@article {Hayat20191040,
	title = {A signaling game-based approach for Data-as-a-Service provisioning in IoT-Cloud},
	journal = {Future Generation Computer Systems},
	volume = {92},
	year = {2019},
	note = {cited By 1},
	pages = {1040-1050},
	doi = {10.1016/j.future.2017.10.001},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034971596\&doi=10.1016\%2fj.future.2017.10.001\&partnerID=40\&md5=da16debd38f36b3509fc36e9edb8e561},
	author = {Hayat, R. and Sabir, E. and Badidi, E. and Elkoutbi, M.}
}
@conference {Routaib2017732,
	title = {An asymmetrical nash bargaining for adaptive and automated context negotiation in pervasive environments},
	booktitle = {2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017},
	year = {2017},
	note = {cited By 0},
	pages = {732-736},
	abstract = {To cope with the energy performance concern of pervasive and Internet-of-Thing (IoT) devices, current pervasive systems require intelligent algorithms that can change the behavior of the devices and the overall network. Making the devices aware of their states and able to adjust their operative modes using context information has the potential to achieve better energy performance. Context information is typically obtained from environmental sensors, device sensors, and from external sources. In this work, we study the marketing of context-aware services through an adaptive context negotiation model. The negotiation process between context consumers and one or several context providers aims to satisfy the preferences of each the negotiating party concerning the quality-of-context (QoC) levels required by the context consumer. The proposed negotiation model uses an asymmetrical {\textquoteright}Power Bargaining{\textquoteright} model in which each negotiating party can influence the other party. It implements a learning algorithm for a symmetrical bargaining model. Numerical evaluation of the model shows the convergence of the Nash Bargaining Solution (NBS) by using a learning algorithm. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/CCNC.2017.7983225},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027412664\&doi=10.1109\%2fCCNC.2017.7983225\&partnerID=40\&md5=70dd3b4a091642d07bcfa73b03f0fd96},
	author = {Routaib, H. and Badidi, E. and Sabir, E. and Elkoutbi, M.}
}
@article {Hayat2017,
	title = {A signaling game-based approach for Data-as-a-Service provisioning in IoT-Cloud},
	journal = {Future Generation Computer Systems},
	year = {2017},
	note = {cited By 0; Article in Press},
	abstract = {The impressive progress in sensing technology over the last few years has contributed to the proliferation and popularity of the Internet of Things (IoT) paradigm and to the adoption of Sensor Clouds for provisioning smart ubiquitous services. Also, the massive amount of data generated by sensors and smart objects led to a new kind of services known as Data-as-a-Service (DaaS). The quality of these services is highly dependent on the quality of sensed data (QoD), which is characterized by a number of quality attributes. DaaS provisioning is typically governed by a Service Level Agreement (SLA) between data consumers and DaaS providers. In this work, we propose a game-based approach for DaaS Provisioning, which relies on signaling based model for the negotiation of several QoD attributes between DaaS providers and data consumers. We consider that these entities are adaptive, rational, and able to negotiate the QoD offering even in the case of incomplete information about the other party. We use in the negotiation between the two parties a Q-learning algorithm for the signaling model and a Multi Attributes Decision Making (MADM) model to select the best signal. Moreover, we empirically validate the MADM model using Shannon{\textquoteright}s entropy. The results obtained in the case of a multi-stages negotiation scenario show the convergence towards the pooling equilibrium. {\textcopyright} 2017.},
	doi = {10.1016/j.future.2017.10.001},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034971596\&doi=10.1016\%2fj.future.2017.10.001\&partnerID=40\&md5=da16debd38f36b3509fc36e9edb8e561},
	author = {Hayat, R. and Sabir, E. and Badidi, E. and Elkoutbi, M.}
}
@conference {Elmachkour201265,
	title = {New insights from a delay analysis for cognitive radio networks with and without reservation},
	booktitle = {IWCMC 2012 - 8th International Wireless Communications and Mobile Computing Conference},
	year = {2012},
	note = {cited By 5},
	pages = {65-70},
	doi = {10.1109/IWCMC.2012.6314179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869156676\&doi=10.1109\%2fIWCMC.2012.6314179\&partnerID=40\&md5=9cfed6da1f67007546e0b300b3572406},
	author = {Elmachkour, M. and Kobbane, A. and Sabir, E. and Koutbi, M.E.}
}
