Learning Automata Based Q-Learning RACH Access Scheme for Cellular M2M Communications

Shinkafi, Nasir A.; Bello, Lawal Muhammad; Shu'aibu, Dahiru Sani; Mitchell, Paul

doi:10.1109/gciot47977.2019.9058419

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…x for class x and is generated c x (t) by comparing it with the expected value of g = 1 − 2e − 1 [18,22], computed as…”

Section: Methodsmentioning

confidence: 99%

“…This scheme minimizes access delay and resource wastage, but causes poor QoS when considering both H2H and M2M devices. To resolve the challenges brought about by the effect of the penalty factor in QL-RACH, LA was used to classify M2M according to QoS classes, thereby producing an LA-based QL-RACH (LA-QL-RACH) access scheme for cellular M2M communications, as discussed in [22]. The scheme includes a mechanism to remove the excessive punishment experienced by M2M devices by regulating the use of a penalty factor in the QL-RACH scheme.…”

Section: Related Workmentioning

confidence: 99%

“…With the help of the feedback, the choice of probability of subsequent actions is updated. P-Model LA is employed in this work which includes a set of environmental responses that take only the binary values of 1 and 0, respectively, for penalty and reward [18,22].…”

Section: Q-learning and Lamentioning

confidence: 99%

“…Numerous RACH access protocols have been proposed to address these challenges. Prominent among them are the reinforcement learning-based techniques such as priority-based learning automata (PLA) [18] and Qlearning RACH (QL-RACH) [19], with associated modification schemes presented in [20][21][22].…”

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confidence: 99%

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Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

et al. 2021

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Machine-to-Machine (M2M) communication, also called machine-type communication (MTC), is predicted to be one of the major applications of current and future cellular communications [1,2]. M2M communication, as defined in [3], enables communication between various devices without or with limited human intervention. Different devices such as sensors, actuators, meters, and radio frequency tags are used as M2M devices to read the status of machines and share information, either on a wireless network, wired network, or a hybrid of both to a target destination [4,5]. M2M devices are an important part of the emerging "Internet of Things" and "Smart City" paradigms [6,7], which are expected to provide solutions to current and future socioeconomic demands.In addition, M2M devices engender new applications in areas such as building and industrial automation, remote and mobile healthcare, and many more, as described in [8]. According to [9], the number of M2M devices is expected to significantly outnumber the world population [10]. This creates a significant gap and makes it practically impossible for humans to control them. Therefore, there is a need for these devices to autonomously interact among themselves.The envisaged growth of M2M applications has led to many research studies on protocols and products oriented to support M2M services. The 6LowPAN protocol suite is a popular technology for low-power devices [11], the IEEE 802.15.4 standard is used for low-bit rate short-range transmission [12], and Zigbee (which utilizes the 802.15.14 standard) is for M2M device interconnection in short-range

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“…x for class x and is generated c x (t) by comparing it with the expected value of g = 1 − 2e − 1 [18,22], computed as…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Q-learning and Lamentioning

confidence: 99%

mentioning

confidence: 99%

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Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

et al. 2021

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“…The growth may lead to congestion during the channel access phase if a massive number of IoT devices access the channel, which leads to significant delay [ 93 ]. Therefore, numerous proposals for controlling the load of the random-access channel have been proposed [ 94 – 97 ]. These studies are mainly based on channel access probability.…”

Section: Machine Learning For Iotmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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Learning Automata Based Q-Learning RACH Access Scheme for Cellular M2M Communications

Cited by 2 publications

References 12 publications

Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

Priority‐based learning automata in Q‐learning random access scheme for cellular M2M communications

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

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