Abstract
This paper formulates a prioritized data gathering problem in noisy wireless sensor networks (WSNs) and solves the problem with a noise-aware evolutionary multiobjective optimization algorithm (EMOA). Unlike existing local search heuristics, the proposed algorithm can seek the Pareto-optimal routing structures with respect to conflicting optimization objectives. Simulation results demonstrate that the proposed algorithm outperforms a traditional EMOA in a noisy WSN.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Krishnamachari, B.: Modeling Data Gathering in Wireless Sensor Networks. In: Wireless Sensor Networks and Applications, III. Signals and Communication Technology, pp. 387–399. Springer, Heidelberg (2007)
Meliou, A., Chu, D., Hellerstein, J., Guestrin, C., Hong, W.: Data gathering tours in sensor networks. In: Proc. of ACM/IEEE IPSN (2006)
Han, Q., Hakarrinen, D., Boonma, P., Suzuki, J.: Quality-aware sensor data collection. Int’l Journal of Sensor Networks 7(3), 127–140 (2010)
Woo, A., Tong, T., Culler, D.: Taming the underlying challenges of reliable multihop routing in sensor networks. In: Proc. SenSys (2003)
Zhao, J., Govindan, R.: Understanding packet delivery performance in dense wireless sensor networks. In: Proc. SenSys (2003)
Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J., Hong, W.: Model-driven data acquisition in sensor networks. In: Proc. VLDB (2004)
Wada, H., Boonma, P., Suzuki, J.: Chronus: A spatiotemporal macroprogramming language for autonomic wireless sensor networks. In: Autonomic Network Management Principles: From Concepts to Applications, Elsevier (in press)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2) (2002)
Goldberg, D., Lingle, R.: Alleles, loci and the traveling salesman problem. In: Proc. 1st Int. Conf. on Genetic Algorithms, pp. 154–159 (1985)
Bianchi, L., Dorigo, M., Gambardella, L., Gutjahr, W.: A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing 8(2) (2009)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4)
Knowles, J., Corne, D.: On metrics for comparing nondominated sets. In: Proc. World on Congress on Computational Intelligence (2002)
Wang, Y.-P., Wing Leung, Y., Ping Wang, Y., Ping Wang, Y.: U-measure: A quality measure for multiobjective programming. Technical Report, Hong kong Baptist University (2003)
Boonma, P., Han, Q., Suzuki, J.: Leveraging biologically-inspired mobile agents supporting composite needs of reliability and timeliness in sensor applications. In: Proc. IEEE FBIT (2007)
Ombuki, B., Ross, B.J., Hanshar, F.: Multi-objective genetic algorithms for vehicle routing problem with time windows. In: Applied Intelligence, vol. 24 (2006)
Tan, K.C., Cheong, C.Y., Goh, C.K.: Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation. European Journal of Operational Research 177(2) (2007)
Beyer, H.-G.: Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. In: Computer Methods in Applied Mechanics and Engineering, vol. 186(2-4) (2000)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments: a survey. IEEE Trans. Evol. Comput. 9(3) (2005)
Goh, C.K., Tan, K.C.: Noise handling in evolutionary multi-objective optimization. In: Proc. of IEEE CEC (2006)
Eskandari, H., Geiger, C.D., Bird, R.: Handling uncertainty in evolutionary multiobjective optimization: SPGA. In: Proc. of IEEE CEC (2007)
Babbar, M., Lakshmikantha, A., Goldberg, D.E.: A modified NSGA-II to solve noisy multiobjective problems. In: Proc. of ACM GECCO (2003)
Teich, J.: Pareto-front exploration with uncertain objectives. In: Proc. of Int’l Conf. on Evol. Multi-Criterion Optimization (2001)
Wormington, M., Panaccione, C., Matney, K.M., Bowen, D.K.: Characterization of structures from x-ray scattering data using genetic algorithms. JSTOR Philosophical Transactions 357(1761), 2827–2848 (1999)
Delibrasis, K., Undrill, P., Cameron, G.: Genetic algorithm implementation of stack filter design for image restoration. In: IEE Proc. VISP, vol. 143(3) (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhu, B., Suzuki, J., Boonma, P. (2012). Evolutionary and Noise-Aware Data Gathering for Wireless Sensor Networks. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-32615-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32614-1
Online ISBN: 978-3-642-32615-8
eBook Packages: Computer ScienceComputer Science (R0)