Search Results (252)

This paper introduces a cost-effective customised Printed Circuit Board (PCB) designed to establish an accurate Internet of Things (IoT) platform integrated with established Digital Twin (DT) models for advanced structural monitoring. The study focuses on developing a low-cost, precise PCB to synchronise real-time data between physical structures and their DT counterparts. The methodology includes a robust communication architecture utilising MQTT protocols, facilitating reliable data transmission and efficient integration with MATLAB for processing. Validation tests demonstrate high accuracy in data capture, with less than 1% deviation from conventional systems across multiple structural damage scenarios. This research highlights the potential of cost-effective PCB solutions for enhancing SHM and developing more resilient, proactive infrastructure management strategies. 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

In this work, the authors introduce an entirely solar-powered LoRa-based WSN consisting of several nodes, two stoplights, and four cameras. The system has been used to monitor the semi-rural area of Panni (FG), Puglia, Italy. The WSN has a totally custom implementation in both the node-gateway side and the gateway-user interface side. In particular, the communication framework is entirely IoT-based, featuring both the MQTT protocol, for the direct control of apparatuses from the system user interface, and the more traditional TCP/IP protocol, implemented on NB-IoT. The proposed system is entirely solar-powered and features a 34.68 mWh/day consumption. Around a single communication session, the average power consumption inside the single node amounts to 1.4 mW. This paper gives an overview of the proposed system, with detailed explanations of each part, and measurements retrieved over a wide period to assess the functionality of the system. Full article

Big data has significantly transformed data processing and analytics across various domains. However, ensuring security and data confidentiality in distributed platforms such as Hadoop remains a challenging task. Distributed environments face major security issues, particularly in the management and protection of large-scale data. In this article, we focus on the cost of secure information transmission, implementation complexity, and scalability. Furthermore, we address the confidentiality of information stored in Hadoop by analyzing different AES encryption modes and examining their potential to enhance Hadoop security. At the application layer, we operate within our Hadoop environment using an extended, secure, and widely used MQTT protocol for large-scale data communication. This approach is based on implementing MQTT with TLS, and before connecting, we add a hash verification of the data nodes’ identities and send the JWT. This protocol uses TCP at the transport layer for underlying transmission. The advantage of TCP lies in its reliability and small header size, making it particularly suitable for big data environments. This work proposes a triple-layer protection framework. The first layer is the assessment of the performance of existing AES encryption modes (CTR, CBC, and GCM) with different key sizes to optimize data confidentiality and processing efficiency in large-scale Hadoop deployments. Afterwards, we propose evaluating the integrity of DataNodes using a novel verification mechanism that employs SHA-3-256 hashing to authenticate nodes and prevent unauthorized access during cluster initialization. At the third tier, the integrity of data blocks within Hadoop is ensured using SHA-3-256. Through extensive performance testing and security validation, we demonstrate integration. Full article

Migrating to Post-Quantum Cryptography (PQC) is critical for securing resource-constrained Internet of Things (IoT) devices against the “harvest-now, decrypt-later” threat. While ML-KEM (CRYSTALS-Kyber) has been standardized under FIPS 203 for general encryption, these devices often operate on unreliable networks suffering from high latency and packet loss. Our recent systematic review identified a critical gap that existing research overwhelmingly focuses on Transport Layer Security (TLS). This leaves the resilience of lightweight protocols like MQTT and CoAP under challenging network conditions largely unexplored. This paper introduces PQC-IoTNet, a novel Software-in-the-Loop (SITL) framework to address this gap. Our three-tier architecture integrates a Python-based IoT client with kernel-level emulation to test the full protocol stack. Validation results comparing Kyber and ECC demonstrate the framework’s ability to capture critical performance cliffs caused by TCP retransmissions. Notably, the framework revealed that while Kyber maintained an 18% speed advantage over ECC at 5% packet loss, both protocols experienced nonlinear latency spikes. This work provides a reproducible blueprint to identify operational boundaries and select resilient protocols for secure IoT systems. Full article

