Research Article
A Bayesian Approach for an Efficient Data Reduction in IoT
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@INPROCEEDINGS{10.1007/978-3-319-93797-7_1, author={Cristanel Razafimandimby and Valeria Loscr\^{\i} and Anna Vegni and Driss Aourir and Alessandro Neri}, title={A Bayesian Approach for an Efficient Data Reduction in IoT}, proceedings={Interoperability, Safety and Security in IoT. Third International Conference, InterIoT 2017, and Fourth International Conference, SaSeIot 2017, Valencia, Spain, November 6-7, 2017, Proceedings}, proceedings_a={INTERIOT \& SASEIOT}, year={2018}, month={7}, keywords={Markov random fields IoT Belief propagation Bayesian Smart node}, doi={10.1007/978-3-319-93797-7_1} }- Cristanel Razafimandimby
Valeria Loscrí
Anna Vegni
Driss Aourir
Alessandro Neri
Year: 2018
A Bayesian Approach for an Efficient Data Reduction in IoT
INTERIOT & SASEIOT
Springer
DOI: 10.1007/978-3-319-93797-7_1
Abstract
Todays, Internet of Things (IoT) is starting to occupy a major place in our everyday lives. It has already achieved a huge success in several sectors and continues to bring us a range of new capabilities and services. However, despite the apparent success, one of issues which must be tackle is the big quantity of data produced and transmitted by the objects. Transmitting these big quantity of data not only increases the energy consumption of objects but can also cause network congestion.
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