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A Specific Heuristic Dedicated to a Coverage/Tracking Bi-objective Problem for Wireless Sensor Deployment

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Abstract

In recent years, wireless sensor networks (WSN) have become very attractive for surveillance applications and particularly for target tracking. When a target has to be located by a WSN, accuracy is an important constraint. Most of the studies made in the WSNs problems deal with either coverage or tracking focus objectives. In this paper, we propose a modification of a previously studied bi-objective sensor placement problem taking into account both coverage and accuracy. The objectives are the minimization of the number of deployed sensors and the minimization of the tracking constraints violations, under the coverage constraints. The non sorting genetic algorithm and multi objective particle swarm optimization have been implemented to solve the problem. A specific heuristic (H3P) based on the mathematical decomposition of the problem has also been proposed. The performances of these algorithms are checked with integer programming results for small size instances, and they are compared on large size instances by multi-objective metrics. Results have shown that implemented metaheuristics provide less optimal solutions than the H3P for the small size instances. The comparison between the algorithms on large size instances set show that the H3P dominates the other implemented methods.

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References

  1. Almiñana, M., & Pastor, J. T. (1994). Two new heuristics for the location set covering problem. Top, 2, 315–328.

    Article  MATH  MathSciNet  Google Scholar 

  2. Almiñana, M., & Pastor, J. T. (1997). An adaptation of SH heuristic to the location set covering problem. European Journal of Operational Research, 100, 586–593.

    Article  MATH  Google Scholar 

  3. Altinel, I. K., Aras, N., Güney, E., & Ersoy, C. (2008). Binary integer programming formulation and heuristics for differentiated coverage in heterogeneous sensor networks. Computer Networks, 52, 2419–2431.

    Article  MATH  Google Scholar 

  4. Andersen, T., & Tirthapura, S. (2009). Wireless sensor deployment for 3d coverage with constraints. In The 6th International Conference on Networked Sensing Systems, pp. 78–81.

  5. Balas, E., & Carrera, M. C. (1996). A dynamic subgradient-based branch-and-bound procedure for set covering. Operations Research, 44, 875–890.

    Article  MATH  MathSciNet  Google Scholar 

  6. Bautista, J., & Pereira, J. (2007). A grasp algorithm to solve the unicost set covering problem. Computers and Operations Research, 34, 3162–3173.

    Article  MATH  MathSciNet  Google Scholar 

  7. Le Berre, M., Hnaien, F., & Snoussi, H. (2013). A multi-objective modeling of k-coverage problem under accuracy constraint. In The 5th international conference on modeling, simulation and applied optimization, pp. 1–6.

  8. Le Berre, M., Rebai, M., Hnaien, F., & Snoussi, H. (2014). A bi-objective model for wireless sensor deployment considering coverage and tracking applications. Accepted for publication in the International Journal of Sensor Networks. http://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijsnet.

  9. Beslay, J. E. (1987). An algorithm for set covering problem. European Journal of Operational Research, 31, 85–93.

    Article  MathSciNet  Google Scholar 

  10. Beslay, J. E. (1990). A lagrangian heuristic for the set covering problem. Naval Research Logistics, 37, 151–164.

    Article  MathSciNet  Google Scholar 

  11. Beslay, J. E., & Chu, P. C. (1996). A genetic algorithm for the set covering problem. European Journal of Operational Research, 94, 392–404.

    Article  Google Scholar 

  12. Caprara, A., Toth, P., & Fischetti, M. (2000). Algorithms for the set covering problem. Annals of Operations Research, 98, 353–371.

    Article  MATH  MathSciNet  Google Scholar 

  13. Caserta, M. (2007). Tabu search-based metaheuristic algorithm for large-scale set covering problems. In K. F. Doerner, et al. (Eds.), Metaheuristics: Progress in complex systems optimization (pp. 43–63). New York: Springer.

