Data Mining - Concepts and Techniques.

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Learning And Soft Computing Support Vector Machines Neural Networks And Fuzzy Logic Models

Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users’ preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

A Joint Multi-Criteria Utility-Based Network Selection Approach for Vehicle-to-Infrastructure Networking

In this paper, we present and evaluate a new propagation model for heterogeneous networks The designed model is multiband, multienvironment, and is usable for short and long distance For this research, a measurement campaign was conducted in Tunis (Tunisia) using continuous wave analog technology It concerns the most used bands (450, 850, 1800, 2100, and 2600 MHz) in rural, suburban, and urban environments Measurements are split into two independent and random sets The first one is used for model training, whereas the second is used for model validation The new model is based on neural networks, uses back propagation algorithm, and obtains its inputs from Standard Propagation Model, to which we have added more parameters such as frequency, environment type, land use distribution, and diffraction loss Model variables are computed from accurate Digital Terrain Model and Land Used maps with 2-m resolution The statistical analysis has shown that the developed model is accurate as we obtained the following metrics: 0235-dB absolute mean error, 6850-dB standard deviation, and 85% correlation factor The obtained simulation results are then compared to SPM and ITU-R P1812-4 prediction, which are taken as reference to highlight the benefit of the new model

A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

Generally empirical models are developed on the basis of measurements achieved in empty buildings. But in practice human bodies and furniture presence induce a considerable fluctuation leading to huge differences between predictions and real measurements. In this paper a new indoor large scale path loss empirical model is presented. The model design, in addition to the considered phenomena in conventional empirical formulation, integrates additional suggestions recommended by electromagnetic techniques such as body shadowing and furniture effects. To achieve this work, a large number of experimental measurements have been carried on and saved in consequently voluminous databases. Their management and exploitation have considered data mining and especially neural networks to perform the new model called neural model. To prove model enhancement and accuracy we compare the “neural model” predictions with measurements. Obtained results show that the mean error is close to zero, the standard deviation is about 4.47 dB with a correlation factor of 97%.

Body Shadowing and Furniture Effects for Accuracy Improvement of Indoor Wave Propagation Models

Indoor radio propagation prediction modeling has been for a long time an important area of interest in research and development. In the literature, many propagation models are classified into empirical and deterministic models. The accuracy of both categories of models can be improved based on model calibration or tuning that uses real measurements collected in a given environment and frequency. Based on the availability of a huge measurement database, we aimed in this paper to develop a new propagation model using artificial neural networks. The new model is inspired from multi-wall one and will be available for the most used system bands, such as GSM, UMTS and WiFi. The model will be a multilayer perceptron and is trained with measured data using a back-propagation learning algorithm. Evaluated model performances show a high improvement in terms of accuracy compared to a calibrated multi-wall model.

A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks

The main goal of 5G cognitive radio in heterogeneous networks is to maintain seamless connectivity and provide satisfying Quality of Service (QoS) by switching from one network to another using Vertical Handovers (VHO) The accuracy and the quickness of VHO decision is the key feature to improve and maintain high QoS levels Recently, many works have been interested to Artificial Neural Networks (ANN) as decisional algorithm for VHO It was considered efficient since it can handle a decision based on prior knowledge acquired during a learning process and takes into account many criteria of QoS This efficiency can be more improved while considering the Quality of Experience (QoE) in the ANN inputs set This paper proposes a modified multi-criteria vertical handoff decision algorithm based on ANN with QoE prediction scheme The proposed mechanism concept serves to improve the accuracy of the vertical handoff decision for radio heterogeneous networks Developed algorithm is compared to classical Fuzzy Logic (FL) and Multi Attribute Decision-Making (MADM) ones Obtained results show that QoE based ANN improve final QoS/QoE satisfaction metrics while reducing delays and the number of executed handoffs

QoE-based vertical handover decision management for cognitive networks using ANN

Seamless handover between different radio access technologies is a great challenge since it needs to guarantee the link continuity and satisfy the subscriber’s quality-of-services (QoS) requirements. The performances of roaming across different technologies depend on the number of implemented handover criteria, which can be large. In this case, multiple attribute decision making (MADM) methods such as simple additive weighting, technique for order preference by similarity to ideal solution and the compromise ranking method called (VIKOR: VlseKriterijumska Optimizacija I Kompromisno Resenje) have been proposed to solve this kind of multi-criteria problems. However, the major shortcomings of these techniques are the high decision delay and the complexity. In this paper, we propose a novel handover (HO) scheme called Fuzzy-MADM. It is based on the combination of one classical MADM method with a fuzzy logic inference system in order to reduce decisional time. The optimal network is selected, based on multiple criteria such as network QoS indicators, speed of mobile station, battery level and signal strength. The efficiency of our approach is evaluated through appropriate simulations. The results related to HO delays, throughput, complexity and number of executed handovers show the performances of our proposal when compared to classical MADM and load balancing methods.

Mohamed Ayadi

Papers

A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

Body Shadowing and Furniture Effects for Accuracy Improvement of Indoor Wave Propagation Models

A Multi-wall and Multi-frequency Indoor Path Loss Prediction Model Using Artificial Neural Networks

QoE-based vertical handover decision management for cognitive networks using ANN

An Enhanced Vertical Handover Based on Fuzzy Inference MADM Approach for Heterogeneous Networks