LISSI Laboratory

Laboratory, France

LISSI Laboratory

Laboratory, France
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Senouci M.R.,Polytechnic School of Algiers | Mellouk A.,LiSSi Laboratory | Oukhellou L.,LiSSi Laboratory | Aissani A.,LRIA Laboratory
GLOBECOM - IEEE Global Telecommunications Conference | Year: 2011

In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme. © 2011 IEEE.


Senouci M.R.,Polytechnic School of Algiers | Mellouk A.,LiSSi Laboratory | Aissani A.,University of Science and Technology Houari Boumediene
International Journal of Ad Hoc and Ubiquitous Computing | Year: 2014

Sensor placement is a fundamental issue in wireless sensor networks (WSNs). The sensor- positions can be predetermined to guarantee the quality of surveillance provided by the WSN. However, in remote or hostile sensor field, randomised sensor placement often becomes the only option. In this paper, we survey existing random node placement strategies. We categorise random placement strategies into simple and compound. An empirical study has been carried out yielding a detailed analysis of random deployment intrinsic properties, such as coverage, connectivity, fault- tolerance, and network lifespan.We also investigate the performance of a hybridisation of the simple diffusion model that places a large number of nodes around the sink and the constant diffusion that provides high coverage and connectivity rates. We show that such hybridisation ensures better performance. The obtained results give helpful design guidelines in using random deployment strategies. Copyright © 2014 Inderscience Enterprises Ltd.


Senouci M.R.,Polytechnic School of Algiers | Mellouk A.,LiSSi Laboratory | Oukhellou L.,UPE | Aissani A.,University of Science and Technology Houari Boumediene
Advances in Intelligent and Soft Computing | Year: 2012

The location of sensors is one of the fundamental design issues in wireless sensor networks. It may affect the fulfillment of the system's requirements and multiple network performance metrics. Assuming that an inherent uncertainty can be associated with sensor readings, it is very important to consider this issue in the deployment 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 optimization problem and possible solutions are discussed. Preliminary experimental analysis demonstrates very promising results of the proposed methodology. © 2012 Springer-Verlag.


Reda S.M.,Polytechnic School of Algiers | Abdelhamid M.,LiSSi Laboratory | Latifa O.,University Paris Est Creteil | Amar A.,LRIA Laboratory
IEEE Wireless Communications and Networking Conference, WCNC | Year: 2012

Deployment is a fundamental issue in wireless sensor networks. Usually the sensor locations are precomputed based on a "perfect" sensor coverage model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is imperative to have practical considerations at the design stage to anticipate this sensing behavior. In this paper, we address four different forms of static wireless sensor networks deployment while considering an evidence-based sensor coverage model. The four problems are formalized as combinatorial optimization problems, which are NP-complete. We propose, E2BDA (Efficient Evidence-Based sensor Deployment Algorithm), a polynomial-time uncertainty-aware deployment algorithm based on a dynamic programming approach. E2BDA is able to determine the minimum number of sensors and their locations to achieve both coverage and connectivity. We compare our proposal to the state-of-the-art deployment strategies, the obtained results show that E2BDA obtains the best performances. © 2012 IEEE.


Senouci M.R.,Polytechnic School of Algiers | Mellouk A.,LiSSi Laboratory | Senouci M.A.,LiSSi Laboratory | Oukhellou L.,UPE
Annales des Telecommunications/Annals of Telecommunications | Year: 2014

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. © 2014 Institut Mines-Télécom and Springer-Verlag France.


Senouci M.R.,Polytechnic School of Algiers | Mellouk A.,LiSSi Laboratory | Aissani A.,LRIA Laboratory
GLOBECOM - IEEE Global Telecommunications Conference | Year: 2012

A Wireless Sensor Network (WSN) consists of many sensors that are densely deployed to monitor a field. The sensor-positions can be predetermined to guarantee the quality of surveillance provided by the WSN. In remote or hostile sensor field, randomized sensor placement often becomes the only option. In this paper, we survey existing stochastic node placement strategies. We categorize stochastic placement strategies into simple and compound. A simulation study has been carried out yielding a detailed analysis of random deployment intrinsic properties, such as coverage, connectivity, fault-tolerance and network lifespan. The obtained results can give helpful design guidelines in using stochastic deployment strategies, and allow engineers to choose the deployment strategy appropriate to the situation and the goals. © 2012 IEEE.


Senouci M.R.,Polytechnic School of Algiers | Boudaren M.E.Y.,Polytechnic School of Algiers | Senouci M.A.,LiSSi Laboratory | Mellouk A.,LiSSi Laboratory
2014 International Conference on Smart Communications in Network Technologies, SaCoNeT 2014 | Year: 2014

This paper presents a smart methodology for deterministic deployment of wireless sensor networks. We discuss a set of factors involved in the wireless sensor network deployment problem, such as building abstract models, using simulation to rank deployment alternatives, and measuring the real performances of the network to verify that they meet the design goals. Network designers can use this methodology to determine the best network topology based on mission specific goals. The methodology can also be exploited to further analyze, compare, and validate deterministic deployment strategies. © 2014 IEEE.


Boumella N.,University of Batna | Boumella N.,LISSI Laboratory | Djouani K.,LISSI Laboratory | Boulemden M.,University of Batna
International Journal of Control, Automation and Systems | Year: 2012

This paper presents two approaches for the design of an interval Type-2 TSK Fuzzy Logic System (FLS), taking into account the uncertainties about the consequent parameters. While the first approach deals with the tuning of the consequent parameters used for the system output computation, the second one, based on a linear Chebyshev fitting, shows more robustness as it considers the consequent sets instead of only the consequent crisp values. This approach deals with all parameters at the consequent level, by taking into account the upper and lower bounds of the consequent set instead of the mean value. The effectiveness of both approaches in terms of robustness to noise is evaluated through the design of IT2 TSK FLS A1-C1 forecasters for the Mackey-Glass time-series. © ICROS, KIEE and Springer 2012.


Senouci M.A.,LISSI Laboratory | Souihi S.,LISSI Laboratory | Hoceini S.,LISSI Laboratory | Mellouk A.,LISSI Laboratory
IEEE Wireless Communications and Networking Conference, WCNC | Year: 2016

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). © 2016 IEEE.


Senouci M.A.,LISSI Laboratory | Hoceini S.,LISSI Laboratory | Mellouk A.,LISSI Laboratory
2016 IEEE International Conference on Communications, ICC 2016 | Year: 2016

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. © 2016 IEEE.

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