Transmitter-Oriented Dual-Mode SWIPT With Deep-Learning-Based Adaptive Mode Switching for IoT Sensor Networks

Park, Jong Jin; Moon, Jong Ho; Lee, Kang-Yoon; Kim, Dong In

doi:10.1109/jiot.2020.2999892

Cited by 21 publications

(19 citation statements)

References 27 publications

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“…We set ρ FS = (1 − 10 −3 ) to normalize the input signal power of each PAPR estimator for fair comparison. We see that the analytical results based on (25) well coincide with the simulation ones for all N . We also confirm that the estimated PAPR value at both PAPR estimators is linearly proportional to N when the HPA nonlinearity is small, as shown in Figs.…”

Section: Resultssupporting

confidence: 71%

“…The performance of the 4 ζ is determined by ζ TcB = χ for the level of decorrelation χ [38]. We select ζ = 0.9 as the lower bound on the channel correlation because of typically ζ ≥ 0.99 at 2.4GHz [25]. proposed algorithm is evaluated using 3.6 × 10 5 test samples, independent of the training samples.…”

Section: Resultsmentioning

confidence: 99%

“…But these ML methods require parallel computing devices (e.g., GPU) to train models and make inferences, which is a heavy burden on low-energy IoT devices. For this reason, the transmitter-oriented asymmetric processing for adaptive mode switching (MS) was proposed via the recurrent neural network (RNN) with long-short term memory (LSTM) to extract the features from temporal correlation of the channel [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture with Deep Learning Adaptive Control

Moon¹,

Jang²,

Kim³

2021

Preprint

Self Cite

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In this paper, we propose a unified SWIPT signal and its architecture design in order to take advantage of both single tone and multi-tone signaling by adjusting only the power allocation ratio of a unified signal. For this, we design a novel unified and integrated receiver architecture for the proposed unified SWIPT signaling, which consumes low power with an envelope detection. To relieve the computational complexity of the receiver, we propose an adaptive control algorithm by which the transmitter adjusts the communication mode through temporal convolutional network (TCN) based asymmetric processing. To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition of low-power IoT devices. Consequently it is foreseen to effectively deploy low-power IoT networks that concurrently supply both information and energy wirelessly to the devices by using the proposed unified SWIPT and adaptive control algorithm in place at the transmitter side.

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Section: Resultssupporting

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture with Deep Learning Adaptive Control

Moon¹,

Jang²,

Kim³

2021

Preprint

Self Cite

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“…IoT sensor network integration with emerging technologies provides efficient methods to handle sensor data's dynamic and complex nature. Furthermore, machine learning and deep learning techniques provide a promising solution towards the analysis of IoT sensor data [14][15][16]. Incorporating these data analysis techniques results in deep insights into sensor data, and provides good knowledge related to hidden data patterns and further decision-making.…”

Section: Introductionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

224

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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“…Millimeter wave (mm-Wave) communication is very promising in terms of high data rate and it is being studied as a key 5G technology to accommodate the expansion of the Internet of Things (IoT) [1][2][3][4][5][6][7]. Because of their smaller wavelength, massive antennas can be deployed in the limited space at the mm-Wave.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT

Chiu

Chien

et al. 2021

Electronics

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In this paper, three different deployment antenna arrays with circular, triangular and rectangular shapes were used to optimize the simultaneous wireless information and power transfer (SWIPT) system for the Internet of Things (IoT). Ray-tracing was employed to channel the model for a real environment. Self-adaptive dynamic differential evolution (SADDE) was used to optimize the harvesting power ratio with bit error rate constrained by the two different resolutions of feed length (high resolution and low resolution). Numerical results show that those three antenna arrays can achieve the goal for information quality in both resolutions. The harvesting power ratio for the circular array is the best and the harvesting power ratio for the rectangular array is the worst. The harvesting power ratio for the low-resolution case is 25% lower than the high-resolution case. However, the circular antenna array is the best deployment in those three different arrays for both high and low resolutions.

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Transmitter-Oriented Dual-Mode SWIPT With Deep-Learning-Based Adaptive Mode Switching for IoT Sensor Networks

Cited by 21 publications

References 27 publications

Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture with Deep Learning Adaptive Control

Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture with Deep Learning Adaptive Control

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT

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