Narrow Band Internet of Things

Chen, Min; Miao, Yiming; Hao, Yixue; Hwang, Kai

doi:10.1109/access.2017.2751586

Cited by 382 publications

(197 citation statements)

References 61 publications

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“…Prior work on AirComp targets sensor networks and thus focuses on narrow-band systems only. The reason is that sensor networks typically require low-rate transmission and communicate over a dedicated narrow-band in practical systems, e.g., LTE for narrow-band Internet-of-Things (NB-IoT) [30]. In contrast, we consider broadband channels due to the transmission of high-dimensional updates in the FEEL systems.…”

Section: B Over-the-air Computationmentioning

confidence: 99%

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

Zhu

Wang

Huang

2020

IEEE Trans. Wireless Commun.

677

489

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To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users.While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the waveform-superposition property of a multi-access channel.Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-andlearning metrics, which are useful for network planning and optimization. The power control ("truncated channel inversion") required for BAA results in a tradeoff between the update-reliability [as measured by the receive signalto-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rises to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results. In addition, we discuss the extensions of BAA to acquire safety against adversary attacks and integrate beamforming for enhancing cell-edge links.

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Section: B Over-the-air Computationmentioning

confidence: 99%

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

Zhu

Wang

Huang

2020

IEEE Trans. Wireless Commun.

677

489

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“…In particular, PSM keeps a device registered with network, but allows it to turn off the functionalities of paging listening and link quality measurements for energy saving. On the other hand, eDRX allows a device to negotiate with a network [23], [116], [119], [120].…”

Section: ) Lte-m: Lte-m Is Fully Compatible With Existing Cellularmentioning

confidence: 99%

“…These applications usually provide a visualization to present information in an intuitive and easy-to-understand way [208], [210] and/or accept interaction based on natural language, e.g., through voice commands, to understand basic human orders and/or respond properly [209]. Human-oriented applications are generally characterized by high data rates (i.e., from tens of Mbps up to tens of Gbps) [119], [211]. However, there are also a few human-oriented applications that require low data rate (e.g., form 1Mbps up to 10Mbps) like intelligent shopping applications that provide information of all items/interactions in a grocery store to the human as its end-user-type [212].…”

Section: A Human-oriented Iot Applicationsmentioning

confidence: 99%

“…1) Data Rate: IoT applications can have different data transmission rates from tens of kbps up to tens of Gbps. Three different application groups can be identified in terms of data rate as follows: 1) high data-rate (greater than 10Mbps), 2) medium data-rate (less than 10Mbps and greater than 100kbps), and 3) low data-rate applications (less than 100 kbps) [119].…”

Section: Nominated Connectivity Technologies For Iot Applicationsmentioning

confidence: 99%

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IoT Connectivity Technologies and Applications: A Survey

et al. 2020

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The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

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“…With the increase of low-data-rate and low power services [1,2], research on LPWAN communication technology develops correspondingly [3]. Based on whether the spectrum is licensed, LPWAN communication technologies [4] are divided into the following types: type 1 includes technologies that run in unlicensed frequency band, such as Lora and Sigfox.…”

Section: Introductionmentioning

confidence: 99%

RADB: Random Access with Differentiated Barring for Latency‐Constrained Applications in NB‐IoT Network

Miao

Tian

Cheng

et al. 2018

Wireless Communications and Mobile Computing

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With the development of LPWA (Low Power Wide Area) technology, the emerging NB-IoT (Narrowband Internet of Things) technology is becoming popular with wide area and low-data-rate services. In order to achieve objectives such as huge amount of connection and wide area coverage within NB-IoT, the problem of network congestion generated by random access of numerous devices should be solved. In this paper, we first introduce the background of NB-IoT and investigate the research on random access optimization algorithm. Then we summarize relevant features of NB-IoT uplink and narrowband physical random access channel and design random access with differentiated barring (RADB), which can improve the insufficiency of traditional dynamic access class barring method. At last, the algorithms proposed in this paper are realized with established NB-IoT model using OPNET Modeler platform, and simulations are conducted. The simulation results show that RADB is able to effectively solve preamble request conflict generated by random access of numerous devices and preferentially provide efficient and reliable random access for latency-sensitive devices.

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Narrow Band Internet of Things

Cited by 382 publications

References 61 publications

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

IoT Connectivity Technologies and Applications: A Survey

RADB: Random Access with Differentiated Barring for Latency‐Constrained Applications in NB‐IoT Network

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