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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. [ABSTRACT FROM AUTHOR]

Air quality is a pressing global concern, resulting in millions of premature deaths annually. Accurate monitoring is vital to assess pollutant levels against established standards. Real-time systems are crucial in addressing air pollution challenges. This study aims to create a cost-effective, energy-efficient real-time air quality monitoring system using LPWAN (LoRa and NB-IoT) and MQTT for data collection. Implementing LoRa, this work highlight the significance of the local environment on transmission stability, including obstacles, elevation, and distance. Despite these factors, LoRa remains functional up to 1.65 km. Comparatively, LoRa excels for diverse applications, while NB-IoT suits areas with limited network coverage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The security and integrity of medical data, in particular, have become significant concerns due to the substantial expansion in the healthcare environment due to the internet of things (IoT). To guarantee data security during transmission, this research suggests a chaotic healthcare monitoring system. This system employs a new 5- dimension (5D) chaotic system in conjunction with the secure hash algorithm version three (SHA3-256), salsa 20, and deoxyribonucleic acid (DNA). Lyapunov's proposed 5D chaos system was tested and passed for several initial periods, yielding a super chaos system (3 positive Lyapunov). Salsa 20 and randomly generated chaotic keys were utilized for encryption and decryption operations. The method we created will assess a patient's body temperature, heart rate, and oxygen saturation levels in the blood and communicate the data to a healthcare center through the Message Queuing Telemetry Transport (MQTT) protocol. The suggested system ensures that patient data is transmitted via the internet or an intranet in a secure, reliable, and readily accessible manner. In several studies involving security analysis, The encryption time is 1.872, the throughput (in bits per second (bps)) is 273.2642, and the memory usage (in megabytes) is 10.6211. The performance analysis confirmed the proposed cryptosystem's capacity to withstand multiple attacks due to its low computational and communication costs and broad key space. [ABSTRACT FROM AUTHOR]

Nowadays, in the most developed countries, modern society is moving towards scenarios in which the self-sufficiency elderly people live alone in their homes. An automatic remote monitoring system using wearable sensors is becoming even more important in Ambient Assisted Living applications. For this type of services, it is important that IoT sensors networks, which are generally composed of devices with limited computing power and storage, implement reliable communication among sensors and the Internet. There are several specialized protocols for the Internet of Things proposed by the scientific community, each characterized by its own levels of Quality of Services. The emergence of new protocols forces the need for developing proxying systems able to intermediate among different types of networks and to translate the relative protocols. In this paper, we propose a complete architecture for monitoring and managing wearable devices, and, in particular, fall detection ones. Our system uses a cross protocol proxy and a device with CoAP and MQTT as application level protocols, while it exploits the NB-IoT at physical and data-link levels. The goal of this work is the performance evaluation of the proposed solution in terms of Throughput, Round Trip Time and Delay. The results highlight the low latency reached by the proposed system architecture thanks to the implemented protocols. [ABSTRACT FROM AUTHOR]

