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Last year at CES, Dreame showed off a robot vacuum prototype with a mechanical arm. But while we were able to see the arm extend and retract, we didn’t see the device, which was described as a prototype at the time, actually grab anything, which was a bit disappointing. This year, though, the company has made its arm-enabled vacuum a reality with the Cyber 10 Ultra. Dreame previewed it recently at IFA in Berlin, but has now confirmed it will be on sale later this year.  The vacuum has an extendable arm that looks pretty similar to the prototype version we saw last year. It extends from the top of the vacuum and has a claw-like device at the end for scooping up objects. According to Dreame, it can pick up items that weigh up to 500 grams (about 1 pound) so it should be able to grab a wider variety of stuff than the Roborock vac we saw last year, which had a 300-gram weight limit for its arm.  The arm can also do more than pick up stuff from the floor. It supports

Robot vacuum companies are once again trying to outdo each other at CES 2026. This year, Chinese appliance maker Dreame is showing off a prototype of a device that can climb up and down an entire flight of stairs. The concept, called the Cyber X, was previewed last year at IFA in Berlin. The vacuum sports a somewhat terrifying set of legs with rubber treads that allow it to autonomously navigate multi-story environments. While Dreame has previously shown off vacuums that can move up smaller steps, it says the Cyber X can climb stairs up to 25cm (9.8 inches) high and slopes up to 42 degrees. It can manage both straight and curved staircases, and can climb a flight of steps in 27 seconds, according to the company. In addition to its legs, the Cyber X also has a built-in water tank to support mopping abilities, and a laser-powered navigation system to help it maneuver up stairs and around other obstacles. It also has a braking system that allows it to stay stable on floors and

Cleaning stairs is just the start.

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I watched Roborock's Saros Rover successfully dodge obstacles, climb stairs, clean the stairs and even dance. It blew me away at CES 2026.

I watched Roborock's Saros Rover successfully dodge obstacles, climb stairs, clean the stairs and even dance. It blew me away at CES 2026.

Dana Wollman / Bloomberg: Chinese robot vacuum maker Roborock unveils the Saros Rover, a concept robot vacuum with two wheel-legs to let it climb steps, as robotics dominates CES  —  Roborock, a Chinese robotic vacuum cleaner brand, unveiled a concept device with two legs that can climb stairs in people's homes …

The Qrevo Curv 2 Flow can tackle wet and dry messes. | Image: Roborock The market leader in robot vacuums has been behind in the latest trend - roller mops. But not for much longer. At CES this week, Roborock announced the Qrevo Curv 2 Flow, its first robot vacuum and mop with a motorized self-cleaning roller mop. The Flow features an extra-wide mop that the company says can clean more surface area in one pass than other models, and it spins at 220rpm with 15 Newtons of downward pressure. It self-cleans with eight water jets and a built-in scraper. The Flow has a "roller shield" that activates when the robot is on carpet, and the mop can extend from the bot to reach along edges. Roborock's current flagship v … Read the full story at The Verge.

Roborock's latest robovacs don't need help getting out of tricky situations. At least, that's one of the perks of the improved AdaptiLift Chassis 3.0 on the Roborock Saros 20 and Saros 20 Sonic announced today at CES 2026. The upgraded chassis in Roborock's new flagship robot vacuum cleaners allows the bots to climb over thresholds up to 3.3 inches tall total, including double-layer thresholds up to 1.7 and 1.57 inches per step. The bots can also clean carpets more effectively with dynamic chassis elevation that automatically adjusts the bots' height for carpets with pile up to 1.2 inches high. The height-adjustable chassis also helps th … Read the full story at The Verge.

Whether this stair-climbing—and cleaning!—robot is the first true whole-home robot vacuum remains to be seen.

The Roborock Rover has garnered the attention that few devices can achieve at CES 2026, and I had the chance to see it in person.

I'm back on the CES show floor, narrowing down the most compelling smart home devices I've spotted so far.

Ring turned up to CES with a whole host of announcements, including a revamped range of home sensors. Ring Sensors (for that is their name) is a new lineup of tools, built on Amazon’s Sidewalk low-power networking protocol. That includes updated versions of its door, window and break glass sensors, as well as a new OBD-II car alarm, motion detectors and panic buttons. You’ll be able to pre-order the new car alarm today, while the rest of the new sensors will be available at some point in March. And, in tandem with that news, Amazon is announcing that Sidewalk is expanding outside of the US, starting in Canada and Mexico. At the same time, the company is launching a number of enhancements to its app platform, including the Ring Appstore. This will let users purchase and integrate with third-party apps which have been built to cater to “specific use cases, from small business operations to everyday needs around the home.” The company added that, in the coming weeks, users will be

Ecovacs adds a mop cover to its flagship robot vacuum  The VergeECOVACS Showcases Its Acceleration Towards Full-Scenario Service Robotics at CES 2026  Yahoo FinanceEcovacs’ latest robot mower adds smarter mapping and an integrated trimmer  PCWorldECOVACS now makes robotic dog companions and pool cleaners  Android HeadlinesECOVACS showcases next generation robotic solutions that work inside and outside your home  techguide.com.au

