AU2020104133A4 - Expected conditional clustered regressive deep multilayer precepted neural learning for iot based cellular network traffic prediction with big data - Google Patents
Expected conditional clustered regressive deep multilayer precepted neural learning for iot based cellular network traffic prediction with big data Download PDFInfo
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Abstract
Expected Conditional Clustered Regressive Deep Multilayer precepted neural Learning for
IOT based Cellular Network Traffic Prediction with Big Data
Abstract:
Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily
basis. Processing such volume of data is a complex task as well as the challenging one. This big
data is applied in the cellular network for traffic prediction. The conventional techniques
handling the large volume of data, but the accurate prediction were not performed since it failed
to accurately learn the spatial and temporal data from raw input. In order to improve the traffic
prediction accuracy with minimum time, Expected Conditional Maximization Clustering and
Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL)
technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of
data over the spatial and temporal aspects of cellular networks. Then the collected data are
trained with multiple layers such as one input layer, two hidden layers, and one output layer. The
spatial and temporal data are given to the input layer for performing the traffic prediction.
Initially, Iterative Expected Conditional Maximization clustering is applied in the first hidden
layer for partitioning the total network data into different groups based on the spatial
information. Then it transferred into the second hidden layers for learning the given input data
using a regression function. The regression function uses the Ruzicka similarity to find the
relationship between the data within the cluster and traffic patterns. Then the analyzed results are
finally transferred into the output layer. The activation function is used at the output layer to
predict the network traffic based on the similarity value with higher accuracy. Finally, the error
rate is calculated for minimizing the prediction error. Experimental evaluation is carried out
using a big dataset with different metrics such as prediction accuracy, false-positive and
prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique
improves the performance of network traffic prediction with higher accuracy and minimum time
as well as the false-positive rate as compared to the state-of-the-art methods.
H i toric Trafic
Data
Real Time Traffic Pmiation rCumI Nr o Future Trafficdata
Fig 1: Proposed IOT based Cellular Network Traffic Prediction with Big Data Model
Description
H i toric Trafic Data
Real Time Traffic Pmiation rCumI Nr o Future Trafficdata
Fig 1: Proposed IOT based Cellular Network Traffic Prediction with Big Data Model
AUSTRALIA Patents Act 1990
The following statement is a full description of this invention, including the best method of performing it known to me:
Field of the Invention:
With the increasing trend of mobile operators and internet access, the data traffic has posed great challenges since the load of the network is constantly increased. Therefore, the network traffic analysis and prediction is an essential part for cellular networks to minimize the load, since it is used for network control and management as well as service provisioning. The several works have been designed in the cellular network traffic prediction, but it has some challenges due to the large volume of temporal and spatial dynamics introduced by different user Internet behaviors.
A Spatial-Temporal Cross-domain neural Network (STCNet) was developed to increase the cellular network traffic prediction with complex patterns. The designed model uses the clustering concept to divide the city into different zones. Though the model reduces the mean square error, the performance of traffic prediction accuracy was not calculated. A Graph Neural Network with Decomposed Cellular Traffic model (GNN-D) was developed in to improve the cellular traffic prediction by learning the spatial and temporal dependencies. The designed model failed to minimize the prediction time of continuously evolving traffic patterns.
Extending Labeled Data (ELD) was introduced in to discover the label of unknown mobile network traffic. The model failed to minimize the mobile network traffic prediction error. A traffic pattern extraction and modeling method were introduced in for processing big cellular data. The method uses the clustering concept to minimize the complexity of prediction, but the accurate traffic prediction was not performed. A Jordan recurrent neural network (JNN) using a firefly algorithm was introduced to forecast the cellular data traffic with minimum error. The time complexity of traffic prediction was not minimized.
A three-layer classifier using machine learning was developed to discover mobile traffic with higher precision. Though the method reduces the false positive rate, the prediction time was not minimized. An application-level traffic prediction method was introduced using traffic big data. The designed method failed to enhance traffic prediction accuracy.
