IOT

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arXiv:2512.07333v1 Announce Type: new Abstract: Maternal mortality in Sub-Saharan Africa remains critically high, accounting for 70% of global deaths despite representing only 17% of the world population. Current digital health interventions typically deploy artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies in isolation, missing synergistic opportunities for transformative healthcare delivery. This paper presents IyaCare, a proof-of-concept integrated platform that combines predictive risk assessment, continuous vital sign monitoring, and secure health records management specifically designed for resource-constrained settings. We developed a web-based system with Next.js frontend, Firebase backend, Ethereum blockchain architecture, and XGBoost AI models trained on maternal health datasets. Our feasibility study demonstrates 85.2% accuracy in high-risk pregnancy prediction and validates blockchain data integrity, with key innovations including

arXiv:2512.06747v1 Announce Type: new Abstract: Large Language Models (LLMs) are emerging as powerful enablers for autonomous reasoning and natural-language coordination in unmanned aerial vehicle (UAV) swarms operating within Internet of Things (IoT) environments. However, existing LLM-driven UAV systems process sensitive operational data in plaintext, exposing them to privacy and security risks. This work introduces PrivLLMSwarm, a privacy-preserving framework that performs secure LLM inference for UAV swarm coordination through Secure Multi-Party Computation (MPC). The framework incorporates MPC-optimized transformer components with efficient approximations of nonlinear activations, enabling practical encrypted inference on resource-constrained aerial platforms. A fine-tuned GPT-based command generator, enhanced through reinforcement learning in simulation, provides reliable instructions while maintaining confidentiality. Experimental evaluation in urban-scale simulations

arXiv:2512.06148v1 Announce Type: new Abstract: A Digital Twin (DT) framework to enhance carbon-based gas plume monitoring is critical for supporting timely and effective mitigation responses to environmental hazards such as industrial gas leaks, or wildfire outbreaks carrying large carbon emissions. We present AIMNET, a one-of-a-kind DT framework that integrates a built-in-house Internet of Things (IoT)-based continuous sensing network with a physics-based multi-scale weather-gas transport model, that enables high-resolution and real-time simulation and detection of carbon gas emissions. AIMNET features a three-layer system architecture: (i) physical world: custom-built devices for continuous monitoring; (ii) bidirectional information feedback links: intelligent data transmission and reverse control; and (iii) digital twin world: AI-driven analytics for prediction, anomaly detection, and dynamic weather-gas coupled molecule transport modeling. Designed for scalable, energy-efficient

The fewer entry points you leave open, the more secure your smart home will be. Here's my guide.

arXiv:2512.05468v1 Announce Type: new Abstract: Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset

Getting into smart home technology doesn't have to be complicated. All sorts of gear can smoothly slot right into uses already filled by non-smart devices.

Samsara Inc. (NYSE:IOT) Q3 2026 Earnings Call Transcript December 4, 2025 Samsara Inc. beats earnings expectations. Reported EPS is $0.15, expectations were $0.1182. Mike Chang: Good afternoon, and welcome to Samsara’s Third Quarter Fiscal 2026 Earnings Call. I’m Mike Chang, Samsara’s Vice President of Corporate Development and Investor Relations. Joining me today are Samsara Chief […]

The fewer entry points you leave open, the more secure your smart home will be.

Chamberlain blocks smart home integrations with its garage door openers — again  The Verge

Chamberlain blocks smart home integrations with its garage door openers — again  The Verge

The MyQ garage door controller is an accessory that can connect Chamberlain Group garage door openers to the MyQ app. The MyQ platform used to support more smart home integrations, but has increasingly become focused on those that require subscriptions. Garage door opener manufacturer The Chamberlain Group has launched a new version of the communication platform that powers its connected garage door openers - and it's bad news for smart home users. The new Security+ 3.0 platform, launching alongside Chamberlain's latest openers, shuts down the workarounds that third-party accessory makers such as Tailwind, Meross, and Ratgdo developed to let you integrate your garage door with Apple Home, Home Assistant, Amazon Alexa, Google Home, and others. Instead, you're pushed into Chamberlain's ad-stuffed MyQ app and a short list of partners

The Dreame Matrix10 Ultra might be the perfect bot for big, complex homes or even commercial properties.

