Message Queuing Telemetry Transport Protocol Research Articles

Integration of MQTT-SN and CoAP protocol for enhanced data communications and resource management in WSNs

Lightweight communication protocols for wireless sensor networks (WSNs) are unfolding for machine to machine (M2M) communications and thus there is always going to be a possible conflict of interest on which protocol is best suited for any particular application. The two protocols of interest in this study are the message queue telemetry transport protocol for sensor network (MQTT-SN), a variant of message queue telemetry transport (MQTT) protocol and the constrained application protocol (CoAP). There have been studies that reveal that these protocols perform differently based on the underlying network conditions. CoAP experience lower delays than MQTT for higher packet loss and higher delays for lower packet loss. MQTT default communication via a broker is easier to scale compared to CoAP direct request-response paradigm. Although this is a huge advantage over CoAP, it presents the single point-of-failure problem. In this paper we propose an integration of MQTT-CoAP protocol using an abstraction layer that enables both MQTT-SN and CoAP protocol to be used in the same sensor node. Resources are managed by directly modifying sensor node configuration using CoAP protocol. Performance evaluation of these protocols under the integrated scenario shows acceptable levels of latency and energy consumption for internet of thing (IoT) operations.

Enhancing MQTT security for internet of things: Lightweight two-way authorization and authentication with advanced security measures

The MQTT (Message Queuing Telemetry Transport) protocol used the pub-sub model for IoT communication. Bolstering security with two-way authentication becomes a critical necessity. This paper presents a novel algorithm that fortifies MQTT with enhanced security measures, utilizing a refined MQTT implementation that integrates with a Merkle tree. HBMQTT Broker increases security as it uses different plugins, like the authentication plugin and the authorization plugin. These plugins serve to add extra layers of protection, reinforcing security protocols and heightening resilience to potential threats. The Merkle tree integration enhances data security during data transmission, effectively distinguishing between authentic and inauthentic data streams. Merkle trees generate tokens, which are used for secure data transmission. The algorithm is tested with four different hacking adversarial attacks: man-in-The-Middle (MITM), malware attack, denial of service (DoS), and phishing attack. Space and time complexity are also calculated for these attacks.

Enabling Lightweight Device Authentication in Message Queuing Telemetry Transport Protocol

Enabling Lightweight Device Authentication in Message Queuing Telemetry Transport Protocol

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.

A security-enhanced scheme for MQTT protocol based on domestic cryptographic algorithm

Recently, the rapid advancement of Industrial Internet of Things (IIoT) technology has led to the utilization of various communication protocols. Among these, the Message Queue Telemetry Transport (MQTT) protocol has become prevalent. Operating on a publish/subscribe model, MQTT’s popularity in IIoT because of simplicity, lightweight nature, and ease of implementation. However, traditional MQTT protocol standards have neglected critical security concerns during the communication process, such as ineffective identity authentication, weak data confidentiality, and a lack of access control functions. To address these challenges, this paper introduces a security-enhanced scheme for the MQTT protocol based on domestic cryptographic algorithms. The proposed method enforces mutual identity authentication between the MQTT Client and the MQTT Broker using digital certificates. It also implements access control through an Access Control List (ACL) and ensures end-to-end data security via the symmetric encryption algorithm SM4. Through comprehensive security analysis and experimental validation, this paper demonstrates that the enhanced scheme effectively rectifies the MQTT protocol’s security deficiencies, achieving these improvements with small overhead.

Developing a smart system for infant incubators using the internet of things and artificial intelligence

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R<sup>2</sup> value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R<sup>2</sup> value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments.

The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.

Priority-enabled MQTT: a robust approach to emergency event messaging

This paper presents priority support in the Internet of Things to support the reliable and timely transmission of messages during emergencies. The Message Queuing Telemetry Transport protocol is a widely used IoT messaging protocol. However, it does not support the timely and fast delivery of emergency messages. In this regard, this paper proposes to classify the messages into three different queues. The RabbitMQ broker manages virtual queues based on the message type, such as First Come First Served, Critical, and Urgent. In addition, the proposed approach stores the messages in the MySQL database for further analysis. To confirm its efficacy, we compare the Urgent and Critical queues with the current First Come First Served technique in an experimental implementation. Wireshark packet analyzer is used to record packets while messages are being transmitted between clients and the broker to examine end-to-end latency, jitter, response time, and total time. The results show that the proposed approach performs better for high-priority emergency messages.

