Multihop Communication Latency Prediction in Wireless Sensor Networks using Dimensionality Reduction and Recurrent Neural Network Architecture

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 2

Year of Publication : 2026

Authors : K. Vinayakan, A. Srinivasa Reddy, M. Vasuki

10.14445/23488549/IJECE-V13I2P118

How to Cite?

K. Vinayakan, A. Srinivasa Reddy, M. Vasuki, "Multihop Communication Latency Prediction in Wireless Sensor Networks using Dimensionality Reduction and Recurrent Neural Network Architecture," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 2, pp. 239-253, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P118

Abstract:

The prediction of Multihop Latency (MHL) in Wireless Sensor Networks (WSNs) faces diverse challenges and is substantially addressed by the unintermittent growth of technologies such as Machine Learning (ML), edge computing, security mechanisms, and hybrid modelling models. Effectively handling the difficulty is crucial for attaining the full potential of prediction methods in diverse settings of the Internet of Things (IoT). Factors, namely transmission delays, node congestion, hop count, and energy constraints, are considered in MHL prediction, and also, ML is widely utilized for forecasting and alleviating latency in dynamic network environments. Recently, Deep Learning (DL) has gained popularity in network routing and is also applied to model the Multihop (MH) communication latency prediction in WSNs. This study presents a Multihop Communication Latency Prediction Using Dimensionality Reduction and Recurrent Neural Network (MCLP-DRRNN) technique in WSNs. The aim is to develop an efficient model for accurately predicting the latency of MH communication in WSNs. Initially, the min-max scaling-based data pre-processing is employed. Furthermore, the walrus optimization algorithm (WOA) technique is used for the Feature Selection (FS) process. Moreover, the Minimal Gated Unit (MGU) technique is employed for classification. Finally, the Group Theory Optimization Algorithm (GTOA) technique is implemented for tuning. The comparison analysis of the MCLP-DRRNN model revealed a superior accuracy value of 99.33% compared to existing techniques under the WSN MH dataset.

Keywords:

Multihop communication, Latency, Wireless Sensor Networks, Minimal Gated Unit, Group Theory Optimisation Algorithm.

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