DERNNet: Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

Abstract

A Wireless Sensor Network (WSN) is constructed with numerous sensors over geographical regions. The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy. As sensor nodes are resource constrained in nature, novel techniques are essential to improve lifetime of nodes in WSN. Nodes energy is considered as an important resource for sensor node which are battery powered based. In WSN, energy is consumed mainly while data is being transferred among nodes in the network. Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer. Moreover, this network is threatened by attacks like vampire attack where the network is loaded by fake traffic. Here, Dual Encoding Recurrent Neural network (DERNNet) is proposed for classifying the vampire nodes s node in the network. Moreover, the Grey Wolf Optimization (GWO) algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points; thereby maximizing battery/ lifetime of the network nodes. The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing (KIDSASR), Risk-aware Reputation-based Trust (RaRTrust) model and Activation Function-based Trusted Neighbor Selection (AF-TNS) in terms of various parameters. These existing methods may lead to wastage of energy due to vampire attack, which further reduce the lifetime and increase average energy consumed in the network. Hence, the proposed DERNNet method achieves 31.4% of routing overhead, 23% of end-to-end delay, 78.6% of energy efficiency, 94.8% of throughput, 28.2% of average latency, 92.4% of packet delivery ratio, 85.2% of network lifetime, and 94.3% of classification accuracy.