Traffic De Correlation Techniques for Countering a Global Eavesdropper in WSNs

0
643
Traffic De correlation Techniques for Countering a Global Eavesdropper in WSNs

Traffic De Correlation Techniques for Countering a Global Eavesdropper in
WSNs

Abstract

Traffic De Correlation. We address the problem of preventing inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of packets transmitted, interpacket times, and traffic directionality, to infer the location of the event, its occurrence time, and the sink location.

We develop a general method of traffic analysis for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most of the existing countermeasures either fail to provide adequate protection or incur high overhead communication and delay.
 
 
To mitigate the impact of eavesdropping, we propose resource-efficient traffic standardization schemes. Compared to the state-of – the-art, our methods reduce overhead communication by more than 50 percent and the end to end delay by more than 30 percent.To do so, we partition the WSN to minimally connected dominating sets that operate in a round-robin fashion. 
 
 
Advantages
  • The proposed Traffic De Correlation Techniques  system reduces the communication and delay overheads by limiting the injected bogus traffic.
  • The proposed system reduces the forwarding delay
  • We compare privacy and overhead of our techniques to prior art and show the savings achieved.
 
Disadvantages
  • First, eavesdroppers are passive devices that are hard to detect.
  • Second, the availability of low-cost commodity radio hardware makes it inexpensive to deploy a large number of eavesdroppers.
  • Third, even if encryption is applied to conceal the packet payload, some fields in the packet headers still need to be transmitted in the clear for correct protocol operation (e.g., PHY-layer headers used for frame detection, synchronization, etc.). These unencrypted fields facilitate accurate estimation of transmission attributes.
  • High communication overhead and increased end-to-end delay for reporting events.