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The Internet of things(IoT) is now considered among  the most emerging technologies aiming to interconnect  heterogeneous smart  devices in several areas without human interaction . From sensing  and identification to data processing and storing. The Building and the monitoring of smart systems like smart homes, smart campus and smart cities pass through several stages on which different tools and platforms coexist to answer this need. In this paper, we present a survey on recent works using IoT tools to build their systems. Particularly, we classify and compare them according  to layers and characteristics that we have defined such as data acquisition, data processing , software used, conformity to standards.  Hence , our study aims to provide a clear vision on available functionalities during the process of building IoT systems based on most used IoT tools and to help users to choose the most adapted tool depending on their needs.

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<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, multi-biometric aims to reduce the False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) which are two standard metrics widely used in the accuracy of biometric sys-tems. In this paper, we will examine the different possible scenario in multi-modal biometric systems using RFID, fingerprint and facial recognition, that can be adopted to merge information and improve the overall accuracy of the system.</span>

Radio frequency Identification (RFID) is currently considered as one of the most used