Modèle de Planification Dynamique en Fonction de la Priorité Multi-Niveau de Données IoT dans les Villes Intelligentes
Pognon, Marie Carria
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Information is at the center of our daily interactions. It infers important decision-making often leading to salutary interventions. This reality is all the more true in the digital age where data is expected more and more quickly to meet the requirements of this new paradigm. In the recent context of the emergence of smart cities, the massive amount of data generated by connected objects (IoT) has led to unprecedented demands in terms of data transfer. The various constraints linked to their number, their characteristics as well as their transmission are even greater and dim the effectiveness, in their regard, of traditional data planning schemes. As a result, the risks of overloads in queues, information loss, and critical emergency data delays are of continuing concern. With the aim of avoiding data loss while prioritizing the transfer of emergency data over the network, we have proposed a dynamic planning model based on the multi-level priority of IoT data in smart cities. It uses the classification of data into four priority levels with preemption, their dynamic scheduling in the queues, the activation of contingency queues, the migration of packets in critical situation and the emergency service of the queues that are on extended hold. The model was implemented as part of a software-defined wireless sensor network with the particularity of hierarchical placement of the latter in levels according to the number of hops that separate them from the base station. It was the subject of a simulation allowing to follow the behavior of the network and the evolution of the data according to the proposed methodology. The performance of the model was evaluated according to three metrics, by varying the size and depth of the network, corresponding respectively to the number of sensors and levels. These indicators are the average data delay, and the data delivery and loss rates. The results show that the proposed model helps prevent data loss and prioritizes the transfer of emergency data over the network. The loss rate does not exceed 4.44% for the simulations carried out and the lowest delivery rate observed is 94.64%. In addition, it guarantees the shortest delays for urgent data.