Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Alekseeva, Daria; Stepanov, Nikolai A.; Veprev, Albert; Sharapova, Alexandra; Lohan, Elena Simona; Ometov, Aleksandr

doi:10.1109/access.2021.3129850

Cited by 19 publications

(16 citation statements)

References 62 publications

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“…Each tree selects a particular prediction and the random forest then averages the different predictions provided by the decision trees, which improves prediction accuracy. The random forest has been studied extensively in the literature (see [52][53][54][55]). Its architecture is shown in Figure 4.…”

Section: Random Forestmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

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Section: Random Forestmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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show abstract

“…Therefore, vehicular networks are in the early stages of challenges related to the exploitation and adaptation of AI tools [6]. Moreover, the implementation of Machine Learning (ML) techniques as a subset of AI could optimize the operation of the networks in predicting failure before it causes a significant reduction in the Quality of Service (QoS) [7]. In VANETs, information about road conditions and other vehicles will be exchanged among communications when the number of sending and receiving packets through these communications increases (i.e., many vehicular users on the road) and traffic occurs in the network, which will cause a delay or decline in important services.…”

Section: Introductionmentioning

confidence: 99%

“…We considered our problem as a classification task. Furthermore, supervised ML algorithms, including Random Forest(RF), K-Nearest Neighbor(KNN), Naive Bayes (NB), Decision Tree(DT) and Support Vector Machines (SVM), are commonly considered for designing predictive models in traffic [7]. Among them, Support Vector Machines (SVM), which can adapt to the dynamic and nonlinear nature of traffic data, have problems with selecting the kernel type and resolving this issue.…”

Section: Introductionmentioning

confidence: 99%

“…When traffic occurs in the network, it causes a significant reduction in QoS and failure in the network. ML, as a subset of AI, can optimize the operation of networks [7]. Making an intelligent prediction of traffic in the network can help us to precisely identify the failure of the network and avoid service degradation, especially for services that are highly dependent on the performance of the network.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri¹,

Pierre²

2023

IEEE Access

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Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK-EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK-EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

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“…In contrast, UL is applied when it is impossible or extremely difficult to obtain labeled samples [16]. The goal is to model the latent or underlying structure of the distribution in the data, e.g., by Clustering.…”

Section: Introductionmentioning

confidence: 99%

On Applicability of Imagery-Based CNN to Computational Offloading Location Selection

2023

Self Cite

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The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said demand is enabling Mobile Edge Computing (MEC) powered by advanced methods of Machine Learning (ML). This paper proposes the application of ML-powered computational offloading strategy in a wireless cellular network by applying the traditional fundamental Travelling Salesman Problem (TSP) on computational offloading location selection. The main specificity of the proposed approach is the use of imagery data. Thus, the paper executes a literature review to identify existing strategies. It further proposes a novel method utilizing the location-like imagery data to identify the most suitable computational location by executing the search for an identified route between locations using the proposed Deep Learning (DL) model. The model was evaluated and achieved MAE -1,575, MSA -10,119,205, R 2 -0.98 on the testing dataset, which outperforms or is comparable with other well-known architectures. Moreover, the training time is proven to be 2-10 times faster. Interestingly, the MAE values are relatively low compared to the target values that should be predicted (despite rather high MSE results), which is confirmed by the almost perfect R 2 value. It is concluded that the proposed neural network can predict the target values, and this solution can be applied to real-world tasks.

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Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Cited by 19 publications

References 62 publications

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

On Applicability of Imagery-Based CNN to Computational Offloading Location Selection

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