IOT

You can already get a solid deal on a Roomba robot vacuum before Black Friday kicks off in earnest. iRobot's entry-level Roomba 104 Vac robot vacuum is available for 40 percent off right now, taking its normal $250 price 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 dirty spots.

Black Friday deals are starting to pop up across the web, and a great one to check out is at Dyson. While we still think you have the best shot to get the steepest discounts the closer to Black Friday we get, some of the discounts on Dyson's site right now are some of the best we've seen. One of those is $600 off the Dyson 360 Vis Nav robot vacuum, which is down to a record low of $400. Dyson was pretty late to the robot-vacuum party, but its entry was (and remains) one of the strongest in the category. It doesn't have a lot of bells and whistles like a self-emptying base or mopping capabilities, but it makes up for that by having probably the best suction power of any robovac we've tested. All kinds of debris will fall in its path: dirt, dust, food crumbs, pet hair and more. It

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Multi-modal sensors generate data that edge AI can turn into actionable insights, provided new devices can be integrated with legacy equipment. The post Edge AI Is Starting To Transform Industrial IoT appeared first on Semiconductor Engineering.

arXiv:2511.11392v1 Announce Type: cross Abstract: This paper presents RadAround, a passive 2-D direction-finding system designed for adversarial IoT sensing in contested environments. Using mechanically steered narrow-beam antennas and field-deployable SCADA software, it generates high-resolution electromagnetic (EM) heatmaps using low-cost COTS or 3D-printed components. The microcontroller-deployable SCADA coordinates antenna positioning and SDR sampling in real time for resilient, on-site operation. Its modular design enables rapid adaptation for applications such as EMC testing in disaster-response deployments, battlefield spectrum monitoring, electronic intrusion detection, and tactical EM situational awareness (EMSA). Experiments show RadAround detecting computing machinery through walls, assessing utilization, and pinpointing EM interference (EMI) leakage sources from Faraday enclosures.

arXiv:2511.11464v1 Announce Type: new Abstract: The routing protocol for low-power and lossy networks (RPL) has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number manipulation. Traditional countermeasures, including protocol-level modifications and machine learning classifiers, can achieve high accuracy against known threats, yet they fail when confronted with novel or zero-day attacks unless fully retrained, an approach that is impractical for dynamic IoT environments. In this paper, we investigate incremental learning as a practical and adaptive strategy for intrusion detection in RPL-based networks. We systematically evaluate five model families, including ensemble models and deep learning models. Our analysis highlights that incremental learning not only restores detection performance on new attack

arXiv:2511.11209v1 Announce Type: new Abstract: IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls. Even when fine-grained controls are available, users are likely overwhelmed by the complexity of setting privacy preferences. This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users that can be semi-automatically assigned or suggested to them. We developed and validated a questionnaire, based on users' privacy concerns regarding confidentiality and control and their requirements towards transparency in TAPs. In an online study (N=301), where participants were informed about potential privacy risks, we clustered users by their privacy concerns and requirements into Basic, Medium and High Privacy clusters. These clusters were then characterized by the users' data sharing preferences, based on a factorial vignette approach, considering the data categories, the data recipient

arXiv:2511.11204v1 Announce Type: new Abstract: We propose to enhance the dependability of large-scale IoT systems by separating the management and operation plane. We innovate the management plane to enforce overarching policies, such as safety norms, operation standards, and energy restrictions, and integrate multi-faceted management entities, including regulatory agencies and manufacturers, while the current IoT operational workflow remains unchanged. Central to the management plane is a meticulously designed, identity-independent policy framework that employs flexible descriptors rather than fixed identifiers, allowing for proactive deployment of overarching policies with adaptability to system changes. Our evaluation across three datasets indicates that the proposed framework can achieve near-optimal expressiveness and dependable policy enforcement.

If you're spending more time fixing your smart home than using it, it could be time to stop relying on the cloud. Here's how.

We surveyed our readers about their favorite smart home devices, including which smart assistants they use and love.

In Mashable's Readers' Choice Smartest Home survey, participants dubbed Google Assistant the best smart assistant in overall satisfaction and likelihood to recommend.

We asked Mashable readers about their favorite smart home devices, including robot vacuums. They crowned Roomba a winner.

Mashable surveyed its readers about the smart home products they use. See which smart home brands earned a Readers' Choice Award.

The SwitchBot S20's roller mop is great on hard floors – just don't try to vacuum anything afterwards.

