Authors:
Laura García
1
;
2
;
Jose M. Jimenez
1
;
2
;
Sandra Sendra
1
;
Jaime Lloret
1
and
Pascal Lorenz
2
Affiliations:
1
Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/ Paranimf nº 1, Grao de Gandía – Gandia, Valencia, Spain
;
2
Network and Telecommunication Research Group, University of Haute Alsace, 34 rue du Grillenbreit, 68008, Colmar, France
Keyword(s):
Fog Computing, Multi-layer, Energy-saving.
Abstract:
As the population of the world keeps increasing, it is necessary for the agriculture to adopt technologies that improve the production and optimize re-sources such as water. This has been done by introducing IoT devices, which has led to smart agriculture or precision agriculture. However, due to the remoteness of the fields, the communication of these devices needs to be per-formed with technologies such as LoRa that has limitations on the amount of data and the number of messages that can be forwarded. Furthermore, as there is no connection to the electric grid, optimizing the energy consumption is a necessity. In this paper, we present a multi-layer fog computing framework for a water quality monitoring and precision agriculture system. Data aggregation techniques are applied at the algorithms provided for the different layers to reduce the amount of data and the number of messages forwarded to the data center so as to improve the performance of the constrained LoRa network and re
duce the energy consumption. Furthermore, the added decision-making provides fault-tolerance to the system if the connection to the Data Center is not available. Simulations were performed for different functioning modes. Results show a reduction of the 80% in the amount of transmitted data and a reduction of 85.33% in the number of for-warded messages for the most restrictive functioning mode.
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