Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the hyper-parameters of long short-term memory (LSTM) network as an efficient time series modeling approach which is widely used in sequence prediction tasks. To prove the superiority of the proposed algorithm, four other optimization algorithms were employed to optimize LSTM, and the results were compared. The evaluation results confirmed the effectiveness of the proposed approach in predicting the traffic of wireless networks accurately. On the other hand, a statistical analysis is performed to emphasize the stability of the proposed approach.

In the context of network traffic analysis and prediction, network traffic management is defined. Use of Internet traffic management is used to control network traffic, minimize congestion, improve information security and make the most of network resources. Internet traffic is growing at an exponential rate due to the widespread use of smartphones and data subscriptions. Accurate network traffic prediction is necessary for network capacity planning in order to handle this tremendous expansion. Network operators will be at danger if they do not have access to a model that accurately and quickly predicts network traffic. Effective network traffic management necessitates proper network management and modeling [

The traditional system administration structure cannot keep up with the rapid advancements in Internet technology and the growing ubiquity of high-tech mobile phones. If we can estimate network traffic patterns using past data, we will be able to allocate and schedule resources more effectively, and improved planning of the network is more likely to take place. For optimal resource allocation through simple bandwidth provisioning and maintaining maximum network utilization, models and forecasts of network traffic are critical. As a result, a robust forecasting model is essential for planning and constructing the network’s capacity. The requirement for a method to dynamically allot bandwidth and minimize congestion is influenced by the dynamic nature of network traffic. We can only accurately measure network traffic if we have a model. With greater Quality of Service (QoS) and better future traffic engineering, reliable network traffic modeling and prediction are necessary. Furthermore, several developers and academics have used linear and non-linear time series models to model network traffic.

Network traffic prediction models can help network engineers choose traffic engineering solutions that can even handle unpredicted future scenarios. In network planning and bandwidth provisioning, network traffic prediction algorithms can play an important role. The planning of network traffic has grown in importance in the scientific community. It’s becoming increasingly difficult for network administrators to keep track of all the people who are using the network at the same time. For this reason, it is critical to keep an eye on the network between the two points and to model the incoming traffic and make predictions about what it will be. To keep tabs on network activity, network traffic analysis is becoming increasingly critical. In order to determine what is happening in the network, network analysts collect and analyze network traffic. There are various different names for network analysis, including “network analysis,” “protocol analysis,” and “packet analysis”. There are several techniques that may be used to analyze network traffic on a packet or level-by-packet basis. It is also possible to do network traffic analysis on a variety of other types of data, including the identity and location of the organizations interacting as well as the length and complexity of their communication streams. As a result, network traffic modeling has become an integral aspect of network design and bandwidth management. The Poisson model is the initial model used in the telephone network to model and forecast arrivals. In spite of this, as contemporary telecommunications have evolved, network traffic has grown enormously and is now more complicated and erratic than speech traffic was previously. To effectively control network traffic, network management and suitable modeling are required.

In order to model and predict network traffic, a lot of academics and developers have worked on these problems. The genetic algorithm [

After a review of the current research models, it can be seen that there are still significant gaps in the field, which will be addressed in the proposed study. To improve network resource efficiency, it is necessary to understand network traffic in order to forecast future demand owing to the complicated in-network data from a variety of network applications [

The Fusion Deep Learning (FDL) model for forecasting traffic speed was described in [

The proposed methodology is based on optimizing the hyperparameters of long short-term memory (LSTM) to boost the prediction accuracy of wireless network traffic volume. Therefore, this section starts by presenting the structure of LSTM and showing the parameters that are optimized, and then the proposed optimization algorithm is presented and discussed.

Because of its ability to simulate complicated time series with time delays of undetermined size, the Long Short-Term Memory (LSTM) model has recently gained favor as a recurrent neural network [

An initial random population is generated by the grey wolf optimizer; the next iterations modify this population. Exports and exploitation are kept under check by GWO. In order to find potential places in the search space, exportation is used. Exploitation is a search strategy that makes use of promising points in the search space to identify the best possible advantageous points. To the problem-solver, each and every one of us is a potential solution. The fitness function is then determined for each solution. In this way, the three major gamma frequencies may be distinguished [

Over the course of iterations, the value of

The average of the three solutions,

The Cinclidsae family of birds includes the Dipper Throated bird, which is renowned for its bobbing or dipping motions while perched. Unlike other passerines, birds are able to dive, swim, and hunt underwater. Flying straight and swiftly with no stops or glides is made possible by the small and flexible wings. The Dipper Throated bird has a unique hunting strategy due to its fast bending motions and white breast. To catch its prey, it plunges headfirst into any turbulent or fast-flowing water. The sinking of the ocean floor has a negative impact on aquatic invertebrates, including aquatic insects, fish, and tiny crustaceans. The dipper uses its hands to move on the ocean floor. It’s possible to catch prey by lowering your head and walking down the bottom of the water with your body bent at an angle. When diving into the water and purposefully submerging, it may use its wings to propel itself through the water and stay below for lengthy periods of time while using its wings to propel itself through the water. The Dipper-Throated Optimization (DTO) technique argues that a flock of birds is swimming and flying in search of food supplies. This matrix may be used to depict the position and speed of a flock of birds (BL, BS). In order to choose features, the binary DTO is used. An alternative approach employs a continuous DTO for fine-tuning neural network parameters during classification [

Based on the formulation of DTO and GWO discussed in the previous sections, we proposed a new optimization algorithm that exploits the advantages of both algorithms to improve the efficiency of the LSTM network and thus boost the prediction accuracy. The steps of the proposed algorithm are listed in Algorithm 1.