Google's long-running streaming device is still useful for far more than binge-watching.

arXiv:2511.10291v1 Announce Type: new Abstract: Despite the advantages of multi-agent reinforcement learning (MARL) for wireless use case such as medium access control (MAC), their real-world deployment in Internet of Things (IoT) is hindered by their sample inefficiency. To alleviate this challenge, one can leverage model-based reinforcement learning (MBRL) solutions, however, conventional MBRL approaches rely on black-box models that are not interpretable and cannot reason. In contrast, in this paper, a novel causal model-based MARL framework is developed by leveraging tools from causal learn- ing. In particular, the proposed model can explicitly represent causal dependencies between network variables using structural causal models (SCMs) and attention-based inference networks. Interpretable causal models are then developed to capture how MAC control messages influence observations, how transmission actions determine outcomes, and how channel observations affect rewards. Data

arXiv:2511.09680v1 Announce Type: new Abstract: This paper presents a unified analytical framework for a two phase underwater wireless optical communication (UWOC) system that integrates Simultaneous Lightwave Information and Power Transfer (SLIPT) using a photovoltaic (PV) panel receiver. The proposed architecture enables self powered underwater sensor nodes by leveraging wide area and low cost PV panels for concurrent optical signal detection and energy harvesting. We develop a composite statistical channel that combines distance dependent absorption, turbulence induced fading characterized by the mixture Exponential Generalized Gamma (EGG )distribution, and beam misalignment due to pointing errors. Based on this model we derive closed form expressions for the probability density function, the cumulative distribution function, the outage probability (OP), the average bit error rate, the ergodic capacity, and the harvested power using Meijer G and Fox H functions. Overall, the paper

arXiv:2511.10291v1 Announce Type: new Abstract: Despite the advantages of multi-agent reinforcement learning (MARL) for wireless use case such as medium access control (MAC), their real-world deployment in Internet of Things (IoT) is hindered by their sample inefficiency. To alleviate this challenge, one can leverage model-based reinforcement learning (MBRL) solutions, however, conventional MBRL approaches rely on black-box models that are not interpretable and cannot reason. In contrast, in this paper, a novel causal model-based MARL framework is developed by leveraging tools from causal learn- ing. In particular, the proposed model can explicitly represent causal dependencies between network variables using structural causal models (SCMs) and attention-based inference networks. Interpretable causal models are then developed to capture how MAC control messages influence observations, how transmission actions determine outcomes, and how channel observations affect rewards. Data

arXiv:2511.09680v1 Announce Type: new Abstract: This paper presents a unified analytical framework for a two phase underwater wireless optical communication (UWOC) system that integrates Simultaneous Lightwave Information and Power Transfer (SLIPT) using a photovoltaic (PV) panel receiver. The proposed architecture enables self powered underwater sensor nodes by leveraging wide area and low cost PV panels for concurrent optical signal detection and energy harvesting. We develop a composite statistical channel that combines distance dependent absorption, turbulence induced fading characterized by the mixture Exponential Generalized Gamma (EGG )distribution, and beam misalignment due to pointing errors. Based on this model we derive closed form expressions for the probability density function, the cumulative distribution function, the outage probability (OP), the average bit error rate, the ergodic capacity, and the harvested power using Meijer G and Fox H functions. Overall, the paper

The Narwal Freo Pro is one of the better-value robot vacuums you can buy today. But it's not the best.

arXiv:2511.09414v1 Announce Type: new Abstract: In dynamic Industrial Internet of Things (IIoT) environments, models need the ability to selectively forget outdated or erroneous knowledge. However, existing methods typically rely on retain data to constrain model behavior, which increases computational and energy burdens and conflicts with industrial data silos and privacy compliance requirements. To address this, we propose a novel retain-free unlearning framework, referred to as Probing then Editing (PTE). PTE frames unlearning as a probe-edit process: first, it probes the decision boundary neighborhood of the model on the to-be-forgotten class via gradient ascent and generates corresponding editing instructions using the model's own predictions. Subsequently, a push-pull collaborative optimization is performed: the push branch actively dismantles the decision region of the target class using the editing instructions, while the pull branch applies masked knowledge distillation to

arXiv:2511.09303v1 Announce Type: new Abstract: This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including radio frequency (RF) communication, optical wireless communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy

arXiv:2511.09111v1 Announce Type: new Abstract: The operational lifetime of energy-harvesting wireless sensor nodes is limited by availability of the energy source and the capacity of the installed energy buffer. When a sensor node depletes its energy reserves, manual intervention is often required to resume node operation. While lowering the duty cycle would help extend the network lifetime, this is often undesirable, especially in time-critical applications, where rapid collection and dissemination of information is vital. In this paper, we propose a context-aware energy management policy that helps balance the two opposing objectives of timely data collection and dissemination with energy conservation. We capture these objectives through the Value of Information (VoI) of observations made by a sensor node and the State of Energy (SoE) of the energy buffer. We formulate the energy management policy as a Model Predictive Control (MPC) problem which computes device sampling and

arXiv:2511.09006v1 Announce Type: new Abstract: The Internet of Things (IoT) is transforming industries by connecting billions of devices to collect, process, and share data. However, the massive data volumes and real-time demands of IoT applications strain traditional cloud computing architectures. This paper explores the complementary roles of cloud, fog, and edge computing in enhancing IoT performance, focusing on their ability to reduce latency, improve scalability, and ensure data privacy. We propose a novel framework, the Hierarchical IoT Processing Architecture (HIPA), which dynamically allocates computational tasks across cloud, fog, and edge layers using machine learning. By synthesizing current research and introducing HIPA, this paper highlights how these paradigms can create efficient, secure, and scalable IoT ecosystems.

