KCLP: A k-Means Cluster-Based Location Privacy Protection Scheme in WSNs for IoT

Abstract

While enjoying the convenience brought by the Internet of Things (IoT), people also encounter many problems with wireless sensor networks (WSNs), the foundation of IoT. Security problems are especially of concern. In this article, we focus on location privacy, which is a major security issue in WSNs, and propose a k-means cluster-based location privacy (KCLP) protection scheme for IoT. To protect the source location, fake source nodes are used to simulate the function of the real sources. Then, to protect the sink location privacy, fake sink nodes and a specific transmission pattern are utilized. In order to improve safety time, a k-means cluster is applied to create clusters and fake packets that must pass through the area. Compared to contrasting algorithms, the KCLP scheme can increase the safety time and reduce delay at minor expense in energy consumption.