An Exponential Smoothing Method was developed to predict the cellular network traffic with lesser complexity. But the error rate was not reduced. A multiple RNN based learning models were developed using a unified multi-task learning method for enhancing the traffic forecasting by using Spatio-temporal correlations among base stations. Though the method minimizes the error rate, the accuracy was not improved. A cellular traffic offloading problem was resolved in by the link prediction with minimum delay. But it failed to analyze the multiple data effectively from the cellular network..
Sensor consolidation remains an important challenge in IoT and Cyber Physical Systems (CPS). Sensor consolidation plays a vital role to grab accurate data from various IoT frameworks. So, selecting a sensor, which should allow into the network and which should not base on decision system by evaluating all aspects. It ultimately generates unique valuable data by satisfying all entail aspects. This entire process would be carried out with our proposed QML approach.
Background and prior art of the invention:
Billions of Internet of Things (IoT) sensor devices are interconnected to perceive data towards a unique platform to, where all sensor data to be analyzed and computed to make certain quality of service. A multivariate Long Short-Term Memory (LSTM) algorithm was designed in to predict the traffic networks by performing the call detail record (CDR) data analysis. The designed algorithm failed to consider the large volume of wireless data and complex data types for network prediction.
A Deep Belief Network was developed to forecast the network traffic using spatiotemporal correlation with minimum error rate. But the traffic prediction time complexity was not minimized. A Deep Traffic Predictor (DeepTP) was developed to forecast the traffic from the cellular network. The model consumed more time for traffic prediction.
A deep-learning-based Cloud Radio Access Network (C-RAN) optimization technique was developed using the Multivariate LSTM model to perform traffic forecasts based on temporal dependence and spatial correlation. But the performance of the deep learning was not improved by considering different traffic patterns. A clustering-based artificial neural network (C-ANN) model was introduced in for classifying the mobile traffic patterns. Though the model increases the accuracy, the false positive rate was not minimized.
A novel approach was introduced in that initiate the users to smooth the traffic temporally. But the method failed to ensure higher prediction accuracy. A measurement-driven model was developed for mobile data traffic prediction using big data collected from a crowded metropolitan area. The traffic parameters spatial correlation was not analyzed to further enhance traffic prediction.
A densely connected Convolutional Neural Networks (CNN) was introduced in to forecast the cellular network traffic with minimum error. But it failed to achieve better performance using a large amount of training data. A Group Method of Data Handling (GMDH) polynomial neural network was developed in to accurate traffic prediction. However, the time complexity of the traffic prediction was not minimized. Various machine learning and statistical methods were developed in for forecasting the voice traffic of the Mobile Telecommunication System. However, the performance of the accurate prediction was not obtained with minimum complexity.
Objects of the Invention:
• A novel approach was introduced in that initiate the users to smooth the traffic.
• Analyze the approaches of learning.
• Various machine learning and statistical methods were developed in for forecasting the voice traffic of the Mobile Telecommunication System.
Summary of the invention:
In this invention, we designed with billions of mobile devices accessing the Internet, cellular traffic has grown extremely in the past few years. Large volumes of data about the cellular traffic are collected at cell towers (i.e. base station) that widely implemented for daily network management. With the increasing volume of big cellular data, traffic prediction plays a challenging one due to temporal and spatial dynamics established by different user behavior. The prediction is the statistical technique used for extracting more relevant information from the large volume of data and predicting future outcomes by collecting and analyzing the current and past events. Accurate cellular network traffic prediction at base station enables to ensure good quality of services. Based on this motivation, cellular network traffic prediction is carried out by employing the deep learning and statistical learning concept. Therefore, the ECMCRR-MPDNL technique is introduced to handle the large volume of cellular data for accurate prediction by deeply learning the higher-level features from the raw datasets using multiple layers.
