G. Zhou, L. Huang, W. Li, and Z. Zhu, Harvesting Ambient Environmental Energy for Wireless Sensor Networks: A Survey, Journal of Sensors, vol.138, issue.1, 2014.
DOI : 10.1109/TIM.2009.2012943
URL : http://doi.org/10.1155/2014/815467

A. Randrianarisaina, O. Pasquier, and P. Charg, Energy consumption modeling of smart nodes with a function approach, Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing, p.16, 2014.
DOI : 10.1109/DASIP.2014.7115645
URL : https://hal.archives-ouvertes.fr/hal-01062240

M. Darienzo, M. Iacono, S. Marrone, and R. Nardone, Petri net based evaluation of energy consumption in wireless sensor nodes, Journal of High Speed Networks, vol.19, issue.4, p.339358, 2013.

A. Suissa, O. Romain, J. Denoulet, K. Hachicha, and P. Garda, Empirical Method Based on Neural Networks for Analog Power Modeling, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol.29, issue.5, p.839844, 2010.
DOI : 10.1109/TCAD.2010.2043759
URL : https://hal.archives-ouvertes.fr/hal-01198824

A. Munir and A. Gordon-ross, An MDP-Based Dynamic Optimization Methodology for Wireless Sensor Networks, IEEE Transactions on Parallel and Distributed Systems, vol.23, issue.4, p.616625, 2012.
DOI : 10.1109/tpds.2011.208

A. Munir, A. Gordon-ross, S. Lysecky, and R. Lysecky, Online algorithms for wireless sensor networks dynamic optimization, 2012 IEEE Consumer Communications and Networking Conference (CCNC), p.180187, 2012.
DOI : 10.1109/CCNC.2012.6181082

D. Banerjee, S. Sen, and A. Chatterjee, Self learning analog/mixed-signal/RF systems: Dynamic adaptation to workload and environmental uncertainties, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), p.5964, 2015.
DOI : 10.1109/ICCAD.2015.7372550

F. A. Aoudia, M. Gautier, and O. Berder, Learning to survive: Achieving energy neutrality in wireless sensor networks using reinforcement learning, 2017 IEEE International Conference on Communications (ICC), 2017.
DOI : 10.1109/ICC.2017.7996978
URL : https://hal.archives-ouvertes.fr/hal-01530098

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, p.1054, 2013.
DOI : 10.1109/TNN.1998.712192

R. S. Sutton, Learning to predict by the methods of temporal differences, Machine Learning, pp.9-44, 1988.
DOI : 10.3758/BF03205056

H. Bellia, R. Youcef, and M. Fatima, A detailed modeling of photovoltaic module using MATLAB, NRIAG Journal of Astronomy and Geophysics, vol.3, issue.1, pp.53-61, 2014.
DOI : 10.1016/j.nrjag.2014.04.001

C. Piedallu and J. C. Ggout, Calcul multi-??chelle du rayonnement solaire pour la mod??lisation pr??dictive de la v??g??tation, Annals of Forest Science, vol.15, issue.I, pp.899-909, 2007.
DOI : 10.1111/j.1654-1103.2004.tb02315.x