It's designed to do your chores, but it'll need some help from folks behind the curtain.

It’s not for everyone, but sometimes my robot vacuum is my only friend.

In an era characterized by rapid technological advancement, the intersection of education and the Internet of Things (IoT) has emerged as a focal point for researchers worldwide. A pivotal study conducted by Wang and Wang introduces an innovative information sharing and tracking platform tailored for educational management. This research not only identifies the significance of […]

arXiv:2511.07428v1 Announce Type: new Abstract: This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical Wireless Communication (OWC) alongside Radio Frequency (RF). We begin by formulating a Mixed-Integer Nonlinear Programming (MINLP) model that jointly considers throughput maximization and delay minimization between access points and IoT nodes under energy and link availability constraints. However, given the intractability of solving such NP-hard problems at scale and the impractical assumption of full channel observability, we propose the Dual-Graph Embedding with Transformer (DGET) framework, a supervised multi-task learning architecture combining a two-stage Graph Neural Networks (GNNs) with a Transformer-based encoder. The first stage employs a transductive GNN that encodes the known graph topology and initial node and link states. The second stage introduces an inductive GNN for temporal refinement, which

The company is reporting major financial problems and an uncertain future. Should you buy a Roomba this Black Friday, and will your existing ones continue to function?

IKEA has dropped a new line of smart home products that include lighting, remote controls, and sensors. Here's everything you need to know.

iRobot's financial struggles arrive just as people are finally writing the Roomba's incredible history.

The best time to make your home smart is at the very beginning. Whether you're building from scratch, renovating, or just moving in, a little bit of work on lighting, security, and energy management can go a long way. Getting started can be tricky, though: there's no obvious path to follow, no starter kit of stuff to buy, no easy answers. And if you pick the wrong first gadget or tie yourself down to the wrong ecosystem, you might be in trouble. In this episode of The Vergecast, David finds himself in precisely this situation. He's a few days away from moving into a new house, wants to do some useful smart home stuff, and has absolutely no … Read the full story at The Verge.

arXiv:2511.05920v1 Announce Type: new Abstract: Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated temperature changes impacting produce shelf life, and compare it against traditional approaches such as Robust Optimization, Distributionally Robust Optimization, and Stochastic Programming. Additionally, we conduct a series of computational experiments using Internet of Things (IoT) sensor data to evaluate the performance of our proposed model. Our study demonstrates that the proposed adaptive model achieves a higher shelf life, extending it by over 18\% compared to traditional optimization models, by dynamically mitigating temperature deviations through a temperature feedback

arXiv:2511.05920v1 Announce Type: cross Abstract: Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated temperature changes impacting produce shelf life, and compare it against traditional approaches such as Robust Optimization, Distributionally Robust Optimization, and Stochastic Programming. Additionally, we conduct a series of computational experiments using Internet of Things (IoT) sensor data to evaluate the performance of our proposed model. Our study demonstrates that the proposed adaptive model achieves a higher shelf life, extending it by over 18\% compared to traditional optimization models, by dynamically mitigating temperature deviations through a temperature feedback

arXiv:2511.07325v1 Announce Type: new Abstract: This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The system can quickly detect and monitor dumped waste using a street-level camera and object detection algorithm. Data was collected from the Sangareddy district in Telangana, India. A series of comprehensive experiments was carried out using the proposed dataset to assess the accuracy and overall performance of various object detection models. Specifically, we performed an in-depth evaluation of YOLOv8, YOLOv10, YOLO11m, and RT-DETR on our dataset. Among these models, YOLO11m achieved the highest accuracy of 92.39\% in waste detection, demonstrating its effectiveness in detecting waste. Additionally, it attains an mAP@50 of 0.91, highlighting its high precision. These findings confirm that the object detection model is

arXiv:2511.07189v1 Announce Type: new Abstract: Due to the recent shortage of resources in the healthcare industry, Remote Patient Monitoring (RPM) systems arose to establish a convenient alternative for accessing healthcare services remotely. However, as the usage of this system grows with the increase of patients and sensing devices, data and network management becomes an issue. As a result, wireless architecture challenges in patient privacy, data flow, and service interactability surface that need addressing. We propose a fog-based Internet of Things (IoT) platform to address these issues and reinforce the existing RPM system. The introduced platform can allocate resources to alleviate server overloading and provide an interactive means of monitoring patients through speech recognition. We designed a testbed to simulate and test the platform in terms of accuracy, latency, and throughput. The results show the platform's potential as a viable RPM system for sound-based healthcare

