Scalability Analysis of a Novel Integer Programming Model to Deal with Energy Consumption in Heterogeneous Wireless Sensor Networks
This paper presents a scalability analysis over a novel integer programming model devoted to optimize power consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off a specific set of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. As the problem instances grows the time spent to the execution turns impractcable.
KeywordsSensor Node Wireless Sensor Network Network Lifetime Total Energy Consumption Demand Point
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- 1.Megerian, S., Potkonjak, M.: Lower power 0/1 coverage and scheduling techniques in sensor networks. Technical Reports Vol. 030001. University of California, Los Angeles (2003)Google Scholar
- 2.Mote battery life calculator, http://www.xbow.com/Support/Sypport_pdf_files/PowerManagement.xls
- 3.ILOG: ILOG CPLEX 9.0 User’s Manual (2003)Google Scholar
- 4.Loureiro, A., Ruiz, L., Mini, R., Nogueira, J.: Redes de sensores sem fio. Simpósio Brasileiro de Computação, Jornada de Atualização de Informática (2002)Google Scholar
- 6.Nakamura, F.G., Quintão, F.P., Menezes, G.C., Mateus, G.R.: Planejamento dinâmico para controle de cobertura e conectividade em redes de sensores sem fio. In: Workshop de Comunicação sem Fio e Computação Móvel, vol. 1, pp. 182–191 (2004)Google Scholar
- 7.Nepomuceno, N.V., Pinheiro, P.R., Coelho, A.L.V.: A Hybrid Optimization Framework for Cutting and Packing Problems: Case Study on Constrained 2D Non-guillotine Cutting. In: Cotta, C., Hemert., J. (eds.) Recent Advances in Evolutionary Computation for Combinatorial Optimization. Springer, Heidelberg (to appear)Google Scholar