Multimetric‐Based Optimization of Cluster Selection and Data Redundancy Elimination Through Copula Variational LSTM in Wireless Sensor Networks

ABSTRACT In wireless sensor networks (WSN), the sensor nodes are deployed randomly where the sensor nodes are not positioned away from each other. The intersection of sensing ranges creates an overlapping area. Every sharing node perceives the same event and generates redundant and associated data if it takes place inside the overlapping area. In this paper, a multimetric‐based optimization of cluster selection and data redundancy elimination through copula variational LSTM in wireless sensor networks (CS‐DRE‐CVLSTM‐WSN) is proposed. Initially, cluster formation using semantic invariant multiview clustering (SIMVC) for data aggregation in WSN by leveraging data characteristics and connectivity patterns is discussed. Then, the clusters formed are given to the Wader Hunt Optimization Algorithm (WHOA) for cluster head selection. For this, a novel method to enhance data redundancy elimination efficiency, considering factors, like trust degree computation, energy efficiency, link quality, path loss, distance from the target node to the base station, and aggregation delay is proposed. The selected clusters are fed into copula variational LSTM (CV‐LSTM) to optimize cluster selection and predict temporal trends in key network metrics. The offline meta RL (OMRL) framework is proposed to suppress redundant data streams and decide which data to transmit or aggregate. The reward system assigns positive values for efficient aggregation and reduced communication costs, while penalizing data loss or unnecessary transmissions. The output is a set of policies for effective data aggregation and redundancy elimination. The performance of the proposed CS‐DRE‐CVLSTM‐WSN method is evaluated with existing methods like the reliable cluster dependent data aggregation scheme for IoT with hybrid deep learning methods (RC‐DAS‐IoT‐HDL), new machine learning‐driven data aggregation for predicting data redundancy in IoT connected WSN (ML‐PDR‐IoT‐WSN), and two vector data prediction techniques for energy‐efficient data aggregation in WSN (TVDP‐EEDA‐WSN), respectively.