Novel AI-Based Scheme for Traffic Detection and Recognition in 5G Based Networks

Volkov, Artem; Ateya, Abdelhamied A.; Muthanna, Ammar; Koucheryavy, Andrey

doi:10.1007/978-3-030-30859-9_21

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…The model can be applied to improve traffic flow and enhance traffic control in real-world scenarios. In [2], Accurate traffic flow data is important for improving travel decision-making, reducing congestion, and lowering carbon emissions. Intelligent transit systems (ITSs) provide better accuracy for traffic flow prediction and are essential for the success of advanced traffic and public transportation systems.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey Paper on Traffic Prediction Using Machine Learning

Srivastava¹,

Aryan²,

Khurana³

2023

IJRASET

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Traffic flow prediction is an essential tool for traffic management, which can assist traffic operators in making informed decisions for traffic control and management. Accurate traffic flow prediction can also help commuters to plan their journeys efficiently and reduce travel time. The use of Support Vector Regression algorithm for traffic flow prediction has shown promising results in various studies, and it can be used to predict traffic flow at different time intervals, such as hourly, daily, weekly, or even yearly. Additionally, the application of machine learning and artificial intelligence techniques can further enhance the accuracy of traffic flow prediction, which can lead to more effective traffic management strategies

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Section: Literature Surveymentioning

confidence: 99%

A Survey Paper on Traffic Prediction Using Machine Learning

Srivastava¹,

Aryan²,

Khurana³

2023

IJRASET

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“…The model was built in a discrete event modeling system. It implements traffic generation in the source node and the foreground traffic served by the network elements through which the selected routes pass [26]. Figure 6 presents the obtained data rate of the simulation during the first ten seconds.…”

Section: Numerical Evaluation and Analysismentioning

confidence: 99%

Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things

Ateya,

Bushelenkov,

Muthanna

et al. 2023

Mathematics

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The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models.

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“…The features of dataset consist of the timestamp, bytes count and the packets count. This work is further extended in [6], for 5G networks using the Recurrent Neural Networks (RNN) and specifically the LSTM.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Regression Approach for Throughput Estimation in an IoT Environment

Hameed

Violos

Santi

et al. 2021

2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &Amp; Communications (GreenCom) An

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The success of Internet of Things (IoT) has significantly increased the volume of data generated by various smart applications. However, as many of these applications are characterized by strict Quality of Service (QoS) requirements, there is a growing need for accurately predicting typical performance parameters such as throughput. This prediction should be based on the applications' traffic profiles and at the same time reflect the network uncertainty that IoT access networks add to the overall communication. In this work, we deployed 6 different smart building applications in a real testbed while creating a considerable traffic contention in an IEEE 802.15.4 access network. After preprocessing the raw data and following a feature engineering mechanism, we apply five different regression learning approaches to each application and predict its throughput. By resorting to several prediction error metrics and time metrics such as training and inference time, we show that the multiple linear regression achieves high accuracy while outperforming other well known machine learning methods.

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Novel AI-Based Scheme for Traffic Detection and Recognition in 5G Based Networks

Cited by 12 publications

References 16 publications

A Survey Paper on Traffic Prediction Using Machine Learning

A Survey Paper on Traffic Prediction Using Machine Learning

Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things

A Machine Learning Regression Approach for Throughput Estimation in an IoT Environment

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