Classification of node degree based on deep learning and routing method applied for virtual route assignment

Lee, Yang-Min

doi:10.1016/j.adhoc.2016.11.007

Cited by 38 publications

(17 citation statements)

References 11 publications

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“…Thus, a wireless ad hoc network (WANET) can be established by using the same devices as nodes to deliver the data packets to the nearest online BS. Moreover, as these devices are hand-held and according to the high possibility that the owners of these devices are on the move, to escape the disastrous area, Mobile Ad Hoc Network (MANET) is more suitable to such a scenario [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed by Lee [ 8 ] uses deep learning to classify the node degree in order to assign virtual routes. This method is proposed to establish communications among MANET nodes in ad hoc topology to establish communications with the nearest BS during a disaster.…”

Section: Introductionmentioning

confidence: 99%

“…Based on this review, the methods that exist in the literature have addressed the problem of establishing temporary communications using MANETs, even by using deep learning [ 8 ] and reinforcement learning [ 25 ]. However, these methods can handle networks with limited speed of nodes, which may be a dramatic limitation to these methods as survivors tend to flee the location of a disaster as fast as possible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Alkadhmi

Uçan

Ilyas

2020

Applied Bionics and Biomechanics

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With the reliance of humans on mobile smart devices that have wireless communication, modules have significantly increased in recent years. Using these devices to communicate with the survivors during a disaster or its aftermath can significantly increase the chances of locating and saving them. Accordingly, a method is proposed in this study to extend the lifetime of the nodes in a Mobile Ad Hoc Network (MANET) while maintaining communications with the nearest base station (BS). Such a methodology allows the rapid establishment of temporary communications with these survivors, as restoring the complex infrastructure is a time-consuming process. The proposed method achieves the longer lifetime of the network by balancing the load throughout the nodes and avoids exhausting those with limited remaining energy. The proposed method has shown significant improvement in the lifetime of the MANET while maintaining similar Packet Delivery Rate (PDR) and route generation time, compared to existing methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Alkadhmi

Uçan

Ilyas

2020

Applied Bionics and Biomechanics

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“…Routing: Deep learning can also improve the efficiency of routing rules. Lee et al exploit a 3-layer deep neural network to classify node degree, given detailed information of the routing nodes [365]. The classification results along with temporary routes are exploited for subsequent virtual route generation using the Viterbi algorithm.…”

Section: G Deep Learning Driven Network Controlmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,383

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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“…It imitates the human brain's communication and information processing mechanisms and procedures the data for object identification, language translation, speech recognition, and decision making. In WSNs, DL is used to tackle many problems, such as abnormality and fault detection, energy harvesting, data efficiency calculation, and routing [40].…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

Abdan

Seno

2021

Preprint

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A wormhole attack is a type of attack on the network layer which reflects the issue of routing protocols. The classification is performed with several methods of machine learning consisting of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Linear Discrimination Analysis (LDA), Naive Bayes (NB), and Convolutional neural network (CNN). Moreover, for feature extraction, we used the properties of nodes, especially nodes speed in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious samples. Results of the classification show that the accuracy of KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method's accuracy is 98.9% and higher than other methods. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.

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Classification of node degree based on deep learning and routing method applied for virtual route assignment

Cited by 38 publications

References 11 publications

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Deep Learning in Mobile and Wireless Networking: A Survey

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

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