nguyen, A. Sadek, S. Shellhammer, and C. Shen. A TV white space spectrum sensing prototype. In DySpan '11.
[35]
R. E. D. Borth and B. Oberlie. Considerations for successful cognitive radio systems in US TV white space. In DySpan '08.
[36]
M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi. Long-range communications in unlicensed bands: the rising stars in the iot and smart city scenarios. IEEE Wireless Communications, October.
[37]
R. Chandra, R. Mahajan, T. Moscibroda, R. Raghavendra, and P. Bahl. A case for adapting channel width in wireless networks. In SIGCOMM '08.
[38]
C.-T. Chen. System and Signal Analysis. Thomson, 1988.
[39]
D. Chen, S. Yin, Q. Zhang, M. Liu, and S. Li. Mining spectrum usage data: a large-scale spectrum measurement study. In MobiCom '09.
[40]
K. Chintalapudi, B. Radunovic, V. Balan, M. Buettener, S. Yerramalli, V. Navda, and R. Ramjee. WiFi-NC: WiFi over narrow channels. In NSDI '12.
[41]
S. Deb, V. Srinivasan, and R. Maheshwar. Dynamic spectrum access in DTV white spaces: Design rules, architecture and algorithms. In MobiCom '09.
[42]
A. Dutta, D. Saha, D. Grunwald, and D. Sicker. SMACK: A smart acknowledgment scheme for broadcast messages in wireless networks. In SIGCOMM '09.
[43]
P. Dutta, S. Dawson-Haggerty, Y. Chen, C.-J. M. Liang, and A. Terzis. Design and evaluation of a versatile and efficient receiver-initiated link layer for low-power wireless. In SenSys '10.
[44]
X. Feng, J. Zhang, and Q. Zhang. Database-assisted multi-AP network on TV white spaces: Architecture, spectrum allocation and AP discovery. In DySpan '11.
[45]
O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis. Collection tree protocol. In SenSys '09.
[46]
D. Gurney, G. Buchwald, L. Ecklund, S. Kuffner, and J. Grosspietsch. Geo-location database techniques for incumbent protection in the TV. In DySpan '08.
[47]
M. Islam, C. Koh, S. Oh, X. Qing, Y. Lai, C. Wang, Y. Liang, B. Toh, F. Chin, G. Tan, and W. Toh. Spectrum survey in Singapore: Occupancy measurements and analyses. In CrownCom '08.
[48]
V. Jaap, R. Janne, A. Andreas, and M. Petri. UHF white space in Europe: a quantitative study into the potential of the 470-790 MHz band. In DySpan '11.
[49]
P. Juang, H. Oki, Y. Wang, M. Martonosi, L. S. Peh, and D. Rubenstein. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. In ASPLOS-X '02.
[50]
H. Kim and K. G. Shin. Fast discovery of spectrum opportunities in cognitive radio networks. In DySpan '08.
[51]
H. Kim and K. G. Shin. In-band spectrum sensing in cognitive radio networks: Energy detection or feature detection? In MobiCom '08.
[52]
S. Kim, S. Pakzad, D. Culler, J. Demmel, G. Fenves, S. Glaser, and M. Turon. Health monitoring of civil infrastructures using wireless sensor networks. In IPSN '07.
[53]
K. Langendoen, A. Baggio, and O. Visser. Murphy loves potatoes: experiences from a pilot sensor network deployment in precision agriculture. In IPDPS '06.
[54]
B. Li, Z. Sun, K. Mechitov, C. Lu, D. Dyke, G. Agha, and B. Spencer. Realistic case studies of wireless structural control. In ICCPS '13.
[55]
D. Liu, Z. Wu, F. Wu, Y. Zhang, and G. Chen. FIWEX: Compressive sensing based cost-efficient indoor white space exploration. In MobiHoc '15.
[56]
C. Lu, A. Saifullah, B. Li, M. Sha, H. Gonzalez, D. Gunatilaka, C. Wu, L. Nie, and Y. Chen. Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proceedings of the IEEE, 104(5):1013--1024, 2016.
[57]
Y. Luo, L. Gao, and J. Huang. HySIM: A hybrid spectrum and information market for TV white space networks. In INFOCOM '15.
[58]
G. Mao, B. Fidan, and B. D. O. Anderson. Wireless sensor network localization techniques. Computer networks, 51(10):2529--2553, 2007.
[59]
E. Meshkova, J. Ansari, D. Denkovski, J. Riihijarvi, J. Nasreddine, M. Pavloski, L. Gavrilovska, and P. Mahonen. Experimental spectrum sensor testbed for constructing indoor radio environmental maps. In DySpan '11.
[60]
R. Murty, R. Chandra, T. Moscibroda, and P. Bahl. SenseLess: A database-driven white spaces network. In DySpan '11.
[61]
