IEEE Access
(Jan 2018)
Addressing RFID Misreadings to Better Infer Bee Hive Activity
Ferry Susanto,
Thomas Gillard,
Paulo De Souza,
Benita Vincent,
Setia Budi,
Auro Almeida,
Gustavo Pessin,
Helder Arruda,
Raymond N. Williams,
Ulrich Engelke,
Peter Marendy,
Pascal Hirsch,
Jing He
Affiliations
Ferry Susanto
ORCiD
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Thomas Gillard
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Paulo De Souza
ORCiD
School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia
Benita Vincent
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Setia Budi
ORCiD
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Auro Almeida
Land and Water, Commonwealth Scientific and Industrial Research Organization, University of Tasmania, Hobart, TAS, Australia
Gustavo Pessin
ORCiD
Sustainable Development, Vale Institute of Technology, Belem, Brazil
Helder Arruda
ORCiD
Sustainable Development, Vale Institute of Technology, Belem, Brazil
Raymond N. Williams
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Ulrich Engelke
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Peter Marendy
ORCiD
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Pascal Hirsch
Data61, Commonwealth Scientific and Industrial Research Organization, Sandy Bay, TAS, Australia
Jing He
Institute of Information Technology, Nanjing University of Finance and Economics, Nanjing, China
DOI
https://doi.org/10.1109/ACCESS.2018.2844181
Journal volume & issue
Vol. 6
pp.
31935
– 31949
Abstract
Read online
This paper proposes a method to address misreadings and consequent inadequacy of radio-frequency identification data for social insect monitoring. Six-month worth field experiment data were collected to demonstrate the application of the method. The data are transformed into a linear combination of the Gaussian model and curve-fitted using an evolutionary algorithm. This results show that the proposed method allows us to improve the quality of data that infer honey bee behavior at the colony level.
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