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.
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Notes
- 1.
In contrast to Woehrle et al. (2007), we use the parameters d 0 = 10m, P t = 0dBm, σ = 4. 0, η = 4. 0, and \({P}_{n} = -115dBm\) here.
- 2.
To get a general operator, the σ{ mut}-values are adapted to the size of the polygon. To this end, we choose \({\sigma }_{\text{ mut},x} = {c}_{\text{ mut}} \cdot X/2\) where X is the width of the enclosing rectangle of the area of interest and c { mut} = 0. 05 is constant. The value of σ{ mut}, y is chosen similarly with respect to Y , the height of the enclosing rectangle.
- 3.
For the computation of the hypervolume indicator, we normalized the number of nodes with the maximal number of nodes occurring during the simulations. As reference point, (1. 01, 1. 01) was chosen; resulting in a maximal indicator value of ≈ 1. 02.
References
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.
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.
Dhillon, S., Chakrabarty, K., & Iyengar, S. (2002). Sensor placement for grid coverage under imprecise detections. In Conference on information fusion (pp. 1581–1587).
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.
Jourdan, D. B. (2006). Wireless sensor network planning with application to UWB localization in GPS-Denied environments. Ph.D. thesis, Massachusetts Institute of Technology.
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.
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.
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).
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).
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.
Schoenauer, M. (1996). Shape representations and evolution schemes. In Conference on evolutionary programming (pp. 121–129). Cambridge, MA: MIT.
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).
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.
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.
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).
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.
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.
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.
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Woehrle, M., Brockhoff, D., Hohm, T., Bleuler, S. (2010). Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs. In: Ehrgott, M., Naujoks, B., Stewart, T., Wallenius, J. (eds) Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Lecture Notes in Economics and Mathematical Systems, vol 634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04045-0_18
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