DESIGN OF A NOVEL REPTILE ROUTE PROBING-BASED OPTIMIZER FOR ANALYZING CLUSTER SELECTION AND ROUTING MECHANISM SUPERIORITY IN WSN FOR HEALTHCARE APPLICATIONS

Abstract

Applications of the Internet of Things (IoT) employing a Wireless sensor network (WSN) experience constraints like limited transmission range, restricted battery, and regular detachments owing to multi-hop data transfer. Because different solutions handle routing and clustering actions differently, providing limited energy consumption and better network lifetime was impossible. This work centers on efficiently collecting information from IoT nodes placed in a distributed environment and linked using WSN. We tackle these interlinked problems, routing and clustering, for IoT-based WSN in an extensive environment and introduce a better clustering and routing to tackle these problems jointly. Better clustering and routing techniques enable area-specific clustering with Superiority towards Cluster Selection and Routing Mechanism (SCSRM) obtained from the transmission range. During clustering, specific cluster heads are chosen to enable fail-over-proof routing, which is optimized using the Reptile Route Probing Algorithm (R2PA) with Firefly features. Finding the shortest path comprising the fewest hops with alternate routing paths is the key to creating an effective routing path in healthcare applications. Modern benchmark protocols are used to compare the outcomes. Simulation and theoretical outcomes of the proposed system enable better network capacity, improved node density management, increased network lifetime and consistent network topology.