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Exploiting Multi–Objective Evolutionary Algorithms for Designing Energy–Efficient Solutions to Data Compression and Node Localization in Wireless Sensor Networks

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Evolutionary Based Solutions for Green Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 432))

Abstract

Wireless sensor network (WSN) technology promises to have a high potential to tackle environmental challenges and to monitor and reduce energy and greenhouse gas emissions. Indeed, WSNs have already been successfully employed in applications such as intelligent buildings, smart grids and energy control systems, transportation and logistics, and precision agriculture. All these applications generally require the exchange of a large amount of data and the localization of the sensor nodes. Both these two tasks can be particularly energy–hungry. Since sensor nodes are typically powered by small batteries, appropriate energy saving strategies have to be employed so as to prolong the lifetime of the WSNs and to make their use attractive and effective. To this aim, the study of data compression algorithms suitable for the reduced storage and computational resources of a sensor node, and the exploration of node localization techniques aimed at estimating the positions of all sensor nodes of a WSN from the knowledge of the exact locations of a restricted number of these nodes, have attracted a large interest in the last years. In this chapter, we discuss how multi–objective evolutionary algorithms can successfully be exploited to generate energy–aware data compressors and to solve the node localization problem. Simulation results show that, in both the tasks, the solutions produced by the evolutionary processes outperform the most interesting approaches recently proposed in the literature.

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References

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

    Article  Google Scholar 

  2. Aspnes, J., Goldenberg, D., Yang, Y.R.: On the Computational Complexity of Sensor Network Localization. In: Nikoletseas, S.E., Rolim, J.D.P. (eds.) ALGOSENSORS 2004. LNCS, vol. 3121, pp. 32–44. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Barr, K.C., Asanović, K.: Energy-aware lossless data compression. ACM Trans. Comput. Syst. 24(3), 250–291 (2006)

    Article  Google Scholar 

  4. Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M., Couach, O., Parlange, M.: SensorScope: Out-of-the-Box Environmental Monitoring. In: Proc. of the 7th Int. Conf. on Information Processing in Sensor Networks, pp. 332–343 (2008)

    Google Scholar 

  5. Biswas, P., Lian, T.C., Wang, T.C., Ye, Y.: Semidefinite programming based algorithms for sensor network localization. ACM Trans. Sen. Netw. 2, 188–220 (2006)

    Article  Google Scholar 

  6. Biswas, P., Liang, T.C., Toh, K.C., Ye, Y., Wang, T.C.: Semidefinite programming approaches for sensor network localization with noisy distance measurements. IEEE Trans. Autom. Sci. Eng. 3(4), 360–371 (2006)

    Article  Google Scholar 

  7. Biswas, P., Ye, Y.: Semidefinite programming for ad hoc wireless sensor network localization. In: Proc. of the 3rd Int. Conf. on Information Processing in Sensor Networks, pp. 46–54 (2004)

    Google Scholar 

  8. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Costa, J.A., Patwari, N., Hero III, A.O.: Distributed weighted–multidimensional scaling for node localization in sensor networks. ACM Trans. on Sensor Networks 2(1), 39–64 (2006)

    Article  Google Scholar 

  10. Croce, S., Marcelloni, F., Vecchio, M.: Reducing power consumption in wireless sensor networks using a novel approach to data aggregation. The Computer Journal 51(2), 227–239 (2008)

    Article  Google Scholar 

  11. Cutler, C.C.: Differential quantization of communication signals. Patent 2 605 361 (1952)

    Google Scholar 

  12. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  13. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ferrière, H.D., Fabre, L., Meier, R., Metrailler, P.: TinyNode: a comprehensive platform for wireless sensor network applications. In: IPSN 2006: Proc. of the 5th Int. Conf. on Information Processing in Sensor Networks, pp. 358–365 (2006)

    Google Scholar 

  15. Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proc. of the 1st IEEE World Congress on Evolutionary Computation, vol. 1, pp. 82–87 (1994)

    Google Scholar 

  16. Huffman, D.: A method for the construction of minimum-redundancy codes. In: Proc. of the IRE, vol. 40(9), pp. 1098–1101 (1952)

    Google Scholar 

  17. Ji, X., Zha, H.: Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling. In: INFOCOM 2004: Proc. of the 23rd Annual Joint Conf. of the IEEE Computer and Communications Societies, vol. 4, pp. 2652–2661 (2004)

    Google Scholar 

  18. Kannan, A.A., Fidan, B., Mao, G.: Analysis of flip ambiguities for robust sensor network localization. IEEE Trans. Veh. Technol. 59(4), 2057–2070 (2010)

    Article  Google Scholar 

  19. Kannan, A.A., Mao, G., Vucetic, B.: Simulated annealing based wireless sensor network localization with flip ambiguity mitigation. In: VTC 2006: Proc. of the 63rd IEEE Vehicular Technology Conf., pp. 1022–1026 (2006)

    Google Scholar 

  20. Kim, S., Kojima, M., Waki, H.: Exploiting sparsity in SDP relaxation for sensor network localization. SIAM J. Optim. 20, 192–215 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  21. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto Archived Evolution Strategy. IEEE Trans. Evol. Comput. 8(2), 149–172 (2000)

    Google Scholar 

  22. LZO homepage (2011), http://www.oberhumer.com/opensource/lzo/

  23. Mao, G., Fidan, B., Anderson, B.D.O.: Wireless sensor network localization techniques. Computer Networks 51(10), 2529–2553 (2007)