Ecovacs adds a mop cover to its flagship robot vacuum  The VergeECOVACS Showcases Its Acceleration Towards Full-Scenario Service Robotics at CES 2026  Yahoo FinanceEcovacs’ latest robot mower adds smarter mapping and an integrated trimmer  PCWorldECOVACS now makes robotic dog companions and pool cleaners  Android HeadlinesECOVACS showcases next generation robotic solutions that work inside and outside your home  techguide.com.au

Aireal is a new AI assistant for the Tapo smart home. | IMAGE: TP-Link Tapo, the smart home brand owned by networking giant TP-Link, is launching an AI assistant at CES this year - because isn't everyone? Aireal is designed to "bring AI into real life" (geddit?), according to the company, and will help you "understand your home, quickly fix Wi-Fi issues and control your devices using natural language." The assistant will work across TP-Link's smart home products and Wi-Fi networking devices and will live in the Tapo and Deco apps. This should allow you to use natural language (via dictation in the app) to create new smart home routines or control devices by describing what you want to happen. For example, "St … Read the full story at The Verge.

Ugreen announced at CES 2026 that it'll be branching into smart home territory with the launch of its SynCare product line of cameras. The series will consist of two indoor cams, the ID500 Pro and ID500 Plus, the OD600 Pro outdoor cam, and the Video Doorbell DB600 Pro. All of this gear will launch in the second half of 2026, with pricing to be announced at IFA 2026. Both indoor cams have 4K capture with pan and tilt support, f1.0 aperture for great low-light performance (and colorized night vision), and use multimodal AI to recognize people, pets, and events. The video doorbell offers head-to-toe 4K capture and intelligent detection for pac … Read the full story at The Verge.

LG’s CLOiD home robot is headed to CES 2026 with a bold demo plan, laundry automation, folding included, plus a breakfast routine that coordinates ThinQ connected appliances through a rolling home hub. The post LG’s CLOiD home robot wants to do your laundry and cook you breakfast appeared first on Digital Trends.

Ugreen makes plenty of things, but you’re probably familiar with the name in the context of its NAS systems (should that be NASes? Who knows). Naturally, the company has turned up to CES 2026 with the former, but it’s also branching out into home security. It’s announcing SynCare, an AI infused all-in-one surveillance platform which, it rather boldly claims, will become an “attentive, integrated guardian” of your home. Leading the pack is the SynCare Video Doorbell with head-to-toe 4K video, intelligent detection and 24/7 recording — especially if you’ve got it hooked up to your Ugreen NAS. That works in tandem with SynCare cameras offering 4K video on a pan-tilt base and, of course, AI to recognise “people, pets and key events.” Ugreen is also offering a tablet, the SynCare Smart Display, a “home hub” to let you manage your cameras from a single place in your home.  The company is quick to highlight the major benefit of an at-home system like this, which is no need to pay

arXiv:2601.02245v1 Announce Type: new Abstract: The rapid increase of Internet of Things (IoT) systems across several domains has led to the generation of vast volumes of sensitive data, presenting significant challenges in terms of storage and data analytics. Cloud-assisted IoT solutions offer storage, scalability, and computational resources, but introduce new security and privacy risks that conventional trust-based approaches fail to adequately mitigate. To address these challenges, this paper presents MOZAIK, a novel end-to-end privacy-preserving confidential data storage and distributed processing architecture tailored for IoT-to-cloud scenarios. MOZAIK ensures that data remains encrypted throughout its lifecycle, including during transmission, storage, and processing. This is achieved by employing a cryptographic privacy-enhancing technology known as computing on encrypted data (COED). Two distinct COED techniques are explored, specifically secure multi-party computation (MPC)

arXiv:2601.01701v1 Announce Type: new Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, with the advent of digital twins and data-driven decision-making, several statistical and machine-learning methods have been proposed. However, these methods face several challenges, such as dependence on only real sensor datasets, limited labeled data, high false alarm rates, and privacy concerns. To address these problems, we propose a suite of digital twin-integrated federated learning (DTFL) methods that enhance global model performance while preserving data privacy and communication efficiency. Specifically, we present five novel approaches: Digital Twin-Based Meta-Learning (DTML), Federated Parameter Fusion (FPF), Layer-wise Parameter Exchange (LPE), Cyclic Weight Adaptation (CWA), and Digital Twin Knowledge Distillation (DTKD). Each method introduces a unique mechanism to combine synthetic and

arXiv:2601.01308v1 Announce Type: new Abstract: The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, primarily due to the opacity of firmware components and the complexity of supply chain dependencies. IoT firmware frequently relies on outdated, third-party libraries embedded within monolithic binary blobs, making vulnerability management difficult. While Software Bill of Materials (SBOM) standards have matured, generating actionable intelligence from raw firmware dumps remains a manual and error-prone process. This paper presents a lightweight, automated pipeline designed to extract file systems from Linux-based IoT firmware, generate a comprehensive SBOM, map identified components to known vulnerabilities, and apply a multi-factor triage scoring model. The proposed system focuses on risk prioritization by integrating signals from the Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), and the CISA

arXiv:2601.01053v1 Announce Type: new Abstract: The proliferation of Internet of Things devices in critical infrastructure has created unprecedented cybersecurity challenges, necessitating collaborative threat detection mechanisms that preserve data privacy while maintaining robustness against sophisticated attacks. Traditional federated learning approaches for IoT security suffer from two critical vulnerabilities: susceptibility to Byzantine attacks where malicious participants poison model updates, and inadequacy against future quantum computing threats that can compromise cryptographic aggregation protocols. This paper presents a novel Byzantine-robust federated learning framework integrated with post-quantum secure aggregation specifically designed for real-time threat intelligence sharing across critical IoT infrastructure. The proposed framework combines a adaptive weighted aggregation mechanism with lattice-based cryptographic protocols to simultaneously defend against model

Linux and open source aren't making headlines at CES 2026, but they're working behind the scenes in embedded, automotive, and edge AI.

