Prediction of Network Traffic in Wireless Mesh Networks Using Hybrid Deep Learning Model

Mahajan, Smita; Kotecha, Ketan

doi:10.1109/access.2022.3140646

Cited by 29 publications

(15 citation statements)

References 50 publications

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“…The smoothed support vector machine (SSVM) has an improvement percentage of 32.35% for a one-step-ahead forecast. In the most recent study [32], the authors proposed a hybrid LSTM and convolution neural network framework for a wireless network. The proposed solution was compared with state-of-the-art techniques, and the effectiveness and superiority of the hybrid architecture were highlighted.…”

Section: Related Workmentioning

confidence: 99%

“…LSTM [32] is a recurrent neural network that forgets and propagates information for a recurrent training period. This can improve the forecast performance.…”

Section: Lstmmentioning

confidence: 99%

“…At every index t, the input sample, xt; past cell state, at − 1; and past hidden state, ht − 1; were considered by LSTM. All temporal relationships in LSTM can be derived using the equations below [32,42]:…”

Section: Lstmmentioning

confidence: 99%

“…ReLU activation can handle this issue, where gradients are calculated faster. However, they are not easily eliminated [32]. The function of a forget gate is to choose what information to retain and what information to remove from ht − 1 and xt.…”

Section: Lstmmentioning

confidence: 99%

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Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.

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Section: Related Workmentioning

confidence: 99%

“…LSTM [32] is a recurrent neural network that forgets and propagates information for a recurrent training period. This can improve the forecast performance.…”

Section: Lstmmentioning

confidence: 99%

Section: Lstmmentioning

confidence: 99%

Section: Lstmmentioning

confidence: 99%

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Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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“…To improve the performance of WMNs, it is essential to study architectures, models, and network topologies. Many research groups have recently completed this [ 3 , 4 , 5 , 6 , 7 , 8 ], where the MRP-WMN has become an important topic. Because this problem is known as NP-hard [ 9 ], conventional algorithms cannot solve it.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm

Bình

Truong

2022

Sensors

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Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the Multi-Verse Optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), respectively.

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Evaluation of the Use of Artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET Networks

Sánchez,

Ludeña-González,

Rohoden

2024

Communications in Computer and Information Science

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Prediction of Network Traffic in Wireless Mesh Networks Using Hybrid Deep Learning Model

Cited by 29 publications

References 50 publications

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

An Efficient Method for Solving Router Placement Problem in Wireless Mesh Networks Using Multi-Verse Optimizer Algorithm

Evaluation of the Use of Artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET Networks

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