@conference{Sarigiannidis2014341,

title = {Predicting multimedia traffic in wireless networks: A performance evaluation of cognitive techniques},

author = { P. Sarigiannidis and K. Aproikidis and M. Louta and P. Angelidis and T. Lagkas},

url = {https://www.researchgate.net/publication/267210651_Predicting_Multimedia_Traffic_in_Wireless_Networks_A_Performance_Evaluation_of_Cognitive_Techniques},

doi = {10.1109/IISA.2014.6878802},

year  = {2014},

date = {2014-01-01},

journal = {IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications},

pages = {341-346},

abstract = {Traffic engineering in networking is defined as the process that incorporates sophisticated methods in order to ensure optimization and high network performance. One of the most constructive tools employed by the traffic engineering concept is the traffic prediction. Having in mind the heterogeneous traffic patterns originated by various modern services and network platforms, the need of a robust, cognitive, and error-free prediction technique becomes even more pressing. This work focuses on the prediction concept as an autonomous, functional, and efficient process, where multiple cutting-edge methods are presented, modeled, and thoroughly assessed. To this purpose, real traffic traces have been captured, including multiple multimedia traffic flows, so as to comparatively assess widely used methods in terms of accuracy. © 2014 IEEE.},

keywords = {automata, extrapolation, markov chains, prediction, wireless networks},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sarigiannidis2013470,

title = {Adaptive sensing policies for cognitive wireless networks using learning automata},

author = { P. Sarigiannidis and M. Louta and E. Balasa and T. Lagkas},

url = {https://www.researchgate.net/publication/267210747_Adaptive_Sensing_Policies_for_Cognitive_Wireless_Networks_using_Learning_Automata},

doi = {10.1109/ISCC.2013.6754991},

year  = {2013},

date = {2013-01-01},

journal = {Proceedings - International Symposium on Computers and Communications},

pages = {470-475},

abstract = {This paper introduces an adaptive spectrum sensing method for cognitive radio wireless networks. The proposed method enhances previously proposed random-based sensing policies, effectively selecting the licensed channels to be sensed by accurately estimating channels' availability, resulting, thus, to high system's resources utilization. The core mechanism of the adaptive method is an enhanced learning automaton, which efficiently interacts with the environment and provides accurate decisions on selecting the channel to be sensed on behalf of the secondary users. A thorough description of the introduced method is provided, while the performance of the enhanced sensing policies is verified through extensive simulation experiment. © 2013 IEEE.},

keywords = {cognitive radio, Learning automata, multi-channel MAC, wireless networks},

pubstate = {published},

tppubtype = {conference}

}