Sahar Khawatmi

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Cooperation among agents across the network leads to better estimation accuracy. However, in many network applications the agents infer and track different models of interest in an environment where agents do not know beforehand which models are being observed by their neighbors. In this work, we propose an adaptive and distributed clustering technique that(More)
In this paper, we study distributed decision-making over mobile adaptive networks where nodes in the network collect data generated by two different models. The nodes need to decide which model to estimate and track. However, they do not know beforehand which model they observe. Therefore, an effective clustering technique is needed. We apply a clustering(More)
There arises the need in many wireless network applications to infer and track different models of interest. Some nodes in the network are informed, where they observe the different models and send information to the uninformed ones. Each uninformed node responds to one informed node and joins its group. In this work, we suggest an adaptive and distributed(More)
To my family I Acknowledgments I would like to thank all people who have supported and inspired me during my doctoral work. I especially wish to thank Prof. Dr.-Ing. Abdelhak Zoubir for supervising this work. It is really an honor and a pleasure to be supervised by an outstanding professor who gave me a highly inspiring mix of freedom in work and research(More)
We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm(More)
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