A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

Peng, Yuhuai; Wang, Jiaying; Tan, Aiping; Wu, Jingjing

doi:10.3390/s19153443

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…However, spectrum resources are limited and do not increase with the increase of connected devices. The limited availability of the communication spectrum therefore is one of the challenges which hinder the massive deployment of smart city based IoT systems [5], [6]. With the development of the IoT communication paradigm, the scarcity of spectrum resources will become more serious [7].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing With Non-Gaussian Noise Over Multi-Path Fading Channels Towards Smart Cities With IoT

Chen²,

Long

et al. 2021

IEEE Access

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As the limited communication spectrum can not meet the demand of the exponential growth of intelligent connected devices in the internet of things(IoT) and typical smart city applications, in this paper, we propose a tractable spectrum sensing method based on Rao detection over non-Gaussian noise, such as generalized Gaussian noise(GGN), Gaussian mixture noise(GMN) and symmetric alpha-stable distribution (SαS) noise, multi-path fading channels environment to alleviate the issue of spectrum scarcity. In this method, there are unknown parameters in the multi-path fading channels. When the probability density function (P.D.F.) of non-Gaussian noise has a closed-form expression, the spectrum sensing method based on Rao detection is used. Otherwise the P.D.F. for SαS noise is estimated firstly by using non-parametric kernel estimation method, which addresses the issue that SαS noise has no closed-form P.D.F. expression, and then the performance of spectrum sensing is derived based on the theory of Rao detection in multi-path fading channels over typical smart city applications. Simulation results show that the accuracy of estimated P.D.F. for SαS noise and the performance of spectrum sensing under different α values over indoor, outdoor, and vehicle fading channels environment.INDEX TERMS Spectrum sensing, non-Gaussian noise, multi-path fading channels, Rao detection, smart cities.

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Section: Introductionmentioning

confidence: 99%

Spectrum Sensing With Non-Gaussian Noise Over Multi-Path Fading Channels Towards Smart Cities With IoT

Chen²,

Long

et al. 2021

IEEE Access

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“…We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.2 of 25 immense opportunities for novel applications of sensor data [3]. The sources of such sensor data are heterogeneous and their integration is also a complex task [4].…”

mentioning

confidence: 97%

“…2 of 25 immense opportunities for novel applications of sensor data [3]. The sources of such sensor data are heterogeneous and their integration is also a complex task [4].…”

mentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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HPDST: Holding pathlength domain scheduled traffic strategy for multi-domain optical networks

Batham¹,

Yadav

2020

Optik

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A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

Cited by 5 publications

References 39 publications

Spectrum Sensing With Non-Gaussian Noise Over Multi-Path Fading Channels Towards Smart Cities With IoT

Spectrum Sensing With Non-Gaussian Noise Over Multi-Path Fading Channels Towards Smart Cities With IoT

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

HPDST: Holding pathlength domain scheduled traffic strategy for multi-domain optical networks

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