In this paper, we conducted a state-of-the-art survey on the current state of IoT platforms suitable for the development of smart city (SC) applications. Both commercial and open-source IoT platforms are presented and compared, addressing various significant aspects and characteristics of SC applications, such as connectivity, communication protocols, dashboards/analytics availability, security, etc. The characteristics of all the investigated platforms were aggregated so that useful outcomes regarding the technological trends of the IoT platforms could be derived. Furthermore, an attempt was made to identify any discrepancies between the needs of smart cities and the capabilities provided by the relevant platforms. Moreover, IoT platforms referring to the domains of industry, agriculture, and asset tracking were also included, alongside platforms that purely target smart cities, as parts of them are also applicable to smart city applications. The results of the comparison proved that there is a lack of open-source IoT platforms targeted at smart cities, which impedes the development and testing of connected smart city applications for researchers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Según un informe de la IEA de 2017, el consumo de energía en los edificios es aproximadamente un tercio del consumo total de energía en el mundo. Además, la iluminación representa el 19% del consumo de electricidad en todo el mundo. Tomando como referencia estos datos, surge la necesidad de implementar una solución que permita reducir dichos consumos. Una forma de hacerlo es la implementación de dispositivos que permitan convertir las áreas de trabajo en espacios inteligentes. El objetivo de este trabajo es la implementación de una oficina inteligente, monitoreando y controlando el consumo energético de los dispositivos eléctricos mediante tecnologías IoT, logrando un ahorro energético significativo y a la vez proporcionando confort al personal que labora en dicha oficina. Para ello, se instalaron diversos sensores tales como sensores para la detección del estado de las puertas (abiertas o cerradas), sensores de presencia (para determinar si se encentran o no personas en áreas específicas) y actuadores, los cuales, con base a las señales transmitidas por los sensores instalados, se pudieron crear escenas que permiten el control de la iluminación de manera automática. La Raspberry Pi 4 se utilizó como controlador central, se integró la herramienta Node-Red para establecer la comunicación entre los dispositivos IoT. Node-Red es una herramienta de desarrollo basada en flujo para la programación visual que permite conectar dispositivos de hardware, API y servicios en línea. Esta herramienta nos facilita la integración de diferentes sensores y marcas, en donde se realizó la programación de diferentes escenas para poder generar un ahorro energético de manera automática. Asimismo, se estableció comunicación con una nube para poder monitorear y controlar de manera remota el sistema implementado mediante el protocolo de comunicación MQTT, el cual es un protocolo de transmisión de datos diseñado para la transición de datos de máquina a máquina. Un servidor, denominado “bróker” de mensajes, recibe transmisiones de dispositivos que “publican” información en “tópicos” particulares y, posteriormente, entregan esos mensajes a otras máquinas que se “suscriben” a esos “tópicos” específicos. Para lograr esto, se utilizaron los servicios de HiveMQ, los cuales permiten conectar hasta 100 dispositivos de manera gratuita. El resultado del proyecto es un sistema que puede ser controlado de manera remota que, debido a las tecnologías implementadas, ha genera un ahorro energético del 6.1%. Este ahorro reduce los costos operativos y apoya los objetivos de sostenibilidad de la oficina. Además, el sistema brinda confort para las personas que trabajan en dicha oficina. También cabe mencionar que este proyecto puede ser escalable, es decir, a medida que la oficina amplíe sus instalaciones, el sistema puede ser modificado para agregar los sensores y actuadores que se requieran para poder controlar dicha ampliación.

Keamanan merupakan salah satu aspek penting di kehidupan manusia. Umumnya, keamanan dapat berarti perlindungan dari bahaya fisik dan non fisik seperti kecelakaan, kebakaran, ataupun pencurian. Kemajuan teknologi yang ada dapat dimanfaatkan untuk meningkatkan keamanan, khususnya untuk kasus pencurian. Sistem kamera pengawas seperti CCTV telah terbukti dalam meningkatkan keamanan. Tetapi, CCTV mengharuskan pengawas untuk memantau layar CCTV secara manual 24/7 untuk melihat adanya pergerakan manusia. Pada penelitian ini, dibangun sistem deteksi menggunakan sensor passive infrared (PIR) yang dikontrol oleh ESP32-CAM. Algoritma single shot detector (SSD) digunakan untuk memproses data citra dan edge computing digunakan untuk mendukung waktu proses deteksi sehingga dapat memberikan informasi secara real-time. Proses transmisi data citra pada edge computing menggunakan protokol MQTT. Setiap ESP32-CAM akan menjadi publisher dan edge akan menjadi subscriber. Dataset yang digunakan untuk melatih algoritma SSD berjumlah 1050 data. Seluruh data dibagi menjadi 3 bagian yaitu 735 data latih (70%), 210 data evaluasi (20%), dan 105 (10%) data uji. Data uji pada penelitian ini terdiri dari 3 jenis data, diantaranya 35 data uji siang hari, 35 data uji sore hari, dan 35 data uji malam hari. Algoritma SSD pada penelitian ini menghasilkan ketepatan deteksi 87.51% mAP pada data uji siang hari, 81.39% mAP pada data uji sore hari, dan 76.82% mAP pada data uji malam hari. Proses dari saat sensor HC-SR501 mendeteksi gerakan hingga informasi sampai ke user membutuhkan rata-rata waktu 2,843 detik.

