Authors :
Shwe Yee Linn; Thanda Win; Hla Myo Tun
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3ydwA7J
DOI :
https://doi.org/10.5281/zenodo.6792007
Abstract :
Smart farming is an innovation in the
agricultural sector that can improve the performance of
the agricultural industry with more automated and datadriven farming methods that connect with the IoT devices
in the farming area. The fast spread of connectivity has
given rise to IoT-based agricultural management
solutions. Most of the existing farming systems, which are
designed using the traditional cloud computing
architecture, are unable to handle massive volumes of data
produced by the connected IoT devices and may have high
latency upon heavy traffic. Consequently, it is preferable
to bring the data processing closer to the source of its
production in order to minimize the latency and network
usage in assisting real-time decisions based on the data
generated. This shows the deployment of application
modules is important for the efficient utilization of
network resources. For this reason, fog computing model
has been proposed in this paper to solve such deployment
issues. The architecture is to be designed for smart
farming, and simulated using iFogSim, which enable to
evaluate the usage of bandwidth and computing resources
as well as latency. The performance results obtained by the
proposed fog computing approach are to be compared to
those of cloud-only implementations in order to give
recommendations for practical use.
Keywords :
Smart Farming; Fog Computing; Cloud Computing; Ifogsim; Module Mapping
Smart farming is an innovation in the
agricultural sector that can improve the performance of
the agricultural industry with more automated and datadriven farming methods that connect with the IoT devices
in the farming area. The fast spread of connectivity has
given rise to IoT-based agricultural management
solutions. Most of the existing farming systems, which are
designed using the traditional cloud computing
architecture, are unable to handle massive volumes of data
produced by the connected IoT devices and may have high
latency upon heavy traffic. Consequently, it is preferable
to bring the data processing closer to the source of its
production in order to minimize the latency and network
usage in assisting real-time decisions based on the data
generated. This shows the deployment of application
modules is important for the efficient utilization of
network resources. For this reason, fog computing model
has been proposed in this paper to solve such deployment
issues. The architecture is to be designed for smart
farming, and simulated using iFogSim, which enable to
evaluate the usage of bandwidth and computing resources
as well as latency. The performance results obtained by the
proposed fog computing approach are to be compared to
those of cloud-only implementations in order to give
recommendations for practical use.
Keywords :
Smart Farming; Fog Computing; Cloud Computing; Ifogsim; Module Mapping