期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：7
页码：1843-1857
出版社：Journal of Theoretical and Applied
摘要：In Wireless Sensor Network (WSN), routing strategies are required for distributing the data from sensor nodes to base station. During the data transmission, the node energy is the key parameter for improving the network lifetime. The conventional routing techniques are developed to perform routing in WSN but it failed to lessen the energy consumption and improve the network lifetime with minimum overhead. In order to overcome the above-said issue, Energy-Efficient Deep Neural Node Classifier based Logit Regressed Routing (EEDNC-LRR) technique is introduced. The main aim of EEDNC-LRR technique is to perform energy efficient routing and increase the reliability of data transmission with maximum network lifetime and minimal overhead. In EEDNC-LRR technique, the sensor nodes are taken to transmit the environmental data. Initially, all sensor nodes have a certain energy level. A sensor node consumes some amount of energy during the sensing of data in WSN. The Deep Neural Node Classifier model is used in EEDNC-LRR technique to classify the node as higher energy nodes and lesser energy nodes based on threshold energy level. From that, the deep neural node classifier model produces the efficient classification results of sensor nodes. This helps to choose the higher energy nodes to reduce the energy consumption and extend the network lifetime while routing the data packets. Subsequently, the higher energy nodes are transmitted to the output layer for efficient routing in WSN. With higher energy nodes, Logit Regression Analysis is carried out to identify the nearest neighbor node through a time of arrival (ToA) to identify the distance between the source nodes and sink node. After selecting the nearest neighbor, the route with a minimum distance between the nodes is discovered for routing in WSN. This in turns, the reliable data packets transmission is achieved with minimum overhead. The simulation is conducted with various parameters namely energy consumption, network lifetime, reliability and routing overhead with respect to a number of sensor nodes and data packets. The simulation results and discussion shows that EEDNC-LRR technique improves the network lifetime, relaibility and also minimize the routing overhead as well as energy consumption.
关键词：WSN; Routing; Deep Neural Node Classifier; Threshold Energy Level; Logit Regression Analysis; Nearest Neighbor Node; Time Of Arrival.