In health care, early detection of diseases is important in order to increase survival rates. Regular monitoring of vital signs is necessary for the early detection of health issues. Due to the high cost, inadequacy, and complexity of monitoring devices, it is challenging for individuals to check their vital signs at home. Consequently, a cost-effective, broadly accessible, and easy-to-use system is necessary for health monitoring. For this purpose, we developed a portable and wireless acquisition electronic device to help patients record physiologically relevant signals such as ECG, EMG, EEG, and EOG for continuous monitoring. The key components of the acquisition system are a portable device, a Wi-Fi router, a SQL server, and a graphical user interface (GUI). In this study, a cost-effective, fairly low-power Internet-of-Things (IoT)-based health monitoring system was built employing a portable device incorporating analog front ends (AFE) and the ESP 32 Wroom-32. Continuous remote monitoring and diagnostics are made possible by including IoT in the architecture. In the proposed monitoring system, the lightweight Message Queue Telemetry Transport (MQTT) protocol was used. A GUI is constructed that shows near-real-time data in a web browser and can be accessed from any operating system. The accuracy of the acquired signals was validated by comparing the individual’s ECG recorded in a remote device through the IoT cloud with a conventional biomedical certified ECG machine. The AFEs were built and evaluated based on the amplitude and bandwidth of ECG, EMG, EEG, and EOG signals. The cost and power analysis, as well as other key parameters are presented. Compared to similar existing boards, our developed system demonstrates high configurable sampling frequency, high Common Mode Rejection Ratio (CMRR) and high transmission throughput with no packet loss while costing significantly less and consuming moderate power. This makes the proposed system suited for the acquisition of multichannel physiological signals for home applications.

Hydroponics is based on nutrients in water. It must be regularly monitored to prevent plant defects. The Internet of Things has become a solution for remote hydroponic monitoring and is currently being tested on the Yuan Hidroponik Kelompok Wanita Tani (KWT). This system will send data every minute, and each data has a possibility of loss in transmission. There is a chance that this system will be implemented in other hydroponic organizations. As more devices are involved, it will affect server resources. This research will compare Message Queue Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) as popular protocols used in IoT. A test with increasing clients shows that at 50 clients HTTP needs 87% CPU, while MQTT needs 22.63% CPU. A test with increasing payload shows that at 10,000 payload HTTP needs 94% CPU while MQTT needs 28.35% CPU. A test with fixed clients and payloads shows that HTTP has a CPU limit based on the clients involved. A transfer time test shows that HTTP needs 177.344 seconds while MQTT needs 3.24 seconds. An acceptance rate is calculated by incrementing the count for every incoming payload. It shows that HTTP can receive 30,000 payloads, unlike MQTT which can only receive 1680 payloads before losses.

Modernisasi pada sektor pertanian diharapkan berpengaruh pada peningkatan kualitas, jumlah produksi, dan kontinuitas suplai hasil tani. Saat ini banyak dikembangkan pertanian cerdas (smart agriculture) dalam greenhouse. Namun, beberapa parameter greenhouse harus diperhatikan supaya pertumbuhan tanaman berlangsung secara optimal. Pada penelitian ini dibuat sebuah sistem pemantauan dan pengendalian beberapa parameter greenhouse berbasis internet of things (IoT) yang memungkinkan petani melakukan pemantauan dan pengendalian kapan pun dan di mana pun. Hal ini juga dapat meningkatkan efisiensi kerja petani dalam melakukan pemantauan dan pengendalian, terlebih jika greenhouse yang dipantau dan dikendalikan lebih dari satu. Data-data hasil pemantauan greenhouse dapat dilihat secara real-time dan tersimpan di server, sehingga memudahkan petani melakukan evaluasi terhadap greenhouse dan tanaman. Beberapa parameter yang dipantau adalah suhu greenhouse, kelembapan greenhouse, dan intensitas cahaya dalam greenhouse, sedangkan parameter yang dikendalikan adalah suhu greenhouse dan kelembapan greenhouse, dengan menggunakan exhaust fan. Proses pengiriman data pemantauan dan pengendalian parameter greenhouse dilakukan menggunakan protokol message queue telemetry transport (MQTT). Pengujian data loss dan delay pada sistem diperlukan untuk mengetahui keandalan alat dalam proses pengiriman dan penerimaan data. Hasil pengujian quality of service (QoS) adalah sebagai berikut: rata-rata data loss pemantauan Gateway-Server sebesar 10,6 %, rata-rata delay pemantauan gateway-server sebesar 1,9 s, dan rata-rata delay pengendalian server-gateway sebesar 7,1 s. Saat nilai parameter suhu greenhouse kurang dari batas ambang maksimal yang ditentukan, sistem akan menyalakan drum fan agar suhu mencapai nilai minimal pada batas ambang.

