A Dynamic Multipath Scheme for Protecting Source-Location Privacy Using Multiple Sinks in WSNs Intended for IIoT

The Industrial Internet of Things (IIoT), which is based on Wireless Sensor Networks (WSNs) and several new technologies such as social and cognitive mobile computing, is expected to play a very important role in daily life. Social and cognitive mobile computing is also based on WSNs because it requires the sharing of the data recorded by the sensor nodes. However, these data are vulnerable to attacks, and it is of utmost importance to protect the privacy and ensure the security of the WSNs. In this study, we focused on the source location privacy (SLP) of WSNs, and propose a dynamic multipath privacy-preserving routing scheme based on multiple sinks (DMPPR) for protecting the privacy. A packet-slicing transmission scheme that generates a large number of dynamic routings based on multiple sink nodes is adopted for transmitting the packets. Local adversaries are considered, and to cope with these adversaries, a transmission loop constructed using real and fake packets is generated to confuse the adversaries during the source detection process. The aim is to break the sociality between the sensor nodes. Simulations performed in MATLAB show that the proposed method outperforms similar existing schemes in terms of the secure time, adversary's capture probability, and node utilization ratio. Moreover, the DMPPR also reduces energy consumption by letting more nodes in the non-hotspot areas participate in the packet transmission process.