M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications, IEEE Transactions on Wireless Communications, vol.16, issue.11, pp.7574-7589, 2017.
DOI : 10.1109/TWC.2017.2751045
URL : https://hal.archives-ouvertes.fr/hal-01778613

S. Dong, S. Duan, Q. Yang, J. Zhang, G. Li et al., MEMS-Based Smart Gas Metering for Internet of Things, IEEE Internet of Things Journal, vol.4, issue.5, pp.1296-1303, 2017.
DOI : 10.1109/JIOT.2017.2676678

M. Gorawski, A. Gorawska, and K. Pasterak, A Survey of Data Stream Processing Tools, pp.295-303, 2014.
DOI : 10.1007/978-3-319-09465-6_31

R. Ranjan, Streaming Big Data Processing in Datacenter Clouds, IEEE Cloud Computing, vol.1, issue.1, pp.78-83, 2014.
DOI : 10.1109/MCC.2014.22

P. Carbone, A. Katsifodimos, S. Kth, S. Sweden, V. Ewen et al., Apache flink: Stream and batch processing in a single engine, 2015.

A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel et al., Proc. of the 2014 ACM SIGMOD Intl. Conf. on Mngmnt of Data, ser. SIGMOD '14, 2014.

S. Kulkarni, N. Bhagat, M. Fu, V. Kedigehalli, C. Kellogg et al., Twitter Heron, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pp.239-250
DOI : 10.1145/2588555.2595641

M. Stonebraker, U. , and S. Zdonik, The 8 requirements of real-time stream processing, ACM SIGMOD Record, vol.34, issue.4, pp.42-47, 2005.
DOI : 10.1145/1107499.1107504

G. Humphreys, M. Houston, R. Ng, R. Frank, S. Ahern et al., Chromium: A stream-processing framework for interactive rendering on clusters, Proc. of the 29 th Conf. on Comp. Graphics and Interactive Techniques, ser. SIGGRAPH, 2002.

F. J. Cangialosi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack et al., The Design of the Borealis Stream Processing Engine, Second Biennial Conference on Innovative Data Systems Research (CIDR 2005), 2005.

Z. Nabi, E. Bouillet, A. Bainbridge, and C. Thomas, Of Streams and Storms, IBM Research Dublin and IBM Software Group Europe, 2014.

J. Samosir, M. Indrawan-santiago, and P. D. Haghighi, An Evaluation of Data Stream Processing Systems for Data Driven Applications, international Conference on Computational Science, pp.439-449, 2016.
DOI : 10.1016/j.procs.2016.05.322

S. Perera, A. Perera, and K. Hakimzadeh, Reproducible experiments for comparing apache flink and apache spark on public clouds [14] https://community.hortonworks.com/questions/106314/spark-vs-flink-vs storm.html. [15] C. Michael. (2017) Open Source Stream Process- ing: Flink vs Spark vs Storm vs Kafka Available: https, 1610.

S. Chintapalli, D. Dagit, B. Evans, R. Farivar, T. Graves et al., Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming, 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.1789-1792, 2016.
DOI : 10.1109/IPDPSW.2016.138

M. A. Lopez, A. G. Lobato, and O. C. Duarte, A Performance Comparison of Open-Source Stream Processing Platforms, 2016 IEEE Global Communications Conference (GLOBECOM), pp.1-6, 2016.
DOI : 10.1109/GLOCOM.2016.7841533

R. Bolze, F. Cappello, E. Caron, M. Dayd, F. Desprez et al., Grid5000: A large scale and highly reconfigurable experimental grid testbed, Intl J. of High Performance Computing ApplicationsMeasurements-using Ganglia, pp.481-494, 2006.
DOI : 10.1177/1094342006070078
URL : https://hal.archives-ouvertes.fr/hal-00684943

D. M. Fernandez, Comparing Hadoop, MapReduce, Spark, Flink, and Storm, 2016.