On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks

Engineering Applications of Artificial Intelligence

Volumn 26, Issue 2, 2013, Pages 669-676

  Manjarres, Diana ^a   Del Ser, Javier ^a   Gil Lopez, Sergio ^a   Vecchio, Massimo ^b   Landa Torres, Itziar ^a   Salcedo Sanz, Sancho ^c   Lopez Valcarce, Roberto ^b  

^a TECNALIA RESEARCH AND INNOVATION   (Spain)

^b UNIVERSITY OF VIGO   (Spain)

^c UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Flip ambiguity; Harmony Search; Localization; Multi objective; Wireless Sensor Networks

Indexed keywords

CONNECTIVITY DEGREE; FLIP AMBIGUITY; GEOMETRICAL CONSTRAINTS; HARMONY SEARCH; LOCAL SEARCH; LOCALIZATION; LOCALIZATION ERRORS; LOCALIZATION SCHEMES; LOCATION INFORMATION; METAHEURISTIC; MULTI OBJECTIVE; NETWORK CONFIGURATION; NETWORK-BASED; NONCONVEX OPTIMIZATION; PARETO ARCHIVED EVOLUTION STRATEGIES; QUALITY INDICATORS; WIRELESS SENSOR;

CONVEX OPTIMIZATION; WIRELESS SENSOR NETWORKS;

SENSOR NODES;

EID: 84872680110     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.06.002     Document Type: Article

References (39)

