Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Abstract EN

Generally, wireless sensor network is a group of sensor nodes which is used to continuously monitor and record the various physical, environmental, and critical real time application data.

Data traffic received by sink in WSN decreases the energy of nearby sensor nodes as compared to other sensor nodes.

This problem is known as hot spot problem in wireless sensor network.

In this research study, two novel algorithms are proposed based upon reinforcement learning to solve hot spot problem in wireless sensor network.

The first proposed algorithm RLBCA, created cluster heads to reduce the energy consumption and save about 40% of battery power.

In the second proposed algorithm ODMST, mobile sink is used to collect the data from cluster heads as per the demand/request generated from cluster heads.

Here mobile sink is used to keep record of incoming request from cluster heads in a routing table and visits accordingly.

These algorithms did not create the extra overhead on mobile sink and save the energy as well.

Finally, the proposed algorithms are compared with existing algorithms like CLIQUE, TTDD, DBRkM, EPMS, RLLO, and RL-CRC to better prove this research study.