Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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    A novel fluid-based modeling approach using extended Hybrid Petri nets for power consumption monitoring in wireless autonomous IoT devices, with energy harvesting capability and triple sleeping strategy
    (Springer Nature, 2024) Oukas, Nourredine; Boulif, Menouar; Arab, Karima
    This paper presents a novel approach to model and monitor the energy dynamics of smart devices within the context of the Internet of Things (IoT). The proposed approach employs eXtended Hybrid Petri nets (xHPN) to emulate the behavior of interconnected smart devices forming a wireless network. The novelty of this study lies in the utilization of a fluidic representation to model the battery behavior of smart devices, allowing for the simulation of continuous energy consumption and replenishment via renewable energy harvesting to reflect real-world scenarios. Furthermore, in order to conserve energy, we introduce a new sleeping mechanism named the Triple Sleeping Strategy (TSS). By considering the mean battery charge and the mean sleeping percentage as evaluation metrics, the experimental study showcases the predictive capabilities of the developed model in simulating the performance of IoT networks prior to their actual deployment. Comparative analysis against recent works that use simple and double sleeping strategies, demonstrates the benefits of our approach, in terms of energy efficiency and device lifespan. For instance, when the device is configured with a 90 % sleeping percentage, TSS maintains a decent mean battery level for ten days, almost 8% higher than the double sleeping strategy. Furthermore, the presented case study demonstrates the ability of the proposed model to select appropriate parameters and configurations such as solar panel area and position, battery capacity, packet length, and deployment zone, to cope with the desired performance criteria.
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    A new petri nets for wsns to model the behaviour of solar-energy harvesting sensors with double sleeping strategy
    (IEEE, 2022) Oukas, Nourredine; Boulif, Menouar; Hadiouche, Hadda; Bengharabi, Chaima
    This paper introduces a new Generalized Stochastic Petri Nets modelling for sensor nodes (SNs) in wireless sensor networks (WSNs). All SNs are equipped with a solar Energy Harvesting (EH) system. Because there is no solar energy source at night, this formulation considers the case where SNs are intelligently configured to use a double sleeping strategy to get a trade-off between energy conservation and good performances. To achieve this aim, the SN joins the standby state most of the time in the night. In the day, the SN awakes most of the time to get good performances thanks to EH. From the proposed modelling, we derive performance formulas that allow determining the most suitable compromise between energy consumption reduction and rapidity of service
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    Solving the unsupervised graph partitioning problem with genetic algorithms: Classical and new encoding representations
    (Elsevier, 2019) Chaouche, Ali; Boulif, Menouar
    The Graph Partitioning Problem (GPP) is one of the most ubiquitous models that operations research practitioners encounter. Therefore, several methods have been proposed to solve it. Among these methods, Genetic Algorithm (GA) appears to carry very promising performances. However, despite the huge number of papers being published with this approach, only few of them deal with the encoding representation and its role in the reported performances. In this paper, we present classical and new encoding representations for the unsupervised graph partitioning problem. That is, we suppose that the number of partition subsets (clusters) is not known apriori. Next, we conduct an empiric comparison to identify the most promising encodings.
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    A New Cut-Based Genetic Algorithm for Graph Partitioning Applied to Cell Formation
    (springer, 2020) Boulif, Menouar
    Cell formation is a critical step in the design of cellular manufacturing systems. Recently, it was tackled by using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. Based on this model, we propose an original encoding representation to solve the problem by using a genetic algorithm. We discuss the performance of this new GA in comparison to some approaches taken from the literature when they are applied to a set of medium sized instances. Given the results we obtained, it is reasonable to assume that the new GA will provide similar results for large real-life instances.
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    Constraint-driven exact algorithm for the manufacturing cell formation problem
    (Inderscience, 2015) Merchichi, Sabrina; Boulif, Menouar
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    Multi-objective cell formation with routing flexibility : a graph partitioning approach
    (Inderscience Enterprises, 2015) Boulif, Menouar; Atif, Karim
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