Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

Chen, Min; Miao, Yiming; Jian, Xin; Wang, Xiaofei; Humar, Iztok

doi:10.1109/tgcn.2018.2873783

Cited by 101 publications

(52 citation statements)

References 30 publications

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“…In [22], the authors compare the performance of dynamic time division duplex with centralized and distributed resource allocation schemes in a dense IoT. The authors in [24] and [25] provide an overview on power consumption, security, spectrum resource management, and deployment of narrow band IoT and cognitive lowpower wide-area networks, respectively. In [26], we considered a system model in which there are periodic and critical messages coexisting in an IoT system and proposed a learning framework that enables the IoT devices to autonomously allocate the limited communication resources for a highly reliable transmission of the critical messages.…”

Section: A Existing Workmentioning

confidence: 99%

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

Park

Saad

2019

IEEE Internet Things J.

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The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multi-state sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delaytolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access request and percentage of devices that learned correctly. Simulation results show that, for a delay threshold of 1.25 ms, the average achieved delay is 0.71 ms and the delay threshold is satisfied with probability 0.87. Moreover, for a massive network, a delay threshold of 2.5 ms is satisfied with probability 0.92. The results also show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delaytolerant, periodic messages.

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Section: A Existing Workmentioning

confidence: 99%

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

Park

Saad

2019

IEEE Internet Things J.

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“…(6) where (7) The parameter P BS,s represents the transmit power of serving base station BS and G j,BS,s represents the channel gain between user j and base station BS on subcarrier s. Similarly the parameter P BS',s represents the transmit power of neighboring base station BS' and G j,BS',s represents the channel gain between user j and neighboring base station BS' on subcarrier s. The N 0 is white noise power spectral density and ∆f represent subcarrier spacing. The channel gain G is represented by the path loss (PL) model based on the urban path-loss and given as (8).…”

Section: B Throughput Measurementmentioning

confidence: 99%

“…7]. Different wireless communication techniques such as LTE, NB-IoT, EC-GSM, Lora, Sigfox are compared using parameters coverage, Spectrum, Bandwidth, Data rate, Battery life in [8]. The software defined radio technology for LTE transmit diversity is explained using USRP N210 and study the BER performance [9].…”

Section: Introductionmentioning

confidence: 99%

Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network

Jadhav*,

Rao

2019

IJITEE

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The wireless communication has become the essential of today’s Internet of Things (IoT) and mobile applications which is increasing exponentially in near future. These applications demand high data rate to speed up the communication among IoT or internet applications. The data rate requirement of individual application is different. To fulfill these demands of every application Long Term Evolution (LTE) integrated with femtocell technology is identified as suitable wireless technology. The running applications on a user equipment changes dynamically. Hence in dense deployment of femtocell and IoT devices, the bandwidth distribution to many active applications becomes more complicated. For multiple running applications per device with available bandwidth constraint, optimal bandwidth allocation to applications has become the most challenging issue. We include the bandwidth allocation to applications during handover of user equipment in LTE-femtocell network. The handover procedure needs efficient cell selection criteria for target cell selection. Hence in our research we focus on bandwidth distribution among applications by proposing “Efficient Bandwidth Distribution” (EBD) mechanism and efficient cell selection by “Dynamic Cell Selection” (DCS) mechanisms to mitigate above mentioned issues.

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“…They extended their work in another article toward improving LPWAN communication over white spaces . Similarly, Chen et al introduced cognitive‐LPWAN (C‐LPWAN) based on an artificial intelligence (AI)‐enabled cognitive engine . They suggested that C‐LPWAN performs better than some known LPWAN technologies such as LoRa (Long Range), NB‐IoT, and LTE‐M in terms of its delay reduction and minimal energy consumption rates.…”

Section: Introductionmentioning

confidence: 99%

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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Cognitive-LPWAN: Towards Intelligent Wireless Services in Hybrid Low Power Wide Area Networks

Cited by 101 publications

References 30 publications

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network

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

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