I've never seen a robot vacuum maneuver as well as Roborock's newest stair-climbing robot, the Saros Rover. Here's why it blew me away.

The Deebot Z12 OmniCyclone won’t get your carpets wet. | Image: Ecovacs Ecovacs launched the second generation of its top-of-the-line robot vacuum and mop at CES 2026 this week. The Deebot X12 OmniCyclone is the follow-up to the $1,500 X11, which launched at IFA in September 2025. Yes, just three months ago. The X12 keeps the same overall design of the X11, including the handy bagless dust bin in the multifunction dock, but adds a stain pretreat feature, a longer roller mop, and a smart cover for the mop to protect your carpets from getting damp. No other specs, pricing, or a release date have been announced yet. The midrange line also got an upgrade with the launch of the T90 Pro Omni, an upgrade from the T80 … Read the full story at The Verge.

Object avoidance is a tough nut to crack on robot vacuums, but Narwal's new Flow 2 claims it has the best yet with a mix of on-board processing an AI cloud computing.

The Eufy Smart Lock E40 offers wide-angle coverage and night vision. Anker is introducing a number of new devices for the new year as part of its Eufy line of smart home appliances, including the Eufy Video Doorbell S4, the Eufy Smart Lock E40, and the Eufy Solar Wall Light Cam S4. New designs and upgraded features are some of the main attractions of this year's models. The Eufy Video Doorbell S4 has a clean, simple design, much improved from predecessors such as the FamiLock S3 Max. The 3K camera provides 9MP resolution and a 180-degree panoramic view of the area surrounding the door. A new AI protocol provides motion sensing and facial recognition. The doorbell can be powered by either battery or wiring an … Read the full story at The Verge.

The robot vacuum also offers up to 30,000Pa of suction, but who cares about that. At CES 2026, Anker debuted the Eufy S2, a powerful robot vacuum and mop hybrid that cleans your floors while also perfuming your home. According to the company, the robovac features a built-in "aromatherapy system" with scent options that include bamboo, sage, bergamot, lychee, citrus, and basil. The idea is to perfume your space while the robot cleans, though how noticeable - or welcome - that will be in everyday use remains to be seen. Aside from trying to make your home smell like a spa, the Eufy S2 also is a powerful cleaning machine - at least on paper. It's rated for up to 30,000Pa of suction, placing it near the top of the category. … Read the full story at The Verge.

It seems like only a few years ago that Anker made nothing more than batteries and chargers. But 15 years into its history, the company's CES portfolio continues to illustrate how much it's expanded. Among other announcements, the company has a new robot vacuum, video doorbell, outdoor light and smart lock. They're all rolling out under Anker's Eufy smart home brand. The company hopes its Eufy Clean Robot Vacuum Omni S2 will be your next robovac. The $1,600 device vacuums with 100 AW suction, and it mops, too. Anker claims the vac works on shag carpets up to about 2 inches (5 cm) in pile height. It has an 11.4-inch rolling mop that applies up to 15 N of downward pressure. As is increasingly common in robovacs, Omni S2 uses AI to identify floor types and adjust several factors on the fly. These include cleaning mode, suction, scrubbing force and wheel height. The machine can also generate lightly oxidizing disinfectants (a hypochlorous acid and ozone water

As of Jan. 5, the Eufy X10 Pro Omni robot vacuum and mop is on sale at Amazon for $494.99. This is 45% off its list price of $899.99.

Zeroth Robotics debuted at CES 2026 with five robots, but only the 15-inch M1 home humanoid has near-term timing and pricing. Preorders are expected in Q1 2026. The post Zeroth wants your family to meet the Zeroth M1 home robot, plus a WALL-E lookalike appeared first on Digital Trends.

LG is pushing improved AI smart home integrations at CES 2026, aiming to create a more seamless and connected home experience.

arXiv:2601.00559v1 Announce Type: new Abstract: Smart home IoT platforms such as openHAB rely on Trigger Action Condition (TAC) rules to automate device behavior, but the interplay among these rules can give rise to interaction threats, unintended or unsafe behaviors emerging from implicit dependencies, conflicting triggers, or overlapping conditions. Identifying these threats requires semantic understanding and structural reasoning that traditionally depend on symbolic, constraint-driven static analysis. This work presents the first comprehensive evaluation of Large Language Models (LLMs) across a multi-category interaction threat taxonomy, assessing their performance on both the original openHAB (oHC/IoTB) dataset and a structurally challenging Mutation dataset designed to test robustness under rule transformations. We benchmark Llama 3.1 8B, Llama 70B, GPT-4o, Gemini-2.5-Pro, and DeepSeek-R1 across zero-, one-, and two-shot settings, comparing their results against oHIT's manually

