skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Byzantine Resilient Locally Optimum Radioactive Source Detection Using Collaborative Sensor Networks

; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Presented at: IEEE Global Conference on Signal and Information Processing, Toronto, Canada, Nov 14 - Nov 16, 2017
Country of Publication:
United States

Citation Formats

Kailkhura, B, Ray, P, Rajan, D, Yen, A, Barnes, P, and Goldhahn, R. Byzantine Resilient Locally Optimum Radioactive Source Detection Using Collaborative Sensor Networks. United States: N. p., 2017. Web.
Kailkhura, B, Ray, P, Rajan, D, Yen, A, Barnes, P, & Goldhahn, R. Byzantine Resilient Locally Optimum Radioactive Source Detection Using Collaborative Sensor Networks. United States.
Kailkhura, B, Ray, P, Rajan, D, Yen, A, Barnes, P, and Goldhahn, R. Fri . "Byzantine Resilient Locally Optimum Radioactive Source Detection Using Collaborative Sensor Networks". United States. doi:.
title = {Byzantine Resilient Locally Optimum Radioactive Source Detection Using Collaborative Sensor Networks},
author = {Kailkhura, B and Ray, P and Rajan, D and Yen, A and Barnes, P and Goldhahn, R},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri May 12 00:00:00 EDT 2017},
month = {Fri May 12 00:00:00 EDT 2017}

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • The Atmospheric Radiation Measurement program operated by U.S. Department of Energy is one of the largest climate research programs dedicated to the collection of long-term continuous measurements of cloud properties and other key components of the earth’s climate system. Given the critical role that collected ARM data plays in the analysis of atmospheric processes and conditions and in the enhancement and evaluation of global climate models, the production and distribution of high-quality data is one of ARM’s primary mission objectives. Fault detection in ARM’s distributed sensor network is one critical ingredient towards maintaining high quality and useful data. We aremore » modeling ARM’s distributed sensor network as a dynamic Bayesian network where key measurements are mapped to Bayesian network variables. We then define the conditional dependencies between variables by discovering highly correlated variable pairs from historical data. The resultant dynamic Bayesian network provides an automated approach to identifying whether certain sensors are malfunctioning or failing in the distributed sensor network. A potential fault or failure is detected when an observed measurement is not consistent with its expected measurement and the observed measurements of other related sensors in the Bayesian network. We present some of our experiences and promising results with the fault detection dynamic Bayesian network.« less
  • For real-time acoustic source localization applications, one of the primary challenges is the considerable growth in computational complexity associated with the emergence of ever larger, active or passive, distributed sensor networks. The complexity of the calculations needed to achieve accurate source localization increases dramatically with the size of sensor arrays, resulting in substantial growth of computational requirements that cannot be met with standard hardware. One option to meet this challenge builds upon the emergence of digital optical-core devices. The objective of this work was to explore the implementation of key building block algorithms used in underwater source localization on anmore » optical-core digital processing platform recently introduced by Lenslet Inc. They investigate key concepts of threat-detection algorithms such as Time Difference Of Arrival (TDOA) estimation via sensor data correlation in the time domain with the purpose of implementation on the optical-core processor. they illustrate their results with the aid of numerical simulation and actual optical hardware runs. The major accomplishments of this research, in terms of computational speedup and numerical accurcy achieved via the deployment of optical processing technology, should be of substantial interest to the acoustic signal processing community.« less
  • A variety of recent applications have led to a great interest in the development and application of sensor networks with the goal of providing more effective detection of moving radioactive sources. This paper endeavors to analyze and evaluate the costs and benefits associated with the use of a network of radiation detectors for applications involving the detection of a moving radioactive source. This analysis is restricted to the one-dimensional case, i.e., to the case where the moving source is constrained to move along a single path. It is found that the relative advantage resulting from sensor dispersal depends upon themore » goals, objectives, and constraints of the measurement scenario. The dispersal of sensors into a network may be advisable or required for operational reasons, but from a statistical perspective does not directly lead to improved performance in terms of detection efficiency and false detection rate.« less
  • Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error basedmore » alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.« less