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EBSCON 2024
"Zebra (US), Infineon (Germany), Inpixon (US), Pozyx (Belgium), Sewio (Czech Republic), Ubisense (UK), Redpoint Positioning (US), Kinexon (Germany), Eliko (Estonia), Infsoft (Germany), Ubitrack (Bulgaria)."Ultra-Wideband (UWB) Indoor Location Market...
Global Info Research announces the release of the report Global UWB Proximity Sensor Market 2025 by Manufacturers Regions Type and Application Forecast to 2031 The report is a detailed and comprehensive analysis presented by region and country type and application ...
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
CTI Connect, a leading provider of wireless broadband and connectivity solutions, delivers robust wireless connectivity for smart factory IoT sensors through
Monitoring von Naturgefahren und Bewegungsanalysen im Sport mithilfe innovativer Sensorsysteme: Forscher*innen am Lehrstuhl für Automation und Messtechnik an der Montanuniversität Leoben arbeiten an verteilten und vernetzten Sensorsystemen: Mit dieser Technologie können beispielsweise erste Anzeichen für Rutschungen und Murenabgängen in erosionsgefährdeten, meist ländlichen Bereichen überwacht werden. Durch die Integration von Sensoreinheiten in Schutzbauten wie Steinschlagnetzen lässt sich deren Zustand kontinuierlich überwachen, wodurch frühzeitig vor vermehrten Steinschlagereignissen und Murenabgängen gewarnt werden kann. Die neuartigen Sensor-Systeme können im Sinne des Gefahrenschutzes aber auch in Staumauern, an Brücken oder Gebäuden zur Anwendung kommen. Eine andere Anwendung der entwickelten Technologie ist der Sport: Bewegungssensoren können hier Performance-Daten von Sportlern etwa während des Schwimmens aufzeichnen, analysieren und auswerten.
The event – jointly organized by MCL and Montanuniversität Leoben, Chair of Automation and Measurement on November 27, 2025 brought together a dedicated audience from academia and industry to exchange ideas and showcase innovations for wireless sensor nodes. We discussed emerging technologies in integrated sensors, edge computing, low power design, IoT system integration as well as application requirements. The inspiring presentations and demonstrations highlighted how research and collaboration can drive the next generation of smart, connected systems.A big thank you to all presenters, partners, and participants for sharing their insights and helping strengthen Austria’s position as a hub for wireless sensor nodes. Today’s exchange of ideas will shape tomorrow’s solutions. ***Die Veranstaltung vom 27. November 2025 brachte führende Köpfe aus Wissenschaft und Industrie zusammen, um Ideen auszutauschen und Innovationen im Bereich autonomer Sensorsysteme zu präsentieren. Das MCL ist stolz darauf, das Symposium gemeinsam mit der Montanuniversität Leoben organisiert zu haben. Im Fokus standen neue Entwicklungen bei integrierten Sensoren, Edge Computing, energieeffizientem Design und der Integration von IoT-Systemen – mit eindrucksvollen Beiträgen, die zeigen, wie Forschung und Zusammenarbeit die nächste Generation smarter, vernetzter Systeme vorantreiben.Ein großes Dankeschön an alle Vortragenden, Partner und Teilnehmer:innen für ihre Beiträge und den offenen Austausch. Unser gemeinsames Ziel war es auch die Rolle Österreichs als Hotspot für fortschrittliche Sensor- und IoT-Technologien zu stärken – die diskutierten Ideen werden die Lösungen von morgen gestalten.
RFID Smart Cabinets Market reached US 935 53 Million in 2023 and is expected to reach US 2 081 23 Million by 2033 growing at a CAGR of 10 2 during the forecast period 2024 2033 Initial investment for RFID ...
The growing volume of data generated by Internet of Things (IoT) devices requires real-time processing architectures that can overcome the latency and bandwidth limitations of centralized cloud infrastructures. Fog computing provides a viable alternative by bringing computational resources closer to end users. This paper presents a fog-based architecture tailored to real-time recommendation systems in a smart shopping mall scenario. The system leverages container-based virtualization using Docker, orchestrated by Kubernetes, and deployed on lightweight fog nodes such as Raspberry Pi boards. We detail the configuration of the infrastructure, its hierarchical deployment, and evaluate its feasibility using performance indicators such as data transfer time and dynamic container management. Results show that the proposed infrastructure achieves low-latency responses and supports concurrent mobile user interactions, validating its effectiveness for real-world fog-based applications.
The Ultra Wideband UWB Market is entering a high growth phase as demand surges for precise indoor positioning low power communication and secure device to device connectivity With rapid expansion across smartphones automotive systems asset tracking and IoT devices UWB ...