This paper presents test results of the performance comparison of 5G standalone (SA) and non-standalone (NSA) networks in the context of gathering data of remote sensors and machines. The study evaluates key network characteristics such as latency, throughput, jitter and packet loss (for UDP protocol only) using standardized tests to gain insights into the impact of these factors on real-time and data-intensive communication. In addition, a range of communication protocols including OPC UA, Modbus, MQTT, AMQP, CoAP, EtherCAT and gRPC were tested to assess their efficiency, scalability and suitability with different send data sizes. By conducting experiments in a controlled hardware environment, we have analyzed the impact of the 5G architecture on protocol behavior and measured the transmission performance at different data sizes and connection configurations. Particular attention is paid to protocol overhead, data transfer rates and responsiveness, which are crucial for industrial automation and IoT deployments. The results show that SA networks consistently offer lower latency and more stable performance, where robust and low-latency data transfer is essential. In contrast, lightweight IoT protocols such as MQTT and CoAP demonstrate reliable operation in both SA and NSA environments due to their low overhead and adaptability. These insights are equally important for time-critical industrial protocols such as EtherCAT and OPC UA, where stability and responsiveness are crucial for automation and control. The study highlights current limitations of 5G networks in supporting both remote sensing and industrial use cases, while providing guidance for selecting the most suitable communication protocols depending on network infrastructure and application requirements. Moreover, the results indicate directions for configuring and optimizing future 5G networks to better meet the demands of remote sensing systems and Industry 4.0 environments. 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

The increasing global connectivity, driven by the expansion of the Internet of Things (IoT), is generating a significant increase in system vulnerabilities. Cyberattackers exploit the computing and processing limitations of typical IoT devices and take advantage of inherent vulnerabilities in wireless networks and protocols to attack networks, compromise infrastructure, and cause damage. This paper presents a shallow learning multiclassifier approach for detecting and classifying cyberattacks on IoT networks. Specifically, it addresses MQTT networks, widely used in the IoT, to detect Denial-of-Service (DoS) and Intrusion attacks, using inter-device communication data as a basis. The use of shallow learning techniques allows this cybersecurity system to be implemented on resource-constrained devices, enabling local network monitoring and, consequently, increasing security and incident response capabilities by detecting and identifying attacks. The proposed system is validated on a real dataset obtained from an IoT system over MQTT, demonstrating its correct operation by achieving an accuracy greater than 99% and F1-score greater than 80% in the detection of Intrusion attacks. 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

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

The exponential proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift in industrial automation and smart city infrastructure. However, this rapid expansion has engendered significant heterogeneity in communication protocols, creating critical barriers to seamless data integration and interoperability. Conventional gateway solutions frequently exhibit limited flexibility in supporting diverse protocol stacks simultaneously and often lack granular user controllability. To mitigate these deficiencies, this paper proposes a novel, modular IoT gateway architecture, designated as MIGS (Modular IoT Gateway System). The proposed architecture comprises four distinct components: a Management Component, a Southbound Component, a Northbound Component, and a Cache Component. Specifically, the Southbound Component employs instance-based isolation and independent task threading to manage heterogeneous field devices utilizing protocols such as Modbus, MQTT, and OPC UA. The Northbound Component facilitates reliable bidirectional data transmission with cloud platforms. A dedicated Cache Component is integrated to decouple data acquisition from transmission, ensuring data integrity during network latency. Furthermore, a web-based Control Service Module affords comprehensive runtime management. We explicate the data transmission methodology and formulate a theoretical latency model to quantify the impact of the Python Global Interpreter Lock (GIL) and serialization overhead. Functional validation and theoretical analysis confirm the system’s efficacy in concurrent multi-protocol communication, robust data forwarding, and operational flexibility. The MIGS framework significantly enhances interoperability within heterogeneous IoT environments, offering a scalable solution for next-generation industrial applications. Full article