W. M. Bolstad and J. M. Curran, Introduction to Bayesian Statistics, 2016.

S. Devarakonda, P. Sevusu, H. Liu, R. Liu, L. Iftode et al., Real-time air quality monitoring through mobile sensing in metropolitan areas, Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, p.15, 2013.

R. K. Ganti, F. Ye, and H. Lei, Mobile crowdsensing: current state and future challenges, IEEE Commun. Mag, vol.49, issue.11, 2011.

A. Gelman, H. S. Stern, J. B. Carlin, D. B. Dunson, A. Vehtari et al., , 2013.

D. Hasenfratz, O. Saukh, S. Sturzenegger, and L. Thiele, Participatory air pollution monitoring using smartphones, pp.1-5, 2012.

M. J. Hausknecht and P. Stone, Deep recurrent q-learning for partially observable mdps, AAAI Fall Symposium Series, pp.29-37, 2015.

F. Ingelrest, G. Barrenetxea, G. Schaefer, M. Vetterli, O. Couach et al., Sensorscope:application-specific sensor network for environmental monitoring, ACM Trans Sens Netw, vol.6, issue.2, pp.1-32, 2010.

L. Kong, M. Xia, X. Liu, G. Chen, Y. Gu et al., Data loss and reconstruction in wireless sensor networks, IEEE Trans. Parallel Distrib. Syst, vol.25, issue.11, pp.2818-2828, 2014.

G. Lample, D. S. Chaplot, G. Lample, and D. S. Chaplot, Playing FPS games with deep reinforcement learning, AAAI Conference on Artificial Intelligence, 2016.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou et al., Playing Atari with deep reinforcement learning, Comput. Sci, 2013.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, p.529, 2015.

R. K. Rana, C. T. Chou, S. S. Kanhere, N. Bulusu, and W. Hu, Ear-phone: an endto-end participatory urban noise mapping system, Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp.105-116, 2010.

M. Roughan, Y. Zhang, W. Willinger, and L. Qiu, Spatio-temporal compressive sensing and internet traffic matrices, IEEE/ACM Trans. Netw. (ToN), vol.20, issue.3, pp.662-676, 2012.

J. Shang, Y. Zheng, W. Tong, E. Chang, and Y. Yu, Inferring gas consumption and pollution emission of vehicles throughout a city, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1027-1036, 2014.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre et al., Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, p.484, 2016.

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang et al., Mastering the game of go without human knowledge, Nature, vol.550, issue.7676, p.354, 2017.

R. Sutton and A. Barto, Reinforcement Learning: An Introduction, 2005.

E. Wang, Y. Yang, J. Wu, W. Liu, and X. Wang, An efficient prediction-based user recruitment for mobile crowdsensing, IEEE Trans. Mob. Comput, vol.17, issue.1, pp.16-28, 2018.

J. Wang, Y. Wang, D. Zhang, F. Wang, Y. He et al., PSAllocator: multi-task allocation for participatory sensing with sensing capability constraints, Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp.1139-1151, 2017.

L. Wang, D. Zhang, A. Pathak, C. Chen, H. Xiong et al., Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.683-694, 2015.

L. Wang, D. Zhang, Y. Wang, C. Chen, X. Han et al., Sparse mobile crowdsensing: challenges and opportunities, IEEE Commun. Mag, vol.54, issue.7, pp.161-167, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01346728

L. Wang, D. Zhang, D. Yang, B. Y. Lim, and X. Ma, Differential location privacy for sparse mobile crowdsensing, Data Mining (ICDM), 2016 IEEE 16th International Conference on, pp.1257-1262, 2016.

L. Wang, D. Zhang, D. Yang, A. Pathak, C. Chen et al., SPACE-TA: cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing, ACM Trans. Intell. Syst.Technol, vol.9, issue.2, pp.1-28, 2017.

L. Xiao, T. Chen, C. Xie, H. Dai, and V. Poor, Mobile crowdsensing games in vehicular networks, IEEE Trans. Veh. Technol, issue.99, pp.1-1, 2017.

L. Xiao, Y. Li, G. Han, H. Dai, and H. V. Poor, A secure mobile crowdsensing game with deep reinforcement learning, IEEE Trans. Inf. ForensicsSecur, issue.99, pp.1-1, 2017.

H. Xiong, D. Zhang, L. Wang, and H. Chaouchi, EMC 3: energy-efficient data transfer in mobile crowdsensing under full coverage constraint, IEEE Trans. Mob. Comput, vol.14, issue.7, pp.1355-1368, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01078224

L. Xu, X. Hao, N. D. Lane, X. Liu, and T. Moscibroda, More with less: Lowering user burden in mobile crowdsourcing through compressive sensing, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.659-670, 2015.

Y. Yang, W. Liu, E. Wang, and J. Wu, A prediction-based user selection framework for heterogeneous mobile crowdsensing, IEEE Trans. Mob. Comput, 2018.

D. Zhang, L. Wang, H. Xiong, and B. Guo, 4W1H in mobile crowd sensing, IEEE Commun. Mag, vol.52, issue.8, pp.42-48, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01078233

Y. Zheng, F. Liu, and H. P. Hsieh, U-Air: when urban air quality inference meets big data, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol.4, pp.1436-1450, 2013.

Y. Zhu, Z. Li, H. Zhu, M. Li, and Q. Zhang, A compressive sensing approach to urban traffic estimation with probe vehicles, IEEE Trans. Mob. Comput, vol.12, issue.11, pp.2289-2302, 2013.