Skip to main content

Advertisement

Log in

A two-objective memetic approach for the node localization problem in wireless sensor networks

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) are emerging as an efficient way to sense the physical phenomenon without the need of wired links and spending huge money on sensor devices. In WSNs, finding the accurate locations of sensor nodes is essential since the location inaccuracy makes the collected data fruitless. In this paper, we propose a two-objective memetic approach called the Three Phase Memetic Approach that finds the locations of sensor nodes with high accuracy. The proposed algorithm is composed of three operators (phases). The first phase, which is a combination of three node-estimating approaches, is used to provide good starting locations for sensor nodes. The second and third phases are then utilized for mitigating the localization errors in the first operator. To test the proposed algorithm, we compare it with the simulated annealing-based localization algorithm, genetic algorithm-based localization, Particle Swarm Optimization-based Localization algorithm, trilateration-based simulated annealing algorithm, imperialist competitive algorithm and Pareto Archived Evolution Strategy on ten randomly created and four specific network topologies with four different values of transmission ranges. The comparisons indicate that the proposed algorithm outperforms the other algorithms in terms of the coordinate estimations of sensor nodes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. This problem happens when a set of nodes are collinear.

  2. In this approach, first, all sensor nodes are categorized into three groups and later by using this information the algorithm can estimate non-anchor node locations with higher accuracy.

References

  1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)

    Article  Google Scholar 

  2. A. Pal, Localization algorithms in wireless sensor networks: current approaches and future challenges. Netw. Protoc. Algorithms 2(1), 45–73 (2010)

    Google Scholar 

  3. O. Banimelhem, S. Khasawneh, Gmcar: grid-based multipath with congestion avoidance routing protocol in wireless sensor networks. Ad Hoc Netw. 10(7), 1346–1361 (2012)

    Article  Google Scholar 

  4. F. Xue, P.R. Kumar, On the coverage and connectivity of large random networks. IEEE/ACM Trans. Netw. 14(SI), 2289–2299 (2006)

    MathSciNet  MATH  Google Scholar 

  5. C. Zhang, Y. Zhang, Y. Fang, Localized algorithms for coverage boundary detection in wireless sensor networks. Wirel. Netw. 15, 3–20 (2009)

    Article  Google Scholar 

  6. K. Kim, A Clustering Algorithm Based on Geographical Sensor Position in Wireless Sensor Networks, in Innovative Algorithms and Techniques in Automation, ed. by T. Sobh, K. Elleithy, A. Mahmood, M. Karim (Industrial Electronics and Telecommunications, Springer, Netherlands, 2007), pp. 245–249

    Google Scholar 

  7. L. Sun, J. Guo, K. Lu, R. Wang, Topology control based on quantum genetic algorithm in sensor networks. Front. Electr. Electron. Eng. China 2, 326–329 (2007)

    Article  Google Scholar 

  8. G. Mao, B. Fidan, Localization Algorithms and Strategies for Wireless Sensor Networks (Premier Reference Source, Information Science Reference, Hershey, 2009)

    Book  Google Scholar 

  9. E. Niewiadomska-Szynkiewicz, Localization in wireless sensor networks: classification and evaluation of techniques. J. Appl. Math. Comput. Sci. 22(2), 281–297 (2012)

    MATH  Google Scholar 

  10. Y. Liu, Z. Yang, Location, localization, and localizability. J. Comput. Sci. Technol. 25(2), 274–297 (2010)

    Article  Google Scholar 

  11. S. Yun, J. Lee, W. Chung, E. Kim, S. Kim, A soft computing approach to localization in wireless sensor networks. Expert Syst. Appl. 36(4), 7552–7561 (2009)

    Article  Google Scholar 

  12. B. Hofmann-Wellenhof, H. Lichtenegger, J. Collins, Global Positioning System: Theory and Practice, 5th edn. (Springer, Berlin, 2001)

    Book  Google Scholar 

  13. E. Niewiadomska-Szynkiewicz, M. Marks, Optimization schemes for wireless sensor network localization. Int. J. Appl. Math. Comput. Sci. 19(2), 291–302 (2009)

    Article  MATH  Google Scholar 

  14. Y. Zhang, L.T. Yang, J. Chen, RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations, 1st edn. (CRC Press Inc, Boca Raton, FL, USA, 2009)

    Book  Google Scholar 

  15. F. Franceschini, M. Galetto, D. Maisano, L. Mastrogiacomo, A review of localization algorithms for distributed wireless sensor networks in manufacturing. Int. J. Comput. Integr. Manuf. 22(7), 698–716 (2009)

    Article  Google Scholar 

  16. M. Gholami, N. Cai, R. Brennan, An artificial neural network approach to the problem of wireless sensors network localization. Robot. Comput.-Integr. Manuf. 29(1), 96–109 (2013)

    Article  Google Scholar 

  17. J. Kuriakose, S. Joshi, R.V. Raju, A. Kilaru, A Review on Localization in Wireless Sensor Networks, in Advances in Signal Processing and Intelligent Recognition Systems, ed. by J. Fagerberg, D.C. Mowery, R.R. Nelson (Springer Netherlands, Netherlands, 2014), pp. 599–610

    Chapter  Google Scholar 

  18. N. Patwari, Location estimation in sensor networks, Ph.D. thesis, Citeseer (2005)

  19. M. Vecchio, R. Lpez-Valcarce, F. Marcelloni, A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks. Appl. Soft Comput. 12(7), 1891–1901 (2012). soft Computing Approaches in the design of energy-efficient wireless systems

