2018 IEEE Applied Power Electronics Conference and Exposition (APEC) 2018
DOI: 10.1109/apec.2018.8341197
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Power management of a self-powered multi-parameter wireless sensor for IoT application

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Cited by 6 publications
(4 citation statements)
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“…This approach for extending battery life has been treated as a default function in many commercial wireless sensors. A number of researchers also attempted to optimize power management to further extend the battery life of WSNs [ 37 , 38 , 39 , 40 ]. However, one key problem that prevents us from realizing long-term aerial–ground sensing is the opportunistic nature of deploying the UAV (gateway) and the sensor nodes.…”
Section: Sensor Activation and Related Workmentioning
confidence: 99%
“…This approach for extending battery life has been treated as a default function in many commercial wireless sensors. A number of researchers also attempted to optimize power management to further extend the battery life of WSNs [ 37 , 38 , 39 , 40 ]. However, one key problem that prevents us from realizing long-term aerial–ground sensing is the opportunistic nature of deploying the UAV (gateway) and the sensor nodes.…”
Section: Sensor Activation and Related Workmentioning
confidence: 99%
“…This latency is not acceptable, especially in time-restricted or quasi real-time applications with reaction times in the range of some milliseconds to some seconds [11]. Researchers, such as in [12,13], are promoting power management to improve the battery life of individual nodes and extend the lifetime of the entire network.…”
Section: Introductionmentioning
confidence: 99%
“…As a result, the latest FDD systems demand more artificial intelligent solutions to incorporate multiple fault events or dynamically changing load profiles in case of incomplete or noisy measurements [44][45][46][47][48][49]. Commonly, the diagnosis and predictions are calculated through motor current signature analysis (MCSA) [50,51], i.e., examining the output signals of the motor stator's current while running on a steady-state operating mood [52][53][54][55][56]. MCSA analyses the time-frequency decomposition of the current signals or by faults' frequencies in the frequency domain.…”
Section: Introductionmentioning
confidence: 99%