RETRACTED: Green corridor: A self-learning and educating algorithm for engineers (industry/students) to send data in emergency situations through wireless sensor network

Recent research emphasized the utilization of rechargeable wireless sensor networks (RWSNs) in a variety of cutting-edge fields like drones, unmanned aerial vehicle (UAV), healthcare, and defense. Previous studies have shown mobile data collection and mobile charging should be separately. In our paper, we created an novel algorithm for mobile data collection and mobile charging (MDCMC) that can collect data as well as achieves higher charging efficiency rate based upon reinforcement learning in RWSN. In first phase of algorithm, reinforcement learning technique used to create clusters among sensor nodes, whereas, in second phase of algorithm, mobile van is used to visit cluster heads to collect data along with mobile charging. The path of mobile van is based upon the request received from cluster heads. Lastly, we made the comparison of our proposed new MDCMC algorithm with the well-known existing algorithms RLLO [32] & RL-CRC [33]. Finally, we found that, the proposed algorithm (MDCMC) is effectively better collecting data as well as charging cluster heads.

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A new mobile data collection and mobile charging (MDCMC) algorithm based on reinforcement learning in rechargeable wireless sensor network

Soni

Chandra

Singh

et al. 2023

IFS

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RETRACTED: Green corridor: A self-learning and educating algorithm for engineers (industry/students) to send data in emergency situations through wireless sensor network

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A new mobile data collection and mobile charging (MDCMC) algorithm based on reinforcement learning in rechargeable wireless sensor network

A new mobile data collection and mobile charging (MDCMC) algorithm based on reinforcement learning in rechargeable wireless sensor network

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