    Chapter  Google Scholar 

  14. Chvatal, V. (1979). A greedy heuristic for the set-covering problem. Mathematics of Operations Research, 4, 233–235.

    Article  MATH  MathSciNet  Google Scholar 

  15. Coello Coello, C. A., & Lechuga, M. S. (2002). Mopso : A proposal for multiple objective particle swarm. In Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1051–1056.

  16. Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm : Nsga-ii. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Article  Google Scholar 

  17. Fisher, M. L., & Kedia, P. (1990). Optimal solution of set covering/partitioning problems using dual heuristics. Management Science, 36, 674–688.

    Article  MATH  MathSciNet  Google Scholar 

  18. Fusco, G., & Gupta, H. (2009). \(\epsilon \)-net approach to sensor k-coverage. In Proceedings of the 4th international conference on wireless algorithms, systems, and applications, pp. 104–114.

  19. Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4, 303–334.

    Article  Google Scholar 

  20. Grossman, T., & Wool, A. (1997). Computational experience with approximation algorithms for the set covering problem. European Journal of Operational Research, 101, 81–92.

    Article  MATH  Google Scholar 

  21. Haddadi, S. (1997). Simple lagrangian heuristic for the set covering problem. European Journal of Operational Research, 97, 200–204.

    Article  MATH  Google Scholar 

  22. Chen, J., Jia, J., Chang, G., Wen, Y., & Song, J. (2009). Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Computer and Mathematics with Applications, 57, 1767–1775.

    Article  MATH  MathSciNet  Google Scholar 

  23. Jia, J., Chen, J., Chang, G., & Tan, Z. (2009). Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computer and Mathematics with Applications, 57, 1756–1766.

    Article  MATH  MathSciNet  Google Scholar 

  24. Jourdan, D.B., & de Weck, O.L. (2004). Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In: 2004 IEEE 59th vehicular technology conference, 2004. VTC 2004-Spring. 5:2466–2470.

  25. Karp, R.M. (1972). Reducibility amnong combinatorial problems. In Complexity of computer computations, pp. 85–103.

  26. Ke, W. C., Liu, B. H., & Tsai, M. J. (2011). The critical-square-grid coverage problem in wireless sensor networks is np-complete. Computer Networks, 55, 2209–2220.

    Article  Google Scholar 

  27. Konstantinidis, A., & Yang, K. (2011). A multi-objective energy efficient dense deployment in wireless sensor networks using a hybrid problem specific moea/d. Applied Soft Computing, 11, 4117–4134.

    Article  Google Scholar 

  28. Konstantinidis, A., Yang, K., Zhang, Q., & Zeinalipour-Yazti, D. (2010). A multi-objective evolutionay algorithm for the deployment and power assignment problem in wireless sensor networks. Computer Networks, 54, 960–976.

    Article  MATH  Google Scholar 

  29. Lan, G., DePuy, G. W., & Whitehouse, G. E. (2007). An effective and simple heuristic for the set covering problem. European Journal of Operational Research, 176, 1387–1403.

    Article  MATH  MathSciNet  Google Scholar 

  30. Lee, J. Y., Seok, J.-H., & Lee, J.-J. (2012). Multiobjective optimization approach for sensor arrangement in a complex indoor environment. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Applications and Reviews, 42, 174–186.

    Article  Google Scholar 

  31. Lopes, F. B., & Lorena, L. A. (1994). Surrogate heirstic for set covering problems. European Journal of Operational Research, 79, 138–150.

    Article  MATH  Google Scholar 

  32. Masazade, E., Varshney, P. K., & Sendur, G. K. (2010). A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 40, 444–457.

    Article  Google Scholar 

  33. Oh, S.C., Tan, C.H., Kong, F.W., Tan, Y.S., Ng, K.H., Ng, G.W., & Tai, K. (2007). Multiobjective optimization of sensor network deployment by a genetic algorithm. In The IEEE congress on evolutionary computation, pp. 3917–3921.

