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Exploring Indoor White Spaces in Metropolises

Exploring Indoor White Spaces in Metropolises. Xuhang Ying, Jincheng Zhang, Lichao Yan Guanglin Zhang , Minghua Chen. Ranveer Chandra. Skyrocketing Wireless Data Demand. Source: Cisco VNI Global Mobile Data Traffic Forecast, 2012-2017.

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Exploring Indoor White Spaces in Metropolises

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  1. Exploring Indoor White Spaces in Metropolises Xuhang Ying, Jincheng Zhang, Lichao Yan Guanglin Zhang, Minghua Chen Ranveer Chandra

  2. Skyrocketing Wireless Data Demand Source: Cisco VNI Global Mobile Data Traffic Forecast, 2012-2017

  3. A Vision: Improve Spectrum Utilization to Satisfy the Growing Demand 15% • Most spectrum are licensed but underutilized Spectrum Occupancy

  4. A Trend: Explore TV White Spaces • “White Spaces” are unoccupied TV channels • FCC allows unlicensed devices to operate in white spaces (2008, 2010) -60 “White spaces” dbm 800 MHz 470 MHz -100 Frequency TV “White Space”

  5. TV White Space Networking Scenario TV 0 MHz 7000 MHz 2400 2500 5180 5300 470 700 54-90 174-216 ISM (Wi-Fi) Signal Strength Vacant Spectrum up to 3x of 802.11g Signal Strength Frequency Frequency

  6. Prior Works and Our Observation • More than 70% of data demand comes from indoors[15] • Most people are indoors 80% of the time[16]

  7. Our Contributions • First large scale measurement in metropolises • 50% and 70% of the TV spectrum are white spaces in outdoors and indoors • WISER design and proto-typing • Data-driven design • WISER prototype identifies 30%~50% more indoor white spaces compared with alternative approaches WISER– White-space Indoor Spectrum EnhanceR

  8. How much more white spaces are indoor?What are their characteristics?

  9. White Space Availability in Hong Kong • A Large-scale measurement study in Hong Kong • Outdoor white space ratio: 50% • Indoor white space ratio: 70% 31 measurement locations Principle TV Station Fill-in TV Station Measurement Location Hardware : USRP + Antenna + Laptop

  10. Indoor White Space Measurement • Experiment Scenario: • 7th floor of a 10-floor office building • 65 measurement locations (cover all rooms and corridors) • Measurement • Across four months • One time profiling every day • Record the signal strengths for all channels at all locations

  11. Indoor White Space Characteristics Indoor white spaces are long-term unstable – one time profiling is not enough Indoor white spaces show spatial variation – single location sensing is not enough

  12. Indoor White Space Correlation TV signal strengths show strong correlation across channels and locations

  13. How to identify the indoor white spaces, without requiring users to sense the spectrum?

  14. Design Space and Solution Comparison Intuition: Exploiting indoor white space correlation to save sensor cost!

  15. WISER Architecture Indoor Positioning System Server Outdoor Sensor Profiled Location Indoor Sensor

  16. Key Challenge: Indoor Sensor Placement One-time spectrum profiling Get the signal strengths Given k sensors to be placed, where are the best locations to place them? Compute Channel- Location clusters Channel-Location clustering Place one sensor per cluster Indoor sensor placement

  17. Channel-Location Clustering • Simple Case: • One channel, locations • What we want: channel-location clusters Compute the proximity matrix Until k clusters Merge two “closest” clusters

  18. Channel-Location Clustering • General Case: • channels, locations • channel clusters, channel-location clusters for channel cluster Compute the proximity matrix Merge two “closest” channel clusters Channel 3,4 Channel 1,2 Repeat procedure for simple case

  19. How well does WISER work?

  20. WISER Experimentation • WISER identifies 30%-50% more indoor white space as compared to baseline approaches. • Implement a WISER prototype on the 7th floor of a campus building • 20 indoor sensors and 1 outdoor sensor • 11 experiments across 4 months • Compare WISER, Outdoor Sensing (OS-only), and One-Time-Profiling (OTP-Only)

  21. How Many Indoor Sensors Are Enough? • Balance between system performance and the total sensor cost

  22. Conclusions • First large scale measurement in metropolises • 50% and 70% of the TV spectrum are white spaces in outdoors and indoors • WISER design and proto-typing • Data-driven design • WISER prototype identifies 30%~50% more indoor white spaces compared with alternative approaches WISER– White-space Indoor Spectrum EnhanceR

  23. References [1] M. McHenry et al., “Chicago Spectrum Occupancy Measurements & Analysis and A Long-term Studies Proposal”, ACM TAPAS, 2006. [2] T. Taheret al., “Long-term Spectral Occupancy Findings in Chicago”, IEEE DySPAN, 2011. [3] M. Islam et al., “Spectrum Survey in Singapore: Occupancy Measurements and Analyses”, IEEE CrownCom, 2008. [4] D. Chen et al., “Mining Spectrum Usage Data: A Large-scale Spectrum Measurement Study”, ACM MobiCom, 2009. [5] M. Nekoveeet al., “Quantifying the Availability of TV White Spaces for Cognitive Radio Operation in the UK”, IEEE ICC joint workshop on cognitive wireless networks and systems, 2009. [6] V. Jaap et al., “UHF White Space in Europe: A Quantitative Study into the Potential of the 470-790MHz band”, IEEE DySPAN, 2011. [7] D. Cabric et al., “Experimental Study of Spectrum Sensing Based on Energy Detection and Network Cooperation”, ACM TAPAS, 2006. [8] H. Kim et al., “Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks”, IEEE DySPAN, 2008. [9] H. Kim et al., “In-band Spectrum Sensing in Cognitive Radio Networks: Energy Detection or Feature Dection?”, ACM MobiCom, 2008. [10] R. Murty et al., “Senseless: A Database-Driven White Space Network”, IEEE Transactions on Mobile Computing, 2012. [11] Y. Yuan et al., “KNOWS: Kognitiv Networking Over White Spaces”, IEEE DySPAN, 2007. [12] R. Borth et al., “Considerations for Successful Cognitive Radio Systems in US TV White Space”, IEEE DySPAN, 2008. [13] P. Bahl et al., “White Space Networking with Wi-Fi Like Connectivity”, ACM Sigcomm, 2009. [14] X. Feng et al., “Database-Assisted Multi-AP Network on TV White Spaces: Architecture, Spectrum Allocation and AP Discovery”, IEEE DySPAN, 2011. [15] V. Chandrasekhar et al., “Femtocell networks: a survey”, IEEE Communications Magazine, 2008. [16] N. Klepeiset al., “The national human activity pattern survey”, Journal of Exposure Analysis and Environmental Epidemiology, 2001.

  24. Thank you! • Minghua Chen (minghua@ie.cuhk.edu.hk) • http://www.ie.cuhk.edu.hk/~mhchen

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