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This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable …
Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.
Mehr Hightech im Skispringen: Die FIS führt ab der kommenden Saison eine neue Anzug-Regel ein. Kontrolliert wird diese mittels Mikrochips.
The federal government has partnered with Mastercard to support 1million farmers across the continent through financial inclusion and contactless payment.
According to Value Market Research the global demand for the RFID Market size is projected to experience substantial growth anticipated to reach a market size of approximately USD 59 88 billion by 2032 up from USD 20 38 billion in ...
According to Value Market Research the global demand for the Internet of Things IoT in healthcare Market Size is expected to experience remarkable growth with the market size projected to reach approximately USD 1 026 77 billion by 2032 up ...
The Europe IoT market size was valued at 2 19 billion in 2021 and is projected to reach 12 30 billion by 2031 growing at a CAGR of 19 0 from 2022 to 2031 The cloud based segment is expected ...
With its new all-in-one solution »Secora Pay Bio« for biometric payment cards Infineon wants to enhance convenience and trust of biometric contactless payment. The solution uses a SoC secure element and a fingerprint sensor within a Biometric Coil on Module package.
Mental distress-induced imbalances in autonomic nervous system activities adversely affect the electrical stability of the cardiac system, with heart rate variability (HRV) identified as a related indicator. Traditional HRV measurements use electrocardiography (ECG), but impulse radio ultra-wideband (IR-UWB) radar has shown potential in HRV measurement, although it is rarely applied to psychological studies. This study aimed to assess early high levels of mental distress using HRV indices obtained using radar through modified signal processing tailored to reduce phase noise and improve positional accuracy. We conducted 120 evaluations on 15 office workers from a software startup, with each 5 min evaluation using both radar and ECG. Visual analog scale (VAS) scores were collected to assess mental distress, with evaluations scoring 7.5 or higher classified as high-mental distress group, while the remainder formed the control group. Evaluations indicating high levels of mental distress showed significantly lower HRV compared to the control group, with radar-derived indices correlating strongly with ECG results. The radar-based analysis demonstrated a significant ability to differentiate high mental distress, supported by receiver operating characteristic (ROC) analysis. These findings suggest that IR-UWB radar could be a supportive tool for distinguishing high levels of mental stress, offering clinicians complementary diagnostic insights.
When LoRaWAN networks are deployed in complex environments with buildings, jungles, and other obstacles, the communication range of LoRa signals experiences a notable reduction, primarily due to multipath propagation, fading, and interference. With the flight advantage of height, mobility, and flexibility, UAV can provide line-of-sight (LOS) communication or more reliable communication in many scenarios, which can be used to enhance the LoRaWAN network’s performance. In this paper, a novel UAV-assisted LoRaWAN network is designed and implemented. Specifically, a UAV-assisted LoRaWAN network system architecture is proposed to improve the LoRaWAN network coverage and communication reliability, in which the UAV architecture of “UAV + Remote Controller + Server” is combined with the traditional LoRaWAN architecture of “End-Device + Gateway + Server”. Then, the implementation of the UAV gateway and the remote controller relay is presented, which play the important role of forwarding LoRaWAN frames transparently in our proposed architecture. In detail, the UAV gateway is developed based on the UAV’s PSDK and classical LoRa packet forwarder, and the remote controller relay is developed based on UAV’s MSDK. The experimental results show that the network coverage and communication reliability of our proposed LoRaWAN network have been significantly improved, effectively supporting a wide range of LoRaWAN applications. Specifically, when the end-device is deployed 1.3 km away with numerous obstacles in the propagation environment, with the UAV altitude advantage and the remote controller’s relay capability, the proposed system achieved an SNR of 5 db and an RSSI of −80 dbm with a packet loss rate of 3%. In comparison, the ground gateway only achieved an SNR of −16 db and an RSSI of −113 dbm with a packet loss rate of 73%.
Verizon Business is supplying German automotive startup Vay Technology with 5G and IoT airtime for its rental fleet of remotely operated EVs in Las Vegas.
… conditions by controlling their Samsung air conditioners, air purifiers … conditioners, and more. Samsung allows access to those … -compliant infotainment units, Samsung is therefore going to … Apple's old trick of making everything itself. But Samsung …
The government on Wednesday launched the first phase of its flagship US$14 billion stimulus handout scheme, which will eventually see an estimated 45 million people receive 10,000 baht each, saying it would spark economic activity.
The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.
The travel and hospitality sectors have long been anchored in traditional payment methods, like cash and credit cards. As consumers and employees voice
MANILA, Philippines — The imposition of fines against motorists entering toll highways without radio frequency identification (RFID) tags has been moved to January 2025, said the Department of
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On September 20, Rocket Lab launched five satellites for the French technology company. Rocket Lab's Electron rocket launched from New Zealand at 7:01 PM ET (Saturday, September 21 at 23:01 GMT or 11:01 NZ time). The satellites were placed into a 400-mile (643-kilometer) high orbit about 66 minutes after launch,…
UWB can take advantage of lateral innovation to enable the smartphone ecosystem to access untapped value
This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.
The Internet of Things (IoT), introduced by Kevin Ashton in the late 1990s, has transformed technology usage globally, enhancing efficiency and convenience but also posing significant security challenges. With the proliferation of IoT devices expected to exceed 29 billion by 2030, securing these devices is crucial. This study proposes an optimized 1D convolutional neural network (1D CNN) model for effectively classifying IoT security data. The model architecture includes input, convolutional, self-attention, and output layers, utilizing GELU activation, dropout, and normalization techniques to improve performance and prevent overfitting. The model was evaluated using the CIC IoT 2023, CIC-MalMem-2022, and CIC-IDS2017 datasets, achieving impressive results: 98.36% accuracy, 100% precision, 99.96% recall, and 99.95% F1-score for CIC IoT 2023; 99.90% accuracy, 99.98% precision, 99.97% recall, and 99.96% F1-score for CIC-MalMem-2022; and 99.99% accuracy, 99.99% precision, 99.98% recall, and 99.98% F1-score for CIC-IDS2017. These outcomes demonstrate the model’s effectiveness in detecting and classifying various IoT-related attacks and malware. The study highlights the potential of deep-learning techniques to enhance IoT security, with the developed model showing high performance and low computational overhead, making it suitable for real-time applications and resource-constrained devices. Future research should aim at testing the model on larger datasets and incorporating adaptive learning capabilities to further enhance its robustness. This research significantly contributes to IoT security by providing advanced insights into deploying deep-learning models, encouraging further exploration in this dynamic field.
By Magda Dabrowska The global cellular IoT connections climbed 24% YoY in 2023 to reach 3.3 billion, despite challenges in the cellular IoT module sector, according to Counterpoint’s latest IoT Connections Tracker. Looking ahead, the market The post Global cellular IoT connections to reach 6. billion by 2030 appeared first on IoT Now News – How to run […]
BMEL's RePack network supports the COPPA innovation project led by the SKZ
“The market's expansion is largely driven by the need for efficient energy management solutions to address rising energy demands and regulatory pressures...
The number of contactless payments in Canada increased by 17% in 2023 to reach 63% of all in-store transactions.
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