An optimized whale based replication node prediction in wireless sensor network.

In recent years, Wireless Sensor Network (WSN) has been used for several applications and various roles like information gathering, monitoring data, transmitting data, etc. However, the key drawback behind this WSN is energy consumption. In WSN, some nodes have been consumed more energy because of malicious events like replication nodes. So, the replication node must be predicted in the initial stage to avoid packet drop and minimize energy consumption. So, this current research proposed a novel Whale-based Node Identity Verification (WbNIV) for detecting replication nodes in the Mobile Wireless Sensor Network. Moreover, the fitness function of the whale is used to identify each node's energy level and for exact replication node detection. Initially, the required numbers of nodes are designed then the details of a node are stored in WbNIV memory. During the monitoring and prediction process, the replication node in the WSN is identified by analyzing the details and behavior of the node. Finally, the proposed WbNIV has gained 98.89% replication node prediction accuracy, recorded the maximum packet drop as 12.8%, and reduced communication delay to 3 s, which is relatively better than the existing models. Thus, it has reduced power consumption up to 30% compared to the existing models. It has also improved the detection accuracy up to 5% than the compared models and has reduced the packet drop up to 10% compared to conventional approaches. [ABSTRACT FROM AUTHOR]