Automatic Speech Recognition (ASR) is an area of research that's constantly evolving, thanks to important advancements like machine learning and deep learning techniques. Its applications are wide-ranging, touching fields like healthcare, public services, and interfaces between humans and machines. What's particularly noteworthy is the pressing need for highquality Arabic datasets to enhance the capabilities of speech recognition on devices that use the Arabic language. In this paper, we introduce a new dataset created with great care, designed specifically for recognizing Arabic speech when spoken by Tamazight speakers. This effort significantly broadens the pool of linguistic resources available for research and practical use. A crucial aspect of developing this dataset is the rigorous quality control applied to the data, which, in turn, improves the accuracy and effectiveness of Arabic speech recognition models. By making use of this innovative dataset, we enable the creation and evaluation of Arabic ASR systems tailored precisely to the needs of Tamazight speakers. This addresses a critical gap in the field of Arabic speech recognition, as it focuses on linguistic groups that have been underrepresented in this technology.

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”. 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 demonstrates the benefits of our approach, in terms of energy efficiency and device lifespan.