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Quality-of-Decision-Driven Machine-Type Communication

Machine-type communication (MTC) has been considered as one of the key enablers for intelligent Internet of Things (IoT) applications. However, existing evaluating metrics, no matter the Quality of Service (QoS) or Quality of Experience (QoE), cannot truly and accurately reflect the quality of MTC when it serves machine decision making. To address this problem, this work proposes a new performance index, Quality of Decision (QoD), to elegantly capture the essence of MTC and precisely depict the corresponding functionality. First, the physical components and logical processes that affect the quality of MTC in analytics-oriented scenarios are carefully studied. Second, through jointly considering the factors of the commonness and individuality, we propose a layered QoD framework capable of independently evaluating and monitoring the quality of data to be acquired, delivered, and processed for enabling machine decision making. Third, we design a typical QoD-driven transmission scheme for video analytics by avoiding over provisioning of sensing and communication capabilities, which shows super efficiency compared to traditional human-perception-oriented approaches. We believe that the utilization of QoD will significantly promote the application of MTC in building various intelligent IoT systems.