arXiv:2601.00556v1 Announce Type: new Abstract: The rapid proliferation of Internet of Things (IoT) technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cybersecurity challenges. This survey aims to provide a comprehensive analysis of vulnerabilities, threats, and defense mechanisms, specifically focusing on the integration of network and application layers within real-time monitoring and decision-making systems. Employing an integrative review methodology, 59 scholarly articles published between 2009 and 2024 were selected from databases such as IEEE Xplore, ScienceDirect, and PubMed, utilizing keywords related to IoT vulnerabilities and security attacks. Key findings identify critical threat categories, including sensor vulnerabilities, Denial-of-Service (DoS) attacks, and public cloud insecurity. Conversely, the study highlights advanced defense approaches leveraging Artificial Intelligence (AI) for anomaly

arXiv:2601.00372v1 Announce Type: new Abstract: Being able to understand the security and privacy (S&P) concerns of IoT users brings benefits to both developers and users. To learn about users' views, we examine Amazon IoT reviews - one of the biggest IoT markets. This work presents a state-of-the-art methodology to identify and categorize reviews in which users express S&P concerns. We developed an automated pipeline by fine-tuning GPT-3.5-Turbo to build two models: the Classifier-Rationalizer-Categorizer and the Thematic Mapper. By leveraging dynamic few-shot prompting and the model's large context size, our pipeline achieved over 97% precision and recall, significantly outperforming keyword-based and classical ML methods. We applied our pipeline to 91K Amazon reviews about fitness trackers, smart speakers and cameras, over multiple years. We found that on average 5% contained S&P concerns, while security camera exhibited the highest prevalence at 10%. Our method detected

arXiv:2601.00341v1 Announce Type: new Abstract: As the transition from 5G to 6G unfolds, a substantial increase in Internet of Things (IoT) devices is expected, enabling seamless and pervasive connectivity across various applications. Accommodating this surge and meeting the high capacity demands will necessitate the integration of NonTerrestrial Networks (NTNs). However, the extensive coverage area of satellites, relative to terrestrial receivers, will lead to a high density of users attempting to access the channel at the same time, increasing the collision probability. In turn, the deployment of mega constellations make it possible for ground users to be in visibility of more than one satellite at the same time, enabling receiver diversity. Therefore, in this paper, we evaluate the impact of multi-receivers in scenarios where IoT nodes share the channel following a non-orthogonal multiple access (NOMA)irregular repetition slotted ALOHA (IRSA) protocol. Considering the impairments

Tokyo, Japan (SPX) Dec 31, 2025 Researchers from Zhengzhou University, the University of Kent, and City University of Hong Kong have developed a framework to evaluate and optimize the spatiotemporal resilience of Internet of Things (IoT)-enabled unmanned system of systems operating in complex missions. The work responds to the growing use of unmanned aerial vehicles and unmanned vehicles in hazardous environments where maintai

In an era marked by rapid technological advancements, the Internet of Things (IoT) stands as a pivotal force reshaping the fabric of daily life and industry. With smart devices becoming omnipresent, the need for robust frameworks to manage such extensive networks effectively is more critical than ever. A novel approach proposed by researchers, led by […]

Lots of time to think about the robot uprising while folding laundry. LG teased that it would be showing off a new robot for a "zero labor home" at CES. We now have a bit more detail on what to expect. The company says that its CLOiD home robot can fetch milk from the fridge, put a croissant in the oven, and even do some laundry, including folding and stacking clothes. CLOiD isn't the first laundry-folding robot we've seen, it's not even the only one at CES this year - SwitchBot's Onero H1 will also be able to tackle your hamper. LG's does seem particularly impressive, at least on paper. While the Onero looks like someone stuck some arms on Stop & Shop's Marty, CLOiD has two fully articulated arms with seven … Read the full story at The Verge.

The new Narwal Flow 2 promises strong cleaning power, complemented by high-end AI features.

LAS VEGAS — LG Electronics will unveil its next-generation artificial intelligence (AI) home robot, LG CLOiD, at CES 2026 when it opens Tuesday (local time) in Las Vegas, the company said Sunday. The robot embodies LG’s vision for the future of AI-powered homes, demonstrating how intelligent machines can autonomously manage household chores, control home appliances and interact naturally with residents by understanding their routines and surrounding environment. The debut of LG CLOiD aligns with LG Electronics’ long-term goal of achieving a “Zero Labor Home, Makes Quality Time” — a living space where advanced appliances and services free people from routine housework and allow them to focus on higher-value activities. LG has been steadily advancing its vision of AI-enabled appliances through its UP appliances platform that provides continuous software updates and subscription-based services designed to reduce maintenance burdens. Visitors at CES 2026 will experience how LG CLOiD brings

The Mui Board is a minimalist, screen-free interface for controlling tech in your home. One of the fun parts of being a tech journalist for over a decade is that occasionally you get to watch a truly unique gadget go from concept to reality - and, eventually, into your living room. That's the case with the Mui Board, a smart home controller built into a piece of wood. The Mui was first demoed at CES in 2019, and I've seen it at several shows over the years, in various iterations, always with the promise that it would ship soon. Well, this year it did (in limited quantities!), and I finally got to try one out in my living room. Mui Board A minimalist smart home controller made from a piece of wood, the Mui Board works with Mat … Read the full story at The Verge.

