Search Results (141)

The development of effective Intrusion Detection Systems (IDS) for Internet of Things (IoT) environments is constrained by the absence of realistic, large-scale datasets, particularly for the Message Queuing Telemetry Transport (MQTT) protocol, which is prevalent in industrial IoT. Existing datasets are frequently limited in scope, imbalanced, or do not capture MQTT-specific attack patterns, thereby impeding the training of accurate machine learning models. To address this gap, the extensible Message Queuing Telemetry Transport (eMQTT) Traffic Generator is introduced as a modular platform capable of simulating both legitimate MQTT communication and targeted denial-of-service (DoS) attacks. The framework features a scalable and reproducible architecture that incorporates protocol-aware attack modeling, automated traffic labeling, and direct export of datasets suitable for machine learning applications. The system produces standardized, configurable, repeatable, and publicly accessible datasets, thereby facilitating reproducible research and scalable experimentation. Experimental validation demonstrates that the simulated traffic aligns with established DoS behavior models. Two high-volume datasets were generated: one representing normal MQTT traffic and another emulating CONNECT-flooding attacks. Machine learning classifiers trained on these datasets exhibited strong performance, with gradient boosting models achieving over 95% accuracy in distinguishing benign from malicious traffic. This work offers a practical solution to the scarcity of datasets in IoT security research. By providing a controlled, extensible, and reproducible traffic-generation platform alongside validated datasets, eMQTT enables systematic experimentation, supports the advancement of IDS solutions, and enhances MQTT security for critical IoT infrastructures. Full article

We developed a real-time alert and data management framework that integrates programmable logic controllers, RS-485 industrial communication, Structured Query Language Server, Message Queuing Telemetry Transport (MQTT), and the nodemation (n8n) automation platform, using a filling machine production line as a case study. The system collects and analyzes the operational status and production line data of the filling machine in real time, storing all information in a database for preservation. Through MQTT, the data is sent to n8n for automated processing. When equipment anomalies occur or data exceed predefined thresholds, the system automatically notifies maintenance personnel via communication software APIs. Additionally, users can query daily production capacity or related data using n8n’s AI functions. This architecture offers low cost, rapid deployment, cross-platform integration, and high flexibility. It not only improves anomaly handling efficiency but also preserves complete historical records, supporting trend analysis, report generation, and decision optimization, thereby assisting the filling production line in achieving long-term stable and intelligent management. Full article

Message Queuing Telemetry Transport (MQTT) is a lightweight publish–subscribe protocol widely deployed in Internet of Things (IoT) systems. Although MQTT defines authentication and authorization mechanisms, their enforcement accuracy, configuration sensitivity, and operational cost under controlled misconfiguration conditions remain insufficiently quantified. This study experimentally quantifies authentication enforcement behavior and Access Control List (ACL) misconfiguration impact within a standards-compliant MQTT deployment under controlled laboratory conditions. Rather than benchmarking a specific software product, the work measures protocol-defined security behavior—including authentication success rate, false acceptance rate (FAR), false rejection rate (FRR), privilege-boundary preservation, authentication latency, and broker CPU utilization—across systematically constructed operational and failure scenarios. Username/password and mutual TLS authentication were evaluated under valid and stress-induced connection conditions, alongside structured ACL policies incorporating wildcard over-permission. Across repeated trials, username/password authentication achieved higher observed connection reliability (≈0.95), while TLS-based authentication provided stronger cryptographic identity assurance at the cost of increased authentication latency (≈42.6 ms vs. 14.8 ms) and higher CPU utilization (≈23.7% vs. 9.4%). No false acceptances were observed within 100 unauthorized trials per configuration, corresponding to a 95% confidence upper bound of <3% for FAR under a binomial model. Under controlled ACL misconfiguration, 22 of 100 evaluated authorization operations accessed topics beyond the originally intended least-privilege scope, yielding a reproducible privilege expansion rate of 0.22. This expansion resulted from wildcard policy semantics rather than an enforcement malfunction. The results provide controlled empirical quantification of reliability–security trade-offs and configuration-driven privilege-boundary behavior within a standards-compliant MQTT deployment. While the findings reflect enforcement behavior as realized in the evaluated implementation and laboratory environment, the proposed measurement framework establishes reproducible criteria for assessing MQTT security enforcement accuracy under controlled conditions. Full article

Electronic waste (e-waste) continues to increase globally, yet conventional bins cannot distinguish hazardous batteries and devices from recyclable metals. This article presents an AI- and IoT-enabled smart dustbin that automatically identifies and segregates general waste, metals, and electronic or battery-based hazards while providing real-time monitoring through a cloud-based dashboard. The system integrates inductive sensing, Time-of-Flight detection, an Espressif Systems Platform 32 (ESP32)-CAM module, and Google Gemini 1.5 Flash for image classification. The prototype achieved a waste segregation accuracy of 93.5% with a total cycle time of 4–6 s per item. The touch-free lid, swift mechanical actuation, and compact 59 × 59 × 100 cm footprint make the dustbin suitable for deployment in campuses, offices, and shopping malls. Dual ESP32 controllers, cloud connectivity through Message Queuing Telemetry Transport (MQTT), Firebase, and a Streamlit web interface enable automated alerts through Discord and email, demonstrating a scalable and energy-efficient approach to sustainable e-waste management. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article

Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m². In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m². The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

Gun violence in U.S. schools not only causes loss of life and physical injury but also leaves enduring psychological trauma, damages property, and results in significant economic losses. One way to reduce this loss is to detect the gun early, notify the police as soon as possible, and implement lockdown procedures immediately. In this project, a novel gun detector Internet of Things (IoT) system is developed that automatically detects the presence of a gun either from images or from gunshot sounds, and sends notifications with exact location information to the first responder’s smartphones using the Internet within a second. The device also sends wireless commands using Message Queuing Telemetry Transport (MQTT) protocol to close the smart door locks in classrooms and announce to act using public address (PA) system automatically. The proposed system will remove the burden of manually calling the police and implementing the lockdown procedure during such traumatic situations. Police will arrive sooner, and thus it will help to stop the shooter early, the injured people can be taken to the hospital quickly, and more lives can be saved. Two custom deep learning AI models are used: (a) to detect guns from image data having an accuracy of 94.6%, and (b) the gunshot sounds from audio data having an accuracy of 99%. No single gun detector device is available in the literature that can detect guns from both image and audio data, implement lockdown and make PA announcement automatically. A prototype of the proposed gunshot detector IoT system, and a smartphone app is developed, and tested with gun replicas and blank guns in real-time. Full article

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Applications of smart cities, smart buildings, smart agriculture systems, smart grids, and other smart systems benefit from Internet of Things (IoT) protocols, networks, and architecture. Wireless Sensor Networks (WSNs) in smart systems that employ IoT use wireless communication technologies between sensors in the Things layer and the Fog layer hub. Such wireless protocols and networks include WiFi, Bluetooth, and Zigbee, among others. However, the payload formats of these protocols are heterogeneous, and thus, they lack a unified frame format that ensures interoperability. In this paper, a lightweight, interoperable frame format for low-rate, small-size Wireless Sensor Networks (WSNs) in IoT-based systems is designed, implemented, and tested. The practicality of this system is underscored by the development of a gateway that transfers collected data from sensors that use the unified frame to online servers via message queuing and telemetry transport (MQTT) secured with transport layer security (TLS), ensuring interoperability using the JavaScript Object Notation (JSON) format. The proposed frame is tested using market-available technologies such as Bluetooth and Zigbee, and then applied to smart home applications. The smart home scenario is chosen because it encompasses various smart subsystems, such as healthcare monitoring systems, energy monitoring systems, and entertainment systems, among others. The proposed system offers several advantages, including a low-cost architecture, ease of setup, improved interoperability, high flexibility, and a lightweight frame that can be applied to other wireless-based smart systems and applications. Full article

With rapid developments of wireless communication and Internet of Things (IoT) technologies, an increasing number of devices and sensors are interconnected, generating massive amounts of data in real time. Among the underlying protocols, Message Queuing Telemetry Transport (MQTT) has become a widely adopted lightweight publish–subscribe standard due to its simplicity, minimal overhead, and scalability. Then, understanding such protocols is essential for students and engineers engaging in IoT application system designs. However, teaching and learning MQTT remains challenging for them. Its asynchronous architecture, hierarchical topic structure, and constituting concepts such as retained messages, Quality of Service (QoS) levels, and wildcard subscriptions are often difficult for beginners. Moreover, traditional learning resources emphasize theory and provide limited hands-on guidance, leading to a steep learning curve. To address these challenges, we propose an AI-assisted, exercise-based learning platform for MQTT. This platform provides interactive exercises with intelligent feedback to bridge the gap between theory and practice. To lower the barrier for learners, all code examples for executing MQTT communication are implemented in Python for readability, and Docker is used to ensure portable deployments of the MQTT broker and AI assistant. For evaluations, we conducted a usability study using two groups. The first group, who has no prior experience, focused on fundamental concepts with AI-guided exercises. The second group, who has relevant background, engaged in advanced projects to apply and reinforce their knowledge. The results show that the proposed platform supports learners at different levels, reduces frustrations, and improves both engagement and efficiency. Full article

The rapid advancement of quantum computing poses significant threats to classical cryptographic methods, such as Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC), which currently secure Internet of Things (IoT) and cloud communications. Post-Quantum Cryptography (PQC), particularly lattice-based schemes, has emerged as a promising alternative. CRYSTALS-Kyber, standardized by the National Institute of Standards and Technology (NIST) as ML-KEM, has shown efficiency and practicality for constrained IoT devices. Most existing research has focused on PQC within the Transport Layer Security (TLS) protocol. Consequently, a critical gap exists in understanding PQC’s performance in lightweight IoT protocols. These are Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP), particularly under adverse network conditions. To address this gap, this paper provides a systematic review of the literature on the network resilience and performance of CRYSTALS-Kyber when integrated into these protocols operating over lossy and high-latency networks. Additional challenges include non-standardized integration, resource limitations, and side-channel vulnerabilities. This review provides a structured synthesis of current knowledge, highlights unresolved trade-offs between security and efficiency, and outlines future research directions, including protocol-level optimization, lightweight signature schemes, and resilience testing of PQC-secured IoT protocols under realistic conditions. Full article