Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks

Stephan, Thompson; Al‐Turjman, Fadi; K., Suresh Joseph; Balamurugan, B.

doi:10.1007/s13042-020-01154-y

Cited by 35 publications

(19 citation statements)

References 48 publications

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“…Ant colony algorithm is employed to search inter-cluster paths with the purpose of extending node usage time. In ESAUC [24], CRSN nodes with higher number of available channels are more likely to become candidate CHs. Among them, those with more residual energy and common channels within competition radius become final CHs.…”

Section: B Current Clustering Routing Protocols For Crsnmentioning

confidence: 99%

Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Wang

2020

IEEE Access

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Cognitive radio sensor network (CRSN) is an intelligent and reasonable combination of cognitive radio and wireless sensor networks. Clustering is an effective method to manage the network topology of CRSN. In order to solve the optimal cluster head selection problem in clustering which is proved to be NP-hard, inspired by ions motion optimization (IMO) algorithm, a novel centralized clustering routing protocol, i.e., IMO-based clustering routing protocol (IMOCRP) which can adapt to the dynamic characteristics of CRSN is presented in this paper. Optimal number of clusters and identity of cluster heads are automatically determined by combining the excellent characteristics of IMO algorithm and the rich resource, comprehensive information at the sink together. Thanks to its centralized structure, IMOCRP proposed in this paper avoids excessive overhead in cluster head selection and cluster formation, which in turn helps conserve energy. Available channel list at each living node is taken into consideration to perform reasonable channel allocation for clusters, and the probability of collisions with primary users can be reduced, which promotes more successful information delivery. Simulation results have shown that IMOCRP can balance network lifetime and effective information collection capability, and it is superior to other competing protocols. INDEX TERMS Cognitive radio sensor network, clustering algorithms, ions motion optimization, computational complexity JIHONG WANG received the B.S. degree in communication engineering from Jilin University, Changchun, Jilin in 2011 and the PhD. degree in communication and information systems from School of Communication Engineering, Jilin University in 2016. She is currently an associate professor and master supervisor in Northeast Electric Power University. Her research interests include cognitive radio sensor networks, routing and resource allocation in wireless networks, energy harvesting and so on SHUO LI was born in Changchun, Jilin, China, in 1995. He obtained a bachelor's degree in communication engineering from Northeast Electric Power University, China, in 2018. In the same year, he studied for a master's degree at Northeast Electric Power University, China, and his research direction is clustering and routing in cognitive radio sensor network. YIYANG GE was born in Changchun, Jilin, China, in 1996. She received B.S. degrees in electronic information science and technology from Northeast Electric Power University, China, in 2019. Her research interests include cluster routing protocol design and resource allocation in cognitive radio wireless sensor networks with energy harvesting.

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Section: B Current Clustering Routing Protocols For Crsnmentioning

confidence: 99%

Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Wang

2020

IEEE Access

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“…It is clear from the data presented above that modulation recognition at low SNR will be a difficult undertaking to accomplish. It has been discovered that when the parameters provided in various configurations with first CNN (7) which has ReLu activation layer with 12 Mini-Batch Size and 256 Epochs are compared with the second CNN which also make use of ReLu activation layer but with 24 Mini-Batch Size and 128 Epochs under various channel circumstances that approximately an improvement in OTA classification accuracy of 4 percent has occurred.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…CR systems face the challenge of detecting a radio signal's modulation type in order to determine what type of communication scheme and transmitter is there. Convolutional neural networks (CNNs) have recently been used to the job of radio modulation identification in order to introduce the notion of deep learning(5)(6)(7)(8) .There has been a lot of recent pioneering work on the use of deep learning to extract signal characteristics and demonstrate the feasibility of utilizing convolutional neural networks (CNNs) for modulation classification. Various techniques like implementing ANN with multiple detectors' test statistics (TSs)(9) , Comparison of DL-based estimator with BEM-LMMSE estimator(10) , Doubly Selective Fading Channel Estimation Using Deep Learning(11) , and combining various CNNs to increase the Modulation Classification accuracy(12) has been proposed.…”

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confidence: 99%

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Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Arjun¹,

Surekha²

2021

IJST

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Background/Objectives: The ability to recognize the type of modulation is a critical function of Cognitive Radio. The objective of this study is to increase the modulation classification efficiency in Over-The-Air (OTA) signals by utilizing channel characteristics that are strong. Methods: In this work, we demonstrate how to classify Over-The-Air modulation using a deep learning technique under various fading channels simulating real-time data. The network recognizes eight different digital modulation schemes and three different analogue modulation methods. Each modulation scheme will consist of 10,000 frames with 1024 samples per frame and a sampling rate of 200 kHz. Each sample will pass through fading channels prior to training, with 80% of samples are for training, 10% for validation, and 10% for testing. Six convolutional layers and one fully linked layer comprise our network. The final convolution layer is followed by a batch normalization layer, an activation layer utilizing rectified linear units (ReLUs), and a maximum pooling layer. As a result, the final convolution layer contains soft-max activation instead of the maximum pooling layer. Findings: Modulation categorization OTA is done with two separate ADALM-PLUTO SDRs working in various channel configurations. Network-I has a forecast accuracy of 91.4 percent using 12 Mini-Batch Size and 256 Epochs, whereas Network-II has a prediction accuracy of 95.3 percent using 24 Mini-Batch Size and 128 Epochs. There are a number of ways in which SDR technology can aid to make computer-generated data more realistic, such as adopting alternative channel models. Novelty: Using Software Defined Radio hardware; the same network was used to analyze various fading situations, such as Rayleigh, Rician or Lognormal distributions, and to optimize the network topology by adjusting hyper-parameters to increase accuracy.

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“…The performance measures considered in this research is depicted in Eq. ( 1) to (6) [24][25][26][27][28][29][30]:…”

Section: Performance Evaluationmentioning

confidence: 99%

Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

Kadry¹,

Al‐Turjman²,

Rajinikanth

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Pneumonia caused by the novel Coronavirus Disease (COVID-19) is emerged as a global threat and considerably affected a large population globally irrespective of their age, race, and gender. Due to its rapidity and the infection rate, the World Health Organization (WHO) declared this disease as a pandemic. The proposed research work aims to develop an automated COVID-19 lesion segmentation system using the Convolutional Neural Network (CNN) architecture called the U-Net. The traditional U-Net scheme is employed to examine the COVID-19 infection present in the lung CT images. This scheme is implemented on the benchmark COVID-19 images existing in the literature (300 images) and the segmentation performance of the U-Net is confirmed by computing the essential performance measures using a relative assessment among the extracted lesion and the Ground-Truth (GT). The overall result attained with the proposed study confirms that, the U-Net scheme helps to get the better values for the performance values, such as Jaccard (>86%), Dice (>92%) and segmentation accuracy (>95%).

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Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks

Cited by 35 publications

References 48 publications

Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Ions Motion Optimization-Based Clustering Routing Protocol for Cognitive Radio Sensor Network

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture

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