LAUSR

Among several new technologies, such as social and cognitive mobile computing, wireless sensor networks (WSNs) constitute the founding pillar of the industrial Internet of Things. These networks are expected to play an increasingly important role in our daily lives. Social and cognitive mobile computing requires the sharing of data recorded by sensor nodes. However, the data can be vulnerable to attacks. It is of utmost importance to protect the users privacy while ensuring the security of the WSNs. This investigation is focused on the source-location privacy (SLP) of WSNs. This article proposes a dynamic multipath privacy-preserving routing (DMPPR) scheme based on multiple sinks for protecting the privacy. Different from single sink schemes, the technique of using multiple sink nodes to protect SLP is discussed in this article. Furthermore, 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 proposed 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 scheme also reduces energy consumption by allowing more nodes in the nonhotspot areas to participate in the packet transmission process.