Chrysso &amp;#x2014; A multi-channel approach to mitigate external interference

Iyer, Venkatraman; Woehrle, Matthias; Langendoen, Koen

doi:10.1109/sahcn.2011.5984929

Cited by 17 publications

(28 citation statements)

References 21 publications

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“…The RSSI sampler represents the burst by the weighted mean power level and the total duration of the subsequence. The previous example contains the non-idle subsequence (3,3), (4,2), (3,3), which corresponds to the RSSI burst:…”

Section: Rssi Samplermentioning

confidence: 99%

“…We show that SpeckSense distinguishes between the predominant sources of interference, and in particular, identifies multiple WiFi access points in the presence of data traffic. We demonstrate the usefulness of SpeckSense by adding it to a multichannel data collection protocol [2]. We evaluate the combined solution on a large-scale indoor testbed and observe a significant improvement in data yield facilitated by avoiding interfered channels.…”

Section: Introductionmentioning

confidence: 96%

“…Typically, IEEE 802.15.4-compliant sensor nodes compete for channel access with an increasing number of WiFi and Bluetooth devices such as laptops, smartphones, and tablet PCs. This results in long contention delays and collisions that degrade sensor network performance [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Several mitigation approaches [1][2][3][4] have been proposed to tackle the problem of external interference in sensor networks. Knowing the type of interference enables a sensor node to choose a suitable mitigation strategy [1,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Lastly, multiple interference detection enables wireless devices to disambiguate external interference from in-network channel traffic. This provides a clearer context for motivating interference mitigation mechanisms as in [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Detecting and Avoiding Multiple Sources of Interference in the 2.4 GHz Spectrum

Iyer

Hermans²,

Voigt

2015

Lecture Notes in Computer Science

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Abstract. Sensor networks operating in the 2.4 GHz band often face cross-technology interference from co-located WiFi and Bluetooth devices. To enable effective interference mitigation, a sensor network needs to know the type of interference it is exposed to. However, existing approaches to interference detection are not able to handle multiple concurrent sources of interference. In this paper, we address the problem of identifying multiple channel activities impairing a sensor network's communication, such as simultaneous WiFi traffic and Bluetooth data transfers. We present SpeckSense, an interference detector that distinguishes between different types of interference using a unsupervised learning technique. Additionally, SpeckSense features a classifier that distinguishes between moderate and heavy channel traffic, and also identifies WiFi beacons. In doing so, it facilitates interference avoidance through channel blacklisting. We evaluate SpeckSense on common mote hardware and show how it classifies concurrent interference under real-world settings. We also show how SpeckSense improves the performance of an existing multichannel data collection protocol by 30%.

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