Dynamic Stochastic Modeling for Optimization of Environmental Measurements
Disertační práceOtevřený přístupDatum publikování
2018
Autoři
Vedoucí práce
Oponent
Název časopisu
Název svazku
Vydavatel
Univerzita Pardubice
Abstrakt
Minimization of energy consumption of environmental measurement systems is important to ensure their extended operational lifetime and low maintenance cost. This needs to be realized without sacrificing on data quality. One possible way to achieving this is the use of energy-aware sampling techniques such as adaptive and event-triggered sampling. In this work, new methods based on these sampling techniques have been developed. The first method produces stochastic models that accurately predict missed and future data with minimal energy. The method also determines the optimal sampling interval. The second method utilizes new type of event-triggered mechanism that adjusts sampling interval so that it adapts to the changes in measurement data. Algorithms have been developed and all methods demonstrated using field data. Obtained results have been thoroughly analyzed from the perspective of approximation error and energy savings. Models have been validated and favorable results obtained. High R-squared values and low values of mean square normalized error have been obtained. Battery lifetime is extended by more than 87% when sampling interval increases from 15 to 30 seconds. Furthermore, about 45% daily savings of energy consumption of analog-to-digital converter has been achieved in a case study analysis involving the new algorithm, an ADC and field data.
Rozsah stran
121 s.
ISSN
Trvalý odkaz na tento záznam
Projekt
Zdrojový dokument
Vydavatelská verze
Přístup k e-verzi
Bez omezení
Název akce
ISBN
Studijní obor
Information, Communication and Control Technologies
Studijní program
Electrical Engineering and Informatics
Signatura tištěné verze
D38235
Umístění tištěné verze
Univerzitní knihovna (studovna)
Přístup k tištěné verzi
Klíčová slova
time series, sampling interval, environmental variables, stochastic, Box-Jenkins, sensor, energy consumption, data quality