Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs

  • Matthias Woehrle
  • Dimo Brockhoff
  • Tim Hohm
  • Stefan Bleuler
Conference paper
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 634)

Abstract

How many wireless sensor nodes should be used and where should they be placed in order to form an optimal wireless sensor network (WSN) deployment? This is a difficult question to answer for a decision maker due to the conflicting objectives of deployment costs and wireless transmission reliability. Here, we address this problem using a multiobjective evolutionary algorithm (MOEA) which allows to identify the trade-offs between low-cost and highly reliable deployments–providing the decision maker with a set of good solutions to choose from. For the MOEA, we use an off-the-shelf selector and propose a problem-specific representation, an initialization scheme, and variation operators. The resulting algorithm is applied to a test deployment scenario to show the usefulness of the approach in terms of decision making.

Keywords

Evolutionary multiobjective optimization Variable-length representation Wireless sensor networks 

Notes

Acknowledgements

Matthias Woehrle and Dimo Brockhoff have been supported by the SNF under grant numbers 5005-67322 and 112079. Tim Hohm has been supported by the European Commission under the Marie Curie RTN SYSTEM, Project 5336.

References

  1. Bai, X., Kuma, S., Xua, D., Yun, Z., & La, T. H. (2006). Deploying wireless sensors to achieve both coverage and connectivity. In Symposium on mobile ad hoc networking and computing (MobiHoc 2006) (pp. 131–142). New York: ACM.Google Scholar
  2. Bleuler, S., Laumanns, M., Thiele, L., & Zitzler, E. (2003). PISA–A platform and programming language independent interface for search algorithms. In Conference on evolutionary multi-criterion optimization (EMO 2003) (Vol. 2632, pp. 494–508) of LNCS.Google Scholar
  3. Dhillon, S., Chakrabarty, K., & Iyengar, S. (2002). Sensor placement for grid coverage under imprecise detections. In Conference on information fusion (pp. 1581–1587).Google Scholar
  4. Grimme, C. (2005). Räuber-Beute-Systeme für die Mehrkriterielle Optimierung. Internal report of the systems analysis research group SYS–5/05, Dortmund University, Computer Science Section.Google Scholar
  5. Jourdan, D. B. (2006). Wireless sensor network planning with application to UWB localization in GPS-Denied environments. Ph.D. thesis, Massachusetts Institute of Technology.Google Scholar
  6. Kotz, D., Newport, C., Gray, R., Liu, J., Yuan, Y., & Elliott, C. (2004). Experimental evaluation of wireless simulation assumptions. In Int’l workshop modeling analysis and simulation of wireless and mobile systems (MSWiM 04) (pp. 78–82). New York: ACM.Google Scholar
  7. Krause, A., Guestrin, C., Gupta, A., & Kleinberg, J. (2006). Near-optimal sensor placements: maximizing information while minimizing communication cost. In Conference on information processing sensor networks (IPSN 2006) (pp. 2–10). New York: ACM.Google Scholar
  8. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Workshop on wireless sensor networks and application (WSNA 2002) (pp. 88–97).Google Scholar
  9. Meier, A., Beutel, J., Lim, R., & Thiele, L. (2007). Design of a high-reliability low-power status monitoring protocol. In Conference on networked sensing systems (INSS 2007) (pp. 2–9).Google Scholar
  10. Rajagopalan, R., Varshney, P. K., Mohan, C. K., & Mehrotra, K. G. (2005). Sensor placement for energy efficient target detection in wireless sensor networks: a multi-objective optimization approach. In Conference on information sciences and systems.Google Scholar
  11. Schoenauer, M. (1996). Shape representations and evolution schemes. In Conference on evolutionary programming (pp. 121–129). Cambridge, MA: MIT.Google Scholar
  12. So, A. M.-C., & Ye, Y. (2005). On solving coverage problems in a wireless sensor network using voronoi diagrams. In Workshop on internet and network economics (WINE 2005) (pp. 584–593).Google Scholar
  13. Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., & Gill, C. (2003). Integrated coverage and connectivity configuration in wireless sensor networks. In Conference on embedded networked sensor systems (SenSys 2003) (pp. 28–39). New York: ACM Press.Google Scholar
  14. Woehrle, M., Brockhoff, D., & Hohm, T. (2007). A new model for deployment coverage and connectivity of wireless sensor networks. Technical Report 278, Computer Engineering and Networks Lab, ETH Zurich, 8092 Zurich, Switzerland.Google Scholar
  15. Xu, N., Rangwala, S., Chintalapudi, K., Ganesan, D., Broad, A., Govindan, R., et al. (2004). A wireless sensor network for structural monitoring. In Conference on embedded networked sensor systems (SenSys 2004) (pp. 13–24).Google Scholar
  16. Zdarsky, F. A., Martinovic, I., & Schmitt, J. B. (2005). On lower bounds for MAC layer contention in CSMA/CA-Based wireless networks. In Workshop on discrete algorithms and methods for MOBILE computing and communications (pp. 8–16). New York: ACM.Google Scholar
  17. Zitzler, E., & Künzli, S. (2004). Indicator-based selection in multiobjective search. In Conference on parallel problem solving from nature (PPSN VIII) (Vol. 3242, pp. 832–842) of LNCS. Birmingham: Springer.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matthias Woehrle
  • Dimo Brockhoff
  • Tim Hohm
    • 1
  • Stefan Bleuler
  1. 1.Computer Engineering and Networks LabETH ZurichZurichSwitzerland

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