Brown Advisory, an investment management company, released its “Brown Advisory Large-Cap Growth Strategy” third-quarter 2025 investor letter. A copy of the letter can be downloaded here. The strategy returned -0.88% (net) during the third quarter, underperforming the benchmark, the Russell 1000 Growth Index. Even though the portfolio has significant exposure to AI, its underweight to […]

Smart home hacking exists, but it's probably not the threat you think it is. Here are the facts, practices to keep you safe and more.

CRM, SNOW, PATH, IRBT, NFLX were among the stocks seen trending among investors on Wednesday, Dec. 3, 2025. read more

Jaures Yip / CNBC: IoT security device maker Verkada hit a $5.8B valuation after new funding, source says totaling $100M, led by CapitalG; it passed $1B in annualized bookings  —  Security technology startup Verkada has reached a $5.8 billion valuation after a new funding round led by CapitalG, Alphabet's venture capital arm, announced Wednesday.

As of Dec. 3, you can get the iRobot Roomba Plus 405 (G185) Combo Robot Vacuum & Mop for $399, down from $799.99.

arXiv:2512.02272v1 Announce Type: new Abstract: This paper proposes a hardware-aware intrusion detection system (IDS) for Internet of Things (IoT) and Industrial IoT (IIoT) networks; it targets scenarios where classification is essential for fast, privacy-preserving, and resource-efficient threat detection. The goal is to optimize both tree-based machine learning (ML) models and compact deep neural networks (DNNs) within strict edge-device constraints. This allows for a fair comparison and reveals trade-offs between model families. We apply constrained grid search for tree-based classifiers and hardware-aware neural architecture search (HW-NAS) for 1D convolutional neural networks (1D-CNNs). Evaluation on the Edge-IIoTset benchmark shows that selected models meet tight flash, RAM, and compute limits: LightGBM achieves 95.3% accuracy using 75 KB flash and 1.2 K operations, while the HW-NAS-optimized CNN reaches 97.2% with 190 KB flash and 840 K floating-point operations (FLOPs). We

If you’ve ever thought about getting a robot vacuum but didn’t want to spend “flagship money,” this is the kind of deal that makes it a lot easier to say yes. The iRobot Roomba 105 Vac Robot Vacuum is currently $149.00 as a limited-time deal, down from $299.99—a full 50% off. You’re getting proper power-lifting […] The post This Roomba robot vacuum is 50% off and actually makes “set it and forget it” cleaning realistic appeared first on Digital Trends.

Smart home products are all about making everyday life a little easier, which is why they're some of the best and most thoughtful gifts you can give. Some people go all out building a multi-gadget IoT kingdom (like our resident smart home expert, Jen Pattison Tuohy), but a smart home is whatever your recipient wants it to be. It can be as simple as wanting a cheap switch-flipping bot to brew you a pot of coffee before you exit the shower in the morning. Having tested many smart home gadgets over the years, we pooled our knowledge to assemble our favorite recommendations for this gift guide. There are practical (and sometimes pricey) picks l … Read the full story at The Verge.

arXiv:2512.01824v1 Announce Type: new Abstract: The growth of the Internet of Things has enabled a new generation of applications, pushing computation and intelligence toward the network edge. This trend, however, exposes challenges, as the heterogeneity of devices and the complex requirements of applications are often misaligned with the assumptions of traditional routing protocols, which lack the flexibility to accommodate application-layer metrics and policies. This work addresses this gap by proposing a software framework that enhances routing flexibility by dynamically incorporating application-aware decisions. The core of the work establishes a multi-hop Wi-Fi network of heterogeneous devices, specifically ESP8266, ESP32, and Raspberry Pi 3B. The routing layer follows a proactive approach, while the network is fault-tolerant, maintaining operation despite both node loss and message loss. On top of this, a middleware layer introduces three strategies for influencing routing

arXiv:2512.00998v1 Announce Type: new Abstract: Continuous energy monitoring is essential for identifying potential savings and predicting the energy requirements of buildings. Energy meters are often located in underground spaces that are difficult to reach with wireless technology. This paper presents an experimental study comparing different Low Power Wide Area Networks (LPWAN) technologies in terms of building penetration and radio coverage. The technologies Low Power Long Range Wide Area Networks (LoRaWAN), Narrow Band Internet of Things (NB-IoT), Sigfox 0G and Wireless Smart Ubiquitous Networks (Wi-SUN) are evaluated experimentally. It also proposes a distributed hybrid IoT architecture that combines multiple LPWAN technologies using an abstraction layer to optimize cost and coverage. Communication is message-based using the publish-subscribe messaging pattern. It is implemented using the MQTT protocol. The abstraction layer decodes the proprietary binary data and converts it to