This paper develops a real-time (RT) transmission and distribution (T&D) co-simulation platform for testing distributed energy resources management system (DERMS) algorithms. The platform consists of a transmission system modeled within a real-time transient-stability type environment interfaced to an active distribution network modeled within a fundamental frequency phasor-domain platform. The data exchange and time synchronization between the T&D models has been established via MQTT communication protocol, enabling the platform to communicate with a large number of DER models in real-time. The developed platform is generic in the sense that it can integrate an arbitrarily sized transient stability-type model of a transmission system with an arbitrarily sized fundamental frequency phasor-domain model of a distribution system. The platform does not impose an inherent limit on the size of the T&D models, and the size is limited by the computational capability of the simulation computer. This feature enables utilizing the platform for simulation studies of large-scale T&D systems. Furthermore, the developed platform enables both off-line and real-time simulations, extending its application from off-line planning-type studies to real-time operation-type and hardware-in-the-loop (HIL) studies. The paper demonstrates the application of the developed platform in a large-scale case study utilizing behind-the-meter (BTM) DERs to provide grid services when controlled by a DERMS. By including the propagation of dynamics between the simulation domains, realistic DER models, and typical communication protocols that are used in the field, the proposed platform enables the testing of DERMS algorithms in a close to real environment, replicating a wide range of power system phenomena that highlight the impact of a DERMS in an actual power system.

Abstract The Internet of Things (IoT) is spreading rapidly around the world, and Message Queue Telemetry Transport (MQTT) is one of the main protocols used to explore device-to-device (D2D) communication. The industry typically requires communication systems that can transmit data continuously while optimizing both bandwidth and transmission time. Due to the vast amount of data that can be lost, companies often find that even short periods of network downtime lead to significant costs. In this paper, we propose a retransmission mechanism to allow sensor nodes to relay missing data via MQTT to a local server when it reconnects after an unexpected disconnection. To assess its performance, several tests in a digital healthcare use case scenario have been designed. Since the procedure involves transferring a considerable amount of data, our main goal is to determine the maximum payload of each message to restore the missing information, while minimizing the retransmission time without information loss.

The Internet of Things (IoT) technology has begun to proliferate in recent years, which simultaneously increases the number of attacks. Owing to the massive volume and multi-dimensional data in IoT, anomaly detection leads to low prediction accuracy and a high false alarm rate. Further, there is a deficit of real-world test datasets for anomaly detection. This work aims to generate a novel real-time anomaly detection dataset and proposes an efficient anomaly detection model using an Improved Grey Wolf Optimization (IGWO)-enabled Long Short-Term Memory (LSTM) network in IoT edge scenarios. Dataset generation is carried out using a testbed setup containing Raspberry Pi 4 and sensors connected by a lightweight Message Queuing Telemetry Transport (MQTT) protocol. An autoencoder is used for feature reduction as it can investigate the input characteristics without sacrificing vital information. The LSTM classifier parameters, such as learning rate, optimizer, and batch size, are tuned precisely using IGWO techniques. The experimental results disclose that the proposed model achieves an accuracy of 99.11% for the testbed dataset, which is better than recent models. To confirm the generalizability of our model, the CICIDS 2017, DS2OS, and MQTTset standard datasets are applied explicitly. The developed model outcomes are statistically verified using the Wilcoxon signed rank test.

Connected and Autonomous Vehicles (CAVs) will be provided with multiple sensing and connectivity options as well as embedded computing and decision-making capabilities. The resulting technological landscape paves the way for the deployment of a plethora of innovative applications involving different stakeholders, such as insurance companies, car repairs, car manufacturers and public authorities. In such a context it is crucial to collect data in an efficient manner, not to burden the vehicle itself and the network infrastructure, while also providing an interoperable data sharing among all the involved players. The Digital Twin (DT) concept can play a key role to properly retrieve, store and share data as well as to exploit them to monitor, predict and improve the vehicle safety and driving experience. This work proposes a comprehensive framework which encompasses the presence of an edge-based DT interacting with the vehicle and the remote applications. It leverages properly specified interfaces and semantic models for different types of data provided by on-board sensing and learning capabilities. A Proof-of-Concept (PoC) has been developed to assess the practicality of the proposal and its performance in terms of communication and computation footprint under a variety of settings.

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the ‘source’ attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57% accuracy and a 92.38% F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56% accuracy and a 98.50% F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27%, F1 score to 99.26%, and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability.

