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AbstractThe project aimed to revolutionize home security by developing innovative, user-friendly, and highly secure smart security devices requiring minimal installation and robust data protection. Through meticulous research and critical design phases, the project successfully achieved its objectives, surpassing initial goals and setting new standards for home security.Objectives & Research Findings:The project aimed to address the critical needs of homeowners, tenants/renters, and property managers by focusing on damage-free installation, easy setup, remote monitoring, and encrypted data transmission. Analysis of burglary statistics emphasized the vulnerability of inadequate door-locking mechanisms, while exploration of sensor technologies identified viable options for detecting door status. Patent analysis provided insights into prior art and innovation trends, guiding design decisions and strategies for intellectual property protection.Critical Design Phase & Achievements:Detailed engineering analysis, testing protocols, risk assessment, and compliance considerations were conducted. Hardware components were carefully selected and tested for optimal performance, and housing units were designed for durability and practical installation. Non-invasive sensors facilitated risk-free installation, while battery-powered sensors ensured portability and longevity. Invasive sensor options were developed for permanent home installation, and user-friendly mobile/web applications provided real-time property security status with secure and encrypted data transmission.Future Recommendations & Conclusion:Future recommendations include extending battery life, enhancing data protection measures, implementing a notification system within the application, and ensuring compatibility with other smart home systems. The project demonstrates how innovative security solutions can evolve alongside technology to meet user needs effectively, setting new benchmarks for convenience, flexibility, and data protection in home security systems. Lessons learned will guide future improvements, ensuring continued innovation and user satisfaction.

Networks for the Internet of Things typically use a gateway to provide connectivity between a low bit rate, low capability sensor network and the broader Internet. The gateway can be subject to very high traffic loads, many concurrent processes and needs to be highly reliable. Functional programming languages such as Erlang and Elixir have proven to be an effective programming paradigm for such scenarios, notably in large-scale telecommunications switches. In this paper, we report on our experience of developing a gateway between a LoRa network and an MQTT broker using the functional programming language Elixir and the more conventional language C++. To obtain an understanding of this approach to development, we first developed an initial prototype on a single-board computer using Elixir. We then developed the same system in C++ and ran experiments to compare the two systems' performance. In order to understand the performance of such systems on low-end IoT devices, we then developed the same system on a low-cost ESP32 micro-controller in both C++ and Elixir. We were able to run the Elixir-based system on an ESP32 micro-controller but found that its performance was significantly poorer than the same system written in C++. We conclude that functional programming has great potential for the development of IoT systems, but work needs to be carried out to improve the supporting libraries and underlying virtual machines. We also note that learning to program in a functional programming language has quite a steep learning curve. [ABSTRACT FROM AUTHOR]

Over the last decade, health information systems (HIS) have undergone significant changes, particularly in embracing flexible frameworks for ongoing development. This evolution underscores the necessity for information technology (IT) infrastructures that rapidly align with clinical processes. The paper investigates the transition of hospital information systems to comprehensive strategies, examining the benefits and challenges involved. It also examines the increasing demands on health data management systems (HDMS) for patient care and biomedical research. The focus is on how the integration of the Internet of Things (IoT) and open-source enterprise resource planning (ERP) systems, such as Odoo, impacts health information management. The study evaluates the effectiveness and implications of combining these technologies. It provides examples of these integrated systems in action, particularly in resource-limited settings, and evaluates their potential to improve care. The document provides a comprehensive review of the current status and evolution of HIS and HDMS, highlighting the importance of integrating IoT and other cutting-edge technologies in healthcare. This is a crucial aspect for developing countries, where these advancements can significantly improve healthcare outcomes. [ABSTRACT FROM AUTHOR]

