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This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.

A congestion-aware decision-driven architecture for information-centric Internet-of-Things applications