Internet of Things
PulseAugur coverage of Internet of Things — every cluster mentioning Internet of Things across labs, papers, and developer communities, ranked by signal.
- instance of federated learning 90%
- used by Hierarchical federated learning using access permissions 90%
- used by federated learning 80%
- used by alphaXiv 70%
- used by ScienceCast 70%
- instance of alphaXiv 70%
- used by Gotit.pub 70%
- used by cloud computing 70%
- used by 6G 70%
- uses edge computing 70%
- instance of edge computing 70%
- used by edge computing 70%
13 day(s) with sentiment data
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Quantum Kernels Explored for Machine Learning Advantages
Researchers have investigated projected quantum kernels (PQKs) as a method to address the computational challenges posed by the vast dimensionality of quantum Hilbert spaces in machine learning. Their study compares unt…
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Survey details GenAI and IoT integration across industries
A systematic survey published on arXiv explores the integration of Generative Artificial Intelligence (GenAI) with the Internet of Things (IoT). The paper highlights the growing interest in this synergy across various s…
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TinyML enables energy-efficient sensor nodes with on-device inference
A new research paper explores the energy consumption of embedded machine learning in sensor nodes, focusing on the trade-offs between on-device inference and data offloading via Low Power Wide Area Networks (LPWAN). The…
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AIoT integrates IoT and AI for enhanced business decision-making
The integration of the Internet of Things (IoT) with Artificial Intelligence (AI), known as AIoT, is enabling businesses to transform data from connected devices into actionable insights. This synergy allows organizatio…
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New Echo State Network models enhance time-series prediction and classification
Two new papers introduce advancements in Echo State Networks (ESNs), a type of reservoir computing model. The first paper proposes CGDT-CESN, a clustered ESN designed for multivariate time-series prediction that uses co…
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AI and IoT combine to enhance water infrastructure monitoring · 2 sources tracked
The integration of the Internet of Things (IoT) with artificial intelligence (AI) is enhancing water infrastructure monitoring. By collecting data from connected sensors, AI can analyze patterns to help maintenance team…
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AI's historical impact on mathematics and future IoT threats
A recent article discusses how machines first "mogged" mathematicians in relation to the Four Colour Theorem, a historical event involving Kenneth Appel and Wolfgang Haken. In a separate development, Anthropic has raise…
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AI worms pose existential threat to IoT devices
AI worms, a new type of malware, are emerging and pose a significant threat, particularly to Internet of Things (IoT) devices. These advanced worms are capable of self-propagation and adaptation, raising concerns about …
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Small AI models may soon self-replicate on IoT devices, study suggests
Researchers have conducted experiments suggesting that small AI models could achieve self-replication on commercial Internet of Things (IoT) devices within months. This development raises potential cybersecurity concern…
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AI applications span automotive manufacturing and NestJS development
Two distinct technology applications are highlighted: one focuses on leveraging AI, IoT, and RTLS to enhance visibility and traceability in modern automotive manufacturing by connecting disparate data systems. The other…
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Anthropic warns small AI models could soon self-replicate across devices
Anthropic researchers have issued a warning that small AI models could soon possess the capability to self-replicate across everyday consumer devices, such as smart TVs and ovens. These AI "worms" could form distributed…
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UAV-based maritime surveillance system uses graph learning for vessel tracking
Researchers have developed a new framework called AMGT for identifying and tracking vessels using unmanned aerial vehicles (UAVs) in maritime surveillance. This system addresses challenges like varied sensing modalities…
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New unsupervised framework detects malicious clients in federated learning
Researchers have developed WaSA, a novel unsupervised framework designed to detect malicious clients in federated learning environments. This system operates offline, before the main training process, by analyzing spect…
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IEEE launches new program to teach AI chip design principles
The Institute of Electrical and Electronics Engineers (IEEE) has launched a new educational program focused on AI chip design principles. This program aims to equip engineers with the knowledge to navigate the increasin…
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IEEE launches AI chip design program to tackle hardware complexity
The IEEE has launched a new educational program focused on AI chip design principles to address the increasing complexity and computational demands of modern AI models. This five-course program, developed by IEEE Educat…
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Connectivity gaps hinder Edge AI adoption for infrastructure
Edge AI's potential is being hampered by connectivity limitations, particularly for infrastructure systems that require reliable, persistent communication rather than just high speeds. Traditional wireless networks, opt…
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Stella Horizon EU Talks launches podcast on AI, IoT, and plant health
A new podcast series from Stella Horizon EU Talks is set to launch, focusing on the intersection of Internet of Things (IoT), AI, and remote sensing technologies. The series aims to explore how these advancements are tr…
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AI optimizes traffic signals, cutting delays and emissions
Researchers have developed a reinforcement learning (RL) system to optimize traffic signal control in urban intersections, particularly for IoT-enabled environments. The system, utilizing Proximal Policy Optimization (P…
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AI-powered monitoring for Internet of Things devices explored
This article explores how AI can be used to monitor the status and performance of Internet of Things (IoT) devices. It poses the question of how to effectively query an AI about the health of these connected devices. Th…
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Small language models evaluated for edge data classification
A new arXiv paper explores the use of small, open-source language models for classifying smart data models (SDMs) in edge computing environments. The research addresses the limitations of existing methods, which are oft…