Skip to content


  • Research Article
  • Open Access

Optimal and Approximate Approaches for Deployment of Heterogeneous Sensing Devices

EURASIP Journal on Wireless Communications and Networking20072007:054731

  • Received: 1 July 2006
  • Accepted: 16 January 2007
  • Published:


A modeling framework for the problem of deploying a set of heterogeneous sensors in a field with time-varying differential surveillance requirements is presented. The problem is formulated as mixed integer mathematical program with the objective to maximize coverage of a given field. Two metaheuristics are used to solve this problem. The first heuristic adopts a genetic algorithm (GA) approach while the second heuristic implements a simulated annealing (SA) algorithm. A set of experiments is used to illustrate the capabilities of the developed models and to compare their performance. The experiments investigate the effect of parameters related to the size of the sensor deployment problem including number of deployed sensors, size of the monitored field, and length of the monitoring horizon. They also examine several endogenous parameters related to the developed GA and SA algorithms.


  • Genetic Algorithm
  • Simulated Annealing
  • Mathematical Program
  • System Application
  • Develop Model


Authors’ Affiliations

Department of Computer Science and Engineering, Southern Methodist University, Dallas, TX 75275-0122, USA
Department of Environmental and Civil Engineering, Southern Methodist University, Dallas, TX 75275-0340, USA