Summary Cloud computing has enabled the accumulation, processing, and review of large volumes of Internet of Things (IoT) data in a more efficient manner. Cloud offerings such as Software as a Service, Infrastructure as a Service, and Platform as a Service are accessible across private, hybrid, public, and community cloud environments. This paper presents a detailed study of cloud computing on IoT with emphasis on privacy concerns for both technologies. The paper examines how cloud computing and IoT have common features and explores the benefits of their union. Furthermore, paper highlights the offering of cloud being used for computing purposes in IoT and how it has the potential to be used to improve its function. The cloud‐based IoT architecture is composed of perception, network, middleware, and application layers that collect, process, and manage data gathered from IoT devices. This data is then used to add value to end‐users. Message Queuing Telemetry Transport, Constrained Application Protocol, Advance Message Queueing Protocol, and Hypertext Transfer Protocol are some of the various cloud‐based IoT standards and protocols. This research paper also explores the various potential use cases of this technology in health care, transportation, smart homes, and agriculture, as well as the challenges involved in integrating IoT with the cloud. The research paper presents two case studies that illustrate the application of cloud architecture that is IoT based on intelligent environment home automation and industrial IoT optimization. The paper concludes by outlining the potential of cloud computing applications in IoT and highlighting future scope in this emerging technology.
In this paper, we explore and validate the feasibility of using electroencephalography (EEG) based brain-computer interfaces (BCIs) to issue basic control commands to unmanned aerial vehicles (UAVs). We focus on integrating human cognitive motor commands with Internet of Things (IoT) devices, enabling hands-free UAV control. In our approach, neural signals captured during motor imagery of a right-hand upward movement and a left-hand downward movement are translated into discrete UAV instructions (conceptually analogous to "hover" and "land" commands). EEG data were acquired from a 14-channel Emotiv Epoc X headset worn by 10 participants, and features such as band power in key frequency bands were extracted. A lightweight decision tree classifier was trained and evaluated in a leave-one-participant-out (LOPO) cross-validation scheme to assess how well the model generalizes across individuals. The results indicate that certain participants can achieve classification accuracies above 65% for the two mental commands, although average accuracy across all subjects was modest (~55%). These findings highlight both the promise and the challenges of EEG-based hands-free drone control. They demonstrate the potential of neural interfaces as a bridge between human thought and machine action in IoT contexts, while also underscoring the need for improved signal processing and personalization to handle inter-subject variability. This work lays important groundwork for more advanced BCI-driven UAV control frameworks, aiming toward intuitive human-IoT interactions in high-impact domains.
The rapid growth of consumer IoT devices has introduced unprecedented challenges in trustworthy anomaly detection against AI-enabled cyber threats, requiring real-time, privacy-preserving, and scalable defense mechanisms. Traditional centralized strategies face critical limitations, including communication bottlenecks, single points of failure, and privacy vulnerabilities when processing distributed consumer data. We propose SwarmSense-DNN, a novel decentralized neural framework employing swarm intelligence for secure, cooperative anomaly detection across distributed IoT environments. The framework integrates autonomous agents with deep neural networks to form a self-organizing defense system that detects evolving anomalies without centralized coordination. It utilizes hierarchical federated learning with graph neural networks and attention mechanisms to capture local and global anomaly behaviors while ensuring data privacy. Extensive experiments demonstrate SwarmSense-DNN’s superior performance: it achieves 95.44% average detection accuracy across five benchmark datasets while reducing communication overhead by 67%. The framework maintains robust resilience against adversarial threats through differential privacy safeguards and demonstrates strong fault tolerance under node failures and AI-enabled attacks.
This paper describes a real-time Extended Reality (XR) Heart Twin based on ECG IoT measurements from wearable devices. Recent research has highlighted the importance of non-Euclidean features, such as wavelet graphs and heart meshes, in conveying information about a user's condition and enabling effective visualization in XR interfaces. We propose an end-to-end architecture to generate an XR Heart Twin from signals acquired by wearable devices. The XR Heart Twin architecture includes IoT communication protocols and middleware for data processing, extending to a web-based XR visualization tool that presents up-to-date features characterizing the user's condition. The proposed architecture enables a scalable monitoring solution suitable for cloud deployment. Examples of outputs presented via the XR interface are shown using ECG signals from publicly available datasets, processed in real time to extract XRrelevant heart features. This approach enables continuous user monitoring through a responsive XR dashboard. Experimental results demonstrate how an XR Heart Twin based on IoT measurements can be developed for healthcare systems using automated deployment capabilities on modern cloud platforms.
