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Subject: search.filters.subject.Energy harvesting

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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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    Energy harvesting based on SLIPT in I2V-VLC system
    (IEEE, 2023) Refas, Souad; Acheli, Dalila; Yahia, Selma; Meraihi, Yassine
    Vehicular connectivity is mostly based on wireless access. The vehicular applications may be limited due to the limited battery life of the equipment involved. To address this issue, a method based on simultaneous light wave information and power transfer (SLIPT) is proposed for harvesting the energy in the Infrastructure-to-Vehicle Visible Light Communication (I2VVLC) system. The purpose of SLIPT is to harvest energy using light sources while decoding information. This article studies the effect of SLIPT in an I2V-VLC system. In this method, the received light from the traffic light source at the vehicle is harvested while decoding the information signal. First, for I2VVLC channel modeling, a recent realistic channel model using the ray-tracing method is utilized. Then, we propose the energy harvesting analysis based on the SLIPT strategy. After that, we investigate the impact of both the longitudinal and lateral distance between the vehicle and the traffic light on the amount of harvested energy. Furthermore, we investigate the relationship between the achievable information rate and the harvested energy amount. The obtained results demonstrate the significant impact of the communication distance and the required information rate on the quantity of harvested energy
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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