 R-learning-based team game model for Internet of things quality-of-service control schemeSungwook Kim International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 1, 1550147716687558 Abstract: In modern times, it has been observed that Internet of things technology makes it possible for connecting various smart objects together through the Internet. For the effective Internet of things management, it is necessary to design and develop service models that ensure appropriate level of quality-of-service. Therefore, the design of quality-of-service management schemes has been a hot research issue. In this work, we formulate a new quality-of-service management scheme based on the IoT system power control algorithm. Using the emerging and largely unexplored concept of the R-learning algorithm and docitive paradigm, system agents can teach other agents how to adjust their power levels while reducing computation complexity and speeding up the learning process. Therefore, our proposed power control approach can provide the ability to practically respond to current Internet of things system conditions and suitable for real wireless communication operations. Finally, we validate the introduced concept and confirm the effectiveness of the proposed scheme in comparison with the existing schemes through extensive simulation analysis. Keywords: R-learning; power control algorithm; Internet of things; team game model; docitive network paradigm; interactive mechanism (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:1:p:1550147716687558 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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