Ubiquitous, Yet Deceptive: Hardware-Based Channel Metrics on Interfered WSN Links

Barać, Filip; Gidlund, Mikael; Zhang, Tingting

doi:10.1109/tvt.2014.2334494

Cited by 23 publications

(15 citation statements)

References 17 publications

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“…The classification accuracy of the proposed method is 72%, while this result is also IEEE 802.15.4 packet size dependent, since packets with a small payload size are partially overlapped with the interferer, thus carrying a small interference fingerprint. In [34] Barac et al use forward error correction (FEC) in order to identify the source of bit errors in a received packet (i.e., multipath fading and attenuation, as well as the IEEE 802.11 b/g interference). Therefore, instead of packet retransmissions (which is used in [32]), the FEC method in [34] emerges as an energy-efficient alternative for interference classification, yielding more than 91% classification rate with just one received packet.…”

Section: Bit Error Pattern-based Interference Classificationmentioning

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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Section: Bit Error Pattern-based Interference Classificationmentioning

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 [17], RSS and the LQI indicators are combined in order to distinguish between the errors caused by signal shadowing from those caused by packet collisions, using non-IEEE 802.15.4-compliant devices. However, both indicators exhibit substantial ambiguity under WLAN interference [18], one of the reasons being the fact that, in most IEEE 802.15.4-compliant transceivers, the two are calculated only on several symbols of the packet. Furthermore, RSS-based channel state prediction is proposed in [13], which is in counterintuitive, having in mind the stochastic nature of interference at industrial locations.…”

Section: Related Workmentioning

confidence: 99%

“…Namely, it has been observed that MFA can sometimes cause a small number (<4) of closely placed corrupted symbols (algorithm lines [3][4][5]. MFA is also attributed to the packets with calculated S E D smaller than 0.2 or equal to 1, with symbol errors placed more than t symbols before the end of the packet (lines 9-10), and to packets with S E D ≤ 0.2 and more correctable than uncorrectable codewords (lines [17][18]. The indicators of WLAN are S E D ∈ (0.2, 1) (line 12), S E D above 0.2 with more uncorrectable than correctable codewords (lines [19][20], or S E D > 0.2, calculated only from correctable codewords (lines 25-26).…”

Section: The Lped Algorithmmentioning

confidence: 99%

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

Barać

Caiola²,

Gidlund

et al. 2014

IEEE Sensors J.

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Wireless sensor network communication in industrial environments is compromised by interference, multipath fading, and signal attenuation. In that respect, accurate channel diagnostics is imperative to selecting the adequate countermeasures. This paper presents the lightweight packet error discriminator (LPED) that infers the wireless link condition by distinguishing between errors caused by multipath fading and attenuation, and those inflicted by interfering wideband singlechannel communication systems (e.g., IEEE 802.11b/g), based on the differences in their error footprints. The LPED uses forward error correction in a novel context, namely, to determine the symbol error density, which is then fed to a discriminator for error source classification. The classification criteria are derived from an extensive set of error traces collected in three different types of industrial environments, and verified on a newly collected set of error traces. The proposed solution is evaluated both offline and online, in terms of classification accuracy, speed of channel diagnostics, and execution time. The results show that in ≥91% of cases, a single packet is sufficient for a correct channel diagnosis, accelerating link state inference by at least 270%, compared with the relevant state-of-the-art approaches. The execution time of LPED, for the worst case of packet corruption and maximum packet size, is below 30 ms with ≤3% of device memory consumption. Finally, live tests in an industrial environment show that LPED quickly recovers from link outage, by losing up to two packets on average, which is only one packet above the theoretical minimum.Index Terms-IWSN, interference recognition, error discrimination, FEC, bit error traces. 1530-437X

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“…The industrial, scientific, and medical (ISM) unlicensed band where industrial wireless sensors net-work (IWSN) nodes are deployed together with Wi-Fi and Bluetooth de-vices has become overcrowded resulting in intense competition for available bandwidth in the ISM band. In addition, unlike traditional wireless sensor networks (WSNs) (Nellore and Hancke, 2016a,b), IWSNs define stricter and stringent QoS requirements (Bara et al, 2015;Baviskar et al, 2015;Dob-slaw et al, 2015;Kruger et al, 2015;Nagarajan and Dhanasekaran, 2015;Papadopoulos, 2015). To meet these goals, different working groups such as WINA, ISA, and the ZigBee working groups have developed various indus-trial standards including WIA-PA, ISA100.11a, WirelessHART, and ZigBee (Batista et al, 2012;Chung et al, 2015;Evans-Pughe, 2003;Miao et al, 2010;Tang et al, 2010;Yang and Dong-Seong, 2013) for specific industrial applications QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN)

Oyewobi

Hancke

2017

Journal of Network and Computer Applications

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Industrial wireless sensor network (IWSN) applications are mostly timebound, mission-critical and highly delay sensitive applications therefore IWSN defines strict, stringent and unique QoS requirements such as timeliness, reliability and availability. In IWSN, unlike other sensor networks, late arrival of packets or delay or disruption to an on-going communication are considered as critical failure. Also, because IWSN is deployed in the overcrowded industrial, scientific, and medical (ISM) band it is difficult to meet this unique QoS requirements due to stiff competition for bandwidth from other technologies operating in ISM band resulting in scarcity of spectrum for reliable communication and/or disruption of ongoing communication. However, cognitive radio (CR) provides more spectral opportunities through opportunistic-use of unused licensed spectrum while ensuring minimal interference to licensed users. Similarly, spectrum handoff, which is a new type of handoff in cognitive radio, has the potential to offer increase bandwidth, reliable, smooth and interference-free communication for IWSNs through opportunistic-use of spectrum, minimal switching-delays, and efficient target channel selection strategies as well as effective link recovery maintenance. As a result, a new paradigm known as cognitive radio industrial wireless sensor network (CR-IWSN) has become the interest of recent research efforts. In this paper, we highlight and discuss important QoS requirements of IWSN as well as efforts of existing IWSN standards to address the challenges. We discuss the potential and how cognitive radio and spectrum handoff can be useful in the attempt to provide real-time reliable and smooth communication for IWSNs.Keywords: cognitive radio, industrial wireless sensor network, opportunistic use, spectrum handoff, quality of service. (Cheng et al., 2016;Silva et al., 2015;Silva and Hancke, 2016). Therefore, reliable and stable communication is of critical importance and top priority in IWSNs (Kumar S et al., 2014;Phala et al., 2016). For instance, in industrial automation; precision and timeliness are important and in industrial monitoring; availability, reliability, delay-intolerance, and robustness of communication links are key (Abu-Mahfouz and Hancke, 2011;Silva and Hancke, 2016). Yet, these requirements can be relatively difficult to achieve when operating in the ISM band due to interference and overcrowding which result in continuous switching, high latency due to switching, reduced link capacity and disruption of on-going communications. However, spectrum handoff can offer increase bandwidth, reliable, smooth and interference-free communication for IWSNs through opportunistic-use of spectrum, minimal switching-delays, and efficient target channel selection strategies as well as effective link recovery maintenance. A well-planned and designed spectrum handoff technique will provide reliable communication by reducing number of spectrum handoffs and maintaining quality communication link for IWSN systems and application...

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Ubiquitous, Yet Deceptive: Hardware-Based Channel Metrics on Interfered WSN Links

Cited by 23 publications

References 17 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

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN)

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