arXiv:2511.06197v1 Announce Type: new Abstract: The rapid proliferation of Internet of Things (IoT) devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting artificial intelligence (AI) and machine learning (ML)-based intrusion detection systems (IDS) to deliberately evade detection, induce misclassification, and systematically undermine the reliability and integrity of security defenses. To address these challenges, we propose a novel adversarial detection model that enhances the robustness of IoT IDS against adversarial attacks through SHapley Additive exPlanations (SHAP)-based fingerprinting. Using SHAP's DeepExplainer, we extract attribution fingerprints from network traffic features, enabling the IDS to reliably distinguish between clean and adversarially perturbed inputs. By capturing subtle

Los Angeles CA (SPX) Nov 11, 2025 Quectel Wireless Solutions has announced a collaboration with Swift Navigation to provide real-time kinematic (RTK) correction solutions that deliver centimeter-level accuracy for a diverse range of Internet of Things (IoT) applications. The partnership integrates Quectel's RTK modules and antennas with Swift's Skylark Precise Positioning Service, enabling equipment manufacturers to streamline a

Things continue to look bleak for the original robot vacuum maker. iRobot’s third-quarter results, released last week, show that revenue is down and “well below our internal expectations due to continuing market headwinds, ongoing production delays, and unforeseen shipping disruptions,” said Gary Cohen, iRobot CEO, in a press release. This meant they had to spend more cash and are now down to under $25 million. “At this time, the Company has no sources upon which it can draw for additional capital,” said Cohen. The Roomba manufacturer has been struggling for several years in the face of increased competition from Chinese manufacturers. A sale to Amazon in 2022 looked to be its lifeline; however, regulatory scrutiny scuppered the deal, and the company was left in further turmoil. It laid off over 30 percent of its staff, lost its

arXiv:2511.04923v1 Announce Type: new Abstract: Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.

Tokyo, Japan (SPX) Nov 10, 2025 ArkEdge Space Inc., based in Tokyo, has confirmed the operational capability of satellite short messaging using compact, battery-powered IoT devices. During the 11th UNISEC-Global Meeting, the company demonstrated how simple ground IoT terminals transmitted short messages to micro-satellites, which were then downlinked to ArkEdge's ground station in real time.

The Roborock Qrevo Edge robot vacuum can vacuum and mop with hot water. It is on sale on amazon for 43% off.

With the holidays approaching, and the cold weather with it, you might start to wish for some new smart-home additions. One of these might be just the thing.

Ikea just took over your smart home  The VergeIKEA announces new Matter-compatible smart home products  EngadgetIkea’s Big Smart-Home Push Arrives With 21 New Matter Devices  ForbesIkea’s new smart home collection is entirely Matter-compatible  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast Company

Ikea just took over your smart home  The VergeIKEA announces new Matter-compatible smart home products  EngadgetIkea’s Big Smart-Home Push Arrives With 21 New Matter Devices  ForbesIkea’s new smart home collection is entirely Matter-compatible  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast Company

IKEA just announced 21 smart home gadgets — here’s the ones I'm buying  Tom's GuideIKEA announces new Matter-compatible smart home products  EngadgetIkea’s Big Smart-Home Push Arrives With 21 New Matter Devices  ForbesIkea’s new smart home collection is entirely Matter-compatible  The VergeIkea just took over your smart home  The Verge

IKEA just announced 21 smart home gadgets — here’s the ones I'm buying  Tom's GuideIKEA announces new Matter-compatible smart home products  EngadgetIkea’s Big Smart-Home Push Arrives With 21 New Matter Devices  ForbesIkea’s new smart home collection is entirely Matter-compatible  The VergeIkea just took over your smart home  The Verge

Hi, friends! Welcome to Installer No. 105, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, hope you've recovered from the clocks falling back, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about David Ellison and Common Crawl and Stephen Colbert, catching up on It's Always Sunny in Philadelphia and The Great British Baking Show, letting TikTok turn me on to a new Olivia Dean song and a new Broadway musical, testing the Boox Palma 2 Pro, spending way too many hours trying to design my new home office / podcast studio, and packing every single thing … Read the full story at The Verge.

Meet my new best friend. Robot vacuums are amazing machines, but they can also be a pain in the arse. In my home, testing a new robot vacuum often means digging it out from under my living room couch or unhooking it from the legs of my lounger. Then there's being woken at 3AM by a cheery "resuming cleaning," getting down on my hands and knees to retrieve a pencil from their brushes or scrub the gunk out of the "self-cleaning" dock. And - my favorite - holding my nose while dumping the contents of a giant dirty water tank into the toilet. Then I met Matic. It's a complete rethink of the household robot. From design and navigation to cleaning performance and mobili … Read the full story at The Verge.

