An Effective Dynamic Cross Propagation Clustering in Wireless Sensor Network using Improved RFCM

Wireless sensor networks (WSNs) often operate in environments that are dynamic and subject to unpredictable changes. Dynamic cross-propagation clustering makes sure that systems continue to be reliable and effective even in the face of changing conditions by allowing clusters to adapt to changes in the network. It is difficult to create a dynamic cluster and choose a routing strategy because of the high energy consumption and network complexity. An Improved Relational Fuzzy C-Means (IRFCM) based dynamic routing selection and clustering method is suggested for the WSN in this study. The effective routing process in a WSN is the goal of suggested IRFCM technique. The primary goals of suggested IRFCM are to locate the cluster head in dynamic cross-propagation system and to minimize energy usage and routing in wireless sensor networks. The recommended approach performs remarkably well, with an energy consumption rate of 10.30, a network time of 5900, and a First Node Death (FND) of 1700. These results show that the proposed approach is effective in improving Dynamic clustering, energy efficiency, network time, and FND. It also performs better with a node count of 300 compared to state of the art techniques, such as CSC-TC and MCR-UWSN.