Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Abstract

Wireless Sensors Networks (WSN) is the self-configured Wireless Ad hoc Networks (WANET) for Internet of Things (IoT) which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes’ (MNs’) presence. Thus, to shun security threats, it is vital to find and isolate those MNs. Consequently, this work proffered a solution for detecting MNs in WSN utilizing every SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission (DT) by means of choosing the Cluster Head (CH) centred on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Extended K-Means (</i> EKM) algorithm clusters the Trusted Nodes (TN), and the t-Distribution based Satin Bowerbird Optimization (t-DSBO) algorithm selects an individual CH for each cluster centred on those nodes’ residual energy. The data of that cluster is transmitted to the Base Station (BS) through the CH. The t-DSBO selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques.