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Optimal multiobjective placement of distributed sensors against moving targets

Published: 02 August 2012 Publication History

Abstract

We consider the optimal deployment of a sparse network of sensors against moving targets, under multiple conflicting objectives of search. The sensor networks of interest consist of sensors which perform independent binary detection on a target, and report detections to a central control authority. A multiobjective optimization framework is developed to find optimal trade-offs as a function of sensor deployment, between the conflicting objectives of maximizing the Probability of Successful Search (PSS) and minimizing the Probability of False Search (PFS), in a bounded search region of interest. The search objectives are functions of unknown sensor locations (represented parametrically by a probability density function), given sensor performance parameters, statistical priors on target behavior, and distributed detection criteria. Numerical examples illustrating the utility of this approach for varying target behaviors are given.

References

[1]
Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. 2002. A survey on sensor networks. IEEE Comm. Mag. 40, 8, 102--114.
[2]
Bar-Shalom, Y., Li, X. R., and Kirubarajan, T. 2001. Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software. Wiley-Interscience.
[3]
Chamberland, J. F. and Veeravalli, V. V. 2003. Decentralized detection in sensor networks. IEEE Trans. Signal Process. 51, 2, 407--416.
[4]
Clouqueur, T., Phipatanasuphorn, V., Ramanathan, P., and Saluja, K. K. 2002. Sensor deployment strategy for target detection. In Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications.
[5]
Coello Coello, C. A., Lamont, G. B., and Van Veldhuizen, D. A. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems 2nd Ed. Springer.
[6]
Conrad, M. and Papenberg, N. 2008. Iterative adaptive Simpson and Lobatto quadrature in MATLAB. Emory University tech. rep. TR-2008-12.
[7]
Das, I. and Dennis, J. E. 1997. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct. Optim. 14, 1, 63--69.
[8]
Das, I. and Dennis, J. E. 1998. Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8, 3, 631--657.
[9]
Deb, K. 2001. Multiobjective Optimization Using Evolutionary Algorithms. Wiley.
[10]
Dhillon, S. S. and Chakrabarty, K. 2003. Sensor placement for effective coverage and surveillance in distributed sensor networks. In Proceedings of the Wireless Communications and Networking Conference, Vol. 3, 1609--1614.
[11]
Estrin, D., Culler, D., Pister, K., and Sukhatme, G. 2002. Connecting the physical world with pervasive networks. IEEE Perv. Comput. 1, 1, 59--69.
[12]
Ferrari, S. 2006. Track coverage in sensor networks. In Proceedings of the American Control Conference. 2053--2059.
[13]
Ferrari, S., Zhang, G., and Wettergren, T. A. 2010. Probabilistic track coverage in cooperative sensor networks. IEEE Trans. Syst. Man. Cyber. Part B: Cyber. 40, 6, 1492--1504.
[14]
Fletcher, R. 1987. Practical Methods of Optimization 2nd Ed. Wiley-Interscience.
[15]
Goldberg, D. E. 1999. Genetic Algorithms. Addison Wesley Longman.
[16]
Han, S. P. 1977. A globally convergent method for nonlinear programming. J. Optimiz. Theory Appl. 22, 3, 297--309.
[17]
Horn, J., Nafpliotis, N., and Goldberg, D. E. 1994. A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the 1st IEEE Conference on Evolutionary Computation (ICEC '94). Vol. 1, 82--87.
[18]
Koopman, B. 1980. Search and Screening: General Principles with Historical Applications. Pergamon Press.
[19]
Martinez, S. and Bullo, F. 2006. Optimal sensor placement and motion coordination for target tracking. Automatica 42, 661--668.
[20]
Miettinen, K. 1998. Nonlinear Multiobjective Optimization. Kluwer.
[21]
Morse, P. M. and Kimball, G. E. 2003. Methods of Operations Research. Dover Publications.
[22]
Musman, S. A., Lehner, P. E., and Elsaesser, C. 1997. Sensor planning for elusive targets. Math. Comput. Model. 25, 3, 103--115.
[23]
Niu, R., Varshney, P. K., and Cheng, Q. 2006. Distributed detection in a large wireless sensor network. Info. Fusion 7, 380--394.
[24]
Papoulis, A. 1991. Probability, Random Variables, and Stochastic Processes, 3rd Ed. McGraw-Hill.
[25]
Penny, D. E. 1999. Multi-sensor management for passive target tracking in an anti-submarine warfare scenario. In Proceedings of the IEE Colloquium on Target Tracking: Algorithms and Applications. Vol. 3, 1--5.
[26]
Powell, M. J. D. 1978. A fast algorithm for nonlinearly constrained optimization calculations. Numer. Anal. 630, 144--157.
[27]
Qi, H., Iyengar, S., and Chakrabarty, K. 2001. Distributed sensor networks-a review of recent research. J. Franklin Inst. 338, 6, 655--668.
[28]
Schaffer, J. D. 1984. Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. thesis, Vanderbilt University.
[29]
Srinivas, N. and Deb, K. 1995. Multiobjective function optimization using nondominated sorting genetic algorithms. Evol. Comput. 2, 3, 221--248.
[30]
Stone, L. D., Stanshine, J. A., and Persinger, C. A. 1972. Optimal search in the presence of Poisson-distributed false targets. SIAM J. Appl. Math. 23, 1, 6--27.
[31]
Van Veldhuizen, D. A. 1999. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations. Ph.D. thesis, Air Force Institute of Technology.
[32]
Wang, H., Yao, K., and Estrin, D. 2005. Information-theoretic approaches for sensor selection and placement in sensor networks for target localization and tracking. J. Comm. Netw. 7, 4, 438--449.
[33]
Washburn, A. 2002. Search and Detection 4th Ed. Institute for Operations Research and the Management Sciences.
[34]
Wettergren, T. A. 2006. The genetic-algorithm-based normal boundary intersection (GANBI) method: An efficient approach to Pareto multiobjective optimization for engineering design. NUWC-NPT tech. rep. 11,741.
[35]
Wettergren, T. A. 2008. Performance of search via track-before-detect for distributed sensor networks. IEEE Trans. Aerospace Electron. Syst. 44, 1, 314--325.
[36]
Wettergren, T. A. and Costa, R. 2009. Optimal placement of distributed sensors against moving targets. ACM Trans. Sen. Netw. 5, 3, Article 26.
[37]
Wettergren, T. A., Streit, R. L., and Short, J. R. 2004. Tracking with distributed sets of proximity sensors using geometric invariants. IEEE Trans. Aerospace Electron. Syst. 40, 4, 1366--1374.
[38]
Willett, P., Alford, M., and Vannicola, V. 1994. The case for like-sensor predetection fusion. IEEE Trans. Aerospace Electron. Syst. 30, 4, 986--1000.
[39]
Zitzler, E. and Thiele, L. 1998. Multiobjective optimization using evolutionary algorithms—A comparative case study. In Proceedings of the International Conference on Parallel Problem Solving from Nature. 292--301.
[40]
Zou, Y. and Chakrabarty, K. 2004. Sensor deployment and target localization in distributed sensor networks. ACM Trans. Embed. Comput. Syst. 3, 1, 61--91.

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cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 8, Issue 3
July 2012
255 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/2240092
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 August 2012
Accepted: 01 April 2011
Revised: 01 April 2011
Received: 01 January 2011
Published in TOSN Volume 8, Issue 3

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Author Tags

  1. Sensor networks
  2. coverage
  3. optimization

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