arXiv:2512.00321v1 Announce Type: new Abstract: Power consumption has become a critical aspect of modern life due to the consistent reliance on technological advancements. Reducing power consumption or following power usage predictions can lead to lower monthly costs and improved electrical reliability. The proposal of a holistic framework to establish a foundation for IoT systems with a focus on contextual decision making, proactive adaptation, and scalable structure. A structured process for IoT systems with accuracy and interconnected development would support reducing power consumption and support grid stability. This study presents the feasibility of this proposal through the application of each aspect of the framework. This system would have long term forecasting, short term forecasting, anomaly detection, and consideration of qualitative data with any energy management decisions taken. Performance was evaluated on Power Consumption Time Series data to display the direct

arXiv:2512.00251v1 Announce Type: new Abstract: The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service (DoS) attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional Generative Adversarial Network framework enhanced with Sinkhorn Divergence, tailored for robust anomaly detection in IoT edge environments. The framework incorporates CTGAN-based synthetic data augmentation to address class imbalance and leverages Sinkhorn Divergence as a geometry-aware loss function to improve training stability and reduce mode collapse. The model is evaluated on exploitative attack subsets from the CICDDoS2019 dataset and compared against baseline deep learning and GAN-based approaches. Results show that SD-CGAN achieves superior detection accuracy, precision, recall, and F1-score while maintaining computational efficiency

arXiv:2512.00161v1 Announce Type: new Abstract: LoRaWAN is a leading standard and technology for low-power, long-range Internet-of-Things (IoT) communications. However, its single-hop architecture results in limited effective range and excessive power consumption for end devices, especially when deployed in large, remote and RF-challenged environments. Existing solutions are either incompatible with LoRaWAN, or limit relaying to a single hop. We present LIMA, a protocol for augmenting an existing or new LoRaWAN deployment with a mesh network of LIMA Routers. LIMA increases the effective coverage range well beyond the maximum LoRa range via multi-hopping, and significantly reduces the energy consumed by end-devices. LIMA requires no changes to the end-device, the servers or the LoRaWAN standard. LIMA builds routes using reverse path forwarding, tunnels LoRaWAN messages over LIMA, provides transparent extension of the existing Adaptive Data Rate (ADR), and suppresses duplicate

arXiv:2512.00035v1 Announce Type: new Abstract: The increasing heterogeneity of hardware and software in the Internet of Things (IoT) poses a major challenge for the portability, maintainability and deployment of software on devices with limited resources. WebAssembly (WASM), originally designed for the web, is increasingly recognized as a portable, secure and efficient runtime environment that can overcome these challenges. This paper explores the feasibility of using WASM in embedded IoT systems by evaluating its performance, memory footprint and energy consumption on three representative microcontrollers: the Raspberry Pi Pico, the ESP32 C6 and the nRF5340. Two lightweight WASM runtimes, WAMR and wasm3, are compared with the native C execution. The results show that while the native execution remains superior in terms of speed and energy efficiency, WASM offers acceptable trade-offs in return for cross-platform compatibility and sandbox execution. The results highlight that WASM is

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In the future, LEDs could serve as data transmitters and energy sources in holistic Internet of Things (IoT) networks. The University of Oulu’s professor Marcos Katz leads SUPERIOT, a Horizon Europe project that aims to develop a flexible IoT system based on the dual-mode use of optical and radio communications. The system will be sustainably powered by printed electronic components made from low-cost, bio-friendly materials. “The SUPERIOT project addresses a critical challenge for the 6G era: how to design IoT systems that meet the demanding performance requirements of future networks while remaining truly sustainable,” Katz said. “As IoT adoption accelerates, projections suggest that hundreds of...