Smart homes are becoming increasingly popular worldwide, and they are mainly based on Internet of Things (IoT) technologies to enable their functionality. However, because IoT devices have limited computing power and resources, implementing strong security measures is difficult, making the use of intrusion detection systems (IDS) an appropriate option. In this study, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)-based IoT networks for smart homes. This is done by studying 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating the performance to find the ideal model. Based on the empirical evaluation, the Generalized Linear Model (GLM) classifier with the random over-sampling technique produced the best detection performance with 100% accuracy and an f-score of 100%, outperforming previous studies. This study also investigated the influence of automatic feature engineering techniques on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time required to classify the attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time.

This study proposes the development of a contactless emergency assistance system designed for individuals with severe physical disabilities (tetraplegia and no voice but normal mouth movements) to address the limitations of traditional emergency bells in sudden emergencies. The core technology of the system includes artificial intelligence facial recognition and fuzzy motion algorithms to identify facial movements. After the auxiliary signal is triggered, the system uses Message Queuing Telemetry Transport (MQTT) technology to activate warning lights and speakers. The signal is then transmitted to the LINE application on a computer or smartphone platform via Wi-Fi, notifying caregivers to provide timely assistance. Experimental results show that the system has a user-friendly interface and an accuracy rate of about 96.69%. This study is the successful development of an emergency assistance system controllable by quadriplegic patients. The one-to-many alarm device can establish a safe net, effectively help individuals with severe physical disabilities proactively seek help, reduce the risk of accidents, and alleviate caregiver shortages. For quadriplegics, this technology offers a unique alternative with significant advantages over commercially available manually operated or voice command call bells.

As the Internet of Things (IoT) industry grows, the risk of network protocol security threats has also increased. One protocol that has come under scrutiny for its security vulnerabilities is MQTT (Message Queuing Telemetry Transport), which is widely used. To address this issue, an automated execution program called fuzz has been developed to verify the security of MQTT brokers. This program is provided with various random and unexpected input data and monitored for different responses, such as acknowledgments, crashes, failures, or memory leaks. To generate a significant number of realistic MQTT protocols, we have proposed a Generative Adversarial Networks (GAN)-based protocol fuzzer called SGANFuzz. Our experimental results show that SGANFuzz has successfully detected 6 vulnerabilities among 7 MQTT implementations, including 3 CVE bugs. Compared to the state-of-the-art fuzzing tools, SGANFuzz has proven to be the most efficient fuzzing tool in terms of vulnerability detection and has expanded the feedback coverage by receiving more unique network responses from MQTT brokers.

The Internet of Things (IoT) is a network of smart sensory objects. These objects collect environmental data and communicate to exchange this gathered data without human intervention. IoT has evolved as a revolutionary technology for over a decade. It is currently, widely applied in a variety of applications like Internet of Vehicles (IoV), Internet of Healthcare Things (IoHT), and the Internet of Everything (IoE). IoHT is one of the most beneficial and significant IoT applications. The quality of the connection in an IoT network is governed by the application layer communication protocol. Application type has a significant impact on the preference of protocol selection. In this paper, the performance of CoAP and MQTT-SN is compared. Based on the performance analysis it was concluded that in case of high traffic conditions network congestion occurs on the medical broker of MQTT-SN, which lowers the network efficiency. A framework is proposed to achieve reliability and time optimization in larger sensor-based healthcare networks. An adaptive switching-based data communication model has been designed. In the proposed model, we have enabled IoHT network to run two application protocols in parallel. The adaptive switching algorithm allows to switch between the two available protocols based on the status of the network condition. Network simulation has been performed using NODE-RED. This tool is used to test the proposed framework in different scenarios. In the end it was concluded that during network congestion conditions at medical broker of MQTT-SN, the adaptive switching algorithm allows the network to switch to CoAP connection for the time efficient and reliable transmission in the IoHT network.

The emergence of global health crises has emphasised the need for efficient and secure methods to verify the individuals’ vaccination and testing status. The research paper proposes an innovative application of the Radio Frequency Identification technology to develop a comprehensive system that ensures the controlled entry of vaccinated and tested individuals into buildings and extends its use to transportation networks. By combining tags, readers and a centralised database, with the extension of using Message Queuing Telemetry Transport, this solution aims to enhance public health safety, restore confidence in public spaces and optimise transportation operations.

