Abstract
An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.
Highlights
THE infrastructure of Wireless Sensor Networks (WSN) is built in an ad hoc way with arranged nodes informing a Base Station (BS) about events
We proposed an Element-Wise Attention Gate (EG)-CRNN structure which combines the advantages of Deep Learning (DL)-recurrent neural networks (RNNs) structure and the Element wise-Attention Gate (EleAttG) structure in order to improve the efficiency of the I-Internet of Things (IoT) network
This paper proposed an Intelligent-IoT architecture to be utilized in health care applications
Summary
THE infrastructure of Wireless Sensor Networks (WSN) is built in an ad hoc way with arranged nodes informing a Base Station (BS) about events. With the advent disruptive IoT involving huge amounts of different types of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role in bringing intelligence to these networks [4], [5]. An increase in the number of sensors that communicate with the IoTrouters in the network leads to raising the traffic load in the sink node buffer. The proficiency of deep learning based on ANNs has been demonstrated in security, routing, traffic management and load balance in an IoT network [10], [12]–[14]. The buffers are limited and data buffering leads to buffer overflow and delay, both of which are important QoS considerations [18]
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