The week's top stories from GTA 6, IKEA, Samsung, Google and more for November 8, 2026.

It's designed to do your chores, but it'll need some help from you and from folks behind the curtain.

While Black Friday can be a great opportunity to score deals on your Christmas shopping, sometimes it's just a way to get the things you need at better prices. Take the iRobot Roomba Max 705 Combo Robot Vacuum & Mop, which is down to $869 from $1,300 at Wellbots. This practical purchase is available for a Black Friday discount by using the code ENGABF430 at checkout.  We're big fans of iRobot, with the company making two of our favorite robot vacuums for 2025. Its Roomba Max 705 Combo offers both a vacuum and a mop to, hopefully, get out any messes this holiday season. The device also comes with an AutoWash Dock, which empties the robovac, washes the mop, dries it with heat and then charges its battery.  The robot vacuum has dual rubber brushes for

arXiv:2511.03753v1 Announce Type: new Abstract: This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images, the proposed approach enables efficient feature extraction through Convolutional Neural Networks (CNNs) while ensuring that sensitive medical data remain local to each device. This work is among the first to experimentally validate GAF-based federated ECG classification across heterogeneous IoT devices, quantifying both performance and communication efficiency. To evaluate feasibility in realistic IoT settings, we deployed the framework across a server, a laptop, and a resource-constrained Raspberry Pi 4, reflecting edge-cloud integration in IoT ecosystems. Experimental results demonstrate that the FL-GAF model achieves a high classification accuracy of 95.18% in a multi-client setup,

Ikea’s new low-cost line is a huge win for Matter and your smart home  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast CompanyIKEA announces new Matter-compatible smart home products  EngadgetThere's something for everyone in IKEA's new range of 21 smart home devices  TechRadarIkea’s Big Smart Home Push Arrives With 21 New Matter Devices  Forbes

Ikea’s new low-cost line is a huge win for Matter and your smart home  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast CompanyIKEA announces new Matter-compatible smart home products  EngadgetThere's something for everyone in IKEA's new range of 21 smart home devices  TechRadarIkea’s Big Smart Home Push Arrives With 21 New Matter Devices  Forbes

As of Nov. 6, the iRobot Roomba Plus 405 robot vacuum and mop is down to $449, its lowest price ever. Shop now at Amazon.

Emergent’s IoT expertise and Connext Network combine with UnCommon Farms’ trusted relationships to support over 600 farmers and ranchers. The post Emergent Connext and UnCommon Farms Form Strategic Partnership to Expand Connectivity and IoT Solutions for Farmers appeared first on CropLife.

As of Nov. 6, Ikea has launched a new and redesigned line-up of smart home devices. The collection focuses on lighting, sensors, and controls,

IKEA announces new Matter-compatible smart home products  EngadgetIkea’s new low-cost line is a huge win for Matter and your smart home  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast CompanyIKEA’s smart home hub now runs Matter 1.3 and can power more gadgets  The Ambient

IKEA announces new Matter-compatible smart home products  EngadgetIkea’s new low-cost line is a huge win for Matter and your smart home  The VergeIkea’s revamped 21-piece smart home range is about to make your life a lot easier  Fast CompanyIKEA’s smart home hub now runs Matter 1.3 and can power more gadgets  The Ambient

IKEA has decided to revamp its smart home product range, and it looks like there's something for everyone.

There’s good news today for HomeKit fans on a budget: IKEA has announced the launch of 21 new smart home products, all of them Matter-compatible. The new lineup comprises 11 different smart bulbs, five types of sensor, three remote controls, and a new smart plug … more…

Ikea’s new 21-product series of bulbs, sensors, and remotes is dirt cheap, idiot-proof, Matter-ready, and designed to work with everything. But it's still years from the promised house of the future.

Ikea is making the smart home cheaper and easier with a new line of low-cost Matter-over-Thread smart home staples, including bulbs, buttons, and sensors. Ikea just announced a bunch of super cheap, colorful Matter-over-Thread devices that will work with any platform, and it feels like Christmas came early for the smart home. The 21 new products include a line of smart bulbs starting at just £4 and two new remote controls that start at just £3 (US pricing is not yet confirmed). Ikea also officially updated its Dirigera hub to a Matter controller and Thread border router to support the new products, which will start to arrive in the US in January. It's a big shift toward a simpler, more open smart home, a vote of confidence in Matter from a major mainstream brand, and great news for users, w … Read the full