Covert channel methods are techniques for improving privacy and security in network communications. These methods consist of embedding secret data within normal network channels, making it more difficult for unauthorized parties to detect such data. This paper presents a new approach for creating covert channels using the Message Queuing Telemetry Transport (MQTT) protocol, widely used in the context of the Internet of Things (IoT). The proposed method exploits storage channels by altering the field length of MQTT messages. Our solution leverages well-known one-way mathematical functions to ensure that data remain hidden from third parties observing the MQTT stream. In this way, we ensure that not only the content of the communication is preserved but also that the communication itself takes place. We conducted a security analysis to show that our solution offers the above-mentioned property even against severe threats, such as an adversary being able to observe all the messages exchanged in the network (even in the clear). Finally, we conducted an overhead analysis of our solution both in terms of the time required to perform the required operations and of the bytes to send. Our study shows that our solution adds no significant time overhead, and the additional overhead in terms of transmitted bytes remains within acceptable limits.

In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments.

Urban farming has been growing in popularity to help secure food needs in urban areas. Hydroponic is one of the methods to grow crops without soil media being an option for urban farming. However, hydroponic farming has its challenges as farmers must carefully monitor the environmental conditions of the plants regularly and adjust the nutrient solution and water circulation based on the environmental conditions. This paper describes designing and implementing an IoT-enabled hydroponic farming system to monitor the environmental condition of plants and control the nutrient supply plants. Our system is built from sensors, actuators, a micro-controller unit (Arduino), and a single-board computer (Raspberry pi) attached to the hydroponic system. The system can monitor the environmental condition of hydroponics through sensors such as temperature, humidity, pH sensors, and Total Dissolve Solid (TDS) sensors and control water pumps to circulate the nutrients to the plant. The Raspberry pi acts as a Message Queuing Telemetry Transport (MQTT) broker for distributing the data from sensors to subscribers. It also controls the nutrient pump to adjust nutrient solution. We use Node-RED installed in the Raspberry pi to build and connect the system to hardware devices. Users can monitor the environmental condition of plants in hydroponics through a web browser on their smartphone and laptop. This IoT-based indoor hydroponic system can automate the delivery of nutrients and water to plants, ensuring they receive the optimal amount at the right time. [ABSTRACT FROM AUTHOR]

The fast evolution of Industry 4.0 and smart manufacturing has led to an increased reliance on MQTT (Message Queuing Telemetry Transport) as a lightweight messaging protocol for efficient machine-to-machine communication. Despite MQTT being well-suited as a standard protocol for the Industrial Internet of Things and the increasing provision of MQTT interfaces by manufacturers for their production machines, the presence of poorly documented, non-machine-readable APIs poses challenges. Consequently, integrating these devices into an existing production environment requires significant effort and is prone to errors. In this paper, we explore diverse methods for extracting valuable information from running MQTT clients, enabling the reconstruction of their communication interfaces with the associated endpoints. This information can then be transformed into a standardized machine-readable format. The AsyncAPI Initiative has defined such an industry standard for defining asynchronous APIs, which includes MQTT. These AsyncAPI Specifications can be used to make a subsequent implementation of programs that interact with these interfaces more fast and reliable. The presented approaches focus on extracting interface information by analyzing network traffic and MQTT messages. We concentrate on observing the behavior of MQTT clients from an external perspective, deliberately disregarding the internal workings and program code of the clients themselves. Furthermore, we discuss an approach to map the data from the intercepted MQTT communication to a AsyncAPI Specification. Through our research, we aim to provide insights into the various techniques that can be employed to reverse engineer MQTT client interfaces effectively. By leveraging these approaches, it is possible to gain valuable knowledge about MQTT client communication interfaces, thus simplifying the subsequent implementation of programs that interact with these interfaces. [ABSTRACT FROM AUTHOR]

In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is spreading rapidly around the world, and Message Queue Telemetry Transport (MQTT) is one of the main protocols used to explore device-to-device (D2D) communication. The industry typically requires communication systems that can transmit data continuously while optimizing both bandwidth and transmission time. Due to the vast amount of data that can be lost, companies often find that even short periods of network downtime lead to significant costs. In this paper, we propose a retransmission mechanism to allow sensor nodes to relay missing data via MQTT to a local server when it reconnects after an unexpected disconnection. To assess its performance, several tests in a digital healthcare use case scenario have been designed. Since the procedure involves transferring a considerable amount of data, our main goal is to determine the maximum payload of each message to restore the missing information, while minimizing the retransmission time without information loss. [ABSTRACT FROM AUTHOR]

