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Exploring indoor white spaces in metropolises

Published: 30 September 2013 Publication History

Abstract

It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of the spectrum demand comes from indoor environments, we carry out a comprehensive study of exploring indoor white spaces. We first present a large-scale measurement of outdoor and indoor TV spectrum occupancy in 30+ diverse locations in a typical metropolis Hong Kong. Our measurement results confirm abundant white spaces available for exploration in a wide range of areas in metropolises. In particular, more than 50% and 70% of the TV spectrum are white spaces in outdoor and indoor scenarios, respectively. While there are substantially more white spaces in indoor scenarios than in outdoor scenarios, there is no effective solution for identifying indoor white spaces. To fill in this gap, we propose the first system WISER (for White-space Indoor Spectrum EnhanceR), to identify and track indoor white spaces in a building, without requiring user devices to sense the spectrum. We discuss the design space of such system and justify our design choices using intensive real-world measurements. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-50% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

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cover image ACM Conferences
MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networking
September 2013
504 pages
ISBN:9781450319997
DOI:10.1145/2500423
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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Published: 30 September 2013

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Author Tags

  1. TV white spaces
  2. clustering algorithms
  3. sensor placement

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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  • (2023)Adaptive Uplink Data Compression in Spectrum Crowdsensing SystemsIEEE/ACM Transactions on Networking10.1109/TNET.2023.323937831:5(2207-2221)Online publication date: Oct-2023
  • (2022)Complexity reduction of ordinary kriging algorithm for 3D REM designPhysical Communication10.1016/j.phycom.2022.10191255:COnline publication date: 1-Dec-2022
  • (2022)Cognitive Long‐range: Towards efficient public communication infrastructure for Internet of ThingsInternational Journal of Communication Systems10.1002/dac.520735:12Online publication date: 25-May-2022
  • (2020)WhiteHaulProceedings of the 18th International Conference on Mobile Systems, Applications, and Services10.1145/3386901.3388950(338-351)Online publication date: 15-Jun-2020
  • (2020)Towards Fine-Grained Indoor White Space SensingGreen, Pervasive, and Cloud Computing10.1007/978-3-030-64243-3_4(45-60)Online publication date: 4-Dec-2020
  • (2019)PerspectiveACM SIGCOMM Computer Communication Review10.1145/3371934.337196649:5(107-109)Online publication date: 8-Nov-2019
  • (2019)A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal ScalesThe 25th Annual International Conference on Mobile Computing and Networking10.1145/3300061.3345450(1-16)Online publication date: 5-Aug-2019
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