  1. Mainwaring A, Polastre J, Szewczyk R, Culler D, Anderson J: Wireless sensor networks for habitat monitoring. Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA '02), September 2002, Atlanta, Ga, USA 88-97.View ArticleGoogle Scholar
  2. Kuorilehto M, Hännikäinen M, Hämäläinen TD: A survey of application distribution in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 2005,2005(5):774-788.MATHView ArticleGoogle Scholar
  3. Nguyen NT, Venkatesh S, West G, Bui HH: Multiple camera coordination in a surveillance system. Acta Automatica Sinica 2003,29(3):408-422.Google Scholar
  4. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E: Wireless sensor networks: a survey. Computer Networks 2002,38(4):393-422. 10.1016/S1389-1286(01)00302-4View ArticleGoogle Scholar
  5. Ilyas M, Mahgoub I: Handbook of Sensor Networks. CRC Press, Boca Raton, Fla, USA; 2005.Google Scholar
  6. Estrin D, Girod L, Pottie G, Srivastava M: Instrumenting the world with wireless sensor networks. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '01), May 2001, Salt Lake, Utah, USA 4: 2033-2036.Google Scholar
  7. Li D, Wong KD, Hu YH, Sayeed AM: Detection, classification, and tracking of targets. IEEE Signal Processing Magazine 2002,19(2):17-29. 10.1109/79.985674View ArticleGoogle Scholar
  8. Chvátal V: A combinatorial theorem in plane geometry. Journal of Computorial Theory (B) 1975, 18: 39-41. 10.1016/0095-8956(75)90061-1MATHView ArticleGoogle Scholar
  9. O'Rourke J: Galleries need fewer mobile guards: a variation on Chvátal's theorem. Geometriae Dedicata 1983,14(3):273-283.MATHMathSciNetView ArticleGoogle Scholar
  10. Chakrabarty K, Iyengar SS, Qi H, Cho E: Grid coverage for surveillance and target location in distributed sensor networks. IEEE Transactions on Computers 2002,51(12):1448-1453. 10.1109/TC.2002.1146711MathSciNetView ArticleGoogle Scholar
  11. Cardei M, Wu J: Coverage in wireless sensor networks. In Handbook of Sensor Networks. Edited by: Ilyas M, Mahgoub I. CRC Press, Boca Raton, Fla, USA; 2004.Google Scholar
  12. Isler V, Kannan S, Daniilidis K: Sampling based sensor-network deployment. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04), September-October 2004, Sendai, Japan 2: 1780-1785.Google Scholar
  13. Liu X, Mahapatra P: On the deployment of wireless sensor nodes. Proceedings of the 3rd International Workshop on Measurement, Modeling, and Performance Analysis of Wireless Sensor Networks, in Conjunction with the 2nd Annual International Conference on Mobile and Ubiquitous Systems, July 2005, San Diego, Calif, USAGoogle Scholar
  14. Hu W, Chou C, Jha S, Bulusu N: Deploying long-lived and cost-effective hybrid sensor networks. Proceedings of the 1st Workshop on Broadband Advanced Sensor Networks (BaseNets '04), October 2004, San Jose, Calif, USAGoogle Scholar
  15. Lee J-J, Krishnamachari B, Kuo C-CJ: Impact of heterogeneous deployment on lifetime sensing coverage in sensor networks. Proceedings of the 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON '04), October 2004, Santa Clara, Calif, USA 367-376.Google Scholar
  16. Wang G, Cao G, La Porta TF: Movement-assisted sensor deployment. IEEE Transactions on Mobile Computing 2006,5(6):640-652.View ArticleGoogle Scholar
  17. Poduri S, Sukhatme GS: Constrained coverage for mobile sensor networks. Proceedings of IEEE International Conference on Robotics and Automation (ICRA '04), April-May 2004, New Orleans, La, USA 165-171.Google Scholar
  18. Howard A, Mataríc MJ, Sukhatme GS: An incremental self-deployment algorithm for mobile sensor networks. Autonomous Robots 2002,13(2):113-126. 10.1023/A:1019625207705MATHView ArticleGoogle Scholar
  19. Dhillon SS, Chakrabarty K, Iyengar SS: Sensor placement for grid coverage under imprecise detections. Proceedings of the 5th International Conference on Information Fusion, July 2002, Washington, DC, USA 2: 1581-1587.View ArticleGoogle Scholar
  20. Zou Y, Chakrabarty K: Sensor deployment and target localization in distributed sensor networks. ACM Transactions on Embedded Computing Systems 2004,3(1):61-91. 10.1145/972627.972631View ArticleGoogle Scholar
  21. Zou Y, Chakrabarty K: Sensor deployment and target localization based on virtual forces. Proceedings of the 22nd Annual Joint Conference on the IEEE Computer and Communications Societies (INFOCOM '03), March-April 2003, San Francisco, Calif, USA 2: 1293-1303.Google Scholar
  22. Lee J-J, Krishnamachari B, Kuo C-CJ: Node aging effect on connectivity of data gathering trees in sensor networks. Proceedings of the 60th IEEE Vehicular Technology Conference (VTC '04), September 2004, Los Angeles, Calif, USA 4630-4634.Google Scholar
  23. Ye W, Heidemann J, Estrin D: An energy-efficient MAC protocol for wireless sensor networks. Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '02), June 2002, New York, NY, USA 3: 1567-1576.Google Scholar
  24. Xu Y, Heidemann J, Estrin D: Geography-informed energy conservation for ad hoc routing. Proceedings of the Annual International Conference on Mobile Computing and Networking (MOBICOM '01), July 2001, Rome, Italy 70-84.View ArticleGoogle Scholar
  25. Gage D: Command control for many-robot systems. Proceedings of the 19th Annual Technical Symposium and Exhibition of the Association for Unmanned Vehicle Systems (AUVS '92), June 1992, Huntsville, Ala, USAGoogle Scholar
  26. Huang C-F, Tseng Y-C: The coverage problem in a wireless sensor network. Proceedings of the 2nd ACM International Workshop on Wireless Sensor Networks and Applications (WSNA '03), September 2003, San Diego, Calif, USA 115-121.Google Scholar
  27. Zhou Z, Das S, Gupta H: Connected K-coverage problem in sensor networks. Proceedings of the 13th International Conference on Computer Communications and Networks (ICCCN '04), October 2004, Chicago, Ill, USA 373-378.Google Scholar
  28. Lee DT, Lin AK: Computational complexity of art gallery problems. IEEE Transactions on Information Theory 1986,32(2):276-282. 10.1109/TIT.1986.1057165MATHMathSciNetView ArticleGoogle Scholar
  29. Gwiazda, Tomasz, Dominik A: Genetic Algorithms Reference Volume I Crossover for Single-Objective Numerical Optimization Problems. Tomasz Gwiazda, Lomianki, Poland; 2006.Google Scholar
  30. Goldberg D: Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic, Boston, Mass, USA; 1989.MATHGoogle Scholar
  31. Randy L, Haupt S: Practical Genetic Algorithms. John Wiley & Sons, New York, NY, USA; 2004.MATHGoogle Scholar


© Rabie Ramadan et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.