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An Intelligent Data Fusion Technique for Improving the Data Transmission Rate in Wireless Sensor Networks

R. Lavanya

Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamil Nadu, India

E-mail Address: lavanyaprabhakaran07@gmail.com

Corresponding author.

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N. Shanmugapriya

Department of Computer Applications, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, Tamil Nadu, India

E-mail Address: spriyanatrajan@gmail.com

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https://doi.org/10.1142/S1469026823410043Cited by:6 (Source: Crossref)

This article is part of the issue:

Special Issue on Smart Fuzzy Optimization for Decision-Making in Uncertain Environments
Guest Editors: Er Meng Joo, Danilo Pelusi and Shiping Wen

Abstract

Wireless Sensor Networks (WSNs) are made up of multiple source-restricted wireless sensor nodes that gather, process, and transmit information. Existing research work proposed energy competence with trust as well as Quality of Service (QoS) multipath routing protocol for improving network lifetime and other QoS parameters, selection criteria for multipath. However, this protocol has some limitations, such as scalability, data redundancy, bandwidth utilization, and network traffic. The most important challenge lies in managing the voluminous data produced by the network’s sensors. As a result of this study, Intelligent Data Fusion Techniques (IDFTs) were presented, which can greatly minimize redundant data, decrease the quantity of transmitting data, broaden the network life cycle, enhance bandwidth utilization, and therefore, resolve the energy and bandwidth usage bottleneck. This paper proposes Improved Whale Optimization Algorithms (IWOAs) for intelligent data fusion where the amount of data collected from sensor sources is reduced and the information offered is enhanced by duplicate data, which also increases data dependability. IWOAs are used to combine the actual information from the cluster’s sensor nodes at the sink node, resulting in increased information and the ability to make local judgments about the particular events. The sink node transmits local decisions to base station on a regular basis that combines the local decisions and provides the ultimate judgment, easing the pressure on the base station to evaluate all of the data. As per the results obtained, the proposed intelligent data fusion method significantly increases the network’s robustness and accuracy.

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