Issue No. 04 - April (2014 vol. 63)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TC.2012.229
Andre L. L. Aquino , Comput. Inst., Fed. Univ. of Alagoas, Maceio, Brazil
Orlando S. Junior , Dept. of Comput. Sci., Pontifical Catholic Univ. of Minas Gerais, Belo Horizonte, Brazil
Alejandro C. Frery , Comput. Inst., Fed. Univ. of Alagoas, Maceio, Brazil
Edler Lins de Albuquerque , Dept. of Adm. & Ind. Chem. Processes, Fed. Inst. of Bahia, Salvador, Brazil
Raquel A. F. Mini , Dept. of Comput. Sci., Pontifical Catholic Univ. of Minas Gerais, Belo Horizonte, Brazil
A wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced.
Wireless sensor networks, Principal component analysis, Algorithm design and analysis, Temperature measurement, Temperature sensors, Indexes, Humidity
A. L. Aquino, O. S. Junior, A. C. Frery, E. Lins de Albuquerque and R. A. Mini, "MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks," in IEEE Transactions on Computers, vol. 63, no. 4, pp. 968-978, 2014.