Smart home devices from brands including Amazon, Philips Hue and Ring have hit record-low prices for Cyber Monday, so grab them while you can.

arXiv:2511.23252v1 Announce Type: new Abstract: Federated Learning (FL) offers a promising approach to collaboratively train machine learning models without centralizing raw data, yet its scalability is often throttled by excessive communication overhead. This challenge is magnified in Internet of Things (IoT) environments, where devices face stringent bandwidth, latency, and energy constraints. Conventional secure aggregation protocols, while essential for protecting model updates, frequently require multiple interaction rounds, large payload sizes, and per-client costs rendering them impractical for many edge deployments. In this work, we present Hyb-Agg, a lightweight and communication-efficient secure aggregation protocol that integrates Multi-Key CKKS (MK-CKKS) homomorphic encryption with Elliptic Curve Diffie-Hellman (ECDH)-based additive masking. Hyb-Agg reduces the secure aggregation process to a single, non-interactive client-to-server transmission per round, ensuring that

arXiv:2511.22359v1 Announce Type: new Abstract: Modern networked systems rely on complex software stacks, which often conceal vulnerabilities arising from intricate interdependencies. A Software Bill of Materials (SBOM) is effective for identifying dependencies and mitigating security risks. However, existing SBOM solutions lack precision, particularly in binary analysis and non-package-managed languages like C/C++. This paper introduces UniBOM, an advanced tool for SBOM generation, analysis, and visualisation, designed to enhance the security accountability of networked systems. UniBOM integrates binary, filesystem, and source code analysis, enabling fine-grained vulnerability detection and risk management. Key features include historical CPE tracking, AI-based vulnerability classification by severity and memory safety, and support for non-package-managed C/C++ dependencies. UniBOM's effectiveness is demonstrated through a comparative vulnerability analysis of 258 wireless router

arXiv:2511.21932v1 Announce Type: new Abstract: Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computational bottlenecks limit real-time deployment at scale. We present a quantum autoencoder (QAE) framework that compresses network traffic into discriminative latent representations and employs quantum support vector classification (QSVC) for intrusion detection. Evaluated on three datasets, our approach achieves improved accuracy on ideal simulators and on the IBM Quantum hardware demonstrating practical quantum advantage on current NISQ devices. Crucially, moderate depolarizing noise acts as implicit regularization, stabilizing training and enhancing generalization. This work establishes quantum machine learning as a viable, hardware-ready solution for real-world cybersecurity challenges.

arXiv:2511.21857v1 Announce Type: new Abstract: This study investigates the effectiveness and efficiency of two variants of the XGBoost regression model, the full-capacity and lightweight (tiny) versions, for predicting the concentrations of carbon monoxide (CO) and nitrogen dioxide (NO2). Using the AirQualityUCI dataset collected over one year in an urban environment, we conducted a comprehensive evaluation based on widely accepted metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), and the coefficient of determination (R2). In addition, we assessed resource-oriented metrics such as inference time, model size, and peak RAM usage. The full XGBoost model achieved superior predictive accuracy for both pollutants, while the tiny model, though slightly less precise, offered substantial computational benefits with significantly reduced inference time and model storage requirements. These results demonstrate the feasibility of deploying

arXiv:2511.21842v1 Announce Type: new Abstract: The rapid expansion of Internet of Things (IoT) deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly Detection Systems (ADS) in identifying emerging and zero-day threats, this study investigates the effectiveness of two unsupervised anomaly detection techniques, Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM), using the TON_IoT thermostat dataset. A comprehensive evaluation was performed based on standard metrics (accuracy, precision, recall, and F1-score) alongside critical resource utilization metrics such as inference time, model size, and peak RAM usage. Experimental results revealed that IF consistently outperformed OC-SVM, achieving higher detection accuracy, superior precision, and recall, along with a significantly

Cyber Monday robot vacuum deals are in full swing, like the Dreame Aqua10 Ultra Roller and Roborock Saros 10R both under $1,000.

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The best of today's Cyber Monday Ring Doorbell deals, curated by smart home experts

This robot is designed to do your chores for you -- but it might need some help from a remote operator.

Here's what the Google Pixel Tablet does better (and worse) than stationary displays like the Nest and Echo Hub.

Cyber Monday robot vacuum deals are in full swing, like the Dreame Aqua10 Ultra Roller and Roborock Saros 10R both under $1,000.

Your guide to the top robot vacuums (and mops) for hassle-free cleaning, as recommended by experienced reviewers.

