A lightweight approach to online detection and classification of interference in 802.15.4-based sensor networks

Hermans, Frederik; Larzon, Lars-Åke; Rensfelt, Olof; Gunningberg, Per

doi:10.1145/2367580.2367582

Cited by 23 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The publications of Hermans et al [33], Rensfelt et al [11], and Hermans et al [20] developing SoNIC are the most comparable to the algorithm presented here and since the latter publication is the most recent and best evaluated one, the authors will refer to it. Hermans et al [20] claim to detect the predominant source of interference in an office environment with 87%.…”

Section: Classification Resultsmentioning

confidence: 90%

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

Newe

O'Keeffe

et al. 2014

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

IEEE 802.15.4 is the technology behind wireless sensor networks (WSNs) and ZigBee. Most of the IEEE 802.15.4 radios operate in the crowded 2.4 GHz frequency band, which is used by many technologies. Since IEEE 802.15.4 is a low power technology, the avoidance of interference is vital to conserve energy and to extend the lifetime of devices. A lightweight classification algorithm is presented to detect the common external sources of interference in the 2.4 GHz frequency band, namely, IEEE 802.11-based wireless local area networks (WLANs), Bluetooth, and microwave ovens. This lightweight algorithm uses the energy detection (ED) feature (the feature behind received signal strength indication (RSSI)) of an IEEE 802.15.4-compliant radio. Therefore, it classifies the interferers without demodulation of their signals. As it relies on time patterns instead of spectral features, the algorithm has no need to change the channel. Thus, it allows the radio both to stay connected to the channel and to receive while scanning. Furthermore, it has a maximum runtime of merely one second. The algorithm is extensively tested in a radio frequency anechoic chamber and in real world scenarios. These results are presented here.

show abstract

Section: Classification Resultsmentioning

confidence: 90%

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

Newe

O'Keeffe

et al. 2014

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Alternatively, by sampling link quality and/or received signal strength values in combination with processing it has been shown to be possible to identify Wi-Fi signals using commodity ZigBee nodes [33] [34], to identify ZigBee signals using commodity Wi-Fi cards [35] or to detect Wi-Fi idle intervals to which ZigBee frame lengths and transmission intervals can be adapted [36]. Other state of the art coexistence techniques utilize e.g.…”

Section: Challenges To Identify and Correctly Classify The Presence Omentioning

confidence: 99%

Sub-GHz LPWAN Network Coexistence, Management and Virtualization: An Overview and Open Research Challenges

Poorter

Hoebeke

Strobbe

et al. 2017

Wireless Pers Commun

View full text Add to dashboard Cite

Abstract. The IoT domain is characterized by many applications that require lowbandwidth communications over a long range, at a low cost and at low power. LPWANs (Low Power Wide Area Networks) fulfill these requirements by using sub-GHz radio frequencies (typically 433 or 868 MHz) with typical transmission ranges in the order of 1 up to 50 kilometers. As a result, a single base station can cover large areas and can support high numbers of connected devices (> 1000 per base station). Notorious initiatives in this domain are LoRa, Sigfox and the upcoming IEEE 802.11ah (or "HaLow") standard. Although these new technologies have the potential to significantly impact many IoT deployments, the current market is very fragmented and many challenges exists related to deployment, scalability, management and coexistence aspects, making adoption of these technologies difficult for many companies. To remedy this, this paper proposes a conceptual framework to improve the performance of LPWAN networks through in-network optimization, cross-technology coexistence and cooperation and virtualization of management functions. In addition, the paper gives an overview of state of the art solutions and identifies open challenges for each of these aspects.

show abstract

“…Willig et al [3] use the Gilbert-Elliott model to describe IEEE 802.11 bit-error process in industrial environments, but the differences between the IEEE 802.11 and 802.15.4 Physical layers cast aside the validity of the conclusions for IEEE 802.15.4 radios [4]. In addition, the studies conducted in anechoic chambers [5] and isolated environments [6] exclude multipath propagation, a phenomenon present in every realistic setup.…”

Section: Related Workmentioning

confidence: 99%

Scrutinizing Bit- and Symbol-Errors of IEEE 802.15.4 Communication in Industrial Environments

Barać

Gidlund

Zhang

2014

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

The knowledge of error nature in wireless channels is an essential constituent of efficient communication protocol design. To this end, this paper is the first comprehensive bitand symbol-level analysis of IEEE 802.15.4 transmission errors in industrial environments. The intention with this paper is to extract the error properties relevant for future improvements of wireless communication reliability and coexistence of radio systems in these harsh conditions. An extensive set of bit-error traces was collected in a variety of scenarios and industrial environments, showing that error behavior is highly dependent on the cause of packet corruption. It is shown that errors inflicted by multipath fading and attenuation exhibit different properties than those imposed by IEEE 802.11 interference. The statistical behavior of these two patterns is concurrently investigated in terms of differences in bit-error distribution, error burst length, channel memory length, and the scale of packet corruption. With these conclusions at hand, abiding to the computational constraints of embedded sensors and the statistical properties of bit-errors, a Reed-Solomon (15,k) block code is chosen to investigate the implications of bit-error nature on practical aspects of channel coding and interleaving. This paper is concluded by a number of findings of high practical relevance, concerning the optimal type, depth, and meaningfulness of interleaving.Index Terms-Error patterns, forward error correction (FEC), IEEE 802.15.4, industrial wireless sensor network (WSN), interleaving.

show abstract

A lightweight approach to online detection and classification of interference in 802.15.4-based sensor networks

Cited by 23 publications

References 13 publications

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

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

Sub-GHz LPWAN Network Coexistence, Management and Virtualization: An Overview and Open Research Challenges

Scrutinizing Bit- and Symbol-Errors of IEEE 802.15.4 Communication in Industrial Environments

Contact Info

Product

Resources

About