@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.}
}
@article {Badidi2018369,
	title = {A DaaS Based Framework for IoT Data Provisioning},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {661},
	year = {2018},
	pages = {369-379},
	doi = {10.1007/978-3-319-67618-0_34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029599349\&doi=10.1007\%2f978-3-319-67618-0_34\&partnerID=40\&md5=e76f069dc00af29313bd86f85b4adfb8},
	author = {Badidi, E. and Routaib, H.}
}
@article {Badidi2018369,
	title = {A DaaS Based Framework for IoT Data Provisioning},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {661},
	year = {2018},
	note = {cited By 0},
	pages = {369-379},
	abstract = {As a result of the remarkable advances in sensing and digital communication technologies, massive amounts of data are generated by Internet of Things (IoT) devices. In this paper, we propose a framework for data provisioning that relies on the Data-as-a-Service (DaaS) cloud delivery model. IoT Data is processed and aggregated at the edge of where it is generated, then, it is stored in the cloud. Since smart connected devices are proliferating at an unprecedented pace, we anticipate that the number of DaaS providers will also grow at the same rate. Therefore, data consumers will seek to find suitable DaaS providers that can provide them with high-quality data. DaaS Brokers will emerge to mediate between data providers and data consumers and help data consumers find appropriate DaaS providers using different ranking algorithms. We propose an algorithm to evaluate the offers of potential DaaS providers based on the quality-of-data (QoD) requirements of the data consumer and using fitness functions associated with each QoD dimension. {\textcopyright} 2018, Springer International Publishing AG.},
	doi = {10.1007/978-3-319-67618-0_34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029599349\&doi=10.1007\%2f978-3-319-67618-0_34\&partnerID=40\&md5=e76f069dc00af29313bd86f85b4adfb8},
	author = {Badidi, E. and Routaib, H.}
}
@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.}
}
@article {Badidi2017611,
	title = {Towards data-as-a-service provisioning with high-quality data},
	journal = {Lecture Notes in Electrical Engineering},
	volume = {397},
	year = {2017},
	note = {cited By 0},
	pages = {611-623},
	abstract = {Given the large amount of sensed data by IoT devices and various wireless sensor networks, traditional data services lack the necessary resources to store and process that data, as well as to disseminate high-quality data to a variety of potential consumers. In this paper, we propose a framework for Data as a Service (DaaS) provisioning, which relies on deploying DaaS services on the cloud and using a DaaS agency to mediate between data consumers and DaaS services using a publish/subscribe model. Furthermore, we describe a decision algorithm for the selection of appropriate DaaS services that can fulfill consumers{\textquoteright} requests for high-quality data. One of the benefits of the proposed approach is the elasticity of the cloud resources used by DaaS services. Moreover, the selection algorithm allows ranking DaaS services by matching their quality-of-data (QoD) offers against the QoD needs of the data consumer. {\textcopyright} Springer Science+Business Media Singapore 2017.},
	doi = {10.1007/978-981-10-1627-1_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996995662\&doi=10.1007\%2f978-981-10-1627-1_48\&partnerID=40\&md5=3699ddd8367859bcdb3853679e370f5f},
	author = {Badidi, E. and Routaib, H. and El Koutbi, M.}
}
@conference {Badidi2008258,
	title = {Practical load distribution models for Web services based environments},
	booktitle = {Proceedings - 2008 International MCETECH Conference on e-Technologies, MCETECH 2008},
	year = {2008},
	note = {cited By 0},
	pages = {258-261},
	doi = {10.1109/MCETECH.2008.21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-50249141635\&doi=10.1109\%2fMCETECH.2008.21\&partnerID=40\&md5=03f0747597b0ef9e4d69b153af0df1cc},
	author = {Badidi, E. and Serhani, M.A. and Esmahi, L. and Elkoutbi, M.}
}
@conference {Jakimi2008521,
	title = {Use cases and scenarios engineering},
	booktitle = {Innovations{\textquoteright}07: 4th International Conference on Innovations in Information Technology, IIT},
	year = {2008},
	note = {cited By 0},
	pages = {521-525},
	doi = {10.1109/IIT.2007.4430418},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-50249112803\&doi=10.1109\%2fIIT.2007.4430418\&partnerID=40\&md5=82fe395e89ce2d783684d6544100e69f},
	author = {Jakimi, A. and Sabraoui, A. and Badidi, E. and Idri, A. and El Koutbi, M.}
}
@conference { ISI:000255983000056,
	title = {Use cases and scenarios engineering},
	booktitle = {2007 INNOVATIONS IN INFORMATION TECHNOLOGIES, VOLS 1 AND 2},
	year = {2007},
	note = {4th International Conference on Innovations in Information Technology, Dubai, U ARAB EMIRATES, NOV 18-20, 2007},
	pages = {274+},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {\textbackslash{} In this paper, we suggest a requirement engineering process that composes (merge) use cases/scenarios and yields a formal specification of the system in form of a high-level Petri net. Use cases and scenarios are acquired in form of diagrams as defined by the Unified Modeling Language (UML). These diagrams are composed and transformed into Petri net specifications and merged to obtain a global Petri net specification capturing the behavior of the entire system. From the global specification, a system prototype can be generated and embedded in a user interface builder environment for further refinement. Based on end user feedback, the use cases and the input scenarios may be iteratively refined. The result of the overall process is a specification consisting of a global Petri net, together with the generated and refined prototype. This paper discusses some activities of this process.},
	isbn = {978-1-4244-1840-4},
	author = {Jakimi, A. and Sabraoui, A. and Badidi, E. and Idri, A. and El Koutbi, M.}
}
