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<p>Data Congestion Prediction in Sensors Based IoT Network</p> <p class="Author"> </p>

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Title Statement <p>Data Congestion Prediction in Sensors Based IoT Network</p> <p class="Author"> </p>
 
Added Entry - Uncontrolled Name Maheshwari, Aastha ; Delhi Technological University Delhi, 110 042, India
Yadav, Rajesh Kumar; Delhi Technological University Delhi, 110 042, India
Nath, Prem ; H N B Garhwal University, Uttrakhand, 246 174 India
 
Uncontrolled Index Term Deep neural network (DNN); Restricted Boltzmann machine (RBM); Internet of Things (IOT); Congestion; Wireless sensor networks (WSNs)
 
Summary, etc. <p>Internet of Things (IoT) becoming the major part of human life and make the life simpler. IoT uses sensor nodes to monitors certain phenomena and transmitting the collected information to the IoT gateway. The size of the network is increase rapidly and causing the congestion in the network and results as network delay, loss of data packets, a decrease in throughput, and poor energy efficiency. It is important it predict the congestion and mitigate the data accordingly. To resolve the problem of congestion, our focus is on predicting the congested node effectively. We propose an optimized deep neural network - Restricted Boltzmann machine (DNN-RBM) based data congestion prediction approach which is used for analyzing and predicting the congested node in the sensors based IoT environment. To enhance the performance of DNN, the weight parameters of DNN are optimized using the Restricted Boltzmann Machine (RBM)-algorithm. The dataset is used to train the model and enable the prediction to find the congested nodes in the network with more accuracy to enhance the performance of the network. The performance factors congestion window, throughput, propagation delay, RTT, number of packets sent, and packet loss are given as input by using DNN-RBM. Predicted results show that the proposed DNN-RBM model predicts congestion with more than 95% accuracy as compared with other models like ANN, DNN-GA.</p> <p> </p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-01-13 13:07:16
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/52658
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 80, ##issue.no## 12 (2021): Journal of Scientific and Industrial Research
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/52658/465570808
http://op.niscair.res.in/index.php/JSIR/article/download/52658/465570814
http://op.niscair.res.in/index.php/JSIR/article/download/52658/465588581