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Ubiquitous wireless technologies like Wi-Fi, Bluetooth, and 5G rely on radio frequency (RF) signals to send and receive data. A new prototype of an energy harvesting module—developed by a team led by scientists from the National University of Singapore (NUS)—can now convert ambient or "waste" RF signals into direct current (DC) voltage. This can be used to power small electronic devices without the use of batteries.
LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.
In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability.
This research studies the application of machine learning to enhance the security of data transport for the. of Things (IoT). Conventional encryption methods might not be adequate in Internet of Things scenarios because of the fluidity and limited resources presented by these contexts. In the course of our research, we make use of fictional Internet of Things data in order to evaluate the effectiveness of machine learning models for anomaly detection, intrusion detection, and data encryption. When it comes to resolving difficulties related to the security of the internet of things (IoT), the findings suggest that machine learning is better to traditional cryptography methodologies. The Internet of Things (IoT) security is going to be strengthened by the introduction of new strategies, dynamic security frameworks, privacy-preserving methodologies, and practical implementations in the near future. It is possible that the implementation of machine learning will make the ecosystem of the Internet of Things (IoT) more safe and efficient, which would, in turn, foster innovation across a wide range of industries.
WESTFORD, Mass., June 28, 2024 /PRNewswire/ -- According to SkyQuest, the global Automotive IoT Market size was valued at USD 102.3 billion in 2022 and is poised to grow from USD 125.32 billion
Chicago, June 28, 2024 (GLOBE NEWSWIRE) -- The global LoRa and LoRaWAN IoT Market size is projected to grow from USD 8.0 billion in 2024 to USD 32.7...
This article delves into the rise of IoT in the AV industry, and how it is transforming various aspects of AV solutions.
UWB has been in existence for several years, but it was only a few years ago that it transitioned from a specialized niche to more mainstream applications. Recent market data indicate a rapid increase in the popularity of UWB in consumer products, such as smartphones and smart home devices, as well as automotive and industrial real-time location systems. The challenge of achieving accurate positioning in indoor environments arises from various factors such as distance, location, beacon density, dynamic surroundings, and the density and type of obstacles. This research used MFi-certified UWB beacon chipsets and integrated them with a mobile application dedicated to iOS by implementing the near interaction accessory protocol. The analysis covers both static and dynamic cases. Thanks to the acquisition of measurements, two main candidates for indoor localization infrastructure were analyzed and compared in terms of accuracy, namely UWB and LIDAR, with the latter used as a reference system. The problem of achieving accurate positioning in various applications and environments was analyzed, and future solutions were proposed. The results show that the achieved accuracy is sufficient for tracking individuals and may serve as guidelines for achievable accuracy or may provide a basis for further research into a complex sensor fusion-based navigation system. This research provides several findings. Firstly, in dynamic conditions, LIDAR measurements showed higher accuracy than UWB beacons. Secondly, integrating data from multiple sensors could enhance localization accuracy in non-line-of-sight scenarios. Lastly, advancements in UWB technology may expand the availability of competitive hardware, facilitating a thorough evaluation of its accuracy and effectiveness in practical systems. These insights may be particularly useful in designing navigation systems for blind individuals in buildings.
The intersection of the Internet of Things (IoT) and the circular economy (CE) creates a revolutionary opportunity to redefine economic sustainability and resilience. This review article explores the intricate interplay between IoT technologies and CE economics, investigating how the IoT transforms supply chain management, optimises resources, and revolutionises business models. IoT applications boost efficiency, reduce waste, and prolong product lifecycles through data analytics, real-time tracking, and automation. The integration of the IoT also fosters the emergence of inventive circular business models, such as product-as-a-service and sharing economies, offering economic benefits and novel market opportunities. This amalgamation with the IoT holds substantial implications for sustainability, advancing environmental stewardship and propelling economic growth within emerging CE marketplaces. This comprehensive review unfolds a roadmap for comprehending and implementing the pivotal components propelling the IoT’s transformation toward CE economics, nurturing a sustainable and resilient future. Embracing IoT technologies, the authors embark on a journey transcending mere efficiency, heralding an era where economic progress harmonises with full environmental responsibility and the CE’s promise.
The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data [...]
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The Internet of Things IoT has revolutionized various industries and logistics is no exception IoT powered logistics involves the integration of IoT technology into the logistics sector to enhance operational efficiency reduce costs and improve customer satisfaction IoT enables real ...
Researchers have utilized AI and IoT voice devices to advance sports training feature recognition, employing sensors for real-time data transmission and analysis. This approach successfully identifies movement patterns and predicts athlete states, enhancing training effectiveness.
GenAI's creative and analytical capabilities, integrated with IoT devices' connectivity and data insights, could lead to remarkable advancements.
Siemens has unveiled a digital twin and AI-powered urban development project within Germany’s capital, Berlin.
Samsung has launched a new credit card with Internet of Things (IoT) features, thanks to its Bluetooth LE connectivity. If ...
Exceptional customer experiences are crucial in modern business, requiring CX transformation and robust technical infrastructure.
RAIN Alliance, the non-profit industry organisation supporting the development and adoption of Ultra High Frequency (UHF) Radio Frequency Identification (RFID), has announced the launch of its extensive RAIN RFID Training Programme.
Leveraging IoT technologies to improve efficiency of an operation, address any challenges and increase profitability is happening in numerous industries, and the oil and gas sector is no exception. What IoT in oil and gas looks like is another question. According to a report put out by Berg Insight earlier this year and reported on
(Bild: Infineon) Das Internet der Dinge wird immer wichtiger, doch wie jeder Trend ist auch das IoT mit neuen Herausforderungen verbunden. Edge AI bietet hier einige Vorteile, denn die Technologie kann Daten lokal verarbeiten und unterstützt dadurch Echtzeitanwendungen. Doch auch bei Edge AI sind einige Hürden zu überwinden.
Energous today announced new partnerships with Annukin, Ecobyte, and Peak Technologies to help accelerate adoption of the company’s solutions worldwide....
Forrester identified top emerging technologies in its latest report, highlighting their potential benefits, use cases, and risks for businesses. The post AI,
RFID Tags Market Expected to Reach $15 Billion by 2032 — Allied Market Research
Im Juni 1974 wurde zum ersten Mal ein Barcode auf einem Produkt an einer Supermarktkasse gescannt. Experten erwarten, dass der Code in einigen Jahren abgelöst sein wird.
Many of these features are still pretty novel for Android users.