The collection includes a new two-button wireless remote that can be used to control more than just lights. | Image: Ikea Ikea announced 21 new Matter-over-Thread devices that can “connect with a wider range of devices and platforms, making it easier for customers to build a smart home across different brands.” The collection includes both entirely new products and updates to existing offerings introducing new functionality and features. The new collection was teased by Ikea’s David Granath during our exclusive interview earlier this year, and the company originally promised support for the Matter smart home standard when it announced its Dirigera hub in May 2022. Although the hub’s Matter support ended up being delayed for several years, it was finally introduced in beta in March 2024. Several months later, Ikea updated its smart hub so all users could use it as a

IKEA has officially announced its range of Matter-compatible smart home products. The Swedish furniture store is releasing 21 new items under the tentpoles of lighting, sensors and control. IKEA teased these releases back in July.  Part of the roll out will include updates to existing categories in order to work with Matter, an open source smart home standard. "We're upgrading our most-appreciated products while also adding new ones to solve even more everyday challenges," Stjepan Begic, product developer at IKEA. "Our focus has been on keeping things simple from setup to daily use, so it’s easy for people to start, use and grow a smart home." As for the products themselves, 11 come as part of the KAJPLATS smart bulb range. They will have a mix of shapes and sizes, along with dimming functionality. Then there's the five smart sensors., starting with an indoor and outdoor motion sensor called MYGGSPRAY, which automatically turns on lights.

arXiv:2511.03661v1 Announce Type: new Abstract: The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1) detecting malicious cyberattacks and (2) identifying faulty device anomalies, leveraging a dataset of 200,000 records. Eight machine learning models are evaluated across three learning approaches: supervised learning (XGBoost, K-Nearest Neighbors (K- NN)), semi-supervised learning (Generative Adversarial Networks (GAN), Variational Autoencoders (VAE)), and unsupervised learning (One-Class Support Vector Machine (SVM), Isolation Forest, Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) Autoencoders). The comprehensive evaluation was conducted across multiple metrics like F1-score, precision, recall, accuracy, ROC-AUC, computational efficiency. XGBoost achieved 99\% accuracy with minimal

arXiv:2511.03538v1 Announce Type: new Abstract: The convergence of the Internet of Things (IoT) and quantum computing is redefining the security paradigm of interconnected digital systems. Classical cryptographic algorithms such as RSA, Elliptic Curve Cryptography (ECC), and Advanced Encryption Standard (AES) have long provided the foundation for securing IoT communication. However, the emergence of quantum algorithms such as Shor's and Grover's threatens to render these techniques vulnerable, necessitating the development of quantum-resilient alternatives. This chapter examines the implications of quantum computing for IoT security and explores strategies for building cryptographically robust systems in the post-quantum era. It presents an overview of Post-Quantum Cryptographic (PQC) families, including lattice-based, code-based, hash-based, and multivariate approaches, analyzing their potential for deployment in resource-constrained IoT environments. In addition, quantum-based

arXiv:2511.03534v1 Announce Type: new Abstract: In recent years, the number of Internet of Things (IoT) devices in smart homes has rapidly increased. A key challenge affecting user experience is how to enable users to efficiently and intuitively select the devices they wish to control. This paper proposes PnPSelect, a plug-and-play IoT device selection solution utilizing Ultra-wideband (UWB) technology on commercial devices. Unlike previous works, PnPSelect does not require the installation of dedicated hardware on each IoT device, thereby reducing deployment costs and complexities, and achieving true plug-and-play functionality. To enable intuitive device selection, we introduce a pointing direction estimation method that utilizes UWB readings from a single anchor to infer the user pointing direction. Additionally, we propose a lightweight device localization method that allows users to register new IoT devices by simply pointing at them from two distinct positions, eliminating the

arXiv:2511.02924v1 Announce Type: new Abstract: The proliferation of Internet of Things (IoT) networks demands security mechanisms that protect constrained devices without the computational cost of public-key cryptography. Conventional Pre-Shared Key (PSK) encryption, while efficient, remains vulnerable due to static key reuse, replay attacks, and the lack of forward secrecy. This paper presents the Dynamic Session Enhanced Key Protocol (DSEKP) - a lightweight session-key rekeying framework, a fully symmetric extension to PSK that derives per-session AES-GCM keys using the HMAC-based Key Derivation Function (HKDF-SHA256) and authenticates session establishment through an HMAC proof in a single init-ack exchange. DSEKP was implemented on an ESP32 IoT sensor node and a Raspberry Pi 5 edge server communicating through a Mosquitto MQTT broker, and benchmarked against a static PSK baseline over more than 6,500 encrypted packets per configuration. The results demonstrate nearly identical

arXiv:2511.02894v1 Announce Type: new Abstract: The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve functionality and user experience. Although machine learning models have advanced HAR, they are increasingly susceptible to data poisoning attacks that compromise the data integrity and reliability of these systems. Conventional approaches to defending against such attacks often require extensive task-specific training with large, labeled datasets, which limits adaptability in dynamic IoT environments. This work proposes a novel framework that uses large language models (LLMs) to perform poisoning detection and sanitization in HAR systems, utilizing zero-shot, one-shot, and few-shot learning paradigms. Our approach incorporates \textit{role play} prompting, whereby the LLM assumes the role of expert to

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A robot vacuum may seem like the perfect solution for keeping your apartment floor clean, but there are some things you should think about before buying one.