In the burgeoning domain of electric vehicle (EV) technology, advancing supportive ecosystems is pivotal. There is a marked global uptrend in the adoption of EVs, necessitating a robust network of EV charging stations. The infrastructure and accompanying systems that govern these stations are integral to these stations. Increased utilization escalates the exigency for expeditious service at these charging points. This study undertakes a comparative analysis of three distinct data communication protocols at the application layer, specifically within the context of EV charging stations. The protocols scrutinized include Hypertext Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), and Websocket. The benchmark for data transmission in this investigation is the delivery of energy information, adhering to the Open Charge Point Protocol (OCPP), a legally standardized open protocol. The data format employed is JavaScript Object Notation (JSON). Data transmission utilizing the three protocols above was intercepted and analyzed using Wireshark, a network protocol analyzer. Parameters such as latency (delay), jitter (variability of latency), and throughput (successful data delivery over a communication channel) were meticulously examined and subsequently represented graphically to enhance the interpretability of the network protocol performance. Despite identical data payloads, the findings reveal distinct transmission characteristics for each protocol. HTTP exhibited superior throughput, peaking at 31,621 bits per second (bps) during real-time data transmission. Conversely, MQTT demonstrated the most favorable latency and jitter metrics, both for real-time and periodic data dispatches. Websocket, however, registered the lowest throughput in real-time transmission, at 4,941 bps. These divergences underscore the importance of protocol selection based on specific performance criteria within EV charging station ecosystems. [ABSTRACT FROM AUTHOR]

Goal: Industrial operations are complex, and data sensors assure safety and reliable information for production improvements. Multiple stakeholders can take advantage of data acquisition for post-analysis and process control. Providing users and systems with friendly access to operating data is fundamental to the digital transition in the industry 4.0 scenario. Linking data and systems over ontologies and Industry Foundation Classes will boost supply chain performance in many layers. This paper presents the concept of valid data points over Uniform Resource Identifiers for sensor time-series into triples stores via Application Programming Interfaces. Design/Methodology/Approach: A streaming data source approach to integrating industrial sensor data and sharing it via Uniform Resource Identifiers is developed and tested using Node-Red with multiple data connection types, such as the Industry Foundation Classes and open-source time series databases. Results: The detailed proof of concept presented valid the feasibility of sharing sensor data via Uniform Resource Identifiers. The findings provide a backbone of a system able to interop Message Queuing Telemetry Transport data, Resource Description Framework datasets and Industry Foundation Classes schema. Limitations of the investigation: The system envisaged was tested using simulated data. However, it is expected to have similar results from real data use. Nevertheless, more research will be needed to implement more features, such as three-dimensional object integration. Practical implications: The solution designed and tested presented can be used in practice for companies that desire to expand via linked data shareability and interoperability. Also, researchers can advance the solution for specific features, such as creating an open-source data query and manipulation language. Originality/Value: This paper examines future deployments of systems-to-systems interoperability targeting user-friendly data shareability. It is meant to be useful for industrial and academic developments. [ABSTRACT FROM AUTHOR]

Deployment of IoT devices into an existing control system has increasingly become a new norm in recent years. Traditional approach of installing a new device into the system involves creating a separate network or infrastructure to ensure that the newly added devices do not affect the current system. However, this may not be the optimal solution for IoT devices, as they are designed to integrate and communicate with the existing systems. Therefore, it is important to understand how to properly deploy IoT devices and address the concerns of engineers. This research shares valuable experiences in deploying and integrating IoT devices into a campus Building Management System It covers considerations and requirements for the devices, as well as the deployment and integration challenges encountered during the process. These valuable experiences can serve as a useful reference for the industry when they need to install IoT devices in their infrastructure. [ABSTRACT FROM AUTHOR]

