Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.
|Titolo della pubblicazione ospite||IEEE 4th International Forum on Research and Technologies for Society and Industry, RTSI 2018 - Proceedings|
|Numero di pagine||6|
|Stato di pubblicazione||Published - 2018|
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