Samantha Subin / CNBC: Armis, which helps businesses secure and manage IoT devices, raised $435M at a $6.1B valuation, up from $4.2B after raising a $200M Series D in October 2024  —  Cybersecurity startup Armis has raised $435 million in a funding round that values the company at $6.1 billion.

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arXiv:2511.02301v1 Announce Type: new Abstract: The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and imbalanced anomaly distributions. To address this gap, we propose a Federated Quantum Kernel Learning (FQKL) framework that integrates quantum feature maps with federated aggregation to enable distributed, privacy-preserving anomaly detection across heterogeneous IoT networks. In our design, quantum edge nodes locally compute compressed kernel statistics using parameterized quantum circuits and share only these summaries with a central server, which constructs a global Gram matrix and trains a decision function (e.g., Fed-QSVM).

VusionGroup stands at the forefront of physical retail’s digital transformation, deploying Internet of Things technology and artificial intelligence

Cities worldwide are becoming smarter. Integrated system tools technologies are used to capture and analyze real-time data to

There are a couple of universal truths about the smart home. Yes, you will come to treat your robot vacuum like a member of the family, and no, you cannot escape the fact that there will always be one light that doesn't turn on even though there's simply no reason it won't. These are just the facts. Beyond that, the only thing we know for sure about the smart home is that it's tricky to get right. You have to navigate a tangle of standards and ecosystems, optimize your control systems, and design a lot of seemingly basic features yourself. The industry seems to be on a path to a much better place! But we're not there yet. Subscribe: Spot … Read the full story at The Verge.

arXiv:2511.00187v1 Announce Type: new Abstract: With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered video-based traffic analysis with high-resolution urban air quality models. We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district, where the system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline. Results are validated against official air quality measurements of nitrogen dioxide (NO2). Compared to traditional models that rely on static emission inventories, the IoT-assisted approach enhances the temporal granularity of urban air

arXiv:2511.00187v1 Announce Type: cross Abstract: With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered video-based traffic analysis with high-resolution urban air quality models. We present a real-world pilot deployment at a road intersection in Barcelona's Eixample district, where the system captures dynamic traffic conditions and environmental variables, processes them at the edge, and feeds real-time data into a high-performance computing (HPC) simulation pipeline. Results are validated against official air quality measurements of nitrogen dioxide (NO2). Compared to traditional models that rely on static emission inventories, the IoT-assisted approach enhances the temporal granularity of urban air

arXiv:2511.01498v1 Announce Type: new Abstract: Person re-identification (ReID) plays a pivotal role in computer vision, particularly in surveillance and security applications within IoT-enabled smart environments. This study introduces the Enhanced Pedestrian Alignment Network (EPAN), tailored for robust ReID across diverse IoT surveillance conditions. EPAN employs a dual-branch architecture to mitigate the impact of perspective and environmental changes, extracting alignment information under varying scales and viewpoints. Here, we demonstrate EPAN's strong feature extraction capabilities, achieving outstanding performance on the Inspection-Personnel dataset with a Rank-1 accuracy of 90.09% and a mean Average Precision (mAP) of 78.82%. This highlights EPAN's potential for real-world IoT applications, enabling effective and reliable person ReID across diverse cameras in surveillance and security systems. The code and data are available at: https://github.com/ggboy2580/EPAN

arXiv:2511.01127v1 Announce Type: new Abstract: Traditional task offloading strategies in edge computing often rely on static heuristics or data-intensive machine learning models, which are not always suitable for highly dynamic and resource-constrained environments. In this paper, we propose a novel task-offloading framework based on Spiking Neural Networks inspired by the efficiency and adaptability of biological neural systems. Our approach integrates an SNN-based decision module into edge nodes to perform real-time, energy-efficient task orchestration. We evaluate the model under various IoT workload scenarios using a hybrid simulation environment composed of YAFS and Brian2. The results demonstrate that our SNN-based framework significantly reduces task processing latency and energy consumption while improving task success rates. Compared to traditional heuristic and ML-based strategies, our model achieves up to 26% lower latency, 32% less energy consumption, and 25\% higher

