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| http://dx.doi.org/10.1007/s12055-019-00818-x | DOI Listing |
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Energy efficient group priority MAC protocol using hybrid Q-learning honey Badger Algorithm (QL-HBA) for IoT Networks.
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Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
Cine-magnetic resonance imaging (MRI) has been used to track respiratory-induced motion of the liver and tumor and assist in the accurate delineation of tumor volume. Recent developments in compressed sensitivity encoding (SENSE; CS) have accelerated temporal resolution while maintaining contrast resolution. This study aimed to develop and assess hepatobiliary phase (HBP) cine-MRI scans using CS.
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CETC POTEVIO SCIENCE & TECHNOLOGY CO., LTD, Guangzhou, 510310, P.R. China.
In recent years, the hydroacoustic communication MAC (Medium Access Control) protocol has attracted wide attention. Hydroacoustic communication networks suffer from issues such as long propagation delays and rapid dynamic changes in network load, which prevent the maximization of network performance through the use of a single transmission mode. In this paper, we propose a Dynamic Switching Transmission MAC protocol called the DSTM-MAC protocol.
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