2.4 GHz IEEE 802.15.4 channel interference classification algorithm running live on a sensor node

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

doi:10.1109/icsens.2012.6411279

Cited by 18 publications

(14 citation statements)

References 2 publications

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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%

“…For compatibility reasons and in order to increase the network detection range, the beacons are usually sent at the lowest data rate (1 or 2 Mb/s). The default beacon frequency period is 100 time units, which is equal to 102.4 ms [26]. The above-stated heterogeneous medium access rules and PHY specifications for WLAN networks render ZigBee systems vulnerable.…”

Section: Ieee 80211mentioning

confidence: 99%

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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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Section: Energy Detection-based Interference Classificationmentioning

confidence: 99%

Section: Ieee 80211mentioning

confidence: 99%

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

Grimaldi

Mahmood

Gidlund

2017

JSAN

View full text Add to dashboard Cite

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“…Then the node will select a channel with the lowest energy level, that is, the least intensity of interference. Zacharias et al (18) proposed a classification algorithm to detect the common external sources of interference in the 2.4 GHz frequency band.…”

Section: Related Workmentioning

confidence: 99%

Device-Free Indoor Localization Based on Data Mining Classification Algorithms

2016

Sensors and Materials

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Indoor localization is used in many applications such as security, health care, location-based services, and social networking. In traditional localization systems, a target person carries a radio device or sensor and the location of this device is taken as the location of the target person. However, there are situations in which a person does not carry a device. In such cases, devicefree localization (DFL) is the best solution. In this paper, we propose a radio frequency (RF)based DFL system using data mining classification algorithms. ZigBee nodes are deployed at the sides of a rectangular area and the area is divided into square grids. First, a model is developed for each classifier by collecting a received signal strength indicator (RSSI) when a person stands at the center of grids. The RSSI of each RF link is taken as an attribute for classifiers. Second, an online dataset is used to test the trained classifiers. RF links that contribute less for classification are removed from the attribute list. We also analyze the effect of ZigBee and WiFi interference on ZigBee-based DFL systems. Among five data mining classifiers, k-nearest neighbors and support vector machine using sequential minimal optimization achieve a classification accuracy of above 90%.

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“…A number of related works exploits the Received Signal Strength (RSS) for interference recognition, where the RSS values are obtained either by continuous spectrum scanning [3], [4], [5] or by analyzing the RSS inside the corrupted packets [6]. Nevertheless, persistent spectrum sampling induces significant overhead and aggravates the network operation under interference.…”

Section: Introduction and Related Workmentioning

confidence: 99%

LPED: Channel diagnostics in WSN through channel coding and symbol error statistics

Barać

Gidlund

Zhang

2014

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Three major obstacles to wireless communication are electromagnetic interference, multipath fading and signal attenuation. The former stems mainly from collocated wireless systems operating in the same frequency band, while the latter two originate from physical properties of the environment. Identifying the source of packet corruption and loss is crucial, since the adequate countermeasures for different types of threats are essentially different. This problem is especially pronounced in industrial monitoring and control applications, where IEEE 802.15.4 communication is expected to deliver data within tight deadlines, with minimal packet loss. This work presents the Lightweight Packet Error Discriminator (LPED) that distinguishes between errors caused by multipath fading and attenuation, and those inflicted by IEEE 802.11 interference. LPED uses Forward Error Correction to determine the symbol error positions inside erroneously received packets and calculates the error density, which is then fed to a discriminator for error source classification. The statistical constituents of LPED are obtained from an extensive measurement campaign in two different types of industrial environments. The classifier incurs no overhead and in ≥90% of cases a single packet is sufficient for a correct channel diagnosis. Experiments show that LPED accelerates link diagnostics by at least 190%, compared to the relevant state-of-the-art approaches.

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2.4 GHz IEEE 802.15.4 channel interference classification algorithm running live on a sensor node

Cited by 18 publications

References 2 publications

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

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

Device-Free Indoor Localization Based on Data Mining Classification Algorithms

LPED: Channel diagnostics in WSN through channel coding and symbol error statistics

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