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

Iyer, Venkatraman; Hermans, Frederik; Voigt, Thiemo

doi:10.1007/978-3-319-15582-1_3

Cited by 35 publications

(26 citation statements)

References 21 publications

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“…Work in [13] proposes a scheme that identifies mitigation strategy with the help of decision tree but does not identify the type of interference. Authors in [14] propose Specksense, which identifies WiFi, periodic and non-periodic traffic. The scheme also helps to mitigate interference by channel black listing.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based lightweight interference mitigation scheme for wireless sensor network

et al. 2020

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The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme's memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.

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Section: Related Workmentioning

confidence: 99%

Machine learning based lightweight interference mitigation scheme for wireless sensor network

et al. 2020

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“…In [15], the identification of concurrent multi-source interference is based on k-means clustering of RSSI-samples. Using RSSI sampling of 21 kHz and sampling window of 3 s, the authors [15] in achieved a classification accuracy of 90 %, which however reduces further if the 802.15.4 network is not silent during observation time. Although, IDI enhancement in the presence of 802.15.4 traffic is addressed in [13] using power variations in CCA, however, the overall detection performance reduces significantly.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

2019

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Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity tradeoff for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90 %-97 % burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

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“…Neural networks can be adopted for learning component of the RPM. Neural networks have been proven effective in interference detection and classification within wireless networks [65,66] as well as in timeseries prediction tasks [67] that are crucial for coordination.…”

Section: Semantic Driven 5g System Architecture Challengesmentioning

confidence: 99%

Big Data in 5G Distributed Applications

Nejković

Visa

Tošić

et al. 2019

Lecture Notes in Computer Science

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Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today's networks will become reality-from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.

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

Cited by 35 publications

References 21 publications

Machine learning based lightweight interference mitigation scheme for wireless sensor network

Machine learning based lightweight interference mitigation scheme for wireless sensor network

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

Big Data in 5G Distributed Applications

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