Publications Scientifiques

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Author: search.filters.author.Boulif, MenouarHas files: No

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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 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