A risk-based sensor placement methodology.

A risk-based sensor placement methodology is proposed to solve the problem of optimal location of sensors to protect population against the exposure to, and effects of, known and/or postulated chemical, biological, and/or radiological threats. Risk is calculated as a quantitative value representing population at risk from exposure at standard exposure levels. Historical meteorological data are used to characterize weather conditions as the frequency of wind speed and direction pairs. The meteorological data drive atmospheric transport and dispersion modeling of the threats, the results of which are used to calculate risk values. Sensor locations are determined via an iterative dynamic programming algorithm whereby threats detected by sensors placed in prior iterations are removed from consideration in subsequent iterations. In addition to the risk-based placement algorithm, the proposed methodology provides a quantification of the marginal utility of each additional sensor. This is the fraction of the total risk accounted for by placement of the sensor. Thus, the criteria for halting the iterative process can be the number of sensors available, a threshold marginal utility value, and/or a minimum cumulative utility achieved with all sensors.

[1]  Jay P. Boris,et al.  Using CT-Analyst to optimize sensor placement , 2004, SPIE Defense + Commercial Sensing.

[2]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[3]  R. I. Sykes,et al.  The representation of dynamic flow effects in a Lagrangian puff dispersion model , 1999 .

[4]  K B Lim A Disturbance Rejection Approach to Actuator and Sensor Placement , 1997 .

[5]  Volkan Isler,et al.  Sensor Placement Algorithms for Triangulation Based Localization , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  H. Ishida,et al.  Plume-Tracking Robots: A New Application of Chemical Sensors , 2001, The Biological Bulletin.

[7]  S. Sitharama Iyengar,et al.  Sensor placement for grid coverage under imprecise detections , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[8]  R. S. Gabruk,et al.  A Second-Order Closure Model for the Effect of Averaging Time on Turbulent Plume Dispersion , 1997 .

[9]  Arthur B. Maccabe,et al.  Radiation detection with distributed sensor networks , 2004, Computer.

[10]  Tanneguy Redarce,et al.  CAD-based range sensor placement for optimum 3D data acquisition , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[11]  Héctor H. González-Baños,et al.  A randomized art-gallery algorithm for sensor placement , 2001, SCG '01.

[12]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[13]  Tanneguy Redarce,et al.  A CAD-based 3D data acquisition strategy for inspection , 2003, Machine Vision and Applications.

[14]  Krishnendu Chakrabarty,et al.  Sensor placement for effective coverage and surveillance in distributed sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[15]  Kevin L. Moore,et al.  Diffusion boundary determination and zone control via mobile actuator-sensor networks (MAS-net): challenges and opportunities , 2004, SPIE Defense + Commercial Sensing.

[16]  Rw Lee Moving the Hazard Prediction and Assessment Capability to a Distributed, Portable Architecture , 2002 .