Performance evaluation of a widely used implementation of the MQTT protocol with large payloads in normal operation and under a DoS attack

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Abstract

The Internet of Things (IoT) is the term coined to encompass the myriad of devices that have some data processing and transmitting capabilities. Due to the increasing number of IoT devices connected to the Internet, network protocols intended for IoT technology have gained interest. This paper analyzes the performance of one of the most popular ones, named MQTT (Message Queuing Telemetry Transport), focused on Mosquitto, a widely used implementation. Our principal metric is the transmission time, defined as the time it takes a message to pass from one client through the broker to another client, since MQTT uses a publish/subscribe model with a broker. We evaluate different scenarios against some base configurations to give a firm comparison on how different factors affect the performance of an MQTT system based on Mosquitto, for payload sizes ranging from 512 to 1, 048, 576 bytes. For example, we assess how different network technologies (Ethernet, WiFi in the 2.4 GHz and 5 GHz bands) and QoS levels may yield better results at different message payload sizes. We also make a broker software comparison, evaluating Mosquitto against ActiveMQ and RabbitMQ. Our experiments exhibited similar results, with a slight advantage for RabbitMQ. Finally, we provide measurements on how DoS attacks can affect the Mosquitto broker, by flooding it with illegal MQTT petitions or making a TCP SYN flood attack. The goal of this study is to help MQTT implementers in making adequate decisions when selecting the different hardware and software solutions, for their MQTT systems.

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APA

Gamess, E., Ford, T. N., & Trifas, M. (2021). Performance evaluation of a widely used implementation of the MQTT protocol with large payloads in normal operation and under a DoS attack. In Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference (pp. 154–162). Association for Computing Machinery, Inc. https://doi.org/10.1145/3409334.3452067

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