With the gradual increase in the proportion of new energy sources in the power grid, there is an urgent need for more flexible resources to participate in short-term regulation. The impact of communication network channel quality will continue to magnify, and factors such as communication latency may directly affect the efficiency and effectiveness of resource regulation. In this context of a large number of flexible demand-side resources accessing the grid, this article proposes a bidirectional channel delay measurement method based on MQTT (Message Queuing Telemetry Transport). It can effectively evaluate the real-time performance of communication links, considering that resources mainly access the grid through the public network. Subsequently, focusing on two typical types of resources on the demand side, namely, split air conditioners and central air conditioners, this article proposes an assessment method for correcting the response capabilities of air conditioning resources considering communication latency. Experimental simulations are conducted, and the results demonstrate that under given communication conditions, this method can more accurately estimate the response capability of air conditioners. This provides a basis for formulating more reasonable scheduling strategies, avoiding excessive or insufficient resource regulation caused by communication issues, and aiding the power grid in achieving precise scheduling. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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The rapid development of the IoT (Internet of Things) demands speed and security of communication between connected entities via the Internet. A suitable communication protocol for this communication in terms of speed optimization is MQTT (Message Queuing Telemetry Transport). However, it has security limitations that make it vulnerable to third-party attacks. This research proposes an IoT communication system and server using the MQTT protocol and Elliptic Curve Cryptography (ECC) algorithms to secure communications. ECC efficiently uses computing resources and has a short key size compared to Rivest Shamir Adleman (RSA), so it is suitable for mutual authentication. In addition, data encryption uses the 128-bit Advanced Encryption Standard (AES), which has good security and computing efficiency. The study included testing the mutual authentication speed of ECC and RSA across different key sizes, demonstrating that ECC consistently outperformed RSA in execution time. This study also compared the speed of mutual authentication between ECC and RSA with key sizes of 256 and 3072 bits, respectively; ECC achieved an average speed of 117.33 ms, whereas RSA took 241.92 ms. Furthermore, this study was also tested for the level of security using ECC as a cryptographic algorithm. The system is tested for security by performing sniffing attacks, brute force attacks, replay attacks, and fingerprint matching accuracy by measuring the False Rejection Rate (FRR) and False Acceptance Rate (FAR). The most suitable threshold value is between 30 and 40 within an Equal Error Rate (ERR) between 20% and 30%. The overall testing results show that the system is time-efficient and resilient to attacks.

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]

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

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

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]

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.

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.

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 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]

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]

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]

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.

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.

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.

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.

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]

In modern large hotels, due to a large number of rooms and complex layouts, it is difficult for customers to find rooms, which increases a lot of workloads for hotel attendants to guide. In this paper, a hotel intelligent guidance system based on face recognition is designed. After entering the customer's facial photos, the room guidance and customer management are carried out through face recognition. With this, hotels can move toward card-free management, green environmental protection, and save on resources. With these improvements, hotel management will be card-free and green. Each monitoring device of the system adopts dual STM32 core architecture, in which STM32H7 is responsible for face recognition, while STM32L4 is the main control chip, which is responsible for data exchange, guest room guidance and other work. The monitoring master not only guides, but also uploads customer check-in information to the cloud platform to facilitate the management of the hotel. The system adopts contactless information collection and guidance, which improves the intelligence and humanization of the hotel, and has a good application prospect. [ABSTRACT FROM AUTHOR]

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

An IoT-based wireless centralized monitoring system has been proposed which is an effective way to monitor and control building systems. The system will serve the purpose of replacing an old and expensive PLC-based and wire-based management system. The system here uses several nodes of networks which are connected through wireless mesh and sending data to the centralized edge computing device (raspberry pi) through MQTT protocol. Then, the edge will update the data on the cloud using Firebase to generate a library and update the data on the cloud on a real-time basis and through the cloud, which will then allow us to monitor and control the system through the website which can be used easily and remotely on any browsing device. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker's bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings. [ABSTRACT FROM AUTHOR]

