J. M. Bahi, W. Elghazel, C. Guyeux, M. Haddad, M. Hakem et al., Resiliency in distributed sensor networks for prognostics and health management of the monitoring targets, Comput. J, vol.59, issue.2, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02131167

J. M. Bahi and C. Guyeux, Abdallah Makhoul, Congduc Pham, Low cost monitoring and intruders detection using wireless video sensor networks, Int. J. Distributed Sens. Netw, 2012.
DOI : 10.1155/2012/929542

URL : https://doi.org/10.1155/2012/929542

L. Breiman, Bagging predictors, Mach. Learn, vol.24, pp.123-140, 1996.
DOI : 10.1007/bf00058655

URL : https://link.springer.com/content/pdf/10.1007%2FBF00058655.pdf

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

D. W. Carman, P. S. Kuus, and B. J. Matt, Constraints and approaches for distributed sensor network security, 2000.

E. Chavez, S. Dobrev, E. Kranakis, J. Opatrny, L. Stacho et al., Halfspace proximal: a new local test for extracting a bounded dilation spanner, International Conference On Principles of Distributed Systems, pp.235-245, 2006.

F. Chiti, A. De-cristofaro, R. Fantacci, D. Tarchi, G. Collodo et al., Energy efficient routing algorithms for application to agro-food wireless sensor networks, IEEE International Conference on Communication (ICC), pp.3063-3067, 2005.

T. G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Mach. Learn, vol.40, pp.139-157, 2000.

W. Elghazel, J. Bahi, C. Guyeux, M. Hakem, K. Medjaher et al., Dependability of wireless sensor networks for industrial prognostics and health management, Comput. Ind, vol.68, pp.1-15, 2015.
DOI : 10.1016/j.compind.2014.10.004

URL : https://hal.archives-ouvertes.fr/hal-01301811

W. Elghazel, K. Medjaher, N. Zerhouni, J. Bahi, A. Farhat et al., Mourad Hakem, Random forests for industrial device functioning diagnostics using wireless sensor networks, IEEE Aerospace conference, pp.1-9, 2015.
DOI : 10.1109/aero.2015.7119275

URL : https://hal.archives-ouvertes.fr/hal-01304669/file/egbfg%2B15%3Aip-author.pdf

A. Farhat, C. Guyeux, A. Makhoul, A. Jaber, and R. Tawil, On the coverage effects in wireless sensor networks based prognostic and health management, Int. J. Sens. Networks (IJSNET), vol.28, issue.2, pp.125-138, 2018.

F. Giorgio and F. Roli, A theoretical and experimental analysis of linear combiners for multiple classifier systems, IEEE Trans. Pattern Anal. Mach. Intelligence, vol.27, issue.6, pp.942-956, 2005.

K. Gabriel and R. Sokal, A new statistical approach to geographic variation analysis, Syst. Zool, vol.18, pp.259-278, 1969.
DOI : 10.2307/2412323

W. Rabiner-heinzelman, A. Chandrakasan, and H. Balakrishna, Energyefficient communication protocol for wireless sensor networks, IEEE Proceedings of the Hawaii International Conference on System Sciences, 2000.

A. Heng, S. Zhang, A. C. Tan, and J. Mathew, Rotating machinery prognostics: State of the art, challenges and opportunities, Mech. Syst. Signal Process, vol.23, pp.724-739, 2009.

K. Tin and . Ho, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell, vol.20, issue.8, pp.832-844, 1998.

P. Jacques, R. Seshagiri, T. V. Prabhakar, H. Jean-pierre, and H. S. Jamadagni, Commonsense net: a wireless sensor network for resource-poor agriculture in the semiarid areas of developing countries, Int. J. Inform. Technol, vol.4, issue.1, pp.51-67, 2007.

K. S. Andrew, D. Jardine, D. Lin, and . Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Process, vol.20, pp.1483-1510, 2006.

Y. Jina, L. Wanga, and Y. Kimb, Xiaozong Yanga, EEMC. An energyefficient multi-level clustering algorithm for large-scale wireless sensor networks, Comput. Netw, vol.52, pp.542-562, 2008.

A. H. Kabashi and J. M. Elmirghani, A technical framework for designing wireless sensor networks for agricultural monitoring in developing countries, International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST), pp.395-401, 2008.
DOI : 10.1109/ngmast.2008.64

L. K. Kait, C. Z. Kai, R. Khoshdelniat, S. M. Lim, and E. H. Tat, Paddy growth monitoring with wireless sensor networks, Conference on Intelligent and Advanced Systems (ICIAS), pp.966-970, 2007.
DOI : 10.1109/icias.2007.4658529

W. Ke, W. Liqiang, C. Shiyu, and Q. Song, An energy-saving algorithm of wsn based on gabriel graph, 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp.1-4, 2009.

J. H. Soonmok-kwon, J. Ko, C. Kim, and . Kim, Dinamic timeout for data aggregation in wireless sensor netwoks, Comput. Netw, vol.55, pp.650-664, 2011.

D. Matula and R. Sokal, Properties of gabriel graphs relevant to geographic variation research and the clustering of points in the plane, Geogr. Anal, vol.12, issue.3, pp.205-222, 1980.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. E. Schapire, A brief introduction to boosting, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.

A. Sharkey and N. Sharkey, Combining diverse neural nets, Knowledge Eng. Rev, vol.12, issue.3, pp.231-247, 1997.

I. Solis and K. Obraczka, The impact of timing in data aggregation for wireless sensor networks, Proceedings of the IEEE International Conference on Communications, pp.3640-3645, 2004.

A. Tsymbal and S. Puuronen, Bagging and boosting with dynamic integration of classifiers, 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases PKDD, pp.116-125, 2000.

K. Tumer and J. Ghosh, Error correlation and error reduction in ensemble classifiers, Connection Sci, vol.8, pp.385-404, 1996.

A. Verikas, A. Gelzinis, and M. Bacauskiene, Mining data with random forests: A survey and results of new tests, Pattern Recognit, vol.44, pp.330-349, 2011.

W. Yang, H. Liusheng, W. Junmin, and X. Hongli, Wireless sensor networks for intensive irrigated agriculture, Consumer Communications and Networking Conference (CCNC), pp.197-201, 2007.

S. Yoo, J. Kim, T. Kim, S. Ahn, J. Sung et al., A2s: automated agriculture system based on wsn, IEEE International Symposium on Consumer Electronics (ISCE), pp.1-5, 2007.

H. Yousefi, N. Mohammad-hossein-yeganeh, A. Alinaghipour, and . Movaghar, Structure-free real-time data aggregation in wireless sensor networks, Comput. Commun, vol.35, issue.9, pp.1132-1140, 2012.