Abstract
Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.
Highlights
The Internet of Things (IoT) promises the integration of the physical world into computer-based systems
We model the performance of data exchange in the IoT by relying on queueing-network models (QNMs) [14,40]
The nonpreemptive priority and multiclass (NPPM) enables the prioritization of messages belonging to different classes when being served
Summary
The Internet of Things (IoT) promises the integration of the physical world into computer-based systems. In [1,2], IoT devices are exploited to monitor offices or homes for possible seismic activity Such an application provides critical information, and it is anticipated to function timely and reliably. Key IoT protocols were evaluated with regard to metrics such as delivery success rates and response times [9,10] Such efforts are protocolspecific; the research community analyzed the performance of well-known interac-. This paper expands upon our previous work [24] that provides a summary of queueing models that represent the aforementioned QoS constraints of IoT applications.
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