SINCE 2004

  • 0

      0 Item in Bag


      Your Shopping bag is empty

      CHECKOUT
  • Notice

    • ALL COMPUTER, ELECTRONICS AND MECHANICAL COURSES AVAILABLE…. PROJECT GUIDANCE SINCE 2004. FOR FURTHER DETAILS CALL 9443117328

    Projects > COMPUTER > 2017 > IEEE > MOBILE COMPUTING

    Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs


    Abstract

    We address the problem of preventing the 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 transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50%; and the end-toend delay by more than 30%. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality


    Existing System


    Proposed System


    Architecture


    FOR MORE INFORMATION CLICK HERE