Computational Intelligence in Wireless Sensor Networks: by Ajith Abraham, Rafael Falcon, Mario Koeppen

By Ajith Abraham, Rafael Falcon, Mario Koeppen

This booklet emphasizes the more and more vital position that Computational Intelligence (CI) equipment are taking part in in fixing a myriad of entangled instant Sensor Networks (WSN) comparable difficulties. The e-book serves as a advisor for surveying numerous state of the art WSN situations during which CI methods were hired. The reader reveals during this booklet how CI has contributed to unravel quite a lot of not easy difficulties, starting from balancing the price and accuracy of heterogeneous sensor deployments to convalescing from real-time sensor disasters to detecting assaults introduced through malicious sensor nodes and enacting CI-based safeguard schemes. community managers, specialists, academicians and practitioners alike (mostly in computing device engineering, laptop technological know-how or utilized arithmetic) take advantage of th e spectrum of winning purposes mentioned during this booklet. Senior undergraduate or graduate scholars may possibly notice during this e-book a few difficulties well matched for his or her personal examine endeavors.

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Extra info for Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges

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Anchorage, AK (2012) 6. : Are all objectives necessary? On dimensionality reduction in evolutionary multiobjective optimization. In: Parallel Problem Solving from Nature-PPSN IX, pp. 533–542. Springer, Berlin (2006) 7. : Inductive learning of snowpack distribution models for improved estimation of areal snow water equivalent. J. Hydrol. (2015) 8. : Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006) 9. : Multisensor Data Fusion. CRC Press, Boca Raton (2001) 10. : ALPS: the age-layered population structure for reducing the problem of premature convergence.

990 Results on Actual Data Table 8 reports the average prediction errors for all of the actual datasets, for both the hierarchical and non-hierarchical methods. The statistical significance of the difference in errors between these two methods is reported in Table 9. The average cost of models trained on the actual datasets for the hierarchical and non-hierarchical methods can be seen in Table 10. Table 11 reports the statistical signficance of the cost differences between the two methods. 016 5 Discussion In this section we reflect on the reason for and meaning of our quantitative results described in Sects.

Average error Table 6 shows that the average error of the hierarchical and the non-hierarchical methods were not significantly different, except for datasets DS1 , DS2 and DS4 where the latter method achieves better prediction accuracy. This is probably due to the characteristics of these datasets, where the difference between the (g) least prediction variances vri s and the greatest ones is large. The majority of sensors in these datasets are not informative but have low costs and the remaining sensors are more informative but come with higher costs.

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