Nowadays billions of smart devices or things are present in Internet of Things (IoT) environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number of interconnected devices is continuously and rapidly growing. These devices communicate with each other and with other services using various communication protocols for the transportation of sensor or event data. These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of problems. IoT also aims to provide secure, bi-directional communication between interconnected devices, such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years, and show how the growth in MQTT research stands out from the rest. We also gather relevant application areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in major digital research archives. Our quantitative evaluation presents some of the important MQTT-related studies published in the past five years, which we compare to discuss the main features, advantages, and limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally, we discuss the relevant findings of our comparison and highlight open issues that need further research and attention. INDEX TERMS IoT, IoT Protocols, MQTT, MQTT Brokers, Survey I. INTRODUCTIONT HE Internet of Things (IoT) connects everyday devices like a fridge, oven, vehicle, washing machine, fitness band, watch, and even shoes to the internet [1]. It enables us to collect monitored data from these devices over a network without any human-to-human or human-to-computer interaction that can be used to improve our life, business or environments [3]. For example, a simple IoT application like an activity or fitness tracker can inform many things about us, such as distance we walked, ran, cycled or swam, pace, pulse rate, (swim) stroke count, calories we burned, sleep quality to help us improve our regimen. Efficient IoT solutions can help us to control these devices remotely from our phones or tablets. No matter it is agriculture, transport, sports, health, military, energy, or entertainment; today the application space of IoT is virtually endless. It is rare to find any industrial area that does not get benefits from this IoT revolution. Social networking and smart city applications can also benefit from this trend [4]. Instead of waiting for monthly or yearly reports, business and industry can get accurate consumer-data in real-time. They can analyze the data to make more informed decisions, which can add value to their business [5].Cloud Computing [6] provides on-deman...

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Presently, Internet of Things (IoT) protocols are at the heart of Machine-to-Machine (M2M) communication. Irrespective of the radio technologies used for deploying an IoT/M2M network, all independent data generated by IoT devices (sensors and actuators) rely heavily on the special messaging protocols used for M2M communication in IoT applications. As the demand for IoT services is growing, the need for reduced power consumption of IoT devices and services is also growing to ensure a sustainable environment for future generations. The Message-Queuing Telemetry Transport or in short MQTT is a widely used IoT protocol. It is a low-resource-consuming messaging solution based on the publish–subscribe type communication model. This paper aims to assess the performance of several MQTT broker implementations (also known as MQTT servers) using stress testing, and to analyze their relationship with system design. The evaluation of the brokers is performed by a realistic test scenario, and the analysis of the test results is done with three different metrics: CPU usage, latency, and message rate. As the main contribution of this work, we analyzed six MQTT brokers (Mosquitto, Active-MQ, Hivemq, Bevywise, VerneMQ, and EMQ X) in detail, and classified them using their main properties. Our results showed that Mosquitto outperforms the other considered solutions in most metrics; however, ActiveMQ is the best performing one in terms of scalability due to its multi-threaded implementation, while Bevywise has promising results for resource-constrained scenarios.

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