Publications Scientifiques

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Author: search.filters.author.Boulif, Menouar

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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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    Evaluating autonomous-energy-harvesting device lifetime for the internet of medical things with a petri net formulation considering battery SoH
    (2022) Oukas, Nourredine; Djouabri, Abderrezak; Boulif, Menouar
    During charging-discharging operations, the batteries of the Internet of Things (IoT) devices are subject to a depletion that should be considered when predicting their lifetime. This paper proposes a new modeling for the IoT autonomous devices (AD) using Colored Generalized Stochastic Petri Nets (CGSPN). The ADs we consider are equipped with an energy harvesting system, and use a wireless link to connect with their neighbors. The CGSPN formulation models AD functionalities, and evaluates their impact on the battery lifetime by considering its state of health (SoH). The conducted analysis shows the ability of the proposed model to predict the ADs’ lifetime which is very critical for medical applications
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    A new generalized stochastic Petri net modeling for energy-harvesting-wireless sensor network assessment
    (John Wiley and Sons Inc, 2023) Oukas, Nourredine; Boulif, Menouar; Eric, Campo; Adrien, van den Bossche
    This paper proposes an energy-harvesting-aware model that aims to assess the performances of wireless sensor networks. Our model uses generalized stochastic Petri nets to define a sensor–neighbors relationship abstraction. The novelty of the proposed formulation is taking into account several real-life considerations such as battery-over breakdowns, unavailability of neighbors, retrial attempts, and sleeping mechanism in a single model. We use TimeNet tool to simulate the network behavior in order to evaluate its performance throughout different formulas after it had reached its steady state. Finally, we present a case study featuring the different solar energy recovery capabilities of the vast Algerian territory. The aim is to show with the presented model how to determine the kind of resources to be acquired in order to cope with the sensor deployment project requirements. The proposed model allows us to ensure that the battery energy level of sensors deployed in Algiers province for example is almost equal to 80% for 100 messages per day and (1 min/2 min) for (awakening time/sleeping time) ratio
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    Sensor performance evaluation for Long-Lasting EH-WSNs by GSPN formulation, considering seasonal sunshine levels and dual standby strategy
    (Springer, 2023) Oukas, Nourredine; Boulif, Menouar
    In this paper, we propose a generalized stochastic Petri net (GSPN) to model the sensor node (SN) interaction in solar energy harvesting wireless sensor networks. Our GSPN formulation models the energy stored in the SN battery by using the quantization principle. Furthermore, it considers that the sensors’ deployment territory is subject to different sunshine levels. In addition, each SN adopts the double sleeping strategy to cope with solar energy shortage in the nighttime. We conduct a comparative analysis between the proposed model and three others taken from the literature to identify which is best suited to predicting the input parameters that extend the network lifetime to fulfill its long-lasting duty with decent performances
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    Fuzzy constraint prioritization to solve heavily constrained problems with the genetic algorithm
    (Elsevier, 2023) Alouane, Basma; Boulif, Menouar
    Genetic algorithms (GAs) are approximate solving methods that have been originally proposed to achieve unconstrained optimization. To handle constrained problems, which is the case for the majority of real-life circumstances, GAs must be equipped with a constraint-handling mechanism. Transformation functions (TFs) are among the constraint-handling approaches that intervene in the phenotypic space. In this paper, we study the impact of considering constraint priorities on the GA performance when it deals with heavily constrained problems. Priorities are set by integrating a constraint order into the TF definition. We consider different TF forms enhanced with a fuzzy inference engine to find the best constraint ordering. Finally, we conduct an experimental study to assess the performance of the proposed approach on the semi-supervised graph partitioning problem. The obtained results show with statistical evidence that the proposed fuzzy method is promising
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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 colored petri net to model message differences in energy harvesting WSNs
    (Springer, 2021) Oukas, Nourredine; Boulif, Menouar
    This paper proposes a modelling for Energy Harvesting Wireless Sensor Networks (EHWSNs) by using Coloured Generalized Stochastic Petri Nets (CGSPN). Given that the transmission process consumes the most part of the energy, we consider it in more detail. Therefore, in contrast to the related works in the literature, the proposed formulation differentiates between messages since energy consumption can significantly differ according to the type of information to send. Furthermore, in order to get a more realistic model, we also consider that the sensor has a limited buffer capacity. We conduct some experiments by feeding the model with varying input parameters to show that it can predict the actual behaviour of the network
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    A new stochastic petri nets modeling for dual cluster heads configuration in Energy-Harvesting WSNs
    (IEEE, 2021) Oukas, Nourredine; Boulif, Menouar
    This paper proposes a new Stochastic Petri Nets modeling to describe the route taken by the packets to reach the base station from any sensor node in wireless sensor networks. This formulation examines the case where the network is structured into clusters. Each cluster contains two leaders: A Cluster Head and a Collector that cooperate to route the packets from the source to the endpoint. This configuration aims to conserve energy by balancing it through the network. Furthermore, given that a sensor node consumes the majority of its power in the communication process that is affected by the distance of the recipient, this formulation associates data gathering and data processing to the Collector whereas it associates the far sending task to the cluster head. From the proposed formulation, we derive performance formulas and we conduct some experimental analysis that allows to determine the most suitable compromise between energy consumption reduction and longevity of service
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    Generalized stochastic petri nets modelling for energy harvesting WSNs considering neighbors with different vicinity levels
    (IEEE, 2020) Oukas, Nourredine; Boulif, Menouar
    In this paper, we use Generalized Stochastic PetriNets (GSPN) formalism to model the communication betweenan SN and its neighbors in wireless sensor networks (WSN).This modelling considers several actual considerations such assensor vacations and retrial calls phenomenon. Furthermore,given that sensor nodes (SN) consume almost all their energy inthe transmission process that varies according to the distanceof the neighbors, our model considers different levels of vicinityfor communicating neighbors. Our study proves that ourmodelling, which provides a more realistic approach todescribe the actual behavior of the WSN, can identify the inputparameter scenario to have a network with a good compromisebetween longevity and performance