Samsara Inc. (NYSE:IOT) is one of the Best AI Stocks to Buy under $50. On December 22, Evercore highlighted Microsoft, Salesforce, and Oracle as its top enterprise software picks for 2026. Besides these names, Samsara Inc. was highlighted as a favored idea, alongside Intuit and Snowflake. According to the analysts, these names pair secular AI […]

The Verge: What to expect at CES 2026: laptops with new chips from Intel, Qualcomm, and AMD, more AI integrations, smart home robotics, smart glasses, and more  —  Expect plenty of laptops, smart home tech, and TVs — and lots of robots.  —  The biggest tech show of the year kicks off next week …

Exclusive: CEO responds to iRobot co-founder's suggestion that Roomba data would be transferred to China.

arXiv:2512.23849v1 Announce Type: new Abstract: Detection-based security fails against sophisticated attackers using encryption, stealth, and low-rate techniques, particularly in IoT/edge environments where resource constraints preclude ML-based intrusion detection. We present Economic Denial Security (EDS), a detection-independent framework that makes attacks economically infeasible by exploiting a fundamental asymmetry: defenders control their environment while attackers cannot. EDS composes four mechanisms adaptive computational puzzles, decoy-driven interaction entropy, temporal stretching, and bandwidth taxation achieving provably superlinear cost amplification. We formalize EDS as a Stackelberg game, deriving closed-form equilibria for optimal parameter selection (Theorem 1) and proving that mechanism composition yields 2.1x greater costs than the sum of individual mechanisms (Theorem 2). EDS requires

Score the self-emptying Shark AI Ultra Robot Vacuum for just $249.99 (save 55%) at Amazon.

Exclusive: There are some intriguing plans for the original robot vacuum brand following recent company takeover.

BlackBerry Limited (NYSE:BB) is one of the best NYSE stocks under $5 to buy. On December 19, RBC Capital reaffirmed its Sector Perform rating on BlackBerry Limited (NYSE:BB) stock and a $4.50 price target. This decision followed BlackBerry’s Q3 FY26 earnings. RBC noted that although BlackBerry beat estimates and raised guidance, the reliance on one-time […]

In a groundbreaking study published in Scientific Reports, researchers led by B. Panjavarnam, along with N. Kanimozhi and S.R. Nisha, have introduced a novel approach to solve one of the pressing challenges in the rapidly expanding field of Internet of Things (IoT) within edge computing environments. This new method revolves around a quantum-inspired enhancement of […]

If you’ve been waiting for a robot vacuum deal that’s more than a token discount, this one qualifies. The bObsweep UltraVision Pet self-empty robot vacuum and mop is $269.99 (compared value $1,129.99), which is an $860 price cut. The big reason it’s worth paying attention is timing: this is being positioned as a very limited-time […] The post Save $860 on a self-empty robot vacuum and mop, now just $269.99 appeared first on Digital Trends.

The US Cyber Trust Mark Program, an Energy Star-style certification for smart home security, could be winding down less than a year after it launched. Safety testing company UL Solutions has announced that it is stepping down as the program's lead administrator, just a few months after the Federal Communications Commission (FCC) began investigating it over ties to China. The Cyber Trust Mark Program hasn't been officially shut down yet, but the loss of its lead administrator leaves it in limbo. It wouldn't be the first security program the FCC axed this year. In November, the FCC rolled back cybersecurity regulations for telecom companies … Read the full story at The Verge.

The Gardyn Studio is that rare piece of tech that looks good inside my home. I can't grow anything. Multiple attempts to create a cottage garden, first in Idaho and now in South Carolina, have brought disappointment. Both are challenging climates, but where others have succeeded, I've been left with little more than a pile of cherry tomatoes for my vast efforts (those things are bulletproof). I'd all but given up on the idea of ever successfully growing my own food - I can't even keep those pots of herbs you buy at the grocery store alive for more than a week - until I met the Gardyn Studio 2. A smart indoor garden, the Gardyn Studio 2 is an automated growing platform that deploys AI to do what I failed to do: w … Read the full story at The Verge.

As CNET's lead vacuum expert, here are my four predictions for robot vacuums at CES 2026 and beyond.

arXiv:2512.22151v1 Announce Type: cross Abstract: As agriculture faces increasing pressure from water scarcity, especially in regions like Tunisia, innovative, resource-efficient solutions are urgently needed. This work explores the integration of indoor vertical hydroponics with Machine Learning (ML) techniques to optimize basil yield while saving water. This research develops a prediction system that uses different ML models and assesses their performance. The models were systematically trained and tested using data collected from IoT sensors of various environmental parameters like CO2, light. The experimental setup features 21 basil crops and uses Raspberry Pi and Arduino. 10k data points were collected and used to train and evaluate three ML models: Linear Regression (LR), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN). The comparative analysis of the performance of each model revealed that, while LSTM showed high predictive capability and accuracy of 99%, its

arXiv:2512.23493v1 Announce Type: new Abstract: In this article, we consider an industrial internet of things (IIoT) network supporting multi-device dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect. A joint link adaptation (LA) and device scheduling (including the order) design is provided, aiming at maximizing the total transmission rate under strict block error rate (BLER) constraints. In particular, a Bayesian optimization (BO) driven Twin Delayed Deep Deterministic Policy Gradient (TD3) method is proposed, which determines the device served order sequence and the corresponding modulation and coding scheme (MCS) adaptively based on the imperfect CSI. Note that the imperfection of CSI, error sample imbalance in URLLC networks, as well as the parameter sensitivity nature of the TD3 algorithm likely diminish the algorithm's convergence speed and reliability. To address such an issue, we proposed a BO based training

