Internet of Things
PulseAugur coverage of Internet of Things — every cluster mentioning Internet of Things across labs, papers, and developer communities, ranked by signal.
- used by MQTT 80%
- used by alphaXiv 70%
- used by federated learning 70%
- used by Gotit.pub 70%
- used by edge computing 70%
- used by 6G 70%
- uses edge computing 70%
- instance of consumer electronics 70%
- instance of Gotit.pub 70%
- instance of alphaXiv 70%
- instance of DagsHub 60%
- affiliated with cloud computing 60%
23 day(s) with sentiment data
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New Receptron model offers hardware-aware edge intelligence for IoT
Researchers have developed a novel neuromorphic-inspired classifier called the Receptron model, designed to overcome the computational and memory limitations of microcontroller units (MCUs) for edge intelligence in IoT …
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IoT surveillance tested for public safety at music festival
Researchers explored the use of Internet of Things (IoT) devices for enhancing security in public cyber-physical spaces. In a field study at a music festival, they tested various configurations of drones, closed-circuit…
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New E-SpecFormer model offers efficient RF spectrum monitoring for IoT
Researchers have developed E-SpecFormer, a new Transformer model designed for efficient RF spectrum monitoring on edge devices. This model incorporates a novel attention mechanism called LiTAN, which reduces computation…
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1NCE AI launches EU-hosted LLM gateway for IoT on AWS Bedrock
1NCE AI has launched a new gateway service designed to integrate Large Language Models (LLMs) into Internet of Things (IoT) applications. This gateway, built on LiteLLM and hosted on AWS Bedrock, aims to provide a robus…
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Survey details deep learning for video coding, targeting consumer electronics
This survey paper examines deep learning-based filtering (DLF) techniques for video coding, focusing on their application in consumer electronics. It addresses the challenge of computational complexity and power consump…
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New benchmark VEHBench evaluates LLM performance in engineering design stages
Researchers have developed VEHBench, a new diagnostic benchmark designed to evaluate Large Language Models (LLMs) in the context of designing vibration energy harvesters (VEHs). This benchmark addresses the limitations …
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MiniApps proposal aligns web standards with AI and IoT applications
MiniApps, which are web-based applications popular in Asia, combine web technologies like JavaScript, CSS, and HTML-like frameworks with native device capabilities. A proposal suggests using MiniApp principles and align…
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AI revolutionizes cybersecurity, escalating both threats and defenses
Artificial intelligence is significantly reshaping the cybersecurity landscape, acting as both a powerful tool for attackers and defenders. Cybercriminals are leveraging AI to automate sophisticated attacks like phishin…
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Visual MAE adapted for time series anomaly detection
Researchers have developed VAN-AD, a novel framework for time series anomaly detection that adapts a visual Masked Autoencoder (MAE) pretrained on ImageNet. This approach aims to improve generalization capabilities acro…
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AI leaders urge immediate action on transformative tech risks · 1 source tracked
A group of prominent figures, including Reid Hoffman, Erik Brynjolfsson, and Yoshua Bengio, have signed an open letter titled "We Must Act Now" cautioning about the rapid advancements in artificial intelligence. The let…
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AI-Powered TuxBot v3 IoT Botnet Detailed
TuxBot v3 is an Internet of Things (IoT) botnet that has been developed using AI. The botnet's creation and functionality are detailed, including any bugs or disclaimers associated with its development.
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New research explores advanced edge intelligence frameworks and optimizations
Recent research papers explore advancements in edge intelligence, focusing on integrating AI with edge computing. One paper introduces Clustered Edge Intelligence (CEI), an intelligence-centric framework for managing an…
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New Lightweight AI Model Enhances Anomaly Detection on Edge Devices
Researchers have developed a new Lightweight MultiScale AutoEncoder (LMSAE) designed for anomaly detection in resource-constrained edge devices. This model utilizes a discrete wavelet transform to extract multi-scale fe…
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New lightweight AI model excels at anomaly detection on edge devices
Researchers have developed a new Lightweight MultiScale AutoEncoder (LMSAE) designed for anomaly detection on resource-constrained edge devices. This model utilizes discrete wavelet transforms to extract multi-scale fea…
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AI revolutionizes industries, creating new capabilities and expanding cyber risks
Artificial intelligence is rapidly evolving into essential infrastructure, accelerating at an unprecedented rate and impacting all industries. The current "Acceleration Era" sees AI converging with other advanced techno…
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AI-Powered Smart Agriculture Platform Features Farm Insights and IoT Integration
This item describes a smart agriculture platform that leverages AI for farm management. The platform offers features such as AI-driven farming insights, crop monitoring, and IoT integration. It also includes analytics d…
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AI Agents Automate IoT Vulnerability Exploitation with 95% Success Rate
Researchers have developed VEXAIoT, an AI agent framework designed to autonomously discover and exploit vulnerabilities in Internet of Things (IoT) systems. This framework utilizes Large Language Model (LLM) agents to p…
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AI agents automate IoT vulnerability exploitation with 95% success rate
Researchers have developed VEXAIoT, an autonomous multi-agent framework designed to discover and exploit vulnerabilities in Internet of Things (IoT) devices. This system leverages Large Language Model (LLM) agents and o…
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Federated Koopman Learning Framework Enhances IoT Anomaly Detection
Researchers have developed FedKAD, a novel federated learning framework designed for anomaly detection in Internet of Things (IoT) systems. This approach utilizes lightweight Koopman representations to learn normal temp…
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New AI framework uses physics-inspired energy models for IoT system explanations
A new framework inspired by statistical mechanics offers a novel approach to explaining the behavior of cyber-physical IoT systems. Unlike traditional methods that focus on correlations or require explicit causal graphs…