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

Abstract

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