A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles

Sharif, Abida; Li, Jian Ping; Saleem, Muhammad Asim; Manogran, Gunasekaran; Kadry, Seifedine; Basit, Abdul; Khan, Muhammad Attique

doi:10.1007/s10845-020-01722-7

Cited by 33 publications

(18 citation statements)

References 27 publications

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“…The resource allocation for moving nodes in CR-IoT networks is a challenging issue. In future research, we will focus resource allocation on vehicular-based IoT networks [33][34][35].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

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The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

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“…The resource allocation for moving nodes in CR-IoT networks is a challenging issue. In future research, we will focus resource allocation on vehicular-based IoT networks [33][34][35].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

Latif

Akraam

Karamat

et al. 2022

Sensors

Self Cite

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“…Referred publications Markov decision process [12,23,24,37,64,70,75,84,96,100,101,104,127,130,133,138,144,153,165,167,170,177,188,191,199], [203, 207, 211, 212, 214, 217, 220, 231, 252, 256-259, 263, 264, 272, 274, 281, 291, 309, 313, 320, 340, 343, 346], [369][370][371][372][373][374][375][376] Multiarmed bandit [61,66,102,198,351,377,378] Dynamic programming [16,19,27,52,68,70,84,90,93,…”

Section: Approachmentioning

confidence: 99%

“…, 32, 33, 38, 41, 45, 46, 51, 64, 68, 70, 72, 82, 83, 85, 86, 91, 94, 96, 100],[102, 104, 106-109, 128, 131, 135, 136, 140, 141, 145, 151, 158, 160],[164, 166, 168, 170, 173, 178, 179, 188, 191, 198, 200, 201, 203, 206, 207, 209, 215-218, 223, 230],[234-236, 239, 243-245, 248, 248],[251,252,254,255,257,272,275,277,279,284,286,288,293,299,300,302,304,305,[307][308][309],[311, 312, 315-318, 321, 323, 328-336, 340, 341, 343-351] Vehicle, unmanned aerial vehicle, drone, aircraft[10,68,79,90,91,151,161,170,181,183,197,217,220,223,228,229,232,244,257,259,266],[272, 277, 282, 299, 305, 309-311, 313, 320, 325, 336, 341, 352-354] …”

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confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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“…A very few studies are available on how IoT and information technology (IT) techniques work together [6]. The major applications where these technologies work together are biometric [7], video surveillance [8], Internet of Vehicles [9], and biomedical [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Azhar

Sharif

Raza

et al. 2021

Sensors

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The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.

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A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles

Cited by 33 publications

References 27 publications

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

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