Black Friday robot vacuum deals are in full swing, like the Dreame Aqua10 Ultra Roller and Roborock Saros 10R both under $1,000.

Grace Kay / Business Insider: Sunday Robotics, which debuted its Memo home robot on Nov. 19, hired at least 10 former Tesla employees, including engineers who worked on Autopilot and Optimus  —  - Sunday Robotics hired several former Tesla staff members to work on its Memo home robot.  — Some Sunday Robotics staff …

I wasn't convinced a robot vacuum was right for my home, but this one proved me wrong.

This year's sales are delivering some of the best robot vacuum deals I've ever seen – here's my pick of the discounts.

The Nuki smart lock is seeing a rare discount this Black Friday. ‘Tis the season to score some serious bargains on smart home gear. With Black Friday in full swing and Cyber Monday coming up fast behind, this is the time to grab a bargain on that smart lock you’ve been eyeing or help your family members enjoy the benefits of the smart home with a connected stocking stuffer or two. There are lots of great deals out there on everything from robot vacuums to holiday lighting, but here I’ve rounded up the stellar deals I spotted on some of my favorite smart home gear. Smart lighting deals Aqara LED Ceiling Light T1M This elegant Aqara ceiling light is $40 off and offers tunable white light with a color-changing LED ring that can be used for notifications. An Aqara hub is required, but the hardwired light works with Matter, Apple Home, Alexa, and Google. Read my

Researchers at Science Tokyo designed the world’s first automatic and adaptive, dual-mode LED-based optical wireless power transmission system, which operates seamlessly under both dark and bright lighting conditions. The system features AI-powered image recognition and can efficiently power multiple devices in order without interruption. Because the system is LED-based, it offers a low-cost and safe solution ideal for building a sustainable indoor Internet of Things infrastructure. Optical wireless power transmission (OWPT) is a technology that reduces the need for traditional power delivery methods such as batteries and cable connections. In OPWT, energy is transmitted through free space, without physical wires, by converting...

Save $450 on the Roborock Qrevo Edge S5A for Black Friday. Now priced at $549.99 at Amazon.

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The Dreame X50 Ultra conquered chairs and stairs where many others have failed. It's a whopping $800 off for Black Friday.

Robot vacuums can be a huge help, keeping your floors clean regularly without much extra work on your part. Black Friday deals often include some of our favorite robovacs, and this year is shaping up to be no different. iRobot's entry-level Roomba 104 Vac robot vacuum is available for 48 percent off right now, bringing it down to a record low of $130. A number of other Roombas are on sale for Black Friday, too. In iRobot's lineup of robot vacuums, the Roomba 104 sits on the low end, adept at vacuuming up dust and hair, but without the mopping ability of its more expensive Max, Plus or Combo counterparts. The Roomba 104 Vac makes for a great first robot vacuum all the same, though, because of its four levels of powerful suction, and easy-to-use app. Like iRobot's other vacuums, the Roomba 104 maps and navigates your home with LiDAR, which helps it avoid obstacles. And using the Roomba Home app, you can schedule it to clean specific rooms, and even spot-clean particularly

arXiv:2511.21336v1 Announce Type: new Abstract: The convergence of the Internet of Things (IoT) and 5G technologies is transforming modern communication systems by enabling massive connectivity, low latency, and high-speed data transmission. In this evolving landscape, Content-Centric Networking (CCN) is emerging as a promising alternative to traditional Internet Protocol (IP)-based architectures. CCN offers advantages such as in-network caching, scalability, and efficient content dissemination, all of which are particularly well-suited to the constraints of the IoT. However, deploying content-centric approaches in 5G-based IoT environments introduces significant security challenges. Key concerns include content authentication, data integrity, privacy protection, and resilience against attacks such as spoofing and cache poisoning. Such issues are exacerbated by the distributed, mobile, and heterogeneous nature of IoT and 5G systems. In this survey, we review and classify existing

arXiv:2511.21156v1 Announce Type: new Abstract: In space-air-ground integrated networks (SAGIN)-enabled IoT networks, secure access has become a significant challenge due to the increasing risks of eavesdropping attacks. To address these threats to data confidentiality, this paper proposes a Digital Twin (DT)-driven secure access strategy. The strategy leverages a virtual replica of the physical SAGIN environment within the DT framework to continuously assess dynamic eavesdropping risks by quantifying secrecy capacity. Operating within this DT framework, an evolutionary game model dynamically balances the DT-updated secrecy capacity against queuing delay, steering IoT devices toward more secure and efficient access decisions. Furthermore, a novel distributed algorithm, integral to the DT operation, is developed to obtain the equilibrium access strategy for each device in a scalable manner. Simulation results demonstrate that the proposed DT-based approach substantially improves the