arXiv:2511.00777v1 Announce Type: new Abstract: Durian plantation suffers from animal intrusions that cause crop damage and financial loss. The traditional farming practices prove ineffective due to the unavailability of monitoring without human intervention. The fast growth of machine learning and Internet of Things (IoT) technology has led to new ways to detect animals. However, current systems are limited by dependence on single object detection algorithms, less accessible notification platforms, and limited deterrent mechanisms. This research suggests an IoT-enabled animal detection system for durian crops. The system integrates YOLOv5 and SSD object detection algorithms to improve detection accuracy. The system provides real-time monitoring, with detected intrusions automatically reported to farmers via Telegram notifications for rapid response. An automated sound mechanism (e.g., tiger roar) is triggered once the animal is detected. The YOLO+SSD model achieved accuracy rates of

arXiv:2511.00271v1 Announce Type: new Abstract: The rapid growth of the Internet of Things (IoT) offers new opportunities but also expands the attack surface of distributed, resource-limited devices. Intrusion detection in such environments is difficult due to data heterogeneity from diverse sensing modalities and the non-IID distribution of samples across clients. Federated Learning (FL) provides a privacy-preserving alternative to centralized training, yet conventional frameworks struggle under these conditions. To address this, we propose a Mist-assisted hierarchical framework for IoT intrusion detection. The architecture spans four layers: (i) Mist, where raw data are abstracted into a unified feature space and lightweight models detect anomalies; (ii) Edge, which applies utility-based client selection; (iii) Fog, where multiple regional aggregators use FedProx to stabilize training; and (iv) Cloud, which consolidates and disseminates global models. Evaluations on the TON-IoT

arXiv:2511.00249v1 Announce Type: new Abstract: The growth in IoT devices means an ongoing risk of data vulnerability. The transition from centralized ecosystems to decentralized ecosystems is of paramount importance due to security, privacy, and data use concerns. Since the majority of IoT devices will be used by consumers in peer-to-peer applications, a centralized approach raises many issues of trust related to privacy, control, and censorship. Identity and access management lies at the heart of any user-facing system. Blockchain technologies can be leveraged to augment user authority, transparency, and decentralization. This study proposes a decentralized identity management framework for IoT environments using Hyperledger Fabric and Decentralized Identifiers (DIDs). The system was simulated using Node-RED to model IoT data streams, and key functionalities including device onboarding, authentication, and secure asset querying were successfully implemented. Results demonstrated

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arXiv:2510.27304v1 Announce Type: new Abstract: With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability to rapid anomaly changes, known as concept drift. In contrast, streaming learning integrates online and incremental learning, enabling seamless updates and concept drift detection to improve robustness. This study investigates anomaly detection in streaming IoT traffic as binary classification, comparing batch and streaming learning approaches while assessing the limitations of current IoT traffic datasets. We simulated heterogeneous network data streams by carefully mixing existing datasets and streaming the samples one by one. Our results highlight the failure of batch models to handle concept drift, but also reveal persisting limitations of current datasets to expose model limitations due to low

arXiv:2510.26989v1 Announce Type: new Abstract: The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE's key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.

arXiv:2510.26941v1 Announce Type: new Abstract: The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation suggestion. Despite advancements, evaluations remain purely qualitative, and the lack of a standardized, objective benchmark for quantitatively measuring AI-based attack analysis and mitigation hinders consistent assessment of model effectiveness. In this work, we propose a hybrid framework combining Machine Learning (ML) for multi-class attack detection with Large Language Models (LLMs) for attack behavior analysis and mitigation suggestion. After benchmarking several ML and Deep Learning (DL) classifiers on the Edge-IIoTset and CICIoT2023 datasets, we applied structured role-play prompt engineering with Retrieval-Augmented Generation (RAG) to guide

The internet was abuzz this week over Neo, 1X's new housekeeping robot - but then the reviews rolled in.

In a world increasingly shaped by technological innovation, the realms of artificial intelligence and smart home design have converged in transformative ways, particularly in catering to the aging population. A recent study conducted by researcher D. Jiang, published in Discover Artificial Intelligence, delves deep into this confluence, revealing vital insights on how AI can enhance […]

Home robots are moving way beyond Roombas. 1X unveiled its NEO helper bot this week, a terrifying $20,000 machine that can perform basic tasks after you've trained it, and more complex tasks via teleoperation. In this episode, Devindra and Engadget's Igor Bonafacic try to figure out why 1X made the Neo look like a murderbot, as well as the future they see for home robots. Also, we discuss last week's AWS outage and our over-reliance on a single cloud provider, as well as Apple's rumored push for OLED devices in 2026.    Devindra also what’s with John Gearty, a former Apple Vision Pro engineer, about the state of Apple’s headset and the world of XR. Subscribe!iTunes Spotify Pocket Casts Stitcher Google Podcasts TopicsInterview with John Gearty, former Apple Vision Pro engineer and founder of PulseJet Studios – 1:30 Robotics company 1X announces Neo, a $20k home assistant that might become autonomous…someday –