Message Queuing Telemetry Transport Protocol Research Articles

Implementation of Smart Farm Systems Based on Fog Computing in Artificial Intelligence of Things Environments.

Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue. This study implements an Artificial Intelligence of Things (AIoT) system based on fog computing on a smart farm. Three experiments are conducted to evaluate the performance of the AIoT system. First, network traffic volumes between systems employing and not employing fog computing are compared. Second, the performance of the communication protocols-hypertext transport protocol (HTTP), message queuing telemetry transport protocol (MQTT), and constrained application protocol (CoAP)-commonly used in IoT applications is assessed. Finally, a convolutional neural network-based algorithm is introduced to determine the maturity level of coffee tree images. Experimental data are collected over ten days from a coffee tree farm in the Republic of Korea. Notably, the fog computing system demonstrates a 26% reduction in the cumulative data volume compared with a non-fog system. MQTT exhibits stable results in terms of the data volume and loss rate. Additionally, the maturity level determination algorithm performed on coffee fruits provides reliable results.

Design of ECG Circuit and Patient Remote Monitoring System Using MQTT Protocol for ECG Signals

In the healthcare sector, leveraging the Internet of Things (IoT) brings significant advantages, especially in remote monitoring of patients' health using electrocardiogram (ECG) signals. This study employs IoT technology to enable remote ECG monitoring, utilizing the MQTT (Message Queuing Telemetry Transport) protocol to establish a web server. The dedicated measuring device integrates the ADAS1000-3 IC with ESP32 for real-time ECG signal acquisition. In this study, we utilize the Pan-Tompkins algorithm to extract QRS complexes from electrocardiographic signals, count heartbeats, and early detect cardiovascular abnormalities. This solution was tested on a sample dataset from the MIT-BIH (Massachusetts Institute of Technology - Beth Israel Deaconess Medical Center) database. Finally, the article proposes to design and develop a device using advanced IC technologies such as IC ADAS1000-3 combined with ESP32. This device will be designed to meet the requirements of real-time processing and data transmission via webserver.

A novel lightweight multi-factor authentication scheme for MQTT-based IoT applications

The present authentication solutions employed in the Internet of Things (IoT) are either inadequate or computationally intensive, given the resource-constrained nature of IoT devices. This challenges the researchers to devise efficient solutions to embed an important security tenet like authentication. In IoT, the most popular machine-to-machine communication protocol used at the application layer is Message Queuing Telemetry Transport (MQTT). However, the MQTT protocol inherently lacks security-related functions, like authentication, authorization, confidentiality, access control, and data integrity, which is unacceptable for IoT-driven mission-critical applications when connected over public networks. In such a situation, the security is hardened by employing a transport layer security protocol like TLS, which entails significant computational overheads. This paper presents a novel scheme to enhance MQTT security by providing a lightweight multi-factor authentication scheme based on Elliptical curve cryptography. The proposed scheme uses a low-cost signature and a fuzzy extractor to correct errors in imprinted biometrics in noisy environments. This scheme attains mutual authentication, generates a securely agreed-upon session key for secret communication, and guarantees perfect forward secrecy. Furthermore, the rigorous informal security analysis shows the proposed scheme resists cryptographic attacks, including known session critical attacks. Furthermore, an empirical study has been carried out to assess the effectiveness of the proposed scheme in the Cooja simulated environment.

Secure and efficient device‐to‐device communication in IoT: The DMBSOA‐enhanced MQTT protocol

AbstractThe Internet of Things (IoT) plays a crucial role in enhancing technology by facilitating data transfer, storage, control, and management across networks. Secure communication within IoT environments remains a significant challenge. This research aims to enhance IoT security by integrating Message Queuing Telemetry Transport (MQTT) and MQTT‐Sensor Network (MQTT‐SN) protocols with the Dual Mutation‐Based Seagull Optimization Algorithm (DMBSOA). MQTT, known for its lightweight and dependable messaging, is widely used in the IoT community but is vulnerable to cyber‐attacks, particularly concerning privacy and authentication. DMBSOA, inspired by seagulls' foraging behavior, optimizes MQTT settings to enhance security, reliability, and performance. The proposed model dynamically adjusts key parameters such as transmission frequency, Quality of service (QoS) levels, and message size to improve energy consumption, throughput, end‐to‐end delay, and packet delivery ratio. A comprehensive system model is presented, comprising publisher, subscriber, and broker nodes, with security mechanisms integrated into the broker to ensure data integrity, authentication, and encryption. MQTT operates over Transmission Control Protocol/Internet Protocol (TCP/IP), while MQTT‐SN uses User Datagram Protocol (UDP), catering to resource‐constrained devices and low‐power modes. The proposed technique attained throughput (68 kB), energy consumption (120 mJ), security level (96%), energy efficiency (98.80%), path loss (22 dB), end‐to‐end delay (35 ms), processing time (230 s) and packet delivery ratio (0.98). The DMBSOA‐optimized MQTT protocol demonstrates superior performance compared to these models, highlighting its potential to meet the evolving demands of IoT security. This research underscores the effectiveness of DMBSOA in enhancing MQTT protocol security and efficiency, providing a promising solution for secure IoT communication.

