@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 {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.}
}
