Abstract
Wireless sensor networks (WSN) are deployed for many applications such as tracking and monitoring of endangered species, military applications, etc. which require anonymity of the origin, known as Source Location Privacy (SLP). The aim in SLP is to prevent unauthorized observers from tracing the source of a real event by analyzing the traffic in the network. Previous approaches to SLP such as Fortified Anonymous Communication Protocol (FACP) employ transmission of real or fake packets in every time slot, which is inefficient. To overcome this shortcoming, we developed three different techniques presented in this paper. Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD) were developed to overcome the anonymity problem against a global adversary (which has the capability of analyzing and monitoring the entire network). Most of the current techniques try to prevent the adversary from perceiving the location and time of the real event whereas our proposed techniques confuse the adversary about the existence of the real event by introducing low rate fake messages, which subsequently lead to location and time privacy. Simulation results demonstrate that the proposed techniques provide reasonable delivery ratio, delay, and overhead of a real event's packets while keeping a high level of anonymity. Three different analysis models are conducted to verify the performance of our techniques. A visualization of the simulation data is performed to confirm anonymity. Further, neural network models are developed to ensure that the introduced techniques preserve SLP. Finally, a steganography model based on probability is implemented to prove the anonymity of the techniques.
Highlights
Wireless Sensor Networks (WSN) consist of homogeneous, small, and low-cost sensor nodes that have limitations in resources and power [1]
We assume that an adversary can use this data set to analyze the Source Location Privacy (SLP), e.g., a neural network can be trained on this data set and the network used to expose the existence of the real event, which could lead to the identity of the source node
Since Dummy Uniform Distribution (DUD) and Dummy Adaptive Distribution (DAD) schemes could fail in delivering real event packets within a certain delay or network lifetime constraint, Controlled Dummy Adaptive Distribution (CAD) is introduced to maximize the delivery ratio and minimize the delay to guarantee the arrival of all packets in the real event to the sink within the required constraints
Summary
Wireless Sensor Networks (WSN) consist of homogeneous, small, and low-cost sensor nodes that have limitations in resources and power [1]. The active tag has a battery and can send signals to sensor nodes, which can be used to simulate the movement of the asset in different locations in the network, whereas a passive tag does not have a battery and cannot send signals. There are many techniques used to counter a global adversary such as separate path routing, network location anonymization, network coding, and dummy data sources [11]. Dummy Data Sources creates fake sources, which create dummy traffic to hide and obfuscate the real traffic inside This approach will be used in the proposed techniques, because it provides higher anonymity than other approaches. After nodes transmit all the real packets they have, the transmission rate is reduced for those nodes to make the average transmission rate equal to other nodes in the network This makes the adversary unable to distinguish the difference between real and fake packets.
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