Towards commoditized real-time spectrum monitoring

Nika, Ana; Zhang, Zengbin; Zhou, Xia; Zhao, Ben Y.; Zheng, Haitao

doi:10.1145/2643614.2643615

Cited by 69 publications

(36 citation statements)

References 22 publications

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“…A recent system [2] has explored the similar concept of spectrum sensing on smartphones by connecting them with a TV dongle (RTL-SDR). While the two systems share the same goal, their approaches differ significantly.…”

Section: Related Workmentioning

confidence: 99%

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A Wireless Spectrum Analyzer in Your Pocket

Zhang

Patro

Leng

et al. 2015

Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications

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We propose Snoopy, a system that can translate one's mobile phone or tablet into a low-cost, yet effective RF spectrum analyzer. Since typical spectrum analyzers are specialized hardware that is both expensive to acquire and cumbersome to carry around, they are rarely available for quick-and-easy spectrum sensing while on the go. To address this challenge, Snoopy augments popular mobile devices with a small attachable hardware unit (RF frequency translator) that can provide a reasonable view of the wireless spectrum across different frequency bands. It achieves this by leveraging the spectral scan functionality available in certain 802.11 NICs (e.g., the Atheros 9280 family of chipsets), which provides an unique lens towards the WiFi spectrum (2.4 GHz). Through the use of suitable frequency translators, such a view can be flexibly shifted to other spectrum bands. Although such a construction might not match the precision of the most sophisticated but expensive spectrum analyzers, we show that by leveraging some carefully designed spectral features, Snoopy can achieve decent accuracy in determining TV whitespaces (512 -698 MHz) -it can detect primary signals at up to -90dBm with an error rate of <15%, while achieving a median error of < 4dB in estimating the power of these signals. These promising results suggest that Snoopy is an intriguing option in bringing the ability of spectrum sensing to the masses, thereby truly enabling crowdsourcing options in this domain.

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Section: Related Workmentioning

confidence: 99%

“…Before delving into the design of Snoopy, we would like to point out that one recent effort [2] has also proposed the use of mobile devices for RF spectrum sensing. The solution is based on a different architecture that uses a RTL-SDR hardware (essentially a TV-dongle) to directly capture measurements at specific parts of a frequency band.…”

Section: Introductionmentioning

confidence: 99%

A Wireless Spectrum Analyzer in Your Pocket

Zhang

Patro

Leng

et al. 2015

Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications

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“…Request permissions from Permissions@acm.org. This problem is due to the fact that today's spectrum measurements are primarily performed by governmental agencies which drive around using expensive and bulky specialized hardware [10,17]. This monitoring approach does not scale well and is not able to cover the pervasive deployment of wireless networks as well as the increasing range of spectrum frequencies being used.…”

Section: Introductionmentioning

confidence: 99%

“…This monitoring approach does not scale well and is not able to cover the pervasive deployment of wireless networks as well as the increasing range of spectrum frequencies being used. Recent suggestions to enable wide-scale and real-time spectrum monitoring have therefore been to build a networked and distributed infrastructure using remote spectrum analyzers [1,10,16], or leverage the masses and to crowdsource the measurement stations by trading-off radio device sensitivity with cooperation [15,17]. Our vision in this work is aligned with these ideas of exploiting the crowd for scalable monitoring of the spectrum's actual usage at different frequencies, locations and times.…”

Section: Introductionmentioning

confidence: 99%

A software-defined sensor architecture for large-scale wideband spectrum monitoring

Pfammatter

Giustiniano

Lenders³

2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

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Today's spectrum measurements are mainly performed by governmental agencies which drive around using expensive specialized hardware. The idea of crowdsourcing spectrum monitoring has recently gained attention as an alternative way to capture the usage of wide portions of the wireless spectrum at larger geographical and time scales. To support this vision, we develop a flexible software-defined sensor architecture that enables distributed data collection in real-time over the Internet. Our sensor design builds upon low-cost commercial off-the-shelf (COTS) hardware components with a total cost per sensor device below $100. The low-cost nature of our sensor platform makes the sensing approach particularly suitable for large-scale deployments but imposes technical challenges regarding performance and quality. To circumvent the limits of our solution, we have implemented and evaluated different sensing strategies and noise reduction techniques. Our results suggest that our sensor architecture may be useful in application areas such as dynamic spectrum access in cognitive radios, detecting regions with elevated electro-smog, or simply to gain an understanding of the spectrum usage for advanced signal intelligence such as anomaly detection or policy enforcement.

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“…Fortunately, the lack of individual sensitivity can be compensated by using cooperative sensing with multiple sensing devices [8]. These sensing devices can be installed as part of the infrastructure for the sole purpose of spectrum sensing [9], or existing user devices can be involved through a crowdsourcing approach [10]. In both cases, the devices involved in the collaborative spectrum sensing are likely to be heterogeneous, thus performing heterogeneous spectrum sensing.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous spectrum sensing: challenges and methodologies

Liu

Chwalisz

Fortuna

et al. 2015

J Wireless Com Network

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Distributed sensing is commonly used to obtain accurate spectral information over a large area. More and more heterogeneous devices are being incorporated in distributed sensing with the aim of obtaining more flexible sensing performance at lower cost. Although the concept of combining the strengths of various sensing devices is promising, the question of how to compare and combine the heterogeneous sensing results in a meaningful way is still open. To this end, this paper proposes a set of methodologies that are derived from several spectrum sensing experiments using heterogeneous sensing solutions. Each of the solutions offers different radio frequency front-end flexibility, sensing speed and accuracy and varies in the way the samples are processed and stored. The proposed methodologies cover four fundamental aspects in heterogeneous sensing: (i) storing experiment descriptions and heterogeneous results in a common data format; (ii) coping with different measurement resolutions (in time or frequency domain); (iii) calibrating devices under strictly controlled conditions and (iv) processing techniques to efficiently analyse the obtained results. We believe that this paper provides an important first step towards a standardized and systematic approach of heterogeneous sensing solutions.

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Towards commoditized real-time spectrum monitoring

Cited by 69 publications

References 22 publications

A Wireless Spectrum Analyzer in Your Pocket

A Wireless Spectrum Analyzer in Your Pocket

A software-defined sensor architecture for large-scale wideband spectrum monitoring

Heterogeneous spectrum sensing: challenges and methodologies

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