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Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques

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Applications of Evolutionary Computation (EvoApplications 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9028))

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Abstract

This work deals with how to efficiently deploy an indoor wireless sensor network, assuming a novel approach in which we try to leverage existing infrastructure. Thus, given a set of low-cost sensors, which can be plugged into the grid or powered by batteries, a collector node, and a building plan, including walls and plugs, the purpose is to deploy the sensors optimising three conflicting objectives: average coverage, average energy cost, and average reliability. Two MultiObjective (MO) genetic algorithms are assumed to solve this issue, NSGA-II and SPEA2. These metaheuristics are applied to solve the problem using a freely available data set. The results obtained are analysed considering two MO quality metrics: hypervolume and set coverage. After applying a statistical methodology widely accepted, we conclude that SPEA2 provides the best performance on average considering such data set.

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References

  1. Mukherjee, J.Y.B., Ghosal, D.: Wireless sensor network survey. Comput. Netw. 52, 2292–2330 (2008)

    Article  Google Scholar 

  2. Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40, 102–114 (2002)

    Article  Google Scholar 

  3. Cheng, X., Narahari, B., Simha, R., Cheng, M., Liu, D.: Strong minimum energy topology in wireless sensor networks: Np-completeness and heuristics. IEEE Trans. Mob. Comput. 2, 248–256 (2003)

    Article  Google Scholar 

  4. Chang, J.H., Tassiulas, L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Netw. 12, 609–619 (2004)

    Article  Google Scholar 

  5. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. (Doctoral dissertation). Swiss Federal Institute of Technology (ETH) (1999)

    Google Scholar 

  6. Yun, J., Kim, J.: Deployment support for sensor networks in indoor climate monitoring. Int. J. Distrib. Sens. Netw., 1–10 (2013)

    Google Scholar 

  7. Zhang, Z., Zhu, J., Ruan, J., Song, G.: Distance measurement for the indoor WSN nodes using WTR method. Int. J. Distrib. Sens. Netw. 2014, 1–13 (2014)

    Google Scholar 

  8. Song, G., Zhuang, W., Song, A.: Self-deployment of mobile sensor networks in complex indoor environments. In: IEEE Conference WCICA, pp. 4543–4546 (2006)

    Google Scholar 

  9. Lin, C.H., King, C.T.: Sensor-deployment strategies for indoor robot navigation. IEEE Trans. Syst. Man Cybern. B Cybern. - Part A: Syst. Hum. 40, 388–398 (2010)

    Article  Google Scholar 

  10. Seok, J.-H., Lee, J.-Y., Oh, C., Lee, J.-J., Lee, H.J.: Rfid sensor deployment using differential evolution for indoor mobile robot localization. In: IEEE Conference IROS, pp. 3719–3724 (2010)

    Google Scholar 

  11. Tarng, J.H., Chuang, B.W., Liu, P.C.: A relay node deployment method for disconnected wireless sensor networks: Applied in indoor environments. J. Netw. Comput. Appl. 32, 652–659 (2009)

    Article  Google Scholar 

  12. Yu, M., Song, J.K., Mah, P.: RNIndoor: A relay node deployment method for disconnected wireless sensor networks in indoor environments. In: ICUFN Conference, pp. 19–24 (2011)

    Google Scholar 

  13. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2000)

    Article  Google Scholar 

  14. Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: Improving the strength pareto evolutionary algorithm. Technical report, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2001)

    Google Scholar 

  15. Lanza-Gutiérrez, J.M., Gómez-Pulido, J.A., Vega-Rodríguez, M.A.: A trajectory-based heuristic to solve a three-objective optimization problemfor wireless sensor network deployment. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 27–38. Springer, Heidelberg (2014)

    Google Scholar 

  16. Lanza-Gutierrez, J.M., Gomez-Pulido, J.A., Vega-Rodriguez, M.A.: Intelligent relay node placement in heterogeneous wireless sensor networks for energy efficiency. Int. J. Robot. Autom. 29, 1–13 (2014)

    Google Scholar 

  17. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C. (eds.): Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  18. Chipcom, A.S.: Smartrf cc2420 preliminary datasheet (2004). http://inst.eecs.berkeley.edu/cs150/Documents/CC2420.pdf

  19. Wilson, R.: Propagation losses through common building materials: 2.4 ghz vs 5 ghz. reflection and transmission losses through common building materials. Technical Report E10589, Magis Networks, Inc. (2002)

    Google Scholar 

  20. Konstantinidis, A., Yang, K.: Multi-objective k-connected deployment and power assignment in wsns using a problem-specific constrained evolutionary algorithm based on decomposition. Comput. Commun. 34, 83–98 (2011)

    Article  Google Scholar 

  21. Deb, B., Bhatnagar, S., Nath, B.: Reliable information forwarding using multiple paths in sensor networks. In: Proceedings of IEEE LCN, pp. 406–415 (2003)

    Google Scholar 

  22. Suurballe, J.W.: Disjoint paths in a network. Networks 4, 125–145 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  23. Lanza-Gutierrez, J.M., Gomez-Pulido, J.A.: Instance sets for indoor optimization in wireless sensor networks (2014). http://arco.unex.es/wsnopt

  24. Mahboubi, H., Moezzi, K., Aghdam, A., Sayrafian-Pour, K., Marbukh, V.: Distributed deployment algorithms for improved coverage in a network of wireless mobile sensors. IEEE Trans. Industr. Inf. 10, 163–174 (2014)

    Article  Google Scholar 

  25. Martins, F., Carrano, E., Wanner, E., Takahashi, R., Mateus, G.: A hybrid multiobjective evolutionary approach for improving the performance of wireless sensor networks. IEEE Sens. J. 11, 545–554 (2011)

    Article  Google Scholar 

  26. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3, 257–271 (1999)

    Article  Google Scholar 

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Acknowledgements

This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the contract TIN2012-30685 (BIO project), and by the Government of Extremadura, with the aid GR10025 to the group TIC015.

Author information

Authors and Affiliations

  1. Department of Computers and Communications Technologies, Polytechnic School, University of Extremadura, Caceres, Spain

    Jose M. Lanza-Gutierrez & Juan A. Gomez-Pulido

  2. Center for Research in Informatics and Communications, Polytechnic Institute of Leiria, Leiria, Portugal

    Jose M. Lanza-Gutierrez, S. Priem-Mendes, M. Ferreira & J. S. Pereira

  3. School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal

    S. Priem-Mendes, M. Ferreira & J. S. Pereira

  4. Instituto de Telecomunicações, Leiria Branch, Leiria, Portugal

    J. S. Pereira

Authors
  1. Jose M. Lanza-Gutierrez
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  2. Juan A. Gomez-Pulido
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  3. S. Priem-Mendes
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  4. M. Ferreira
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  5. J. S. Pereira
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Corresponding author

Correspondence to Jose M. Lanza-Gutierrez .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

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Lanza-Gutierrez, J.M., Gomez-Pulido, J.A., Priem-Mendes, S., Ferreira, M., Pereira, J.S. (2015). Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary Techniques. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-16549-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16548-6

  • Online ISBN: 978-3-319-16549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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