L. Atzori, A. Iera, and G. Morabito, The Internet of Things: A survey, Computer Networks, vol.54, issue.15, pp.2787-2805, 2010.

J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, Internet of Things (IoT): A vision, architectural elements, and future directions, Future Generation Computer Systems, vol.29, issue.7, pp.1645-1660, 2013.

M. Coronado and C. A. Iglesias, Task automation services: Automation for the masses, IEEE Internet Computing, vol.20, pp.52-58, 2016.

M. Wu, Y. Wang, and Z. Liao, A new clustering algorithm for sensor data streams in an agricultural IoT, Proceedings -2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, pp.2373-2378, 2013.

A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, Internet of things for smart cities, IEEE Internet of Things Journal, vol.1, issue.1, pp.22-32, 2014.

S. Greengard, Association for Computing Machinery, Communications of the ACM, vol.57, issue.9, p.12, 2014.

J. A. Stankovic, Research Directions for the Internet of Things, Internet of Things Journal, IEEE, vol.1, issue.1, pp.3-9, 2014.

J. B. Borges-neto, T. H. Silva, R. M. Assunção, R. A. Mini, and A. A. Loureiro, Sensing in the collaborative internet of things, Sensors (Switzerland), vol.15, issue.3, pp.6607-6632, 2015.

S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, Time-series clustering -A decade review, Information Systems, vol.53, pp.16-38, 2015.

X. Wang, K. Smith, and R. Hyndman, Characteristic-Based Clustering for Time Series Data, Data Mining and Knowledge Discovery, vol.13, issue.3, pp.335-364, 2006.

P. Montero and J. A. Vilar, TSclust : An R Package for Time Series Clustering, Journal of Statistical Software, vol.62, pp.1-43, 2014.

B. D. Fulcher, M. A. Little, and N. S. Jones, Highly comparative timeseries analysis: the empirical structure of time series and their methods, Journal of The Royal Society Interface, vol.10, issue.83, 2013.

J. Lin, E. Keogh, S. Lonardi, and B. Chiu, A symbolic representation of time series, with implications for streaming algorithms, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery -DMKD '03, p.2, 2003.

O. A. Rosso, H. A. Larrondo, M. T. Martin, A. Plastino, and M. A. Fuentes, Distinguishing Noise from Chaos, Physical Review Letters, vol.99, issue.15, p.154102, 2007.

L. Zunino, M. C. Soriano, I. Fischer, O. A. Rosso, and C. R. Mirasso, Permutation-information-theory approach to unveil delay dynamics from time-series analysis, Physical Review E, vol.82, issue.4, 2010.

O. A. Rosso, L. Zunino, D. G. Pérez, A. Figliola, H. A. Larrondo et al., Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach, Physical Review E, vol.76, issue.6, p.61114, 2007.

A. L. Aquino, H. S. Ramos, A. C. Frery, L. P. Viana, T. S. Cavalcante et al., Characterization of electric load with Information Theory quantifiers, Physica A: Statistical Mechanics and its Applications, vol.465, pp.277-284, 2017.

B. A. Gonçalves, L. Carpi, O. A. Rosso, and M. G. Ravetti, Time series characterization via horizontal visibility graph and Information Theory, Physica A: Statistical Mechanics and its Applications, vol.464, pp.93-102, 2016.

C. Bandt and B. Pompe, Permutation Entropy: A Natural Complexity Measure for Time Series, Physical Review Letters, vol.88, issue.17, p.174102, 2002.

L. Zunino, M. C. Soriano, and O. A. Rosso, Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach, Physical Review E, vol.86, issue.4, 2012.

P. Lamberti, M. Martin, A. Plastino, and O. Rosso, Intensive entropic non-triviality measure, Physica A: Statistical Mechanics and its Applications, vol.334, issue.1-2, pp.119-131, 2004.

P. Barnaghi, W. Wang, L. Dong, and C. Wang, A Linked-Data Model for Semantic Sensor Streams, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, pp.468-475, 2013.

C. Perera, A. Zaslavsky, C. H. Liu, M. Compton, P. Christen et al., Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things, IEEE Sensors Journal, vol.14, issue.2, pp.406-420, 2014.

Y. Qin, Q. Z. Sheng, and E. Curry, Matching Over Linked Data Streams in the Internet of Things, IEEE Internet Computing, vol.19, issue.3, pp.21-27, 2015.

S. Maurus and C. Plant, Skinny-dip: Clustering in a Sea of Noise Samuel, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining -KDD '16, pp.1055-1064, 2016.