Using Data Mules to Preserve Source Location Privacy in Wireless Sensor Networks

Wireless sensor networks (WSNs) have many promising applications for monitoring critical regions, such as in military surveillance and target tracking. In such applications, privacy of the location of the source sensor is of utmost importance as its compromise may reveal the location of the object being monitored. Traditional security mechanisms, like encryption, have proven to be ineffective as location of the source can also be revealed by analysis of the direction of traffic flow in the network. In this paper, we investigate the source-location privacy issue. We first propose a semi-global eavesdropping attack model which we show as being more realistic than the local or global eavesdropping attack model discussed in literature. In this model, we use a linear-regression based traffic analysis technique and show that it is effective in inferring the location of the data source under an existing source-location preserving technique. To measure source location privacy against this semi-global eavesdropping, we define an α-angle anonymity model. Additionally, we adapt the conventional function of data mules to design a new protocol for securing source location privacy, called the Mules-Saving-Source (MSS) protocol, which provides α-angle anonymity. We analyze the delay incurred by using data mules in our protocol and examine the association between privacy preservation and data delay in our protocol through simulation.