Snow

Saifullah, Abusayeed; Rahman, M.; Ismail, Dali; Lu, Chenyang; Chandra, Ranveer; Liu, Jie

doi:10.1145/2994551.2994552

Cited by 57 publications

(4 citation statements)

References 23 publications

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“…The SM separates the receiving path from the transmitting path while ensuring that the CR‐LPWAN GW uses a common antenna for the transceiver. Such a design has been elaborated and prototyped by Saifullah et al Nevertheless, several other LPWAN proposals often suggest that half duplex mode can also be used . In particular, the popular LoRa SX1276 transceiver series are based on the half‐duplex mode, which makes it suited for CR‐LPWAN systems.…”

Section: Methodsmentioning

confidence: 99%

“…There are several efforts directed toward developing effective CR‐LPWAN systems. For example, Saifullah et al focused on improving scalability, asynchronous, and bidirectional communication in LPWAN systems. However, with regard to integrating CR in LPWAN, they considered only the use of the geolocation database technology in order to identify white spaces.…”

Section: Related Workmentioning

confidence: 99%

“…Most of these works were conducted to address specific problems associated with LPWANs such as interference, spectrum underutilization, and limited transmission range. For example, Saifullah et al developed a new technology called sensor network over white spaces (SNOW), wherein they deployed an innovative physical (PHY) layer design called distributed orthogonal frequency division multiplexing (D‐OFDM) to ensure multiple access and bidirectional communication in LPWAN. They extended their work in another article toward improving LPWAN communication over white spaces .…”

Section: Introductionmentioning

confidence: 99%

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Adaptive threshold techniques for cognitive radio‐based low power wide area network

Onumanyi

Abu‐Mahfouz

Hancke

2020

Trans Emerging Tel Tech

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Some low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR-LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)-based applications. However, in order to be effective under dynamic sensing conditions, CR-LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR-LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR-LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR-LPWAN systems.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive threshold techniques for cognitive radio‐based low power wide area network

Onumanyi

Abu‐Mahfouz

Hancke

2020

Trans Emerging Tel Tech

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“…The deployment of dense IoT devices has led to research on the coexistence of heterogeneous networks 16‐20 . Wireless devices that follow different communication standards 21‐24 compete for limited spectrum resources at the same time and interfere with each other's communication, resulting in low transmission efficiency and spectrum waste.…”

Section: Related Workmentioning

confidence: 99%

A reliable nonfeedback transmission mechanism for asymmetric channels based on machine learning

Liu

et al. 2020

Trans Emerging Tel Tech

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Cross‐technology communication (CTC) enables data communication between heterogeneous wireless devices, which becomes the focus of current research. Due to the difference of communication distance, the transmission mode of the heterogeneous devices is mostly one‐hop transmission and multihop return. The mechanism of ensuring reliable transmission by sending ACK (ACKnowledge character) leads to longer transmission delays in asymmetric channels, especially for the ZigBee networks with low duty cycle. To solve this problem, this article proposes a new nonfeedback mechanism to reduce the communication delay on asymmetry channel. Through the method of fitting error, the random forest algorithm is improved, so that the training model can predict the current packet loss rate by judging channel conditions, receiver position, payload and other factors, so that the transmitter can accurately calculate the number of packets to be sent. The experimental results show that the loss rate predicted by this algorithm makes the transmission reliability be as high as 99.78%. Compared with the existing CTC technologies such as WeBee and NetCTC, the nonfeedback transmission mechanism put forward by us improves the transmission efficiency by 91.39% and 24.15%. The results show that the proposed mechanism greatly reduces transmission delay and improves channel utilization.

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LPWAN Technologies

Herrero

2021

Textbooks in Telecommunication Engineering

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Snow

Cited by 57 publications

References 23 publications

Adaptive threshold techniques for cognitive radio‐based low power wide area network

Adaptive threshold techniques for cognitive radio‐based low power wide area network

A reliable nonfeedback transmission mechanism for asymmetric channels based on machine learning

LPWAN Technologies

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