    Article  Google Scholar 

  20. L. Doherty, K. S. J. Pister, L. El Ghaoui, Convex position estimation in wireless sensor networks, in: INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 3, 2001, pp. 1655–1663

  21. M. Naraghi-Pour, G.C. Rojas, A novel algorithm for distributed localization in wireless sensor networks. ACM Trans. Sen. Netw. 11(1), 1:1–1:25 (2014). doi:10.1145/2632150

    Article  Google Scholar 

  22. A. Kannan, G. Mao, B. Vucetic, Simulated annealing based localization in wireless sensor network, in: Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on, 2005, pp. 2 pp. –514

  23. A. Kannan, G. Mao, B. Vucetic, Simulated annealing based wireless sensor network localization with flip ambiguity mitigation, in: Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd, Vol. 2, 2006, pp. 1022–1026

  24. Q. Zhang, J. Wang, C. Jin, J. Ye, C. Ma, W. Zhang, Genetic algorithm based wireless sensor network localization, in: Natural Computation, 2008. ICNC ’08. Fourth International Conference on, Vol. 1, 2008, pp. 608–613

  25. A. O. Sá, N. Nedjah, L. Macedo Mourelle, Computational Science and Its Applications – ICCSA 2014: 14th International Conference, Guimarães, Portugal, June 30 – July 3, 2014, Proceedings, Part V, Springer International Publishing, Cham, 2014, Ch. Genetic and Backtracking Search Optimization Algorithms Applied to Localization Problems, pp. 738–746

  26. Localization algorithm in wireless sensor networks based on multiobjective particle swarm optimization, Int. J. Distrib. Sens. Netw. (2015)

  27. D. Manjarres, J.D. Ser, S. Gil-Lopez, M. Vecchio, I. Landa-Torres, S. Salcedo-Sanz, R. Lopez-Valcarce, On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks. Eng. Appl. Artif. Intell. 26(2), 669–676 (2013)

    Article  Google Scholar 

  28. M. Sayadnavard, A. Haghighat, M. Abdechiri, Wireless sensor network localization using imperialist competitive algorithm, in: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 9, 818–822 (2010)

  29. G. Iacca, F. Neri, E. Mininno, Y.-S. Ong, M.-H. Lim, Ockhams razor in memetic computing: three stage optimal memetic exploration. Inf. Sci. 188, 17–43 (2012)

    Article  MathSciNet  Google Scholar 

  30. F. Caraffini, F. Neri, G. Iacca, A. Mol, Parallel memetic structures. Inf. Sci. 227, 60–82 (2013)

    Article  MathSciNet  Google Scholar 

  31. P.C. Pop, O. Matei, A memetic algorithm approach for solving the multidimensional multi-way number partitioning problem. Appl. Math. Modell. 37, 9191–9202 (2013)

    Article  MathSciNet  Google Scholar 

  32. H. Brandner, S. Lessmann, S. Vo, A memetic approach to construct transductive discrete support vector machines. Eur. J. Oper. Res. 230(3), 581–595 (2013)

    Article  Google Scholar 

  33. P.-J. Chuang, C.-P. Wu, An effective pso-based node localization scheme for wireless sensor networks, in: Parallel and Distributed Computing, Applications and Technologies, 2008. PDCAT 2008. Ninth International Conference on, 2008, pp. 187–194

  34. Q.H. Nguyen, Y.-S. Ong, M.-H. Lim, A probabilistic memetic framework. IEEE Trans. Evolut. Comput. 13(3), 604–623 (2009)

    Article  Google Scholar 

  35. N. Krasnogor, Studies on the theory and design space of memetic algorithms, Ph.D. thesis (2002)

  36. H. Hoos, T. Sttzle, Stochastic Local Search: Foundations and Applications (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2004)

    Google Scholar 

  37. H. Karl, A. Willig, Protocols and Architectures for Wireless Sensor Networks (John Wiley & Sons, UK, 2005)

    Book  Google Scholar 

  38. F. Neri, V. Tirronen, Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  39. P. Merz, Memetic algorithms for combinatorial optimization problems: Fitness landscapes and effective search strategies (2001)

  40. M. Huang, S. Chen, Y. Wang, Minimum cost localization problem in wireless sensor networks, in: Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010, pp. 1–9

  41. S. Garca, A. Fernndez, J. Luengo, F. Herrera, A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10), 959–977 (2009)

    Article  Google Scholar 

  42. S. Holm, A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)

    MathSciNet  MATH  Google Scholar 

  43. M. Aziz, M.-H. Tayarani-N, An adaptive memetic particle swarm optimization algorithm for finding large-scale latin hypercube designs. Eng. Appl. Artif. Intell. 36, 222–237 (2014)

    Article  Google Scholar 

  44. H. Babaei, J. Karimpour, A. Hadidi, A survey of approaches for university course timetabling problem, Computers & Industrial Engineering 86,43–59 (2015) applications of Computational Intelligence and Fuzzy Logic to Manufacturing and Service Systems. doi:10.1016/j.cie.2014.11.010. http://www.sciencedirect.com/science/article/pii/S0360835214003714

  45. C.K. Goh, K.C. Tan, A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evolut. Comput. 13(1), 103–127 (2009)

    Article  Google Scholar 

  46. H. R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, in: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), vol. 1, 2005, pp. 695–701

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahdi Aziz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aziz, M., Tayarani-N, MH. & Meybodi, M.R. A two-objective memetic approach for the node localization problem in wireless sensor networks. Genet Program Evolvable Mach 17, 321–358 (2016). https://doi.org/10.1007/s10710-016-9274-8

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-016-9274-8

Keywords

Navigation