Internet has changed and evolved since its origin and in recent time the communication channel for the internet has developed a flexible system design to accommodate newer devices and customized services. Internet of Medical Things (IoMT) is one such blooming service for larger data processing and customization. In this paper, we propose a novel interdependency attribute protocol for authentication and enhancing security aspects of IoMT communication. The protocol is developed on remote monitoring and Message Queuing Telemetric Transport (MQTT) protocol foundation values to secure data transmission and communication via active public domain internet and cloud storage. The approach is developed on machine learning models for attribute customization and classification. The model has been validated with AWS open pipeline cloud platforms for IoMT devices customization. The IoMT device cloud server evaluation and authentication via interdependency attribute protocol has outperformed the existing security models and analysis with Machine learning tools. [ABSTRACT FROM AUTHOR]

Nowadays, the cybersecurity of Internet of Thing (IoT) environments is a big challenge. The analysis of network traffic and the use of automated estimators built up with machine learning techniques have been useful in detecting intrusions in traditional networks. Since the IoT networks require new and particular protocols to control the communications between the different devices involved in the networks, the knowledge acquired in the study of general networks may be unuseful some times. The goal of this paper is twofold. On the one hand, we aim to obtain a consistent dataset of the network traffic of an IoT system based on the Message Queue Telemetry Transport protocol (MQTT) and undergoing certain type of attacks. On the other hand, we want to characterize each of these attacks in terms of the minimum possible number of significant variables allowed by this protocol. Obtaining the data set has been achieved by studying the MQTT protocol in depth, while its characterization has been addressed through a hybrid (filter/wrapper) feature selection algorithm based on the idea behind the minimum-redundancy maximum-relevance (mRMR) algorithm. The dataset, together with the feature selection algorithm, carries out a characterization of the different attacks which is optimal in terms of the accuracy of the machine learning models trained on it as well as in terms of the capability of explaining their underlying nature. This confirms the consistency of the dataset. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

The landfill is a place where waste reaches the last stage in its management since it began to arise in the source, collection, transportation, processing, and disposal. Muara Fajar landfill is one of the landfills in Pekanbaru City whose garbage increase exceeds the capacity of waste storage land so that it can produce 800 tons of garbage per day in dry weather and 1000 tons per day in rainy weather. The problem caused by the management of the Muara Fajar landfill is air pollution due to the process of decomposing organic matter anaerobically which has the potential to cause unpleasant odors and health problems. With this problem, a system was built to monitor and detect hazardous gases in landfill, namely H2S gas using MQ-136 sensors, CH4 gas by MQ-4 sensors, and CO2 and NH3 gases using MQ-135 sensors. This system is implemented with the IoT protocol, MQTT Broker to receive and send monitoring data to the Website with notifications from the telegram web application. Based on testing the value of ppm in CO2 Gas with a danger status of 1000-4000 ppm, H2S Gas is 10-400 ppm, NH3 is included in the status of be careful which is 0.03 ppm, and CH4 with normal status because the value of ppm levels is below 10000 ppm

QoS is an arguable feature of Message Queuing Telemetry Transport Protocol (MQTT) that is employed under constrained environment for communication over an unreliable network in smart cities. It gives extra control to the client for matching its needs according to the application but with confiscated network performance. For a reliable end-to-end service assurance, guaranteed QoS and controlled scalability, MQTT based IoT must be examined for various network parameters. In this work, an improved TCP based transparent MQTT network is proposed for massively deployed connected devices in smart cities. The mathematical modeling is carried out by analyzing end-to-end quality assurance. Network latency, content delivery rate, number of subscribe/publish requests and services offered by the MQTT broker for various topic IDs are then verified on physical network. These parameters are examined over a transparent gateway-based network for verifying the agreement of devised probability based mathematical model with the actual content delivery, service rate and request hold time at virtual machine based MQTT broker, local network broker and remote server respectively. The Improved MQTT model surpasses Apollo, RabbitMQ and Mosquitto server by evidencing the message queueing delay of 3.5 ms for QoS-0 and 3.6 ms for QoS-1 service IoT.