Mitigating the Effects of RF Interference through RSSI-Based Error Recovery

Hauer, Jan-Hinrich; Willig, Andreas; Wolisz, Adam

doi:10.1007/978-3-642-11917-0_15

Cited by 40 publications

(43 citation statements)

References 12 publications

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“…For instance, Liang et al [20] studied the interplay between 802.11 and 802.15.4 and applied a resilience forward error coding scheme against interference. Analogously, some solutions focused on exploiting the temporal effects of interference induced on PHY hints, such as variations in soft errors (softPHY) [14], [15] or RSSI variations [33] to localize interfered segments, hence adapt standard ARQ to retransmit only the interfered segments.…”

Section: Related Workmentioning

confidence: 99%

CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

Hithnawi

Shafagh

et al. 2016

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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International audience—Low-power wireless devices suffer notoriously from Cross-Technology Interference (CTI). To enable coexistence , researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies

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Section: Related Workmentioning

confidence: 99%

CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

Hithnawi

Shafagh

et al. 2016

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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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%

“…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%

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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“…For instance, reed solomon coding can be employed to mitigate the 802.11 impact on 802.15.4 networks, as suggested by Liang et al [10]. Furthermore, partial packet recovery mechanisms are used to exploit the temporal effects of interference induced on the PHY hints, such as variations in soft errors (softPHY) [18] or RSSI variations [7,16] to determine boundaries of the interfered fractions on the received corrupted packets. Recent research efforts utilize advancements in MIMO for interference cancellation [23] and to facilitate spectral usage efficiency and harmonize coexistence across different technologies.…”

Section: Related Workmentioning

confidence: 99%

Understanding the impact of cross technology interference on IEEE 802.15.4

Hithnawi

Shafagh

Duquennoy

2014

Proceedings of the 9th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization

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Over the last few decades, we witnessed notable progress in wireless communication. This has led to rapid emergence of heterogeneous wireless technologies that share the RF spectrum in an un-coordinated way. Such a coexistence introduces high uncertainty and complexity to the medium, affecting reliability and availability of wireless networks. This problem aggravates for technologies operating in the lightly regulated, yet crowded ISM bands. To address coexistence of different technologies in the scarce RF spectrum, provide proper interference-aware protocols, and mitigation schemes, we need to develop a good understanding of the interaction patterns of these technologies. In this paper, we provide a thorough study of the implications of Cross Technology Interference (CTI) on the particularly vulnerable low-power IEEE 802.15.4 wireless networks. We identify the underlying vulnerabilities that hamper 802.15.4 to withstand CTI. We show that the uncertainty that CTI induces on the wireless channel is not completely stochastic; CTI exhibits distinct patterns that can be exploited by interference-aware protocols.

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Mitigating the Effects of RF Interference through RSSI-Based Error Recovery

Cited by 40 publications

References 12 publications

CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

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

Understanding the impact of cross technology interference on IEEE 802.15.4

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