, Pairwise Ranking Aggregation in a Crowdsourced Setting, 2013.

V. Hock-hee-ang, . Gopalkrishnan, K. Wee, S. Ng, and . Hoi, Communication-efficient classification in P2P networks, Proc. Joint Eur. Conf. on Mach. Learn. and Knowl. Disc. in Databases, pp.83-98, 2009.

C. Borcea, M. Talasila, and R. Curtmola, Mobile Crowdsensing, 2016.

R. A. Bradley and M. E. Terry, Rank analysis of incomplete block designs: I. The method of paired comparisons, Biometrika, vol.39, issue.3/4, pp.324-345, 1952.

R. Busa-fekete and E. Hüllermeier, A Survey of Preference-Based Online Learning with Bandit Algorithms, pp.18-39, 2014.

A. Chiuso, F. Fagnani, L. Schenato, and S. Zampieri, Gossip algorithms for simultaneous distributed estimation and classification in sensor networks, IEEE Jnl Sel. Top. Sig. Proc, vol.5, issue.4, pp.691-706, 2011.

F. Fagnani, C. Sophie-m-fosson, and . Ravazzi, A distributed classification/estimation algorithm for sensor networks, SIAM Jnl on Cont. and Optim, vol.52, issue.1, pp.189-218, 2014.

J. Furnkranz and E. Hullermeier, Preference Learning, 2011.

L. Galluccio, B. Lorenzo, and S. Glisic, Sociality-aided new adaptive infection recovery schemes for multicast DTNs, IEEE Trans. Veh. Techn, vol.65, issue.5, pp.3360-3375, 2016.

R. Gentz, S. X. Wu, H. Wai, A. Scaglione, and A. Leshem, Data injection attacks in randomized gossiping, IEEE Transactions on Signal and Information Processing over Networks, vol.2, issue.4, pp.523-538, 2016.

T. Graepel, Score-based bayesian skill learning, 2012.

B. Guo, Z. Wang, Z. Yu, Y. Wang, N. Y. Yen et al., Mobile crowd sensing and computing: the review of an emerginghumanpowered sensing paradigm, ACM Computing Surveys, vol.48, issue.1, 2015.

B. Kailkhura, S. Brahma, and P. K. Varshney, Data falsification attacks on consensus-based detection systems, IEEE Transactions on Signal and Information Processing over Networks, vol.3, issue.1, pp.145-158, 2017.

B. Kantarci and H. T. Mouftah, Reputation-based sensing-as-a-service for crowd management over the cloud, Proc. IEEE ICC, pp.3614-3619, 2014.

W. Li, L. Galluccio, F. Bassi, and M. Kieffer, Distributed faulty node detection in delay tolerant networks: Design and analysis, IEEE Transactions on Mobile Computing, issue.99, pp.1-1, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01327472

W. Li, F. Bassi, L. Galluccio, and M. Kieffer, Peerassisted individual assessment in a multi-agent system, Automatica, vol.83, pp.351-360, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01576605

R. D. Luce, Individual choice behaviour: A theoretical analysis, 1959.

P. Luo, H. Xiong, K. Lü, and Z. Shi, Distributed classification in peer-to-peer networks, Proc. Int. Conf. on Knowl. Discov. and Data Mining, pp.968-976, 2007.

J. Ren, Y. Zhang, K. Zhang, and X. Shen, SACRM: Social Aware Crowdsourcing with Reputation Management in mobile sensing, Comp. Commun, vol.65, 2015.

B. Nihar, S. Shah, M. Balakrishnan, and . Wainwright, A permutation-based model for crowd labeling: Optimal estimation and robustness, 2016.

G. Suarez-tangil, J. E. Tapiador, P. Peris-lopez, and A. Ribagorda, Evolution, detection and analysis of malware for smart devices, IEEE Communications Surveys Tutorials, vol.16, issue.2, pp.961-987, 2014.

L. Thurstone, A law of comparative judgement, Psychological Review, vol.34, pp.273-286, 1927.

H. T. Waiy, A. E. Ozdaglarz, and A. Scaglione, Identifying susceptible agents in time varying opinion dynamics through compressive measurements, Proc. IEEE ICASSP, pp.4114-4118, 2018.

Z. Yu and M. Van-der-schaar, Reputation-based incentive protocols in crowdsourcing applications, Proc. IEEE INFOCOM, pp.2140-2148, 2012.

W. Zamora, C. T. Calafate, J. Cano, and P. Manzoni, A survey on smartphone-based crowdsensing solutions, Mobile Information Systems, p.26, 2016.

H. Zhu, L. Fu, G. Xue, Y. Zhu, M. Li et al., Recognizing exponential inter-contact time in vanets, Proc. IEEE INFOCOM, pp.1-5, 2010.