CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks

Lee, Taegyeom; Jo, Ohyun; Shin, Kyung‐Seop

doi:10.3390/electronics9010143

Cited by 17 publications

(11 citation statements)

References 10 publications

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“…Machine learning could be an effective means of investigating these characteristics given its ability to yield appropriate parameters by considering various external environmental parameters together at the same time. This has been achieved more broadly for communication networks using a variety of processes [12], [13]. In addition, researchers have previously applied machine learning technology to underwater networks in order to overcome poor link quality [14]- [18].…”

Section: Previous Studiesmentioning

confidence: 99%

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Byun

Cho

et al. 2021

IEEE Access

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Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

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Section: Previous Studiesmentioning

confidence: 99%

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Byun

Cho

et al. 2021

IEEE Access

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“…Communication in wireless ad-hoc networks, due to its requirement of adapting to constant environmental changes, provides an optimal playground for the application of reinforcement learning. Over the past few years, a lot of work has been done optimizing selected aspects of the MAC-layer [13,18,24] up to replacing the whole MAC-layer by learning agents [6,16,17].…”

Section: Related Workmentioning

confidence: 99%

“…There are plenty of works proposing MAC protocols based on RL which learn whether a transmission is likely to be successful at a given time or not [6,16,17]. Most of these protocols abstract time into frames, which are further subdivided into slots.…”

Section: Introductionmentioning

confidence: 99%

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QMA: A Resource-efficient, Q-Learning-based Multiple Access Scheme for the IIoT

Meyer¹,

Turau²

2021

Preprint

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Contention-based wireless channel access methods like CSMA and ALOHA paved the way for the rise of the Internet of Things in industrial applications (IIoT). However, to cope with increasing demands for reliability and throughput, several mostly TDMA-based protocols like IEEE 802.15.4 and its extensions were proposed. Nonetheless, many of these IIoT-protocols still require contentionbased communication, e.g., for slot allocation and broadcast transmission. In many cases, subtle but hidden patterns characterize this secondary traffic. Present contention-based protocols are unaware of these hidden patterns and can therefore not exploit this information. Especially in dense networks, they often do not provide sufficient reliability for primary traffic, e.g., they are unable to allocate transmission slots in time. In this paper, we propose QMA, a contention-based multiple access scheme based on Q-learning, which dynamically adapts transmission times to avoid collisions by learning patterns in the contention-based traffic. QMA is designed to be resource-efficient and targets small embedded devices. We show that QMA solves the hidden node problem without the additional overhead of RTS / CTS messages and verify the behaviour of QMA in the FIT IoT-LAB testbed. Finally, QMA's scalability is studied by simulation, where it is used for GTS allocation in IEEE 802.15.4 DSME. Results show that QMA considerably increases reliability and throughput in comparison to CSMA/CA, especially in networks with a high load.

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“…To reduce the collisions between the system and client effectively, the access method is designed using rule-based algorithms and RL. RL is also utilized as a means of selecting the appropriate channel [16][17][18][19]. RL is applied to the problem of choosing a route to escape to a destination by avoiding obstacles.…”

Section: Learning From Demonstrationmentioning

confidence: 99%

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Kim

Lee

2020

Applied Sciences

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Complex problems require considerable work, extensive computation, and the development of effective solution methods. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. However, problem solving often involves human expertise and guidance. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. In previous studies, only binary numbers have been used for this purpose. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods.

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CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks

Cited by 17 publications

References 10 publications

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

QMA: A Resource-efficient, Q-Learning-based Multiple Access Scheme for the IIoT

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

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