MQTT, a popular IoT messaging protocol, is frequently associated with numerous vulnerabilities, the majority of which are critical. Many IoT devices that utilize MQTT are susceptible to cyberattacks such as denial-of-service and buffer overflow. In this paper, we unveil a novel Denial of Service (DoS) attack in the MQTT protocol, referred to as Slow Subscribers, which has the potential to cause MQTT brokers to become single points of failure. Unlike existing MQTT DoS attacks, Slow Subscribers can occur on a single compromised node and could potentially disrupt a MQTT broker with minimal subscription permissions. We evaluated the reliability of Mosquitto and NanoMQ, two popular MQTT messaging brokers, to determine the effect of Slow Subscribers. According to the findings of our investigation, NanoMQ outperforms Mosquitto in response to the Slow Subscribers attack at QoS level 0. We also determine that the response to Slow Subscribers at QoS 2 is the worst for both broker implementations. In addition, the results of our experiments indicate that Eclipse Mosquitto achieves a higher rate of reliability than NanoMQ on cloud deployments whereas NanoMQ has proven to be well-suited for edge environments, especially edge IoT devices that require the use of QoS levels 0 and 1. Finally, we propose a Resilient Middleware for Message Queue Telemetry Transport (Remistry) framework that is capable of detecting misconfigurations while providing granular support of resource commitment errors, in particular the out-of-memory (OOM) problems for effectively mitigating the impact of Slow Subscribers attacks on MQTT brokers. [ABSTRACT FROM AUTHOR]

The Internet-of-Things concept has evolved from providing network connectivity for devices in our physical world to composing complex tasks with the representations of these things in service mashups. Since most of these devices (e.g. wireless sensors, tagged objects) are resource-constrained, it is easy to realize that such objects provide basic services individually and thus, motivates the need to aggregate such services for complex tasks. However, the existence of fragmentation in protocols and standards for device management and service enablement has been a challenge for the domain. In this work, we propose a resource-oriented device management and service enablement architecture that addresses this problem. We also present a smart home application scenario to demonstrate the performance and feasibility of our approach. [ABSTRACT FROM AUTHOR]

Greenhouses have long been important in the advancement of agricultural operations because they provide regulated settings for optimal plant growth. With the introduction of real-time monitoring and automation capabilities, the Internet of Things (IoT) integration into greenhouse systems represents a revolutionary change. This abstract delves into the wider field of greenhouse technology, highlighting the role that IoT plays in improving agricultural in controlled environments. Conventional greenhouses provide plants with a protected environment, but they might not be as accurate or flexible. Intelligent control of environmental conditions is made possible by the introduction of IoT-enabled greenhouses, which utilize data exchange protocols, actuators, and sensors that are networked. The project aims to elevate traditional greenhouse models by integrating Node-RED and MQTT technologies. Transitioning from a Blynk-based prototype showcases the system's versatility. Other key components, including NodeMCU, sensors for real-time data, and LED lighting, collaborate to redefine controlled environment agriculture. The Raspberry Pi serves as a central hub, facilitating seamless communication through Node-RED and MQTT. This advanced greenhouse system harmonizes cutting-edge technologies, showcasing a commitment to sophistication and adaptability in agricultural practices.

This paper presents ''An Open IoT Edge Computing System for Monitoring Energy Consumption in Buildings.'' Implemented at the Faculty of Electromechanical Engineering of the University of Colima, this innovative platform optimizes the pre-existing network infrastructure. Backed by the emerging edge and fog computing paradigms, the solution prioritizes the security of sensitive electrical data, providing detailed power, voltage, and current measurements. Designed to be compatible with various IoT technologies, the research includes practical results that support and validate its effectiveness, highlighting the benefits of edge/fog computing compared to purely cloud-based architectures. It also highlights the versatility of the platform to monitor various variables, emphasizing its applicability in different contexts and monitoring needs.

The prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computationa requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC.

Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates in discrete temporal rounds, during which the broker constructs a Merkle hash tree (MHT) from the messages received. Then the broker publishes the root on the blockchain and the MHT itself on IPFS. This mechanism guarantees that both publishers and subscribers can verify the integrity of the message exchanged. Furthermore, the interactions with the blockchain made by the publishers and the broker ensure they cannot deny having sent the exchanged messages. We provide a detailed security analysis, showing that under standard assumptions, the proposed solution achieves both data integrity and accountability. Additionally, we provided an experimental campaign to study the scalability and the throughput of the system. Our results show that our solution scales well with the number of clients. Furthermore, from our results, it emerges that the throughput reduction depends on the integrity check operations. However, since the frequency of these checks can be freely chosen, we can set it so that the throughput reduction is negligible. Finally, we provided a detailed analysis of the costs of our solution showing that, overall, the execution costs are relatively low, especially given the critical security and accountability benefits it guarantees. Furthermore, our analysis shows that the higher the number of subscribers in the system, the lower the costs per client in our solution. Again, this confirms that our solution does not present any scalability issues.