Integrating Blockchain, MQTT, and Machine Learning for Enhanced IoT Applications: A Comprehensive Survey

This extensive research investigates the integration of blockchain, MQTT and machine learning on the Internet of Things (IoT), a field ripe for transformation with technologies. These three technologies are blockchain and Message Queuing Telemetry Transport (MQTT). Machine learning is a foundational pillar, each offering unique benefits to enhance data exchange, security and decision making in interconnected IoT environments. Our study aims to explore the synergies among these technologies and the implications of their combined usage on the IoT. I delve into how their integration strengthens data security, enables communication, and facilitates data-driven decision-making across IoT scenarios. The study examines types of blockchain technology and the significance of MQTT in IoT communication. Additionally, I explore the implementation of machine learning models. Our primary focus is on exploring how combining blockchain and MQTT can enhance data sharing. I address challenges such as privacy concerns, scalability issues and consensus processes. To illustrate the impact of this convergence, I present practical examples from industries like supply chain management, healthcare services, and finance. Furthermore, this research also encompasses themes such as interoperability, among systems standardization measures, edge computing applications, and privacy-oriented machine learning approaches.