TY - GEN
T1 - A multi-agent architecture for quantified fruits
T2 - 28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
AU - Briot, Jean Pierre
AU - Do Nascimento, Nathalia Moraes
AU - De Lucena, Carlos José Pereira
PY - 2016
Y1 - 2016
N2 - The concept of Quantified Self is about connected objects self-monitoring their human owner (e.g., a watch measuring heart rate, etc.). A natural transposition is in self-monitoring arbitrary things, therefore named Quantified Things. In this paper, we present the case of self-monitoring agricultural products. We discuss the rationales for the design of a Quantified Fruit multi-agent architecture for self-monitoring and self-prediction of the maturation of fruits. The architecture includes 6 different types of agents, the 2 more specific ones being respectively, the self-controller equipped with various sensors and the self-prediction module. Our current implementation uses an Arduino microcontroller board with 5 sensors (measuring respectively: temperature, light, humidity, hydrogen and methane). The prediction module uses a neural network. We have implemented the architecture and have conducted various experiments, storing bananas in diverse settings: room, refrigerator, in a box, with other fruits, etc. The paper discusses the architecture, its current implementation, experiments and current results. Future issues (scalability, collaborative prediction, etc.) are also addressed.
AB - The concept of Quantified Self is about connected objects self-monitoring their human owner (e.g., a watch measuring heart rate, etc.). A natural transposition is in self-monitoring arbitrary things, therefore named Quantified Things. In this paper, we present the case of self-monitoring agricultural products. We discuss the rationales for the design of a Quantified Fruit multi-agent architecture for self-monitoring and self-prediction of the maturation of fruits. The architecture includes 6 different types of agents, the 2 more specific ones being respectively, the self-controller equipped with various sensors and the self-prediction module. Our current implementation uses an Arduino microcontroller board with 5 sensors (measuring respectively: temperature, light, humidity, hydrogen and methane). The prediction module uses a neural network. We have implemented the architecture and have conducted various experiments, storing bananas in diverse settings: room, refrigerator, in a box, with other fruits, etc. The paper discusses the architecture, its current implementation, experiments and current results. Future issues (scalability, collaborative prediction, etc.) are also addressed.
UR - http://www.scopus.com/inward/record.url?scp=84988447997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988447997&partnerID=8YFLogxK
U2 - 10.18293/SEKE2016-102
DO - 10.18293/SEKE2016-102
M3 - Conference contribution
AN - SCOPUS:84988447997
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 369
EP - 374
BT - Proceedings - SEKE 2016
PB - Knowledge Systems Institute Graduate School
Y2 - 1 July 2016 through 3 July 2016
ER -