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. 2010;10(3):2150-68.
doi: 10.3390/s100302150. Epub 2010 Mar 16.

Data driven performance evaluation of Wireless Sensor Networks

Alejandro C Frery  1 , Heitor S Ramos, José Alencar-Neto, Eduardo Nakamura, Antonio A F Loureiro
Affiliations

Data driven performance evaluation of Wireless Sensor Networks

Alejandro C Frery et al. Sensors (Basel). 2010.

Abstract

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed.

Keywords: reconstruction; sampling; simulation; statistical modeling; wireless sensor networks.

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Figures

Figure 1.
Figure 1.
A WSN as a sampling/reconstruction process.
Figure 2.
Figure 2.
Gaussian random fields.
Figure 3.
Figure 3.
Samples of 100 spatial point processes.
Figure 4.
Figure 4.
General setup and alternatives for clustering, aggregation and reconstruction.
Figure 5.
Figure 5.
The influence of sensor deployment using SKATER, coarse data set.
Figure 6.
Figure 6.
The influence of sensor deployment using SKATER, fine data set.
Figure 7.
Figure 7.
Reconstruction errors: three clustering/aggregation procedures (LEACH, SKATER and Pointwise, top, middle and bottom lines), four granularities (scales 5, 10, 15, 20, first to fourth column) and seven deployment point processes (−1000, −30, −15, 0, 5, 15 and 30 in different colors)
See this image and copyright information in PMC

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