    Article  MATH  Google Scholar 

  24. Marcelloni, F., Vecchio, M.: A simple algorithm for data compression in wireless sensor networks. IEEE Commun. Lett. 12(6), 411–413 (2008)

    Article  Google Scholar 

  25. Marcelloni, F., Vecchio, M.: An efficient lossless compression algorithm for tiny nodes of monitoring wireless sensor networks. The Computer Journal 52(8), 969–987 (2009)

    Article  Google Scholar 

  26. Marcelloni, F., Vecchio, M.: Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Information Sciences 180(10), 1924–1941 (2010)

    Article  Google Scholar 

  27. Marcelloni, F., Vecchio, M.: A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks. Evolutionary Intelligence 3, 137–153 (2010)

    Article  Google Scholar 

  28. Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 2nd edn. Springer, New York (1994)

    MATH  Google Scholar 

  29. O’Neal, J.: Differential pulse-code modulation (PCM) with entropy coding. IEEE Trans. Inf. Theory 22(2), 169–174 (1976)

    Article  MATH  Google Scholar 

  30. Patwari, N., Ash, J.N., Kyperountas, S., Hero III, A.O., Moses, R.L., Correal, N.S.: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag. 22(4), 54–69 (2005)

    Article  Google Scholar 

  31. Peralta, L.M.R.: Collaborative localization in wireless sensor networks. In: SENSORCOMM 2007: Proc. of the 2007 Int. Conf. on Sensor Technologies and Applications, pp. 94–100 (2007)

    Google Scholar 

  32. Pradhan, S., Kusuma, J., Ramchandran, K.: Distributed compression in a dense microsensor network. IEEE Signal Process. Mag. 19(2), 51–60 (2002)

    Article  Google Scholar 

  33. Sadler, C.M., Martonosi, M.: Data compression algorithms for energy-constrained devices in delay tolerant networks. In: SenSys 2006: Proc. of the 4th Int. Conf. on Embedded Networked Sensor Systems, pp. 265–278 (2006)

    Google Scholar 

  34. Salomon, D.: Data Compression: The Complete Reference, 4th edn. Springer, London (2007)

    Google Scholar 

  35. Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., Estrin, D.: Lightweight Temporal Compression of microclimate datasets. In: LCN 2004: Proc. of the 29th Annual IEEE Int. Conf. on Local Computer Networks, pp. 516–524 (2004)

    Google Scholar 

  36. Sensirion homepage (2011), www.sensirion.com

  37. Severi, S., Abreu, G., Destino, G., Dardari, D.: Understanding and solving flip-ambiguity in network localization via semidefinite programming. In: GLOBECOM 2009: Proc. of the 28th IEEE Conf. on Global Telecommunications, pp. 3910–3915 (2009)

    Google Scholar 

  38. SimIt-ARM homepage (2011), http://simit-arm.sourceforge.net/

  39. Srinivas, N., Deb, K.: Multiobjective optimization using Nondominated Sorting in Genetic Algorithms. IEEE Trans. Evol. Comput. 2(3), 221–248 (1994)

    Google Scholar 

  40. Tseng, P.: Second–order cone programming relaxation of sensor network localization. SIAM J. Optim. 18(1), 156–185 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  41. Vecchio, M., López-Valcarce, R., Marcelloni, F.: A study on the application of different two-objective evolutionary algorithms to the node localization problem in wireless sensor networks. In: ISDA 2011: Proc. of the 11th IEEE Int. Conf. on Intelligent Systems Design and Applications, pp. 1008–1013 (2011)

    Google Scholar 

  42. Vecchio, M., López-Valcarce, R., Marcelloni, F.: A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks. Applied Soft Computing 12(7), 1891–1901 (2012)

    Article  Google Scholar 

  43. Wang, Z., Zheng, S., Ye, Y., Boyd, S.: Further relaxations of the semidefinite programming approach to sensor network localization. SIAM J. Optim. 19(2), 655–673 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  44. Xiong, Z., Liveris, A., Cheng, S.: Distributed source coding for sensor networks. IEEE Signal Process. Mag. 21(5), 80–94 (2004)

    Article  Google Scholar 

  45. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. IEEE Trans. Evol. Comput. 8(2), 173–195 (2000)

    Google Scholar 

  46. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. Int. Center for Numerical Methods in Engineering (CIMNE), Barcelona (2002)

    Google Scholar 

  47. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  48. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, G.V.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans. Evol. Comput. 7, 117–132 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy

    Francesco Marcelloni

  2. Departamento de Teoría de la Señal y las Comunicaciones, University of Vigo, C/ Maxwell s/n, 36310, Vigo, Spain

    Massimo Vecchio

Authors
  1. Francesco Marcelloni
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  2. Massimo Vecchio
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Correspondence to Francesco Marcelloni .

Editor information

Editors and Affiliations

  1. , Department of Electrical and Computer, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Samee Ullah Khan

  2. Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, Cracow, 31-155, Poland

    Joanna Kołodziej

  3. , Department of Computer Science, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Juan Li

  4. School of Information Technologies, University of Sydney, Sydney, 2006, New South Wales, Australia

    Albert Y. Zomaya

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Marcelloni, F., Vecchio, M. (2013). Exploiting Multi–Objective Evolutionary Algorithms for Designing Energy–Efficient Solutions to Data Compression and Node Localization in Wireless Sensor Networks. In: Khan, S., Kołodziej, J., Li, J., Zomaya, A. (eds) Evolutionary Based Solutions for Green Computing. Studies in Computational Intelligence, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30659-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-30659-4_8

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