A Reliable Publish–Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses

Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish–subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. Nevertheless, IoT technology is required to effectively handle a considerable volume of subscriptions or topic adjustments from several clients concurrently. In addition, subscription throughput is an essential factor of the pub-sub mechanism, as it directly influences the speed at which messages may be sent to subscribers. The primary focus of this paper pertains to a performance assessment of the proposed message categorization architecture for the Message Queue Telemetry Transport (MQTT) broker. This architecture aims to establish a standardized approach to pub-sub topics and generate new topics with various performance characteristics. We also standardize the form of MQTT protocol broker topic categorization and payload based on greenhouse specifications. The establishment of topic classification enhances the operational effectiveness of the broker, reduces data volume, and concurrently augments the number of messages and events transmitted from the greenhouse environment to the central server on a per-second basis. Our proposed architecture is validated across multiple MQTT brokers, including Mosquitto, ActiveMQ, Bevywise, and EMQ X, showing enhanced flexibility, extensibility, and simplicity while maintaining full compatibility with greenhouse environments. Key findings demonstrate significant improvements in performance metrics. The message processing time for the proposed Active MQ broker was increased approximately five-fold across all QoS levels compared to the original. Subscription throughput for the Bevywise MQTT Route 2.0 broker at QoS0 reached 1453.053, compared to 290.610 for the original broker. The number of messages in the Active MQ broker at QoS0 surged from 394.79 to 1973.95. These improvements demonstrate the architecture’s potential for broader IoT applications in pub-sub systems.

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.

Internet of Things Network Performance: Impact of Message and Client sizes and Reliability Levels

Consistent and timely message transmission is pivotal in the success of Internet of Things (IoT) communications. IoT applications typically involve resourceconstrained devices with limited processing power, memory capacity, and battery life. These characteristics necessitate the use of specialized communication protocols for IoT applications. Message Queue Telemetry Transport (MQTT) is a widely adopted protocol within the IoT ecosystem. Ensuring the appropriate message size and reliability level is crucial for achieving successful MQTT communication. This article empirically assesses MQTT protocol performance across various Quality of Service (QoS) levels while considering different message and client sizes. Initially, we conducted a comparative analysis of message transfer performance between HTTP and MQTT protocols. Subsequently, we delved into the MQTT protocol’s performance across three distinct configurations: one publisher-one subscriber, multiple publishers-one subscriber, and multiple publishersmultiple subscribers. We also examined how message and client sizes impact message transfer latency. When employing a 100-byte payload, we observed that the time delay in a network comprising 150 clients is 60.71% greater than in a network with 50 clients. Similarly, with a message size of 2500 bytes, a network with 50 clients requires 96% less time to deliver than a network with 150 clients.

Securing MQTT: unveiling vulnerabilities and innovating cyber range solutions

The Message Queuing Telemetry Transport (MQTT) protocol, widely used in various industries for Machine-to-Machine (M2M) communication, plays a central role in the IoT architecture. However, despite its widespread adoption, an analysis conducted on the shodan search engine revealed up to 540531 vulnerabilities worldwide as of April 2024, underscoring the security challenges facing this protocol. How can an innovative cyber range system be designed to assess and improve the security of MQTT protocols, focusing on in-depth vulnerability analysis, authentication strategy development, and targeted countermeasures against specific attacks? The study includes an analysis of relevant articles, followed by classification into five different categories. Furthermore, the presentation of a cyber range that exposes various attacks, such as replay attacks, Man-In-The-Middle (MITM), Denial of Service (DoS)/Distributed DoS(DDoS), and cam-hackers, enhances the understanding of MQTT protocol vulnerabilities, emphasizes the critical importance of strengthening security, and serves as a reference for new researchers in the field.

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.