arXiv:2512.22860v1 Announce Type: new Abstract: Securing blockchain-enabled IoT networks against sophisticated adversarial attacks remains a critical challenge. This paper presents a trust-based delegated consensus framework integrating Fully Homomorphic Encryption (FHE) with Attribute-Based Access Control (ABAC) for privacy-preserving policy evaluation, combined with learning-based defense mechanisms. We systematically compare three reinforcement learning approaches -- tabular Q-learning (RL), Deep RL with Dueling Double DQN (DRL), and Multi-Agent RL (MARL) -- against five distinct attack families: Naive Malicious Attack (NMA), Collusive Rumor Attack (CRA), Adaptive Adversarial Attack (AAA), Byzantine Fault Injection (BFI), and Time-Delayed Poisoning (TDP). Experimental results on a 16-node simulated IoT network reveal significant performance variations: MARL achieves superior detection under collusive attacks (F1=0.85 vs. DRL's 0.68 and RL's 0.50), while DRL and MARL both attain

arXiv:2512.22690v1 Announce Type: new Abstract: Motion capture remains costly and complex to deploy, limiting use outside specialized laboratories. We present Mesquite, an open-source, low-cost inertial motion-capture system that combines a body-worn network of 15 IMU sensor nodes with a hip-worn Android smartphone for position tracking. A low-power wireless link streams quaternion orientations to a central USB dongle and a browser-based application for real-time visualization and recording. Built on modern web technologies -- WebGL for rendering, WebXR for SLAM, WebSerial and WebSockets for device and network I/O, and Progressive Web Apps for packaging -- the system runs cross-platform entirely in the browser. In benchmarks against a commercial optical system, Mesquite achieves mean joint-angle error of 2-5 degrees while operating at approximately 5% of the cost. The system sustains 30 frames per second with end-to-end latency under 15ms and a packet delivery rate of at least 99.7%

arXiv:2512.22488v1 Announce Type: new Abstract: Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift. Existing solutions typically rely on periodic classifier retraining, resulting in high computational overhead and the risk of catastrophic forgetting. To address these challenges, this paper proposes a scalable framework for adaptive IoT threat detection that eliminates the need for continuous classifier retraining. The proposed approach trains a classifier once on latent-space representations of historical traffic, while an alignment model maps incoming traffic to the learned historical latent space prior to classification, thereby preserving knowledge of previously observed attacks. To capture inter-instance relationships among attack samples, the

Nikkei Asia: Shares of Shenzhen-based OneRobotics opened flat in their HK debut after the Chinese home robotics maker raised $210M by selling 22M+ shares at about $9.50 each  —  HONG KONG — Shares of OneRobotics opened flat on Tuesday in their trading debut after the Chinese household robotics maker …

Samsung's newest device lineup includes Q-Series soundbars and WiFi speakers.

CES 2026 promises big AI opportunities for the smart home. Here are my top four predictions.

Chinese household appliances maker Dreame Technology will present gifts of gold and a trip to Antarctica to employees, on top of their year-end bonuses, as the company boosted its position as one of the world’s leading vendors of robot vacuum cleaners. The additional largesse was revealed over the weekend by Dreame founder and CEO Yu Hao in his WeChat Moments post. Yu said every employee will receive a one-gram gold bonus in addition to their standard year-end payout. The company also planned to...

arXiv:2512.21817v1 Announce Type: new Abstract: Intelligent IoT systems increasingly rely on large language models (LLMs) to generate task-execution methods for dynamic environments. However, existing approaches lack the ability to systematically produce new methods when facing previously unseen situations, and they often depend on fixed, device-specific logic that cannot adapt to changing environmental conditions.In this paper, we propose Method Decoration (DeMe), a general framework that modifies the method-generation path of an LLM using explicit decorations derived from hidden goals, accumulated learned methods, and environmental feedback. Unlike traditional rule augmentation, decorations in DeMe are not hardcoded; instead, they are extracted from universal behavioral principles, experience, and observed environmental differences. DeMe enables the agent to reshuffle the structure of its method path-through pre-decoration, post-decoration, intermediate-step modification, and step

arXiv:2512.21801v1 Announce Type: new Abstract: AI data centers which are GPU centric, have adopted liquid cooling to handle extreme heat loads, but coolant leaks result in substantial energy loss through unplanned shutdowns and extended repair periods. We present a proof-of-concept smart IoT monitoring system combining LSTM neural networks for probabilistic leak forecasting with Random Forest classifiers for instant detection. Testing on synthetic data aligned with ASHRAE 2021 standards, our approach achieves 96.5% detection accuracy and 87% forecasting accuracy at 90% probability within plus or minus 30-minute windows. Analysis demonstrates that humidity, pressure, and flow rate deliver strong predictive signals, while temperature exhibits minimal immediate response due to thermal inertia in server hardware. The system employs MQTT streaming, InfluxDB storage, and Streamlit dashboards, forecasting leaks 2-4 hours ahead while identifying sudden events within 1 minute. For a typical