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TCL’s IoT-powered solutions integrate seamlessly with smart home platforms, enabling users to schedule usage, detect faults, and track energy consumption patterns directly from their smartphones The post AI and IoT take centre stage as Gulf cooling demand surges appeared first on Gulf Business.

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Rabbi Menachem Perl, head of the Tzomet Institute, says it is halachically possible, as long as there is no direct activation on Shabbat, strict conditions are observed and all preparations are made in advance.

With vet bills skyrocketing, could preventative pet care using advances in AI and IoT help pet parents understand their furry friends better?

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arXiv:2511.19103v1 Announce Type: new Abstract: The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is particularly problematic in resource-constrained and remote environments where bandwidth is limited, and battery-dependent devices further emphasize the problem. Moreover, in domains such as agriculture, consecutive sensor readings often have minimal variation, making continuous data transmission inefficient and unnecessarily resource intensive. To overcome these challenges, we propose an analytical prediction algorithm designed for edge computing environments and validated through simulation. The proposed solution utilizes a predictive filter at the network edge that forecasts the next sensor data point and triggers data transmission only when the deviation from the predicted value exceeds a predefined

arXiv:2511.18498v1 Announce Type: new Abstract: Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart

arXiv:2511.18412v1 Announce Type: new Abstract: In this work, we present ioPUF+, which incorporates a novel Physical Unclonable Function (PUF) that generates unique fingerprints for Integrated Circuits (ICs) and the IoT nodes encompassing them. The proposed PUF generates device-specific responses by measuring the pull-up and pull-down resistor values on the I/O pins of the ICs, which naturally vary across chips due to manufacturing-induced process variations. Since these resistors are already integrated into the I/O structures of most ICs, ioPUF+ requires no custom circuitry, and no new IC fabrication. This makes ioPUF+ suitable for cost-sensitive embedded systems built from Commercial Off-The-Shelf (COTS) components. Beyond introducing a new PUF, ioPUF+ includes a complete datapath for converting raw PUF responses into cryptographically usable secret keys using BCH error correction and SHA-256 hashing. Further ioPUF+ also demonstrate a practical use case of PUF derive secret keys in

arXiv:2511.18368v1 Announce Type: new Abstract: Autonomous Aerial Vehicle (AAV)-assisted Internet of Things (IoT) represents a collaborative architecture in which AAV allocate resources over 6G links to jointly enhance user-intent interpretation and overall network performance. Owing to this mutual dependence, improvements in intent inference and policy decisions on one component reinforce the efficiency of others, making highly reliable intent prediction and low-latency action execution essential. Although numerous approaches can model intent relationships, they encounter severe obstacles when scaling to high-dimensional action sequences and managing intensive on-board computation. We propose an Intent-Driven Framework for Autonomous Network Optimization comprising prediction and decision modules. First, implicit intent modeling is adopted to mitigate inaccuracies arising from ambiguous user expressions. For prediction, we introduce Hyperdimensional Transformer (HDT), which embeds

arXiv:2511.18334v1 Announce Type: new Abstract: Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know")

arXiv:2511.18240v1 Announce Type: new Abstract: The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in mitigating Distributed Denial-of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) face significant limitations, including poor adaptability to evolving and zero-day attacks, reliance on static signatures and labeled datasets, and inefficiency on resource-constrained edge gateways. Moreover, most existing DRL-based IDS studies overlook sustainability factors such as energy efficiency and carbon impact. To address these challenges, this paper proposes two novel Deep Reinforcement Learning (DRL)-based IDS: DeepEdgeIDS, an unsupervised Autoencoder-DRL hybrid, and AutoDRL-IDS, a supervised LSTM-DRL model. Both DRL-based IDS are validated through theoretical analysis and experimental evaluation on edge gateways. Results demonstrate that AutoDRL-IDS achieves 94% detection accuracy using labeled

