R-learning-based team game model for Internet of things quality-of-service control scheme

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.

Technological advances in wireless sensor networks enabling diverse Internet of Things applications

“…4 The next paper reports a new IoT system power control scheme based on an average-reward reinforcement learning algorithm for Quality-of-Service provision. 5 There are two papers relevant to the localization and the positioning systems. One paper proposes anchor-free localization algorithm to achieve high localization accuracy with flexible structure, low cost, and high stability, 6 while the other paper, 7 rather than focusing on indoor positioning accuracy, studied store layout optimization by linking the customers' positioning data with the transaction data, rather than focusing on the indoor positioning accuracy.…”