arXiv:2512.21589v1 Announce Type: new Abstract: Smart home automation that adapts to a user's emotional state can enhance psychological safety in daily living environments. This study proposes an emotion-aware automation framework guided by the emotional Biologically Inspired Cognitive Architecture (eBICA), which integrates appraisal, somatic responses, and behavior selection. We conducted a proof-of-concept experiment in a pseudo-smart-home environment, where participants were exposed to an anxiety-inducing event followed by a comfort-inducing automation. State anxiety (STAI-S) was measured throughout the task sequence. The results showed a significant reduction in STAI-S immediately after introducing the avoidance automation, demonstrating that emotion-based control can effectively promote psychological safety. Furthermore, an analysis of individual characteristics suggested that personality and anxiety-related traits modulate the degree of relief, indicating the potential for

arXiv:2512.21374v1 Announce Type: new Abstract: Smart home IoT systems rely on authentication mechanisms to ensure that only authorized entities can control devices and access sensitive functionality. In practice, these mechanisms must balance security with usability, often favoring persistent connectivity and minimal user interaction. This paper presents an empirical analysis of authentication enforcement in deployed smart home IoT devices, focusing on how authentication state is established, reused, and validated during normal operation and under routine network conditions. A set of widely deployed consumer devices, including smart plugs, lighting devices, cameras, and a hub based ecosystem, was evaluated in a controlled residential environment using passive network measurement and controlled interaction through official mobile applications. Authentication behavior was examined during initial pairing, over extended periods of operation, after common network changes, and under replay

arXiv:2512.21368v1 Announce Type: new Abstract: The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects specific to the IoT environment. This study aims to bridge this gap. This research presents a thorough survey focused on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a holistic understanding of lightweight ciphers, emphasizing their security strength, which is an essential consideration for real-time and resource-constrained applications. Furthermore, we propose two taxonomies: one for classifying IoT applications based on their

Robovacs have come a long way in the decade I've been testing them for... but there's still room for improvement.

A paralysed Chinese man who can move only one finger and one toe has successfully developed a smart farm control system and founded a start-up. The determination of Li Xia, who is reliant on a ventilator, has deeply moved countless people across China. Li, 36, from Chongqing in southwestern China, was diagnosed with muscular dystrophy at the age of five. He was forced to drop out of school in the fifth grade. Since then, studying kept him going. He was most fascinated by physics and computer...

Whether you were on the nice list or just decided to treat yourself, I’m gifting you with three Alexa features you’ll be glad you tried.

CLOiD's technical design emphasizes dexterity and precision, departing from earlier LG home robots that relied primarily on wheels and voice assistance. The new model features two articulated arms, each powered by motors with seven degrees of freedom, enabling multidirectional joint movement comparable to that of a human arm.Read Entire Article

LG's CLOiD robot will help out around the house thanks to AI and various sensors and limbs.

Aqara is introducing a new presence sensor that is a natural addition for HomeKit users who want reliable automations without the need for a camera. The new Aqara FP300 combines mmWave presence sensing with a traditional PIR sensor and also adds on light, temperature, and humidity readings. For Apple users, the big story is Thread and Matter support, allowing it to easily integrate with the Home app for fast, room-aware automations. more…

LG is getting ready to take the wraps off a new robot it claims is capable of performing a "wide range" of household chores. The robot, called LG CLOiD, will make its debut at CES next month, featuring two articulated arms and five individually actuated fingers on each hand. Though we only have a description and two images showing LG CLOiD's five-fingered hands, it already seems a lot different from the two-wheeled home companion LG revealed last year, which came with a handle attached to its head rather than two arms. This image appears to show LG CLOiD grabbing a towel. | Image: LG" data-portal-copyright="Image: LG"> LG says CLOiD's motor-equipped arms operate with seven degrees of freedom, indicating a more human-like range of motion an … Read the full story at The Verge.

It's one of the most frustrating problems you can experience with your robot vacuums – here's how to restore your connection.

In a remarkable stride towards the future of education technology, a new automated English essay scoring system has emerged, harnessing the unparalleled capabilities of deep learning algorithms integrated with the Internet of Things (IoT). The system, which was extensively developed by researcher Tiantian W., promises not only to streamline the grading process but also to […]

arXiv:2512.21144v1 Announce Type: new Abstract: The proliferation of Internet-of-things (IoT) infrastructures and the widespread adoption of traffic encryption present significant challenges, particularly in environments characterized by dynamic traffic patterns, constrained computational capabilities, and strict latency constraints. In this paper, we propose DMLITE, a diffusion model and large language model (LLM) integrated traffic embedding framework for network traffic detection within resource-limited IoT environments. The DMLITE overcomes these challenges through a tri-phase architecture including traffic visual preprocessing, diffusion-based multi-level feature extraction, and LLM-guided feature optimization. Specifically, the framework utilizes self-supervised diffusion models to capture both fine-grained and abstract patterns in encrypted traffic through multi-level feature fusion and contrastive learning with representative sample selection, thus enabling rapid adaptation to

arXiv:2512.20997v1 Announce Type: new Abstract: The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has emerged as a promising paradigm, integrating reasoning, planning, and adaptation to enable QoE-aware network management. In this paper, we explore the integration of agentic AI into QoE-aware network slicing for IIoT. We first review the network slicing management architecture, QoE metrics for IIoT applications, and the challenges of dynamically managing heterogeneous network slices, while highlighting the motivations and advantages of adopting agentic AI. We then present the workflow of agentic AI-based slicing management, illustrating the full lifecycle of AI agents from processing

arXiv:2512.20639v1 Announce Type: new Abstract: In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized

arXiv:2512.20623v1 Announce Type: new Abstract: Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural

