A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

Zacharias, Sven; Newe, Thomas; O'Keeffe, Sinéad; Lewis, Elfed

doi:10.1155/2014/265286

Cited by 12 publications

(21 citation statements)

References 29 publications

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“…Research works in [6][7][8] propose algorithms to identify type of interference. Studies in [7,8] decide on features of interfering signal by evaluating RSSI values. Then, with a fixed set of conditions, the signal is classified as bluetooth, WiFi or microwave interference.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…The default beacon interval for WiFi is 102.4ms [7]. The sum of maximum channel clear duration and maximum channel busy duration (duty cycle duration) for WiFi should not exceed this value.…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning based lightweight interference mitigation scheme for wireless sensor network

et al. 2020

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“…In [19,26], Zacharias et al propose a lightweight interference classification method in which a series of conditions are tested to identify the dominant source of interference. In these works, a node collects the RSSI samples on a single channel over a duration of one second at a sampling frequency of 8 kHz.…”

Section: Energy Detection-based Interference Classificationmentioning

confidence: 99%

“…The introduced mechanism is able to discriminate among interference from IEEE 802.11 networks, even when no terminal is associated with the access point, RF leakage from MWO and an IFC. Differently from other methods in the literature, such as [19][20][21][22], the proposed method does not rely on features based on the periodicity of IEEE 802.11 beacons. This fact, in conjunction with the novel classification scheme based on multiple SVMs, helps to ensure good classification performance while requiring an extremely limited sensing time.…”

Section: Introductionmentioning

confidence: 99%

An SVM-Based Method for Classification of External Interference in Industrial Wireless Sensor and Actuator Networks

Grimaldi

Mahmood

Gidlund

2017

JSAN

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In recent years, the adoption of industrial wireless sensor and actuator networks (IWSANs) has greatly increased. However, the time-critical performance of IWSANs is considerably affected by external sources of interference. In particular, when an IEEE 802.11 network is coexisting in the same environment, a significant drop in communication reliability is observed. This, in turn, represents one of the main challenges for a wide-scale adoption of IWSAN. Interference classification through spectrum sensing is a possible step towards interference mitigation, but the long sampling window required by many of the approaches in the literature undermines their run-time applicability in time-slotted channel hopping (TSCH)-based IWSAN. Aiming at minimizing both the sensing time and the memory footprint of the collected samples, a centralized interference classifier based on support vector machines (SVMs) is introduced in this article. The proposed mechanism, tested with sample traces collected in industrial scenarios, enables the classification of interference from IEEE 802.11 networks and microwave ovens, while ensuring high classification accuracy with a sensing duration below 300 ms. In addition, the obtained results show that the fast classification together with a contained sampling frequency ensure the suitability of the method for TSCH-based IWSAN.

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“…In this regard, interference classification is prerequisite towards mitigation. Recent work on interference classification [6,7] addresses the problem by mapping RSSI observations or patterns of corrupted packets to a known class of interference such as WiFi, Bluetooth or microwave ovens. Such designs are intrinsically constrained by a direct mapping of channel observations to a fixed number of interference classes.…”

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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A Lightweight Classification Algorithm for External Sources of Interference in IEEE 802.15.4-Based Wireless Sensor Networks Operating at the 2.4 GHz

Cited by 12 publications

References 29 publications

Machine learning based lightweight interference mitigation scheme for wireless sensor network

Machine learning based lightweight interference mitigation scheme for wireless sensor network

An SVM-Based Method for Classification of External Interference in Industrial Wireless Sensor and Actuator Networks

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

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