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.
Anotace v angličtině
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.
Klíčová slova
-
Klíčová slova v angličtině
Time series, sampling interval, environmental variables, stochastic, Box-Jenkins, sensor, energy consumption, data quality
Rozsah průvodní práce
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Jazyk
AN
Anotace
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.
Anotace v angličtině
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.
Klíčová slova
-
Klíčová slova v angličtině
Time series, sampling interval, environmental variables, stochastic, Box-Jenkins, sensor, energy consumption, data quality
Zásady pro vypracování
While trying to secure high data quality during measurement of environmental parameters by sensor nodes, conventional sampling theorems like Nyquist, Nyquist-Shannon and Compressed-Sensing also known as Sub-Nyquist, trade-off energy-efficiency and other vital gains such as - savings in application memory of sensor nodes' processing units, use of lower frequencies of industrial-scientific and medical band and their inherent benefits in industrial operations, among others. Similarly, commonly used sampling techniques of fixed-rate sampling, adaptive and compressive samplings are yet to yield the much-desired savings in power consumption and benefits mentioned above. Therefore, a twofold need arises. First is the need to establish a novel and reliable approach that determines optimum sampling rates of sensing units which balances the trade-off between gain in power consumption and loss of data. Second, is the need to establish models of sufficient high quality which take into account both the deterministic and probabilistic natures of the time series of the environmental parameters.
Zásady pro vypracování
While trying to secure high data quality during measurement of environmental parameters by sensor nodes, conventional sampling theorems like Nyquist, Nyquist-Shannon and Compressed-Sensing also known as Sub-Nyquist, trade-off energy-efficiency and other vital gains such as - savings in application memory of sensor nodes' processing units, use of lower frequencies of industrial-scientific and medical band and their inherent benefits in industrial operations, among others. Similarly, commonly used sampling techniques of fixed-rate sampling, adaptive and compressive samplings are yet to yield the much-desired savings in power consumption and benefits mentioned above. Therefore, a twofold need arises. First is the need to establish a novel and reliable approach that determines optimum sampling rates of sensing units which balances the trade-off between gain in power consumption and loss of data. Second, is the need to establish models of sufficient high quality which take into account both the deterministic and probabilistic natures of the time series of the environmental parameters.
Seznam doporučené literatury
Artiola, J., Pepper, I., and Brusseau, M., Environmental Monitoring and Characterization, 5th edition, San Diego, USA: Elsevier Academic Press, 2006. ISBN 9788131200889.
Spiegel, M., and Stephens, L., Theory and Problems of Statistics, Third Edition, Schaum's Outline Series McGraw-Hill Companies Inc, 1999, New York, USA. ISBN 0-07-060281-6.
Brockwell, P., and Davis, R., Introduction to Time Series and Forecasting, 2nd edition, New York: Springer-Verlag Publishing, 2002. ISBN 387953515.
Seznam doporučené literatury
Artiola, J., Pepper, I., and Brusseau, M., Environmental Monitoring and Characterization, 5th edition, San Diego, USA: Elsevier Academic Press, 2006. ISBN 9788131200889.
Spiegel, M., and Stephens, L., Theory and Problems of Statistics, Third Edition, Schaum's Outline Series McGraw-Hill Companies Inc, 1999, New York, USA. ISBN 0-07-060281-6.
Brockwell, P., and Davis, R., Introduction to Time Series and Forecasting, 2nd edition, New York: Springer-Verlag Publishing, 2002. ISBN 387953515.
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Po představení doktoranda Obiora Sam Ezeora byla komise seznámena se stanoviskem školitele k disertační práci a osobě disertanta. Doktorand seznámil komisi se svojí disertační prací formou prezentace. Poté byly předneseny posudky oponentů a doktorand zodpověděl otázky a reagoval na připomínky oponentů. V následné veřejné diskusi disertant odpovídal na otázky členů komise, které jsou uvedeny na samostatných listech. Komise posoudila disertační práci a rozhodla, že disertační práce není plagiát. Na závěr proběhlo tajné hlasování. Protokol o výsledcích hlasování tvoří samostatnou přílohu.\par