Protecting Data Source Location Privacy in Wireless Sensor Networks against a Global Eavesdropper

Abstract

Data source location privacy (DSLP) is of great importance for some asset monitoring applications in wireless sensor networks (WSNs). Besides the source simulation (SS) method to protect the DSLP against a global eavesdropper in WSNs, other existing methods are based on the panda-hunter game model (PHGM) without considering the communication between data sources and reporter sources, which can cause them to be ineffective. Moreover, there are two limitations in SS. First, the reporter source cannot generate effective event reports. Second, it is unsuitable to track multiobjects accurately. To address the former issue, an improved source simulation (ISS) method is proposed which adjusts the event report strategy. To solve the latter issue, an updated-panda-hunter game model (UPHGM) is proposed and a formal model of the DSLP issues is also presented. Then, based on the UPHGM, an energy-efficient grid-based pull (GBP) scheme is designed to protect the DSLP by combining a light-weight security object collection scheme with an effective grid partition method. Analysis and simulation results show that GBP outperforms SS and ISS in terms of energy cost on the whole.