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework.

The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O's algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.

E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol

Message Queuing Telemetry Transport (MQTT) enables asynchronous confirmation of message reception by brokers but lacks a way for publishers to know when subscribers receive their messages without adding additional communication overhead. This paper addresses this problem by improving MQTT to establish end-to-end communication between a publisher and subscribers, reducing message exchanges, using what is called End-to-End MQTT (E-MQTT). In E-MQTT, a publisher sets the number of responses that it will wait for when it sends a message. After the broker collects the response messages from subscribers, it sends one aggregated response back to the publisher. The publisher also can receive the response message synchronously or asynchronously. Experimental results consistently show that E-MQTT outperforms traditional MQTT in terms of delay, especially when the publisher needs to monitor when its query message is received by subscribers. Although E-MQTT packets are slightly larger due to additional fields, the difference in packet size compared to MQTT is not significant.

Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO2, and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low-performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35–76% is accomplished with an adequate balance between performance and energy efficiency.

Research and deployment of a Python-based software framework for large-scale physical experiment control

This paper presents a python-based, open-source framework for slow control systems used in particle physics experiments. The framework includes several functions such as display, data query, alarm, log and remote monitoring, etc. The data communication utilizes the MQTT (Message Queuing Telemetry Transport) protocol, ensuring real-time and reliable data transmission. The object-oriented programming approach ensures each functional module's independence and reusability, making them easily interchangeable and extendable through configuration files in various applications. Finally, two case deployment applications of the framework are provided, where both of them are tested with Docker.

Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach

In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. The proposed approach incorporates LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models in a hybrid architecture to leverage their complementary strengths. This research highlights the utilization of the developed model alongside the MQTT (Message Queuing Telemetry Transport) protocol to facilitate real-time end-to-end data transmission. The deployed AI model performs the classification of cognitive workload based on the received data. The main findings of this research reveal an impressive accuracy of 98% in cognitive workload classification, validating the efficacy of the suggested BDGN approach. This study emphasizes the significance of leveraging EEG-based approaches in Industry 5.0 applications for cognitive workload management.

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

SummaryThis paper proposes an IoT‐enabled soil monitoring system using wireless sensor network for automatic irrigation in agricultural applications, especially for lemongrass plants, where an automated control system is required for irrigation applications. This can solve the problem of the water crisis, which is faced by the farmers during the cultivation of the crop in the field. This controls the water supply in the irrigation process using an IoT communication system. A system architecture for soil monitoring and controlling irrigation using IoT technique is designed where the different sensors and actuators like humidity, soil moisture, temperature, pump, and so forth are connected with a node microcontrol unit and message queuing telemetry transport (MQTT) protocol for enhancing communication capabilities. This wireless sensor network gives feedback to the system. This provides automation by the on/off pump system during drip irrigation. The sensor data are displayed on a PC or mobile phone through wireless communication and an IoT cloud platform. An experimental testing setup is developed and the experimental performance of a soil monitoring system using IoT technique for automatic drip irrigation has been carried out and soil moisture data are also stored in a cloud server for analytics. The performance shows that the MQTT protocol sends data within 48 s to the IoT cloud so that the data can be acquired in a faster manner. This shows that this kind of soil monitoring system is suitable for automatic drip irrigation, which enhances the farming process and overcomes the water crises in the agricultural system by reducing the wastage of water.

