Node Activities Learning(NAL)Approach to Build Secure and Privacy-Preserving Routing in Wireless Sensor Networks

Abstract

Wireless networks are becoming the most popular in today communication systems, where users prefer to have wireless connectivity regardless of its geographic location. But the open environment of wireless communication increasing threat on the wireless networks under diverse network circumstances. The random and dynamic activity increases the  vulnerability due to the complete dependency on the intermediate nodes which frequently join and leave the network. It is extremely significant to have a secure routing in such a dynamic network to preserve the data privacy. In this paper, we propose a secure and privacy routing based on Node Activities Learning (NAL) approach. This approach knows the runtime activities of the node to predict the probability of activity transformation for the intentional and unintentional activities which interrupt the data communication and affects the privacy. The mean of privacy is decided based on the node individual trust factor. It also suggests a method for the node which loses their trust due to the unintentional activities. A simulation-based evaluation study shows positive improvisation in secure routing in different malicious node environment.