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Standardized low-power wireless communication technologies for distributed sensing applications.

Vilajosana X, Tuset-Peiro P, Vazquez-Gallego F, Alonso-Zarate J, Alonso L - Sensors (Basel) (2014)

Bottom Line: Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability.Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off.In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.

View Article: PubMed Central - PubMed

Affiliation: Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC) C/Roc Boronat 117, Barcelona 08018, Spain. xvilajosana@uoc.edu.

ABSTRACT
Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability. Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off. Industrial sensing applications, such as those being deployed in oil refineries, have stringent requirements on data reliability and are being built using new standards. Despite the benefits of these new technologies, industrial shifts are not happening due to the enormous technology development and adoption costs and the fact that new standards are not well-known and completely understood. In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.

No MeSH data available.


Forward error correction (FEC) coding schemes with the convolutional encoder and matrix interleaver.
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f7-sensors-14-02663: Forward error correction (FEC) coding schemes with the convolutional encoder and matrix interleaver.

Mentions: One particular case is implementing the FEC encoding scheme at the physical layer, which is based on two subsystems connected in series, as depicted in Figure 7. First, a non-recursive convolutional encoder with a rate r = 1/2 and a constraint length k = 4. Second, a 4 × 4 matrix interleaver that separates adjacent data to ensure that bursty data errors caused by fading can be treated by the convolutional code error corrector. Compared to an uncoded channel, the FEC encoding scheme provides an asymptotic coding gain of 4.8 dB in an AWGN (additive white gaussian noise) channel [26], thus enabling to extend the communication distance under ideal conditions. However, such a gain is reduced in real-life conditions to 2-3 dB, due to channel impairments, such as fading and shadowing. The FEC encoding and decoding process is asymmetric, that is, the code to implement the coder and the decoder are different. During the encoding process, the microcontroller first shifts the data stream through a nibble mapper that selects the appropriate output given the input bits. Such a process can be implemented using a mapping table and, thus, is computationally efficient. Next, the output of the encoder is passed through the interleaver. The process is also computationally efficient, because the matrix transformation is small, and thus, it takes a few steps. Upon reception, the deinterleave process is similar to the interleave process. However, the decoding process is much more computationally intensive. Because the errors introduced by the channel are random, a Viterbi algorithm needs to be executed to select the path in the output stream that has the maximum likelihood decoding.


Standardized low-power wireless communication technologies for distributed sensing applications.

Vilajosana X, Tuset-Peiro P, Vazquez-Gallego F, Alonso-Zarate J, Alonso L - Sensors (Basel) (2014)

Forward error correction (FEC) coding schemes with the convolutional encoder and matrix interleaver.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958241&req=5

f7-sensors-14-02663: Forward error correction (FEC) coding schemes with the convolutional encoder and matrix interleaver.
Mentions: One particular case is implementing the FEC encoding scheme at the physical layer, which is based on two subsystems connected in series, as depicted in Figure 7. First, a non-recursive convolutional encoder with a rate r = 1/2 and a constraint length k = 4. Second, a 4 × 4 matrix interleaver that separates adjacent data to ensure that bursty data errors caused by fading can be treated by the convolutional code error corrector. Compared to an uncoded channel, the FEC encoding scheme provides an asymptotic coding gain of 4.8 dB in an AWGN (additive white gaussian noise) channel [26], thus enabling to extend the communication distance under ideal conditions. However, such a gain is reduced in real-life conditions to 2-3 dB, due to channel impairments, such as fading and shadowing. The FEC encoding and decoding process is asymmetric, that is, the code to implement the coder and the decoder are different. During the encoding process, the microcontroller first shifts the data stream through a nibble mapper that selects the appropriate output given the input bits. Such a process can be implemented using a mapping table and, thus, is computationally efficient. Next, the output of the encoder is passed through the interleaver. The process is also computationally efficient, because the matrix transformation is small, and thus, it takes a few steps. Upon reception, the deinterleave process is similar to the interleave process. However, the decoding process is much more computationally intensive. Because the errors introduced by the channel are random, a Viterbi algorithm needs to be executed to select the path in the output stream that has the maximum likelihood decoding.

Bottom Line: Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability.Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off.In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.

View Article: PubMed Central - PubMed

Affiliation: Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC) C/Roc Boronat 117, Barcelona 08018, Spain. xvilajosana@uoc.edu.

ABSTRACT
Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability. Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off. Industrial sensing applications, such as those being deployed in oil refineries, have stringent requirements on data reliability and are being built using new standards. Despite the benefits of these new technologies, industrial shifts are not happening due to the enormous technology development and adoption costs and the fact that new standards are not well-known and completely understood. In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.

No MeSH data available.