Abstract:
Smart farming is essential to increase the crop production for which IOT in agricultural parameters
is crucial for the growth of crops. To achieve this, modern technology is required to enable
accuracy in fertilizing, watering and adding pesticides to the crops as well as monitoring the
conditions of environment. Now a days, more and more sophisticated sensors are developed but to
achieve this on larger scale managing them efficiently is very significant.
We want to achieve sustainability in large scale farms by improving communication between the
Wireless Sensor Network nodes and the Base Station through monitoring the energy and
communication of sensor nodes through machine learning algorithms. The idea is to make
multihop communication efficient, by balancing the energy consumption of sensors. The nodes
with more energy will be used as relay to transmit data over the distance. The path selection will
done based on remaining energy of the sensor nodes. Reinforcement learning is proposed to select
best paths among the fields towards the base station. Reinforcement Learning is the area of
machine learning in which is concerned with how involved agents are supposed to take actions in
a specified environment to maximize the reward and to achieve a common goal. Reinforcement
learning.
In our network, a large number of sensors are deployed on large scale fields, reinforcement
learning is used to find the best path towards a base station. After a number of successful paths
have been developed, they are then used to transmit the sensed data from the fields. The simulation
results have shown better performance over shortest paths and broadcasting techniques that were
tested against reinforcement learning.