Chronic stress significantly affects cardiovascular, psychological, and immune health, and contributes to conditions such as hypertension and depression. Recent advances in wearable technology, such as electrocardiogram (ECG) sensors, have enabled continuous, non-invasive monitoring of physiological stress responses. This paper introduces CardioMind, a lightweight deep learning model designed for real-time stress detection using ECG data. It processes short ECG segments to extract heart rate variability (HRV) features across the time, frequency, and non-linear domains. To improve generalization across individuals, we propose subject-specific baseline normalization based on resting states. The model was trained and evaluated on the WESAD dataset, a widely used benchmark for wearable stress detection. CardioMind achieved strong performance across subjects, reaching an average accuracy of 90.8% using leave-one-subject-out validation. Its low resource requirements and real-time processing make it a strong candidate for integration into Internet of Things (IoT)-enabled wearable devices for early stress detection and mental health support. To the best of our knowledge, CardioMind is among the first models to integrate robust HRV-based features with subject-specific normalization in a lightweight architecture suitable for real-world, wearable deployment.
Efficient and evenly distributed irrigation is a crucial factor in greenhouse seedling cultivation. However, conventional sprayers utilization is often associated with water wastage and uneven water distribution. In this study, an IoTbased automatic plant-watering robot was designed and developed, which controllable via Arduino Cloud and equipped with an automatic push button to enhance operational flexibility. The robot development was performed using prototype-based design approach. The robot was built using an ESP32 as the main microcontroller, a 12 V water pump, an L298N motor driver, and a push button used as a motion limit sensor. The results demonstrated that water usage was more efficient with the watering robot, achieving savings of $\mathbf{8 1. 6 7 \%}$ compared to the conventional sprayer method. The watering process performed by the robot was proven to be more controlled and evenly distributed, although slightly higher moisture levels were observed in the center area compared to the left and right sides. In the watering time evaluation, 2 minutes were required by the robot to match the volume of water dispensed by the sprayer in 1 minute, due to the intermittent watering pattern employed by the robot. These findings highlight potential application of the proposed technology in greenhouse seedling cultivation, thus promoting better trade-off between sustainability and economic benefits in the agriculture sector.
In a vulnerable environment, an IoT device with limited resources poses a significant security threat. As IoT networks become more complex, efficiency, scalability, and adaptability become more important. A lightweight IDS, driven by machine learning, is proposed for IoT environments in this paper. A proposed approach utilizes a combination of models such as Decision Trees, K-Nearest Neighbors, Support Vector Machines, and Multi-Layer Perceptrons, as well as feature selection and traffic modelling techniques, to detect intrusions accurately and efficiently. Benchmark datasets (KDD-99, NSL-KDD, and UNSW-NB15) are used to validate the system, demonstrating its competitive performance across a variety of attack categories.
Wireless sensor networks (WSNs) are critical for industrial IoT, healthcare, and environmental monitoring, yet limited energy resources constrain their reliability and longevity. This paper presents a hybrid reinforcement learning framework to optimize simultaneous wireless information and power transfer (SWIPT) in MIMO-enabled WSNs, enabling energy-autonomous and sustainable sensing. By integrating Sequential Parametric Convex Approximation (SPCA) with SARSA (State–Action–Reward–State–Action) and Qlearning, the framework employs power-splitting and time-switching techniques to enhance routing efficiency and energy harvesting in dynamic sensor fields. A nonlinear energy harvesting model captures practical circuit constraints, such as diode sensitivity and leakage currents, improving prediction accuracy. Simulations in a 1000×1000 m² area with distributed sensor nodes show up to 20% improvement in energy efficiency and 15% increase in data throughput over baseline methods. These advancements position the framework as a transformative solution for energy-constrained WSNs in 5G/6G-enabled IoT and smart sensor applications, paving the way for sustainable large-scale deployments.
New Jersey US State The global Ultra Wideband UWB Module market in the Information Technology and Telecom category is projected to reach USD 5 50 billion by 2031 growing at a CAGR of 18 5 from 2025 to 2031 With ...
New Jersey US State The global Rain Rfid Solutions market in the Information Technology and Telecom category is projected to reach USD 3 5 billion by 2031 growing at a CAGR of 15 5 from 2025 to 2031 With rising ...
New Jersey US State The global Iot Smart Sensors Consumption market in the Information Technology and Telecom category is projected to reach USD 28 3 billion by 2031 growing at a CAGR of 12 3 from 2025 to 2031 With ...
Use code ONLINE30 to get 30 off on global market reports and stay ahead of tariff changes macro trends and global economic shifts How Large Will the Smart Lamps Market Size By 2025 In recent times the smart lamps market ...
The latest research publication on the Global Veterinary IoT Solutions Market 2025 2032 offers a comprehensive and data driven analysis of the industry landscape The report delivers actionable insights into the market s evolving dynamics backed by in depth quantitative ...
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