An active model for ranging by deep convolutional neural network and elephant herding optimization algorithm (DCNN-EHOA) in WSNs. Issue 2 (8th February 2021) - Rajasekhar Reddy, Adireddy; Narayana Rao, Appini
Abstract : Purpose: In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user's data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods. Design/methodology/approach: This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem. Findings: In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services. Originality/value: The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.