1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci Wireless sensor networks: a survey Comput. Networks 38 2002 393 422 (Pubitemid 34181630)
2. Alippi, C.; Vanini, G.; 2006. A RSSI-based and calibrated centralized localization technique for wireless sensor networks. In: Proceedings of Fourth IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 301-305. (Pubitemid 44612595)
3. Aspnes, J.; Goldenberg, D.; Yang, Y.; 2004. On the computational complexity of sensor network localization. In: Algorithmic Aspects of Wireless Sensor Networks. Lecture Notes in Computer Science, vol. 3121. Springer-Verlag, pp. 32-44.
4. P. Biswas, T.C. Liang, K.C. Toh, Y. Ye, and T.C. Wang Semidefinite programming approaches for sensor network localization with noisy distance measurements IEEE Trans. Autom. Sci. Eng. 3 4 2006 360 371 (Pubitemid 44574330)
5. Biswas, P.; Ye, Y.; 2004. Semidefinite programming for ad-hoc wireless sensor network localization. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks. ACM Press, pp. 46-54.
6. Bleuler, S.; Laumanns, M.; Thiele, L.; Zitzler, E.; 2003. PISA - a platform and programming language independent interface for search algorithms. Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 2632. Springer-Verlag, pp. 494-508.
7. CA. Coello, GB. Lamont, and DA. Van Veldhuizen Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) 2006 Springer-Verlag New York Inc
8. J.A. Costa, N. Patwari, and A.O. Hero Distributed weighted- multidimensional scaling for node localization in sensor networks ACM Trans. Sensor Networks 2 1 2006
9. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast and elitist multi-objective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 6 2002 182 197 (Pubitemid 34555372)
10. J. Del Ser, M. Matinmikko, S. Gil-Lopez, and M. Mustonen Centralized and distributed spectrum channel assignment in cognitive wireless networks: a harmony search approach Appl. Soft Comput. 12 2 2012 921 930
11. J. Del Ser, M.N. Bilbao, S. Gil-Lopez, M. Matinmikko, and S. Salcedo-Sanz Iterative power and subcarrier allocation in rate-constrained orthogonal multicarrier downlink systems based on hybrid harmony search heuristics Eng. Appl. Artif. Intell. 24 5 2011 748 756
12. R. Forsati, A.T. Haghighat, and M. Mahdavi Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing Comput. Commun. 31 10 2008 2505 2519
13. Z.W. Geem, J.H. Kim, and G.V. Loganathan A new heuristic optimization algorithm: harmony search Simulation 76 2 2001 60 68 (Pubitemid 32543074)
14. Z.W. Geem, K.S. Lee, and Y. Park Application of harmony search to vehicle routing Am. J. Appl. Sci. 2 2005 1552 1557
15. Geem, Z.W. (Ed.), 2009. Music-Inspired Harmony Search Algorithm: Theory and Applications. Studies in Computational Intelligence, vol. 191. Springer.
16. S. Gil-Lopez, J. Del Ser, and I. Olabarrieta A novel heuristic algorithm for multiuser detection in synchronous CDMA wireless sensor networks IEEE Int. Conf. Ultra Modern Commun. 2009 1 6
17. Hansen, M.P.; Jaskiewicz, A.; 1998. Evaluating the Quality of Approximations to the Non-dominated Set. Technical Report IMM-REP1998-7, Technical University of Denmark.
18. Hu, L.; Evans, D.; 2004. Localization for mobile sensor networks. In: Proceedings of the 10th International Conference on Mobile Computing and Networking, pp. 45-57. (Pubitemid 40061930)
19. B. Jackson, and T. Jordan Connected rigidity matroids and unique realizations of graphs J. Combinatorial Theory Ser. B 94-1 2005 1 29 (Pubitemid 40398188)
20. Ji, X.; Zha, H.; 2004. Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling. In: Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 2652-2661.
21. D. Kalyanmoy, S. Agrawal, A. Pratap, and T. Meyarivan A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II IEEE Trans. Evol. Comput. 6 2 2000 182 197
22. A.A. Kannan, B. Fidan, and G. Mao Analysis of flip ambiguities for robust sensor network localization IEEE Trans. Veh. Technol. 59 4 2010 2057 2070
23. Kannan, A.A.; Mao, G.; Vucetic, B.; 2005. Simulated annealing based localization in wireless sensor network. In: Proceedings of the IEEE Conference on Local Computer Networks. IEEE Computer Society, pp. 513-514. (Pubitemid 44815639)
24. Kannan, A.A.; Mao, G.; Vucetic, B.; 2006. Simulated annealing based wireless sensor network localization with flip ambiguity mitigation. In: Proceedings of the 63rd IEEE Vehicular Technology Conference, pp. 1022-1026. (Pubitemid 46508966)
25. J.D. Knowles, and D.W. Corne Approximating the nondominated front using the pareto archived evolution strategy Evol. Comput. 8 2 2000 149 172
26. Y.B. Ko, and N.H. Vaidya Location-aided routing (LAR) in mobile ad hoc networks Wireless Networks 38-4 2000 307 321 (Pubitemid 32018664)
27. Landa-Torres, I.; Gil-Lopez, S.; Salcedo-Sanz, S.; DelSer, J.; Portilla-Figueras, J.A.; 2011. A novel grouping harmony search algorithm for the multiple-type access node location problem. Expert Syst. Appl. 39 (5), 5262-5270, (2012).
28. Liang, T.C.; Wang, T.C.; Ye, Y.; 2004. A Gradient Search Method to Round the Semidefinite Programming Relaxation for Ad hoc Wireless Sensor Network Localization. Standford University Technical Report.
29. T.W. Liao Two hybrid differential evolution algorithms for engineering design optimization Appl. Soft Comput. 10 4 2010 1188 1199
30. T. Liu, P. Bahl, and I. Chlamtac Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks IEEE J. Sel. Areas Commun. 16 6 1998 922 936
31. Niculescu, D.; Nath, B.; 2003. Ad-hoc positioning system (APS) using AoA. In: Proceedings of the 20st Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1734-1743.
32. Savvides, A.; Han, C.C.; Srivastava, M.; 2001. Dynamic fine-grained localization in ad-hoc networks of sensors. In: 7th ACM International Conference on Mobile Computing and Networking, pp. 166-179. (Pubitemid 33006713)
33. P. Tseng Second-order cone programming relaxation of sensor network localization SIAM J. Optim. 18 1 2007 156 185 (Pubitemid 351271326)
34. Vecchio, M.; Lopez-Valcarce, R.; Marcelloni, F. A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks. Appl. Soft Comput.; in press, 〈 http://dx.doi.org/10.1016/j.asoc.2011.03.012 〉, 12 (7), 1891-1901.
35. Z. Wang, S. Zheng, Y. Ye, and S. Boyd Further relaxations of the semidefinite programming approach to sensor network localization SIAM J. Optim. 19 2 2008 655 673
36. Z. Yang, and Y. Liu Understanding node localizability of wireless ad-hoc and sensor networks IEEE Trans. Mobile Comput. 99 1 2011
37. R. Zhang, and L. Hanzo Iterative multiuser detection and channel decoding for DS-CDMA using harmony search IEEE Signal Process. Lett. 16 10 2009 917 920
38. E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, and V. Grunert da Fonseca Performance assessment of multiobjective optimizers: an analysis and review IEEE Trans. Evol. Comput. 7 2 2003 117 132
39. Zitzler, E.; Thiele, L.; 2009. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study, vol. 1498. Springer, pp. 292-301.