Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Lohi, Shantanu; Aote, Shailendra S.; Jogekar, Ravindra N.; Gorewar, Harish V.; Jaiswal, Ajay A.; Ganorkar, Sandeep S.; Lohi-Bode, Snehal A.; Metkar, Rajesh M.; Chiwhane, Shwetambari

doi:10.1080/03772063.2023.2185298

IETE Journal of Research

2023

DOI: 10.1080/03772063.2023.2185298

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Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Shantanu Lohi¹,

Shailendra S. Aote²,

Ravindra N. Jogekar

et al.

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Cited by 2 publications

References 33 publications

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Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

Hema,

Raj,

2024

Int J Communication

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SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

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Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

Hema,

Raj,

2024

Int J Communication

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Exploration of low-resource language-oriented machine translation system of genetic algorithm-optimized hyper-task network under cloud platform technology

Liu,

Chen,

et al. 2023

J Supercomput

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Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Cited by 2 publications

References 33 publications

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

Exploration of low-resource language-oriented machine translation system of genetic algorithm-optimized hyper-task network under cloud platform technology

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