Abstract

Wireless Sensor Networks (WSNs) have been widely deployed to monitor valuable objects. In these applications, the sensor node senses the existence of objects and transmitting data packets to the sink node (SN) in a multi hop fashion. The SN is a powerful node with high performance and is used to collect all the information sensed by the sensor nodes. Due to the open nature of the wireless medium, it is easy for an adversary to trace back along the routing path of the packets and get the location of the source node. Once adversaries have got the source node location, they can capture the monitored targets. Thus, it is important to protect the source node location privacy in WSNs. Many methods have been proposed to deal with this source location privacy protection problem, and most of them provide routing path diversity by using phantom node (PN) which is a fake source node used to entice the adversaries away from the actual source node. But in the existing schemes, the PN is determined by the source node via flooding, which not only consumes a lot of communication overhead, but also shortens the safety period of the source node. In view of the above problems, we propose two new grid-based source location privacy protection schemes in WSNs called grid-based single phantom node source location privacy protection scheme (SPS) and grid-based dual phantom node source location privacy protection scheme (DPS) in this paper. Different from the idea of determining the phantom node by the source node in the existing schemes, we propose to use powerful sink node to help the source node to determine the phantom node candidate set (PNCS), from which the source node randomly selects a phantom node acting as a fake source node. We evaluate our schemes through theoretical analysis and experiments. Experimental results show that compared with other schemes, our proposed schemes are more efficient and achieves higher security, as well as keeping lower total energy consumption. Our proposed schemes can protect the location privacy of the source node even in resource-constrained wireless network environments.

Highlights

  • As an important part of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) are widely used in civilian and military applications

  • We focus on the source node location privacy protection in WSNs, and our aim is to deal with the aforementioned problems, advance the existing researches and improve the security performance of source node

  • The sending phantom node candidate set (SPNCS) is composed of nodes selected from M grids close to the source node (SoN), and the receiving phantom node candidate set (RPNCS) is composed of nodes selected from M grids far from the SoN; (3) The sink node (SN) sends the two phantom node candidate set (PNCS) and the grid number of each node in them to the SoN; (4) Once receiving the PNCSs, the SoN randomly selects a node from the SPNCS and a node from the RPNCS as the sending phantom node (SPN) and the receiving phantom node (RPN), respectively

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Summary

Introduction

As an important part of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) are widely used in civilian and military applications. Celal Ozturk et al first considered the source node location privacy problem of WSN in Reference [6], using Panda-Hunter game model, and based on this model they proposed to use a fake source called phantom node (PN) to entice the adversaries away from the actual source node [7]. In their Panda-Hunter model, shown, the monitored target is Panda, which is high-value and needs protection.

Related Works and Issues
Our Motivations and Contributions
Organization of the Paper
Network Model
Attack Model
Security Model
SPS: Grid-Based Single Phantom Node Source Location Privacy Protection Scheme
The Initialization Phase
Step 1
DPS: Grid-Based Dual Phantom Node Source Location Privacy Protection Scheme
The Routing Phase
Performance Analysis and Simulation
Security Performance Analysis
Security Performance Analysis of our Proposed Schemes
Conclusion
Analysis of Communication Overhead
Communication Overhead of the Phantom Node Determination Phase
Communication Overhead of the Routing Phase
Comparison of the Total Communication Overhead
Comparison of the Performance Simulation
Comparison of Safety Period
Comparison of Communication
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Findings
Conclusions
Full Text
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