# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
###### 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. Internet of Things; queueing models; middleware; QoS analysis 2021 13 87 13040087 1  
18 February 2021  
Footnote 1: [[https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet)]([https://www.mdpi.com/journal/futureinternet](https://www.mdpi.com/journal/futureinternet))  
**Keywords:** Internet of Things; queueing models; middleware; QoS analysis +
Footnote †: journal:  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Copyright:** © 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **1. Introduction**  
The Internet of Things (IoT) promises the integration of the physical world into computer-based systems. IoT devices, featuring sensing capabilities, are deployed in many application domains, including community spaces, smart buildings, and intelligent transportation. By exploiting the information that IoT devices can provide, peoples' quality of life and safety can be substantially improved. For instance, 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. Hence, identifying a methodology for the analysis and performance modeling of IoT applications is of great significance. IoT devices can be inexpensive, low-powered, and mobile; regarding their deployment environment, applications that utilize them have different characteristics. More precisely, the following quality-of-service (QoS) constraints can be identified:  
(i) limited memory of low-cost devices, (ii) limited data availability or validity, (iii) intermittent (mobile) device connectivity, and (iv) urgent data delivery. Furthermore, IoT applications are built on application programming interfaces (APIs) and reliable or unreliable protocols that introduce additional QoS constraints [3, 4]. Hence, these constraints pose important challenges for IoT performance analysis. Existing efforts [5, 6, 7] concerning both the design and the evaluation of mobile systems under particular constraints (e.g., limited resources or intermittent availability) rely on queueing theory [8]. Key IoT protocols were evaluated with regard to metrics such as delivery success rates and response times [9, 10].