Data prediction approaches for efficient data transmission using optimized Leibler distance matrix-based data aggregation in wireless sensor network

Abstract

In wireless sensor networks, sensor nodes are distributed in different geographically dispersed areas, sink nodes are used for sensing data and sink nodes are used for data collected from different sensor nodes. Therefore, data collection is a key issue in wireless sensor networks. In addition to computational overhead, many challenging security issues have been introduced to in-network data processing. Data collection and communication methods are also proposed based on the Optimized Leibler Distance Matrix-based data aggregation (OLDMDA) algorithm. These sensor nodes belong to the category forming the OLDMDA plane data aggregation technology, which includes homogeneous nodes and data collection protocols. Leibler Distance Matrix for Node Location to find the location and multi sink node selection is based on node location distance using a Leibler matrix. In this distance matrix to collect the information of node energy, transmission rate to identify the higher performance node on the network. The Optimized Johnson's shortest path algorithm to solve the path problem source to destination. In this path selection method proposed to find the all the possible multi path randomly generate the graph. The performance of the new technology has been implemented using the network emulator (NS2). Simulation results of the proposed method OLDMDA higher performance with respect to the average packet transfer rate and metrics, as compared with the delay and network lifetime.