Objective of the invention: The primary objective of this invention is to bring a new system for forecast the cellular network traffic with minimum error. But it failed to achieve better performance using a large amount of training data. A Group Method of Data Handling (GMDH) polynomial neural network was developed to accurate traffic prediction. However, the time complexity of the traffic prediction was not minimized. Various machine learning and statistical methods were developed for forecasting the voice traffic of the Mobile Telecommunication System. However, the performance of the accurate prediction was not obtained with minimum complexity.
Statement of the invention:
With billions of mobile devices accessing the Internet, cellular traffic has grown extremely in the past few years. Large volumes of data about the cellular traffic are collected at cell towers (i.e. base station) that widely implemented for daily network management. With the increasing volume of big cellular data, traffic prediction plays a challenging one due to temporal and spatial dynamics established by different user behavior. The prediction is the statistical technique used for extracting more relevant information from the large volume of data and predicting future outcomes by collecting and analyzing the current and past events. Accurate cellular network traffic prediction at base station enables to ensure good quality of services. Based on this motivation, cellular network traffic prediction is carried out by employing the deep learning and statistical learning concept. Therefore, the ECMCRR-MPDNL technique is introduced to handle the large volume of cellular data for accurate prediction by deeply learning the higher-level features from the raw datasets using multiple layers.
Brief description of the proposed architecture: Figure 1 illustrates the flow process of the proposed ECMCRR-MPDNL technique to obtain better traffic prediction with minimum time. Initially, the big dataset is taken as input for predictive analytics. The numbers of spatial and temporal data are collected from the big dataset. After that, the input data analyzed using feed forward multilayer percepted deep neural learning. Then the input data is transferred in only one direction from the input nodes of the deep architecture and then analyzed in the hidden layers using regression function.
Abstract: Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily basis. Processing such volume of data is a complex task as well as the challenging one. This big data is applied in the cellular network for traffic prediction. The conventional techniques handling the large volume of data, but the accurate prediction were not performed since it failed to accurately learn the spatial and temporal data from raw input. In order to improve the traffic prediction accuracy with minimum time, Expected Conditional Maximization Clustering and Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL) technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of data over the spatial and temporal aspects of cellular networks. Then the collected data are trained with multiple layers such as one input layer, two hidden layers, and one output layer. The spatial and temporal data are given to the input layer for performing the traffic prediction. Initially, Iterative Expected Conditional Maximization clustering is applied in the first hidden layer for partitioning the total network data into different groups based on the spatial information. Then it transferred into the second hidden layers for learning the given input data using a regression function. The regression function uses the Ruzicka similarity to find the relationship between the data within the cluster and traffic patterns. Then the analyzed results are finally transferred into the output layer. The activation function is used at the output layer to predict the network traffic based on the similarity value with higher accuracy. Finally, the error rate is calculated for minimizing the prediction error. Experimental evaluation is carried out using a big dataset with different metrics such as prediction accuracy, false-positive and prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique improves the performance of network traffic prediction with higher accuracy and minimum time as well as the false-positive rate as compared to the state-of-the-art methods.
Claims (5)
1. The proposed approach provides accurate data by consolidating potential sensor to make accurate decision in terms of computation and analysis to meet latency sensitive constraints.
2. To minimize the traffic prediction error rate i.e. false positive rate, Multilayer Percepted Deep Neural Learning effectively providing accurate results with a lesser mean square error. Based on the Ruzicka similarity values, the higher possibility of traffic is correctly predicted at the output layer
3. Sensor contiguity rate model remain used to assess the surrounding coverage rate by tracking and meeting the objective of area.
4. Our proposed approach provides absolute accurate data with minimal data cost during the entire span of each sensor.
5. Subsequently, the novel ECMCRR-MPDNL outcomes of the measurement minimize the cellular network traffic prediction time.
Fig 1: Proposed IOT based Cellular Network Traffic Prediction with Big Data Model
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