Message Queue Research Articles

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.

TRAFFIC FLOW PREDICTION USING MACHINE LEARNING

As a result of the ongoing increase in the number of vehicles, there are increasingly severe traffic bottlenecks. The amount of time and money that users of transportation spend traveling will immediately change as a result. To some extent, this issue can be mitigated by projecting future traffic patterns. Three machine learning algorithms—Linear Regression, Decision Tree, and Support Vector Machine—are used in this work to predict the traffic flow after the data has been preprocessed using Selenium, OSS, and Message Queue. Next, we examine real-time Beijing traffic data as it shows up on the Baidu map. According to this study, Random Forest is the most accurate of all three techniques, with an accuracy of 0.719. Second are logistic regression and SVM. Keywords - Traffic FlowPrediction; Decision Tree; Random Forest; SVM; Baysian Ridge; CNN; LSTM

APPLICATION OF FUZZY MODELS TO CONTROL THE STREAM OF MESSAGES IN DATA TRANSMISSION INFRASTRUCTURES

The report presents one of the problems that arises in distributed data transmission infrastructures - node overload in data transmission when there is unstable communication between nodes. As a solution, a mechanism for controlling the message flow intensity based on a fuzzy model is proposed. The developed fuzzy control model has demonstrated its effectiveness in the network for collecting and transmitting quotes of financial instruments from various trading platforms to the information-analytical system of a financial company. Message queues at the sources have significantly decreased, while minimizing losses of quotes on unstable communication channels. Additionally, the possibility of more flexible adjustment of fuzzy control by expanding the number of metrics of input parameters of the fuzzy model has been explored.

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.

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.

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.

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

AbstractThis 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.

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.

EVENT DRIVEN KUBERNETES AUTOSCALER FOR CLOUD NATIVE APPS

Kubernetes and Docker have emerged to be a key solution for managing and organizing containers in microservice applications. With autoscaling, one of Kubernetes key features that dynamically modifies resources like CPU and memory to fit changing demands, it gives us a strong and efficient way to manage our application. Yet, the conventional autoscaling methods, such as the Horizontal Pod Autoscaler (HPA), often have difficulties on customary metrics, like CPU and memory utilization, which may not be fully suitable with the constant changes in the modern applications. This project aims at solving this problem by suggesting a unique solution which is Event Driven Kubernetes Autoscaler for Cloud Native Apps that is specifically made to work with the scaling needs of the application based on the message queue traffic. The custom scaling algorithm is an important part of the project, which unlike the previous traditional autoscalers who needed predefined metrics and did not provide much flexibility, the custom Metric based Autoscaler allows developers to select their own metrics and scaling parameters, thus providing flexibility and great user experience as they can meet the performance needs of all the applications. Keywords: Kubernetes, Docker, Microservices, Autoscaling, Cloud-Native, Horizontal Pod Autoscaler (HPA)

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.

Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.

Analysis and Performance Comparison of IoT Message Transfer Protocols Applying in Real Photovoltaic System

The adoption of reliable and real-time communication technology is an absolute necessity for the advancement of Internet of Things (IoT) applications. Messaging protocols such as MQTT, AMQP, and HTTP are frequently used for communication with resource-constrained IoT devices. However, choosing a suitable and effective messaging protocol presents a daunting challenge for organizations, as it depends on the specific characteristics and messaging requirements of the IoT system. Therefore, it is crucial to have a comprehensive understanding of three established messaging protocols, such as the Hypertext Transfer Protocol (HTTP), the Message Queuing Telemetry Transport (MQTT), and the Advanced Message Queuing Protocol (AMQP), to appropriately apply them in practical projects. In this paper, information technology solutions are provided for a chain of solar farms to improve harvest productivity, facilitate warning notifications, and enable remote control. Subsequently, a detailed comparative analysis is performed, considering various interconnected criteria, to gain valuable insight into the strengths and limitations of these protocols. The results show that MQTT and AMQP play a role in enhancing overall efficiency and speed within the framework of our suggested photovoltaic system.

Technical Report: Development and validation of continuous monitoring system for calves based on commercially available sensor for humans

In the last decade, development of continuous sensor based monitoring systems for farm animals (Precision Livestock Farming (PLF)) has markedly increased with the aim to automate and improve disease detection. In calves, multiple technologies exist, but most detect only a single parameter, and none are available for continuous heart rate and electrocardiogram (ECG). The objective of this paper was to develop and validate a sensor system, derived from a commercially available sensor for humans, for heart (ECG, heart rate), movement (inertial measurement unit with nine degrees of freedom (IMU9)) and temperature data. Data is sent over Bluetooth Low Energy (BLE) to a gateway equipped with a built-in camera for monitoring individual calves. The gateway transmits the data using the Message Queuing Telemetry Transport (MQTT) protocol to a server for long term storage. The data can also be shown on a real-time dashboard. Results indicate a very high correlation for heart rate compared with a widely used veterinary sensor (Pearson correlation coefficient (r) = 0.98), and a high level of accuracy for the movement data for lying versus standing detection (94.3 %). However, a lower correlation between sensor temperature and rectal temperature measurements was observed (r = 0.56). In conclusion, the adapted Movesense system exhibits the capability to accurately measure heart rate and detect the position (lying – standing) of calves but was insufficient as such for temperature monitoring. This sensor system has potential not only for disease detection, but equally for continuous, non-invasive heart rate monitoring, which can be used as an indicator (besides others) for animal welfare.

MQTree: Secure OTA Protocol Using MQTT and MerkleTree.

The escalating advancement in Software-Defined Vehicles (SDVs) necessitates a formidable strategy for firmware updates, where traditional methods often fall short of guaranteeing absolute integrity. Although decentralization has been explored in studies for firmware integrity verification using blockchain technology, it lacks comprehensive validation in the context of automotive over-the-air (OTA) updates. By recognizing the limitations of current practices and the partial validation of decentralized approaches, such as blockchain, in the automotive sector, our study introduces a novel mechanism for firmware over-the-air (FOTA) updates. This mechanism is grounded in the widely adopted message queuing telemetry transport (MQTT) protocol, integral to the Internet of Things (IoT) domain, and leverages Merkle tree-based blockchain verification to fortify the fidelity and efficiency of firmware updates. Our proposed solution not only prioritizes the stability crucial to automotive OTA updates but also ensures that performance is not compromised. This dual focus on reliability and efficiency represents a significant stride forward in the development of secure, scalable SDV firmware update protocols.

Utilization of MQTT and Radio GPS in Pet Monitoring

This research presents an effective and efficient solution for tracking pets using Message Queuing Telemetry Transport (MQTT) based on radio GPS. The study creates a device and application capable of monitoring the whereabouts of pets to prevent their loss by displaying the latitude and longitude of the pet's location. This application provides real-time monitoring and exhibits high accuracy, with minimal differences in latitude and longitude compared to both animal tracking applications and Google Maps. The application's update speed is also impressive, with a delay of only 4.25 seconds. By continuously developing and enhancing this technology, it is expected to provide broader benefits to the pet-loving community.