Multiagent Deep-Reinforcement-Learning-Based Virtual Resource Allocation Through Network Function Virtualization in Internet of Things

Shah, Hurmat Ali; Zhao, Lian

doi:10.1109/jiot.2020.3022572

Cited by 43 publications

(25 citation statements)

References 32 publications

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“…In [80], Hurmat et al embedded MADRL with actors and critics in both centralized agent, which is the data center, and distributed agents, which are VNFs. Both centralized and distributed agents select actions to maximize their individual rewards, and hence resources, particularly transmission opportunities, are allocated in a competitive (X.2.1) manner.…”

Section: Hurmat's Madrl Approach With Actors and Criticsmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

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Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

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Section: Hurmat's Madrl Approach With Actors and Criticsmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

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“…Shah and Zhao [138] proposed a multi-agent virtual resource allocation scheme for IoT based on Deep Reinforcement Learning. They accessed network resources using the Network Function Virtualization (NFV) approach, then handle resource allocation in IoT networks using the Deep Reinforcement Learning (DRL) algorithm.…”

Section: For Resource Allocation and Management In Iotmentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

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“…Machine learning (ML) and artificial intelligence (AI) are therefore expected to play a pivotal role in the design of all aspects of wireless networks. This has led to a surge in the number of published works exploring ML/AI-based data-driven solutions for solving challenges associated with different aspects of wireless network design, including radio propagation [145]- [148], wireless signal identification [149], access control and routing protocols [150] and radio resource management [151], [152]. ML-and AI-enabled relaying in IoT networks has also received significant research attention.…”

Section: Machine Learning and Ai For Iot Relayingmentioning

confidence: 99%

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

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The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

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Multiagent Deep-Reinforcement-Learning-Based Virtual Resource Allocation Through Network Function Virtualization in Internet of Things

Cited by 43 publications

References 32 publications

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Relaying in the Internet of Things (IoT): A Survey

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