Green communication in IoT networks using a hybrid optimization algorithm

Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy; Kaluri, Rajesh; Srivastava, Gautam; Parizi, Reza M.; Khan, Mohammad S.

doi:10.1016/j.comcom.2020.05.020

Cited by 112 publications

(53 citation statements)

References 29 publications

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“…Optimizer algorithms are used to fine-tune the NN's properties, which include updating weight and learning rates, to minimize losses and converge in a minimum amount of time that leads to better performance [12,27]. There are different types of optimizers in DNN, namely Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent, Nesterov Accelerated Gradient, Adagrad, AdaDelta and Adam.…”

Section: Optimization Functionsmentioning

confidence: 99%

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu

Alazab

Kaluri

et al. 2021

Complex Intell. Syst.

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142

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Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.

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Section: Optimization Functionsmentioning

confidence: 99%

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu

Alazab

Kaluri

et al. 2021

Complex Intell. Syst.

Self Cite

142

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“…Also solar energy harvesting has been a major point of discussion among various studies along with other forms of energywind, wave and ocean currents. The world at this point is progressing towards green technologies where energy optimization plays a vital role [17,18]. Based on such thorough analysis of the various applications of IoT in marine and other sectors, it has been observed that not much research have been conducted focusing on predicting the sustainability of battery life.…”

Section: Introductionmentioning

confidence: 99%

Roughsets-based Approach for Predicting Battery Life in IoT

Kaluri¹,

Rajput²,

Xin³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water. Firstly, the missing values in the data are replaced with the attribute mean. Later, one-hot encoding technique is applied for achieving data homogeneity followed by the standard scalar technique to normalize the data. Then, rough set theory is used for feature extraction, and the resultant data is fed into a Deep Neural Network (DNN) model for the optimized prediction results. The proposed model is then compared with the state of the art machine learning models and the results justify its superiority on the basis of performance metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Test Variance Score.

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“…Although attention has been given to the deployment structure, energy optimization in sensor nodes poses a significant challenge in WSNs. In recent years, several clustering approaches have been aimed at addressing the efficient energy consumption issues in WSNs [ 4 , 5 , 9 , 10 , 11 , 12 ]. In most of the existing works, including LEACH [ 9 ], HEED [ 10 ], PEGASIS [ 13 ], enhanced LEACH and GBCR [ 14 ], and SPIN [ 15 ], the CH is selected based on the highest residual energy and the CH to CH data transmission is based on the coverage range and energy level.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks

Krishnasamy¹,

Dhanaraj

Gopal

et al. 2020

Sensors

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Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

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Green communication in IoT networks using a hybrid optimization algorithm

Cited by 112 publications

References 29 publications

Hand gesture classification using a novel CNN-crow search algorithm

Hand gesture classification using a novel CNN-crow search algorithm

Roughsets-based Approach for Predicting Battery Life in IoT

A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks

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