arXiv:2511.18235v1 Announce Type: new Abstract: The rapid growth of the Internet of Things (IoT) has given rise to highly diverse and interconnected ecosystems that are increasingly susceptible to sophisticated cyber threats. Conventional anomaly detection schemes often prioritize accuracy while overlooking computational efficiency and environmental impact, which limits their deployment in resource-constrained edge environments. This paper presents \textit{EcoDefender}, a sustainable hybrid anomaly detection framework that integrates \textit{Autoencoder(AE)}-based representation learning with \textit{Isolation Forest(IF)} anomaly scoring. Beyond empirical performance, EcoDefender is supported by a theoretical foundation that establishes formal guarantees for its stability, convergence, robustness, and energy-complexity coupling-thereby linking computational behavior to energy efficiency. Furthermore, experiments on realistic IoT traffic confirm these theoretical insights, achieving up

arXiv:2511.18230v1 Announce Type: new Abstract: As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System (IDS) framework that integrates lightweight machine learning (ML) based IDS models with pre-trained large language models (LLMs) to improve detection accuracy, semantic interpretability, and operational efficiency at the network edge. The system evaluates six ML-based IDS models: Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model on low-power edge gateways, achieving accuracy up to 98 percent under real-world cyberattacks. For anomaly detection, the system transmits a compact and secure telemetry snapshot (for example, CPU usage, memory usage, latency, and energy

arXiv:2511.18045v1 Announce Type: new Abstract: The exponential growth of the Internet of Things (IoT) ecosystem has amplified concerns regarding device reliability, interoperability, and security assurance. Despite the proliferation of IoT security guidelines, a unified and quantitative approach to measuring trust remains absent. This paper introduces SCI-IoT (Secure Certification Index for IoT), a standardized and quantitative framework for trust scoring, evaluation, and certification of IoT devices. The framework employs a six-tier grading model (Grades A-F), enabling device profiling across consumer, industrial, and critical infrastructure domains. Within this model, 30 distinct Trust Tests assess devices across dimensions such as authentication, encryption, data integrity, resilience, and firmware security. Each test is assigned a criticality-based weight (1.0-2.0) and a performance rating (1-4), converted to a normalized percentage and aggregated through a weighted computation

arXiv:2511.17531v1 Announce Type: new Abstract: Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase tree construction and scheduling, often suffer from high computational overhead and suboptimal delays due to their static nature. To address this, we propose a novel Q-learning framework that unifies aggregation tree construction and scheduling, modeling the process as a Markov Decision Process (MDP) with hashed states for scalability. By leveraging a reward function that promotes large, interference-free batch transmissions, our approach dynamically learns optimal scheduling policies. Simulations on static networks with up to 300 nodes demonstrate up to 10.87% lower latency compared to a state-of-the-art heuristic algorithm, highlighting its robustness for delay-sensitive IoT applications. This

Modern devices, from fitness trackers and smart garments to Internet of Things (IoT) sensors, require compact and sustainable power sources. In new research published in Scientific Reports, scientists present an energy harvester based on a horizontally mounted vial half-filled with a biodegradable ferrofluid.

I test robot vacuums for a living, and these are the only Black Friday deals that are actually worth your time and money.

arXiv:2511.16822v1 Announce Type: new Abstract: In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT). Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically Distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior

Robot vacuums can be a huge help, keeping your floors clean regularly without much extra work on your part. Black Friday deals often include some of our favorite robovacs, and this year is shaping up to be no different. iRobot's entry-level Roomba 104 Vac robot vacuum is available for 40 percent off right now, bringing it down to a record low of $150. A number of other Roombas are on sale for Black Friday, too. In iRobot's lineup of robot vacuums, the Roomba 104 sits on the low end, adept at vacuuming up dust and hair, but without the mopping ability of its more expensive Max, Plus or Combo counterparts. The Roomba 104 Vac makes for a great first robot vacuum all the same, though, because of its four levels of powerful suction, and easy-to-use app. Like iRobot's other vacuums, the Roomba 104 maps and navigates your home with LiDAR, which helps it avoid obstacles. And using the Roomba Home app, you can schedule it to clean specific rooms, and even spot-clean particularly

It's designed to do your chores -- with some help from folks behind the curtain.

