MQTT-CT: İntelligent MQTT protocol with cloud integration

Abstract

The MQTT protocol, named Message Queuing Telemetry Transport, has become widely recognized as a superior communication protocol in the Internet of Things (IoT) community. However, conventional MQTT protocols described in existing literature have limitations in supporting distributed environments and scalability. To address these limitations, a more advanced MQTT protocol called MQTT-ST has been developed, which offers bridging capabilities within distributed environments, making it an attractive choice for IoT systems. We have created a better version of our MQTT protocol called MQTT-CB. Our upgraded MQTT-ST protocol has added features like intelligence, scalability, and distribution using containers, making it easy to transport and deploy. Moreover, we've made deploying a cloud-based architecture that takes advantage of cloud technology even simpler. Our research focuses on enhancing the MQTT-ST protocol by incorporating intelligence capabilities. We utilize LSTM (Long Short-Term Memory) network, a cutting-edge deep-learning model that can capture intricate patterns over time. In addition, our protocol uses predictive algorithms that enable it to anticipate retransmitted packets, dynamically adjust the number of brokers in real-time, and reduce brokers when clients are inactive. We have extensively tested our protocol MQTT-CB with MQTT-ST. As a result, MQTT-CB performs better than traditional MQTT-ST protocols in reducing latency between subscribers and publishers. This provides better efficiency and responsiveness in IoT systems. Furthermore, our protocol adapts to publication rate changes and provides robustness in dynamic environments. MQTT-CB is a dependable and effective means of communication for IoT applications. Its ability to seamlessly adapt to changing conditions makes it ideal for IoT systems deployed in distributed environments. MQTT-CB opens up new possibilities for IoT solutions that can operate effectively in various scenarios where scalability, intelligence, and distribution capabilities are crucial for success. In summary, MQTT-CB significantly advances MQTT-ST protocols, introducing intelligence, scalability, and distribution to enable efficient and reliable communication between IoT devices. Furthermore, with its integration of the predictive LSTM algorithm, MQTT-CB optimizes the performance of the MQTT-ST protocol, showing the way for enhanced IoT applications with improved responsiveness and adaptability in distributed environments. The content of this thesis, including the methodology and results presented in all sections, is based on my research paper titled "MQTT-CB: Cloud Based Intelligent MQTT Protocol".