DEVELOPMENT OF AN IOT DEVICE BASED ON A GEIGER COUNTER

The threat of radiation exposure is always present in modern world. In recent years, this threat has become even more pronounced due to Russian aggression on Ukrainian territory. Therefore, the need for remote data collection and analysis of radiation background information has become even more important, and research in the field of creating small, low-cost devices that can monitor radiation levels of a certain area in real-time and provide information to local government agencies responsible for the safety of populated areas can be considered relevant. Radiation background level monitoring is essential to ensuring that people are informed of any potential threats to their health. It is proposed to develop a dense network of monitoring stations that can collect and transmit data about background radiation in real time. Measurement results will be processed and stored on a resource that is available to both local authorities and public. The Message Queue Telemetry Transport (MQTT) protocol is used to transmit information, as it is durable, lightweight, and efficient. A hardware and software system for collecting and transmitting radioactivity values has been developed. The system is based on a module with Geiger counter tube, which is used to measure the level of radiation in the area. The module is connected to a microcontroller, which processes the data and transmits it to a central server. To summarize and process the measurement results, the Majordomo broker is used. It is installed on a remotely accessible network storage. In conclusion, the proposed system can ensure the safety of the public, allowing authorities, experts and trained personnel to quickly and efficiently respond to any potential radiation threats, protecting people from radiation exposure.

Performance Analysis of MQTT Over Websocket for IoT Applications

Abstract With the growing popularity of the Internet of Things (IoT), the development of applications harnessing this technology is becoming increasingly widespread and accessible. Moreover, high-performance, and real-time applications necessitate the use of a low-latency protocol. Given these factors, this study aims to analyse Message Queue Telemetry Transport (MQTT) protocol used in combination with WebSocket for IoT applications. The comparison employs experimental tests used to determine data transfer capabilities, latency, and other performance metrics.

Post-Quantum Authentication in the MQTT Protocol

Message Queue Telemetry Transport (MQTT) is a common communication protocol used in the Internet of Things (IoT). MQTT is a simple, lightweight messaging protocol used to establish communication between multiple devices relying on the publish–subscribe model. However, the protocol does not provide authentication, and most proposals to incorporate it lose their lightweight feature and do not consider the future risk of quantum attacks. IoT devices are generally resource-constrained, and postquantum cryptography is often more computationally resource-intensive compared to current cryptographic standards, adding to the complexity of the transition. In this paper, we use the postquantum digital signature scheme CRYSTALS-Dilithium to provide authentication for MQTT and determine what the CPU, memory and disk usage are when doing so. We further investigate another possibility to provide authentication when using MQTT, namely a key encapsulation mechanism (KEM) trick proposed in 2020 for transport level security (TLS). Such a trick is claimed to save up to 90% in CPU cycles. We use the postquantum KEM scheme CRYSTALS-KYBER and compare the resulting CPU, memory and disk usages with traditional authentication. We found that the use of KEM for authentication resulted in a speed increase of 25 ms, a saving of 71%. There were some extra costs for memory but this is minimal enough to be acceptable for most IoT devices.

Software and Hardware Complex Development toMonitor Production Activities Based on the YOLOv8 Neural Network

The article discusses the features of software and hardware complex development to monitor the efficiency of employees by means of an ESP8266 microcontroller and video cameras with a YOLOv8 neural network that meets the requirements of a 'smart shop'. The purpose of this study is to justify and evaluate the working concept of the created software and hardware complex in the form of a working prototype. Examples of current developments of similar monitoring systems realized within the paradigm of the Internet of Things, are given. However, the systems found in international practice focus on processing images of moving personnel only and do not analyze the technical parameters of the equipment this personnel work on. This study attempts to overcome this limitation. A schematic diagram of the system operation consisting of four key modules has been developed: a data collection module from machine-tools, an employee identification module, a primary data processing module, a web server with software application to manage the entire system in real time. The MQTT (Message Queuing Telemetry Transport) protocol is used for communication between devices and the server in the designed system to provide quality of data transmission under conditions of channel bandwidth limitations. Combined analysis of data from both machine-tools (control of the load of the electric motor) and the movement of workers in the space of the production room is proposed within the concept of developing such systems for the first time. Evaluation tests on the monitored data showed the operability of the entire complex, the problem of object automatic detection in the camera image flow and their location within the coordinate system of the production room was solved.

Integration of Machine Learning Solutions in the Building Automation System

This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building management solutions, HomeAssistant. The System uses communication based on the Message Queue Telemetry Transport (MQTT) protocol. The example machine learning function described in this publication detects anomalies in the electricity waveforms and raises the alarm. This information determines power quality and detects system faults or unusual power consumption. Recently, increasing electricity prices on global markets have meant that buildings must significantly reduce consumption. Therefore, a fundamental element of energy consumption diagnostics requires detecting unusual forms of energy consumption to optimise the use of individual devices in home and office installations.

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using single- and multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.