Ikea has made some of its Matter-compatible smart home devices even cheaper.  The VergeIkea’s New Matter over Thread Products - Bulb, Contact Sensor, Leak Sensor (video) -  Homekit News and Reviews

Ikea has made some of its Matter-compatible smart home devices even cheaper.  The VergeIkea’s New Matter over Thread Products - Bulb, Contact Sensor, Leak Sensor (video) -  Homekit News and Reviews

arXiv:2512.20004v1 Announce Type: new Abstract: Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings. First, we demonstrate the effectiveness of graph-based classification using a Graph Neural Network (GNN)-based classifier to generate API graph embeddings. The graph embeddings are combined with Permission and Intent features to train multiple machine learning and deep learning models for Android malware detection. The proposed classification approach achieves an accuracy of 98.33 percent on the CICMaldroid dataset and 98.68 percent on the Drebin dataset. However, graph-based deep learning models are vulnerable, as attackers can add fake relationships to evade detection by the classifier. Second, we propose a Generative Adversarial Network

arXiv:2512.19945v1 Announce Type: new Abstract: Securing Internet of Things (IoT) firmware remains difficult due to proprietary binaries, stripped symbols, heterogeneous architectures, and limited access to executable code. Existing analysis methods, such as static analysis, symbolic execution, and fuzzing, depend on binary visibility and functional emulation, making them unreliable when firmware is encrypted or inaccessible. To address this limitation, we propose a binary-free, architecture-agnostic solution that estimates the likelihood of conceptual zero-day vulnerabilities using only high-level descriptors. The approach integrates a tri-LLM reasoning architecture combining a LLaMA-based configuration interpreter, a DeepSeek-based structural abstraction analyzer, and a GPT-4o semantic fusion model. The solution also incorporates LLM computational signatures, including latency patterns, uncertainty markers, and reasoning depth indicators, as well as an energy-aware symbolic load

All I want for Christmas is a Dyson robovac redemption – can the Spot+Scrub Ai deliver it?

This morning, I asked my Alexa-enabled Bosch coffee machine to make me a coffee. Instead of running my routine, it told me it couldn't do that. Ever since I upgraded to Alexa Plus, Amazon's generative-AI-powered voice assistant, it has failed to reliably run my coffee routine, coming up with a different excuse almost every time I ask. It's 2025, and AI still can't reliably control my smart home. I'm beginning to wonder if it ever will. The potential for generative AI and large language models to take the complexity out of the smart home, making it easier to set up, use, and manage connected devices, is compelling. So is the promise of a " … Read the full story at The Verge.

Resideo sued by Nebraska AG over rebranding footage-leaking Chinese cameras

These feature-packed home security cameras offer crisp 4K footage, solar charging, local storage for your videos, and AI smarts too – with no extra fees.

iRobot, the maker of Roomba, filed for bankruptcy due to intense Chinese competition and failed regulatory approvals. read more

arXiv:2512.19488v1 Announce Type: new Abstract: The widespread deployment of Internet of Things (IoT) devices requires intrusion detection systems (IDS) with high accuracy while operating under strict resource constraints. Conventional deep learning IDS are often too large and computationally intensive for edge deployment. We propose a lightweight IDS that combines SHAP-guided feature pruning with knowledge-distilled Kronecker networks. A high-capacity teacher model identifies the most relevant features through SHAP explanations, and a compressed student leverages Kronecker-structured layers to minimize parameters while preserving discriminative inputs. Knowledge distillation transfers softened decision boundaries from teacher to student, improving generalization under compression. Experiments on the TON\_IoT dataset show that the student is nearly three orders of magnitude smaller than the teacher yet sustains macro-F1 above 0.986 with millisecond-level inference latency. The results

arXiv:2512.19361v1 Announce Type: new Abstract: The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains, where perishable goods are highly susceptible to environmental conditions. Existing methods often lack adaptability to dynamic conditions and fail to optimize decision making in real time. To address these challenges, we propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction. This hybrid architecture captures temporal dependencies within sensor data, enabling robust and adaptive decision making. In alignment with interpretable artificial intelligence principles, a rule-based classifier environment is employed to provide transparent ground truth labeling of spoilage levels based on domain-specific thresholds. This structured design allows the agent to operate within clearly defined semantic boundaries,

arXiv:2512.19131v1 Announce Type: new Abstract: Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against single points of failure. However, statistical heterogeneity arising from non-identically distributed local data creates a fundamental challenge: nodes must learn personalized models adapted to their local distributions while selectively collaborating with compatible peers. Existing approaches either enforce a single global model that fits no one well, or rely on heuristic peer selection mechanisms that cannot distinguish between peers with genuinely incompatible data distributions and those with valuable complementary knowledge. We present Murmura, a framework that leverages evidential deep learning to enable trust-aware model personalization in DFL. Our key insight is that epistemic uncertainty from Dirichlet-based evidential models directly indicates peer compatibility: high

arXiv:2512.18604v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and limited battery capacity of UAVs. To address these issues, we formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it. Furthermore, we propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs and utilizes a mediator Q-network over a double deep Q-network (DDQN) to accelerate and stabilize training and improve performance. Experimental results in both simulated and real-world environments demonstrate the