R. Murty, G. Mainland, I. Rose, A. Chowdhury, A. Gosain, J. Bers, and M. Welsh. CitySense: An urban-scale wireless sensor network and testbed. In HST '08.
[62]
M. Nekovee. Quantifying the availability of TV white spaces for cognitive radio operation in the UK. In ICC '09.
[63]
E. Obregon and J. Zander. Short range white space utilization in broadcast systems for indoor environment. In DySpan '10.
[64]
R. Obregon, L. Shi, J. Ferrer, and J. Zander. Experimental verification of indoor TV white space opportunity prediction model. In CrownCom '10.
[65]
S. Roberts, P. Garnett, and R. Chandra. Connecting africa using the tv white spaces: From research to real world deployments. In LANMAN '15.
[66]
A. Saifullah, D. Gunatilaka, P. Tiwari, M. Sha, C. Lu, B. Li, C. Wu, and Y. Chen. Schedulability analysis under graph routing for WirelessHART networks. In RTSS '15.
[67]
A. Saifullah, S. Sankar, J. Liu, C. Lu, B. Priyantha, and R. Chandra. CapNet: A real-time wireless management network for data center power capping. In RTSS '14.
[68]
A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu, and Y. Chen. Near optimal rate selection for wireless control systems. ACM Transactions on Embedded Computing Systems, 13(4s):1--25, 2013.
[69]
M. Sha, G. Hackmann, and C. Lu. Energy-efficient low power listening for wireless sensor networks in noisy environments. In IPSN '13.
[70]
C.-S. Sum, M.-T. Zhou, L. Lu, R. Funada, F. Kojima, and H. Harada. IEEE 802.15.4m: The first low rate wireless personal area networks operating in TV white space. In ICON '12.
[71]
Y. Sun, O. Gurewitz, and D. B. Johnson. RI-MAC: A receiver-initiated asynchronous duty cycle mac protocol for dynamic traffic loads in wireless sensor networks. In SenSys '08.
[72]
S. Sur and X. Zhang. Bridging link power asymmetry in mobile whitespace networks. In INFOCOM '15.
[73]
R. Szewczyk, A. Mainwaring, J. Polastre, J. Anderson, and D. Culler. An analysis of a large scale habitat monitoring application. In SenSys '04.
[74]
T. Taher, R. Bacchus, K. Zdunek, and D. Roberson. Long-term spectral occupancy findings in Chicago. In DySpan '11.
[75]
D. Tse and P. Viswanath. Fundamentals of Wireless Communication. Cambridge University Press, 2005.
[76]
X. Wang, J. Chen, A. Dutta, and M. Chiang. Adaptive video streaming over whitespace: SVC for 3-tiered spectrum sharing. In INFOCOM '15.
[77]
M. Wellens, J. Wu, and P. Mahonen. Evaluation of spectrum occupancy in indoor and outdoor scenario in the context of cognitive radio. In CrownCom '07.
[78]
G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. Fidelity and yield in a volcano monitoring sensor network. In OSDI '06.
[79]
L. Yang, W. Hou, L. Cao, B. Zhao, and H. Zheng. Supporting demanding wireless applications with frequency-agile radios. In NSDI '10.
[80]
X. Ying, J. Zhang, L. Yan, G. Zhang, M. Chen, and R. Chandra. Exploring indoor white spaces in metropolises. In MobiCom '13.
[81]
J. Zhang, W. Zhang, M. Chen, and Z. Wang. WINET: Indoor white space network design. In INFOCOM '15.
[82]
T. Zhang, N. Leng, and S. Banerjee. A vehicle-based measurement framework for enhancing whitespace spectrum databases. In MobiCom '14.
[83]
X. Zhang and E. W. Knightly. WATCH: WiFi in active TV channels. In MobiHoc '15.

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cover image ACM Conferences
SenSys '16: Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM
November 2016
398 pages
ISBN:9781450342636
DOI:10.1145/2994551
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]

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Publication History

Published: 14 November 2016

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Author Tags

  1. OFDM
  2. White space
  3. Wireless Sensor Network

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  • (2023)Boosting Reliability and Energy-Efficiency in Indoor LoRaProceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation10.1145/3576842.3582327(396-409)Online publication date: 9-May-2023
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