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.
Generally, mobile wireless sensor network (MWSN) senses the sensitive and typical kind of events in various application areas along with the frequent mobility of sensor nodes as compared to traditional wireless sensor network. Due to mobility feature, MWSN extended the working of WSN. To design MWSN, certain key factors like energy efficiency, mobility, data routing, localization and charging strategy are involved. Mobile sensor nodes consume extra amount of energy due to mobility along with sensing and data routing task which exhaust network lifetime rapidly. In this research study, authors have proposed two wireless chargers (fixed and mobile) for sensor nodes. The main difference between fixed and mobile wireless charger is: fixed wireless charger invites mobile sensor nodes for wireless charging at base station whereas mobile wireless charger travels to the position of mobile sensor nodes for wireless charging. Both chargers fulfilled the charging request of sensor nodes efficiently. This is the first research study to propose such type of charging strategy for mobile wireless sensor network (MWSN) as per the available literature. The RL (reinforcement learning) technique is used here for optimization purpose of charging cost. These wireless chargers simulated in MATLAB enviorment and results showed that there is a significant improvement in network lifetime due to these charging strategies when compared to the existing algorithms.
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