Secure Tracking in Sensor Networks

Abstract

Target tracking is a canonical issue in sensor networks research. However, tracking security has gained little or no attention. Once a sensor node is compromised, it will be able to inject false location information into the network, and those nodes receiving such information will suffer greatly in terms of tracking precision. This paper, to the best of our knowledge, is the first to explore the topic of security in the context of Bayesian tracking for sensor networks. We propose to activate more than one nodes at each time step, and use a relaxation labeling algorithm to detect malicious nodes whose reports are then removed. Simulations based on both linear and nonlinear motion models demonstrate that out algorithm works better than simply averaging over the results based on the redundant sets of nodes.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA491533

Entities

People

  • Chih-chieh G. Chang
  • Cliff Wang
  • Wesley E. Snyder

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Central Processing Units
  • Detectors
  • Environmental Monitoring
  • Filters
  • Homeland Security
  • Kalman Filters
  • Multitarget Tracking
  • Networks
  • North Carolina
  • Probabilistic Models
  • Probability
  • Security
  • Sensor Networks
  • Sequential Monte Carlo Methods
  • Target Tracking
  • Targets

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Educational Psychology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference