IoT device-based data acquisition system with on-board computation of variables for cow behaviour recognition
Computers and Electronics in Agriculture, 2021•Elsevier
Improved raw data handling is one of the requirements that research should fulfil in the
design of IoT device-based data acquisition systems for enhancing the overall system
performance. In this study, the system was composed of low-cost acceleration sensors
broadcasting to a Raspberry PI. The main aim of this study was to develop firmware for the
acceleration sensors with the purpose of maximising the battery life-time and minimising the
information loss during data transfer while allowing high accuracy in the discrimination of the�…
design of IoT device-based data acquisition systems for enhancing the overall system
performance. In this study, the system was composed of low-cost acceleration sensors
broadcasting to a Raspberry PI. The main aim of this study was to develop firmware for the
acceleration sensors with the purpose of maximising the battery life-time and minimising the
information loss during data transfer while allowing high accuracy in the discrimination of the�…
Abstract
Improved raw data handling is one of the requirements that research should fulfil in the design of IoT device-based data acquisition systems for enhancing the overall system performance. In this study, the system was composed of low-cost acceleration sensors broadcasting to a Raspberry PI.
The main aim of this study was to develop firmware for the acceleration sensors with the purpose of maximising the battery life-time and minimising the information loss during data transfer while allowing high accuracy in the discrimination of the cow lying-standing behaviour. The attainment of these goals was achieved by using an aggregated accelerometer variable, computed on-board of the IoT device, together with the idea of saving in the payload the most recent variables.
The comparisons conducted between the base firmware installed on the device and the new firmware developed showed the outstanding performance of the latter in terms of Raspberry PI CPU usage, storage memory occupation, and packet loss. The algorithms of lying-standing behaviour discrimination were implemented and assessed by using the new firmware, producing excellent values of the most used accuracy measures.
Elsevier
Showing the best result for this search. See all results