Message Queuing Telemetry Transport 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.

Neobots: an open-source platform for a low-cost neonatal incubator with internet of things approach

<div align="center">A baby incubator implements the internet of things (IoT) with an architectural design combining several scientific fields, such as networks, software, and hardware. Furthermore, this research develops an open-source platform called Neobots, including open-source program code to create a baby incubator. Then an overview of the system includes sending sensor data to the IoT Broker with the message queuing telemetry transport (MQTT) protocol and automatically storing data in the database. The results of the comparison value on each temperature sensor with a temperature sensor at the midpoint with an error of less than 0.7°C. Then testing the fuzzy between the Neobots program and the simulation in MATLAB got an error rate of 0-28.27%. In addition, in less than 10 minutes, the system response can adjust the temperature conditions to a setpoint value of 34°C from 29°C, and the average error value is 0.35°C during 1 hour of the Fuzzy implementation on the incubator. Then transfer data from the incubator to the database in a room without noise and full noise to get results for lost data less than 16.41% and 42.14%, delay rates between 0-6 seconds and 0-7 seconds with testing for 1 hour at every 1 second.</div>

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.

An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.

IoT based Efficient and Secure Building Architecture with Constrained Application Protocol (CoAP)

The Internet of Things (IoT), one of the newest technologies, has great potential for many industry businesses. Smart building technology is one of the biggest IoT industries. IoT is used on a regular basis in many different industries. It is used in a variety of contexts, including physical defense, e-health, smart buildings, grids, cities, homes, asset management, and transportation management. IoT-level, networked smart buildings offer affordable assistance. Comfort, accessibility, security, and energy management are necessary for commercial space. Naturally, IoT-based solutions can satisfy these requirements. With the depletion of the energy supply and the increase in demand for energy, there has been a growing focus on energy usage and building maintenance. This paper uses emerging IoT technology to demonstrate a smart building design that is both safe and energy-efficient. Constrained Application Protocol (CoAP) is a crucial internet transmission protocol that assigns a unique address to every device. Data is transferred using this application layer protocol outside of secure channels. While there are several recommended techniques to protect sensitive data transfer, one likely way to improve the security aspect of CoAP for authentication and end-to-end security is through the use of Datagram Transport Layer Protection (DTLS).This paper propose a smart building design in which the Internet of Things is used to control the operation of all technological components. The simulation results show that compared to the situation of employing Message Queuing Telemetry Transport (MQTT), adopting CoAP in a smart building saves energy use by around 34.86%. In order to increase security in a smart building, this article also explains how to include the DTLS protocol inside of CoAPutilising enhancements from the Certificate Authority (CA).Finally, using the ContikiCooja (CC) simulator to simulate system performance and data collecting in terms of latency, throughput, and resource consumption, CoAP is utilized to modify orderer and peer nodes for performance research in smart building.

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.

Enabling Lightweight Device Authentication in Message Queuing Telemetry Transport Protocol

Enabling Lightweight Device Authentication in Message Queuing Telemetry Transport Protocol

IoT 가상환경 플랫폼에서의 무결성 보장 시스템

In this paper, we propose a system that improves the data integrity of IoT(Internet of Things) devices in the virtual environment by combining Hyperledger Indy and MQTT(Message Queuing Telemetry Transport). The system complements the limitations of the centralized system by realizing a DPKI(Decentralized Public Key Infrastructure) structure that utilizes a distributed network in publish-subscribe(pub/sub) pattern communication. Digital signature technology was applied to ensure the data integrity of IoT devices and communication scenarios between the four core components of the client, IoT device, broker, and blockchain, as well as a topic structure using a decentralized identifier to ensure safety in the virtual environment. We present a systematic method for transparent data exchange. To prove the performance of the proposed system, this paper conducted experiments on four scenarios and evaluated communication performance in a virtual environment. The experimental results confirmed that the proposed system provides a reliable IoT data communication structure in a virtual environment.

A Performance Analysis of Security Protocols for Distributed Measurement Systems Based on Internet of Things with Constrained Hardware and Open Source Infrastructures.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal's effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

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.

Designing a Secure and Scalable Service Agent for IoT Transmission through Blockchain and MQTT Fusion

With the rapid development of the Internet of Things (IoT) in recent years, many IoT devices use communication systems to transmit data. Data packets are inevitably at risk of tampering during data transmission, which can lead to information errors and damage during communication. Since IoT terminals are often operated under human supervision, it is essential to improve and ensure the transmission of information, avoid data tampering, and ensure the accuracy of packet transmission. This research successfully improves the message transmission method of IoT communication and the communication model by combining it with blockchain architecture. Combining communication protocols with blockchain serial connections eliminates the need to control operating and managing processes. By replacing message transmission with coverage, the reliability of data transmission in the IoT communication system is improved, and the flexibility of the data transmission process is enhanced. Through practical verification through experimentation, the study successfully improved the incapability of effectively constructing a cross-coded message transmission mode in the Message Queuing Telemetry Transport (MQTT) communication protocol, removed a single layer of encryption rule, and created an encryption mode capable of providing complex arrangement organization.

Enhancing MQTT Security in the Internet of Things with an Enhanced Symmetric Algorithm

The Internet of Things (IoT), which connects billions of gadgets to expedite operations and enhance our lives, has completely changed the way we interact with our environment. With MQTT (Message Queuing Telemetry Transport) emerging as a popular communication protocol within the IoT ecosystem, the vast proliferation of networked devices has, however, presented serious security challenges. In order to strengthen MQTT security, this study suggests using an improved symmetric algorithm.Existing MQTT implementations frequently rely on simple security safeguards, making them susceptible to dangers like data manipulation, eavesdropping, and unauthorised access. Our research presents a novel symmetric algorithm designed to meet the particular needs of MQTT communication as a defence against these weaknesses. In order to protect the confidentiality and integrity of data transferred between IoT devices and brokers, this algorithm provides powerful encryption mechanisms. Additionally, it optimises resource usage to take into account the limitations of IoT devices, which frequently have constrained computational and memory resources.

Beta Hebbian Learning for intrusion detection in networks with MQTT Protocols for IoT devices

Abstract This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.

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.

IoT based security and privacy implementation in smart home

IoT based security and privacy implementation in smart home

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.

Industrial Internet of Things: A Review of Improvements Over Traditional SCADA Systems for Industrial Automation

This review article provides an overview of the potential of the Industrial Internet of Things (IIoT) to revolutionize industrial automation. The IIoT is the Internet of Things (IoT) but in an industrial context, i.e., IIoT is used more to connect machines and devices in industrial environments. The IIoT has the potential to benefit from advances in artificial intelligence, particularly machine learning and deep learning, to increase efficiency and productivity and reduce overhead costs. We provide an overview of the supervisory control and data acquisition system, a definition of IIoT, and how IIoT can offer industry greater potential for system integration to improve automation and optimization. In addition, five of the major IIoT protocols are discussed, namely, message queue telemetry transport, advanced messaging queuing protocol, constrained application protocol, data distribution service, and open platform communication unified architecture. We then identified key IIoT improvements for industrial automation. These are; efficient and low-cost systems, digital twin, machine failure prediction, real-time remote monitoring, and security. We then discussed the key research in the literature for each category. We presented some public IIoT datasets so that researchers can use them to develop new learning models to improve the security of IIoT systems. Finally, we discussed some of the limitations, recommendations, and future perspectives for developing IIoT-enabled systems.