Abstract
This work proposes a proactive execution architecture for the information-aware internet of things (IoT) decisions. Herein, complying to offering a proactive congestion awareness along with elevating the quality of the decisions’ supporting information is the pivotal attribute. Firstly, to establish an anticipatory plan towards the incoming different traffic congestion situations, a Proactive Differentiative Random Early Detection (PA-DRED) algorithm is proposed. PA-DRED emphasizes the role of future load forecasting at the admission judgement procedures. PA-DRED anchors on a double-phase load prediction strategy to accurately profile the future traffic load trends. This forecasted load trend is then consolidated with the decision’s degree of emergency to formulate a proactive weighted admission criteria. Secondly, in order to optimize the execution of the admitted decisions, an adaptive information-aware priority-driven scheduling algorithm is presented. The conducted simulations reveal the robustness of the proposed architecture at furnishing the scalability and high availability against various congestion degrees. Additionally, it has been clearly demonstrated that the proposed architecture outperforms the state-of-art counterparts.
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Darwish, R.R. (2021). A Proactive Decision-Driven Architecture for Information-Aware Internet-of-Things Applications. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_25
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DOI: https://doi.org/10.1007/978-3-030-69717-4_25
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