Get over $1,299 off the Roborock Saros Z70 during the Black Friday Amazon sale. Now down to $1,299.99 at Amazon.

Elon Musk seems to think Optimus will be a hit with consumers, and claimed it will claim much of Tesla's stock value.

It's a good time to pick up a robot vacuum on sale for Black Friday. Shark machines are some of our favorites, and we're seeing a number of models discounted for Black Friday. But this one, the Shark AI Ultra robot vacuum, is probably the best deal for most people this year. It's 58 percent off and down to an all-time low of $250. One of this model's standout features is its bagless design. Like many robovacs, it has an auto-empty station. But here, you can remove part of the base, dump its contents in the garbage, and lock it back in place. The base holds up to 60 days of dirt and debris, and you'll never need to order bag refills. The Shark AI Ultra has strong suction and decent obstacle avoidance (via LiDAR). The robovac cleans in a matrix grid. It auto-maps your home and supports Google Assistant

Plus: Omega debuts a new Seamaster Planet Ocean, and DJI has a new action camera.

A grounded take on home automation that cuts through the hype and highlights gear that genuinely improves comfort, security, and routine.

In a groundbreaking advancement for agricultural technology, researchers have unveiled an IoT-driven hybrid AI model specifically designed for the health monitoring of cows. This innovative system stands to revolutionize dairy farming and cattle management, optimizing both the health of livestock and the operational efficiency of farms. By integrating the Internet of Things (IoT) with artificial […]

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Matter 1.5 update brings support for smart home cameras  9to5GoogleSmart Home Cameras From Amazon, Google Could Soon Work Together  Bloomberg.comCamera support could be the boost Matter needs  The VergeMatter 1.5 adds security cameras and much more for the first time  9to5MacMatter 1.5 may finally fix the biggest headache in buying security cameras - here's how  ZDNET

Matter 1.5 update brings support for smart home cameras  9to5GoogleSmart Home Cameras From Amazon, Google Could Soon Work Together  Bloomberg.comCamera support could be the boost Matter needs  The VergeMatter 1.5 adds security cameras and much more for the first time  9to5MacMatter 1.5 may finally fix the biggest headache in buying security cameras - here's how  ZDNET

Semios’ On-Farm Innovation: A Conversation with General Manager Stephen Pistoresi A Familiar Valley Name Returns to the Spotlight When Ag Meter host Nick welcomed his next guest, it felt like ... Read More The post Semios Advances Smart Farming with Automation & Precision appeared first on AgNet West.

The smart home standard Matter is introducing support for cameras, a long awaited device type for the universal standard. more…

Roborock's flagship Saros 10R floor cleaner boasts hyper-advanced obstacle avoidance, unmatched cleaning, and a 34% discount through Dec. 1.

Will Knight / Wired: Sunday Robotics unveils Memo, a fully autonomous home robot capable of tasks like making espresso and loading dishwashers, set to launch in beta in 2026  —  Sunday Robotics has a new way to train robots to do common household tasks.  The startup plans to put its fully autonomous robots in homes next year.

Health tech startup Ultrahuman has launched Ultrahuman Home, a smart home platform designed to improve metabolic health by integrating real-time … Continue reading "Ultrahuman unveils smart home system to boost metabolic health" The post Ultrahuman unveils smart home system to boost metabolic health appeared first on Longevity.Technology - Latest News, Opinions, Analysis and Research.

arXiv:2511.15278v1 Announce Type: new Abstract: The proliferation of IoT devices in shared, multi-vendor environments like the modern aircraft cabin creates a fundamental conflict between the promise of data collaboration and the risks to passenger privacy, vendor intellectual property (IP), and regulatory compliance. While emerging standards like the Cabin Secure Media-Independent Messaging (CSMIM) protocol provide a secure communication backbone, they do not resolve data governance challenges at the application layer, leaving a privacy gap that impedes trust. This paper proposes and evaluates a framework that closes this gap by integrating a configurable layer of Privacy-Enhancing Technologies (PETs) atop a CSMIM-like architecture. We conduct a rigorous, empirical analysis of two pragmatic PETs: Differential Privacy (DP) for statistical sharing, and an additive secret sharing scheme (ASS) for data obfuscation. Using a high-fidelity testbed with resource-constrained hardware, we