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Sophie Zareei, Department of Information Science


Energy harvesting modelling for self-powered fitness gadgets


Owheo G34 - 1:00 pm, Friday 28 July


The idea of employing scavenged energy from human motion to run electronic devices is attracting increased attention of many researchers worldwide. However, the level of human activities varies during a day from sitting for several hours to running on a treadmill. This talk will present analysis of kinetic energy generated by human motions. We also will describe energy harvesting modelling techniques which estimate the required time for a device to store needed energy to a run fitness gadget. Two large, real-world data sets are employed to obtain empirical results for different motion scenarios. Moreover, the talk will bring the study of energy flow behaviour of mobile sensor nodes considering the dense coupling between data and energy queues. Based on verified Markovian modelling, we derive a closed-form solution for the optimal battery size for a node.

Last modified: Wednesday, 26-Jul-2017 08:42:17 NZST

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