It’s possible to simulate any stationary time series system using the formula: [y(t-1), y(t-2),…, y(t-m)]. A single function termed f(.) cannot accurately predict the full-time series for a non-stationary time series, which necessitates a collection of functions, each of which has its own set of independent components. Because of this, non-stationary time series can be modeled as:

The ARMA process is a well-established statistical model and forecasting method for stationary time series. Moving Average (MA) value aims to reduce the influence of unknown series beginning value by increasing the order of Auto Regression (AR) value captures the seasonal pattern of time series and leads to an improved approximation of stationary time series. It is possible to eliminate series dependence on unknown beginning values by combining the AR and MA processes, resulting in very accurate predictions of stationary time series. Time series decomposition into transitory (stationary) and permanent (non-stationary) components can help improve forecasting and approximation for non-stationary data, but it is not possible to use a simple ARMA process in this case because time series only represents one realization of a set of stochastic processes.

Permanent components are represented by ^{p}(t)^{k}(t)

In this case,

The parameters of LSTM that are optimized using the proposed algorithm are listed in

Parameter | Initial value |
---|---|

Max epochs | 200 |

Max iterations | 500 |

Mini batch size | 120 |

Num hidden | 250 |

Shallow hidden layer size | [30 50] |

Using the optimized LSTM, wireless data traffic is predicted using time series derived from data extraction. Time series data extraction from wireless traffic is made possible by the CRAWDAD repository’s wireless trace [

Once the wireless trace has been described, the FARIMA model may be used to predict network traffic on a variety of time scales based on this model. Four phases are involved in the construction of the FARIMA model. In the first step, the traffic data is normalized, and the zero mean time series is obtained. When the Hurst Parameter (H) is calculated using the Variancetime plot of a normalized trace’s Variancetime, the Hurst Parameter (d) is approximated using the Hurst Parameter (H). Fractionally differing

Normalize |
P | q | d | Mean | Variance |
---|---|---|---|---|---|

1 min | 4 | 2 | 0.457 | 0.531 | 0.0076 |

10 s | 4 | 2 | 0.258 | 0.265 | 0.0045 |

1 s | 4 | 2 | 0.026 | 0.0336 | 0.00088 |

On the other hand, the prediction results using the optimized LSTM based on the proposed optimization approach are presented in

Normalize |
P | q | d | Mean | Variance |
---|---|---|---|---|---|

1 s | 4 | 2 | 0.00126 | 0.000236 | 0.00003 |

10 s | 4 | 2 | 0.0123 | 0.001265 | 0.0005 |

1 min | 4 | 2 | 0.0347 | 0.0311 | 0.00059 |

To confirm the effectiveness of the proposed approach, the plots in

Moreover, the plot shown in

ANOVA table | SS | DF | MS | F (DFn, DFd) | |
---|---|---|---|---|---|

Treatment (between columns) | 2.9E-07 | 4 | 7.26E-08 | F (4, 65) = 52.34 | |

Residual (within columns) | 9.01E-08 | 65 | 1.39E-09 | ||

Total | 3.8E-07 | 69 |

DTO+FGW | PSO | GWO | WOA | GA | |
---|---|---|---|---|---|

Number of values | 14 | 14 | 14 | 14 | |

Actual median | 0.00009 | 0.00008 | 0.0001 | 0.0002 | |

Theoretical median | 0 | 0 | 0 | 0 | |

Sum of positive ranks | 105 | 105 | 105 | 105 | |

Sum of signed ranks (W) | 105 | 105 | 105 | 105 | |

0.0001 | 0.0001 | 0.0001 | 0.0001 | ||

Sum of negative ranks | 0 | 0 | 0 | 0 | |

Exact or estimate? | Exact | Exact | Exact | Exact | |

Significant (alpha = 0.05)? | Yes | Yes | Yes | Yes | |

Discrepancy | 0.00009 | 0.00008 | 0.0001 | 0.0002 |

Network management relies heavily on Quality of Service (QoS) since it may assist in determining the volume of traffic on a particular network. Traffic predictions have grown increasingly crucial as networks become increasingly complicated and diversified. It is possible to alleviate network congestion by predicting traffic volumes more accurately. Time series prediction methods are used to predict future traffic numbers. By creating a link between the present and expected traffic quantities, it may be possible to boost the network’s performance this way. In order to increase network quality of service, it has been recommended that time series models should be improved. In this paper, we improved the prediction accuracy by optimizing the parameters of LSTM network using the proposed optimization algorithm based on a hybrid of grey wolf and dipper throated optimization methods. We increase the performance of LSTM time series model by employing metaheuristic optimization techniques. Real-world data from a distinct network was used to test and evaluate the proposed approach. Tests conducted with current time series models demonstrate that using the proposed approach greatly decreased the prediction results. On the other hand, the proposed approach is compared to other methods and proved to achieve better performance. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. Overall, the network traffic volume prediction is improved significantly using the proposed approach.

Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

The authors declare that they have no conflicts of interest to report regarding the present study.