A secure routing protocol for improving the energy efficiency in wireless sensor network applications for industrial manufacturing

Abstract

Wireless sensor networks (WSNs) are critical in manufacturing contexts because they provide continuous tracking, digitization, and data collection. However, they frequently come under threat to security concerns and have limited energy resources due to the growing number of devices and data in Industrial manufacturing, thereby affecting the quality of data transfer. Consequently, previous works have concentrated on predicting novel methods and processes to offer security in WSNs but the threat persists. This study has significance because it solves two major issues in WSNs: security and energy efficiency. This study aims to improve network lifetime, save operational costs, and increase the security and reliability of industrial monitoring systems by proposing a secure and energy-efficient routing protocol. The study suggested a Machine Learning-based Secure Routing Protocol (MLSRP) for WSN to obtain better energy efficiency and overall performance to deliver an efficient tightened security for WSN in comparison to the existing approaches along with reduced data loss. According to a Multi-Criteria-based Decision Making (MCDM) paradigm, the MLSRP performs clustering, cluster head election, and data routing while analyzing certain network elements that affect the node, route, and data quality. The proposed framework was implemented and simulated using the NS2 simulator tool, and the outcomes are compared with the existing system for performance analysis. The proposed approach for secured and accurate data transfer achieves 98.78%. The study could have practical consequences for industries desiring efficient, secure, and long-lasting IoT solutions.