Abdelhamid Mellouk

Utility function-based TOPSIS for network interface selection in Heterogeneous Wireless Networks

In Heterogeneous Wireless Networks (HWNs), the mobile terminals are equipped with multiple access network interfaces (GSM, UMTS, LTE, WiFi, Bluetooth, etc.), to provide the possibility for mobile end-users to rank the networks and dynamically select the best one at anytime and anywhere, which is well known as Always Best Connected (ABC). In such environment, the major issue is network interface selection, which is a decision making problem with multiple alternatives (networks) and attributes (network characteristics, application requirements, terminal capacities, and user needs). In this context, many approaches have been proposed. Multi Attribute Decision Making (MADM) algorithms present a promising solution for multi-criteria decision making problems. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of MADM algorithms, which is widely adopted. TOPSIS ranks the available networks based on their scores, with the highest being the best. TOPSIS suffers from couple limitations. First is the ranking abnormality, e.g. if a low ranking network is disconnected then the order of higher ranking networks changes, which results in the selection of a less desirable network. Second is the selection strategy, where TOPSIS simply selects the network with highest score regardless of whether or not it satisfies the user and/or application needs. In this paper, we propose a new strategy based on utility function to remedy these shortcomings. The effectiveness of our strategy is evaluated through simulations. Obtained results show clearly that our strategy eliminates the rank reversal (ranking abnormality) phenomenon, and enhances the ranking quality by considering application and/or user needs.

Belief Functions in Telecommunications and Network Technologies: An Overview

In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks, robotics, and finance. This is due to the fact that imperfect information permeates the real-world applications, and as a result, it must be incorporated into any information system that aims to provide a complete and accurate model of the real world. Although, it is in an early stage of development relative to classical probability theory, evidence theory has proved to be particularly useful to represent and reason with imperfect information in a wide range of real-world applications. In such cases, evidence theory provides a flexible framework for handling and mining uncertainty and imprecision as well as combining evidence obtained from multiple sources and modeling the conflict between them. The purpose of this paper is threefold. First, it introduces the basics of the belief functions theory with emphasis on the transferable belief model. Second, it provides a practical case study to show how the belief functions theory was used in a real network application, thereby providing guidelines for how the evidence theory may be used in telecommunications and networks. Lastly, it surveys and discusses a number of examples of applications of the evidence theory in telecommunications and network technologies.

Using the Belief Functions Theory to Deploy Static Wireless Sensor Networks

The location of sensors is one of the fundamental design issues in wireless sensor networks. It may affect the fulfillment of the systems requirements and mul- tiple network performance metrics. Assuming that an inherent uncertainty can be associated with sensor readings, it is very important to consider this issue in the de- ployment process to anticipate this sensing behavior. This paper addresses the issue of uncertainty-aware sensor networks deployment by exploiting the belief functions reasoning framework. An evidence-based coverage model is proposed and some possible extensions are discussed. The deployment problem is formulated as an op- timization problem and possible solutions are discussed. Preliminary experimental analysis demonstrates very promising results of the proposed methodology.

A subscriber classification approach for mobile cellular networks

Abstract The classification of subscriber types in mobile cellular networks is valuable for network service providers since it provides a mechanism to plan network services by better understanding subscriber behaviour in a network. Mobile networks contain vast repositories of data that store valuable information regarding subscriber behaviour. In this paper, a new approach for subscriber classification in mobile cellular networks is proposed. The proposed approach considers network traffic generated from a mobile cellular network operator in South Africa. The proposed approach makes use of a difference histogram approach for feature extraction and a fuzzy c-means clustering algorithm to classify traffic data into subscriber classes. To validate the proposed approach, a comparative analysis of two different multi-resolution feature extraction approaches, the empirical mode decomposition (EMD) approach and the discrete wavelet packet transform (DWPT) are compared with results obtained with the difference histogram (DH) approach. It is shown that the difference histogram provides better clustering results when compared to the two multi-resolution approaches demonstrating the potential of the difference histogram approach.

QoE-based network interface selection for heterogeneous wireless networks: A survey and e-Health case proposal

In Heterogeneous Wireless Networks, mobile users use a terminal with multiple access interfaces for non-real-time or real-time applications (services). In such environment, the major issue is Always Best Connected (ABC), which means that the mobile nodes rank the network interfaces and select the best one at anytime and anywhere. To meet the ABC requirements, many network interface selection strategies have been proposed in the literature, using various technologies. This paper surveys existing approaches and discusses their advantages and limitations. The paper also highlights open issues in this area of research and proposes a new QoE-based approach for interface selection based on TOPSIS algorithm for e-Health use case. The effectiveness of our approach is evaluated through simulations. Obtained results show clearly that our approach ensures the best QoE for user, and eliminates a major inconvenient due to rank reversal (ranking abnormality).

Inductive routing based on energy and delay metrics in wireless sensor networks

Wireless Sensor Networks (WSN) represent actually one of the challenging type of Delay-tolerant networks due their sparse connectivity and hard coverage. In addition, the emergence of applications with different types of traffic in these networks, assurance of Quality of Service (QoS) becomes more and more important. In recent years, lot of research has been conducted to improve the QoS in WSN. In this article, we introduce a Quality of Service (QoS) routing algorithm based on dynamic state-dependent policies. The proposed algorithm uses a bio-inspired approach based on trial/error paradigm to optimize two QoS different criteria: Energy and end-to-end delay. Our proposal, called EDEAR Energy and Delay Efficient Adaptive Routing, is based on explorer agent who is responsible for collecting information in terms of energy and delay by using continuous learning parameters on the network and update routing maintained at each node of the network. The exploration of routes has been optimized by proposing a new algorithm based on multipoint relay for energy consumption, thus reducing the overhead generated by the packets exploration. Numerical results obtained with NS simulator for different levels of traffics load and mobility show that EDEAR gives better performances compared to traditional approaches.

Using empirical mode decomposition for subscriber behaviour analysis in cellular networks in South Africa

This paper looks at the potential benefit of using empirical mode decomposition (EMD) for the decomposition of time series data generated from a typical cellular network in South Africa. It is shown that a robust method for the extraction of features that correlate to subscriber behaviour can be conducted by decomposing time series tele-traffic data into finite set of components generated iteratively using the EMD approach. The extracted features are useful for the planning and estimation of future demand in wireless cellular networks especially in areas where subscriber socio-economic factors play a vital role in subscriber demand.

Efficient classification based on multi-scale traffic data extraction patterns of cellular networks

Africa has witnessed an incredible boom in the number of mobile subscribers in mobile networks across Africa. With the rise in demand for capacity in cellular networks, greater pressure is being placed on the network planner. Customer segmentation has been traditionally used in cellular network planning to better understand customer demands and needs. By developing more accurate profiling methods, operators are in a better position to market products and forecast future demand more accurately. This work looks at the extraction of frequency patterns from traffic signals originating from a typical mobile network using multi-scale analysis. By studying the features extracted, the classification of typical subscribers in the network can be conducted more efficiently and with greater granularity.