  34. Pampara, G., & Engelbrecht, A.P. (2011). Binary artificial bee colony optimization. In The IEEE symposium on swarm intelligence, pp. 1–8.

  35. Pessoa, L. S., Resende, M. G. C., & Ribeiro, C. C. (2010). A hybrid lagrangean heuristic with grasp and path-relinking for set k-covering. Technical report, AT&T Labs Research.

  36. Rebai, M., Khoukhi, I., Snoussi, H., & Hnaien, F. (2013). Linear models for the total coverage problem in wireless sensor networks. In: The 5th international conference on modeling, simulation and applied optimization, pp. 1–4.

  37. Ren, Z.-G., Feng, Z.-R., Ke, L.-J., & Zhang, Z.-J. (2010). New ideas for applying ant colony optimization to the set covering problem. Computers & Industrial Engineering, 58, 774–784.

    Article  Google Scholar 

  38. Roth, R. (1969). Computer solutions to minimum cover problems. Operations Research, 17, 455–465.

    Article  MATH  Google Scholar 

  39. Rourke, J. O. (1987). Art gallery theorems and algorithms. Oxford: Oxford University Press.

    MATH  Google Scholar 

  40. Solar, M., Parada, V., & Urrutia, R. (2002). A parallel genetic algorithm to solve the set covering problem. Computers & Operations Research, 29, 1221–1235.

    Article  MATH  MathSciNet  Google Scholar 

  41. Wang, X., & Wang, S. (2011). Hierarchical deployment optimization for wireless sensor networks. IEEE Transactions on Mobile Computing, 10, 1028–1041.

    Article  Google Scholar 

  42. Wei, L.-C., Kang, C.-W., & Chen, J.-H. (2009). A force-driven evolutionary approach for multi-objective 3d differentiated sensor network deployment. In The IEEE 6th international conference on mobile adhoc and sensor systems, pp. 983–988.

  43. Wu, Y., Li, M., Cai, Z., & Zhu, E. (2008). A distributed algorithm to approximate node-weighted minimum \(\alpha \)-connected (\(\theta \), k)-coverage in dense sensor networks. In Proceedings of the 2nd annual international workshop on frontiers in algorithmics, pp. 221–232.

  44. Ye, F., Zhong, G., Cheng, J., Lu, S., & Zhang, L. (2003). Peas: A robust energy conserving protocol for long-lived sensor networks. In Proceedings of the 23rd international conference on distributed computing systems, pp. 28–37.

  45. Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad-hoc and Sensor Wireless Networks, 1, 89–124.

    Google Scholar 

  46. Zhu, C., Zheng, C., Shu, L., & Han, G. (2011). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35, 619–632.

    Article  Google Scholar 

  47. Zitzler, E., & Thiele, L. (1998). An evolutionary algorithm for multiobjective optimization: The strength pareto approach. In Technical Report 43, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich.

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Acknowledgments

This work is supported by the CapSec-SURECAP project funded by Région Champagne Ardenne and FEDER (Fonds Européen de Développement Régional).

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Authors and Affiliations

  1. ICD LM2S, UMR 6281, CNRS, University of Technology of Troyes, 10000, Troyes, France

    Matthieu Le Berre, Maher Rebai & Hichem Snoussi

  2. ICD LOSI, UMR 6281, CNRS, University of Technology of Troyes, 10000, Troyes, France

    Faicel Hnaien

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  1. Matthieu Le Berre
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  2. Maher Rebai
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  4. Hichem Snoussi
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Correspondence to Matthieu Le Berre.

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Le Berre, M., Rebai, M., Hnaien, F. et al. A Specific Heuristic Dedicated to a Coverage/Tracking Bi-objective Problem for Wireless Sensor Deployment. Wireless Pers Commun 84, 2187–2213 (2015). https://doi.org/10.1007/s11277-015-2548-2

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