IOT

From UV lights to jukebox style mop dispensers, robo-mops are more advanced than ever.

Shark’s newest robot vacuum uses a camera and UV light to detect stains. | Photo by Jennifer Pattison Tuohy / The Verge The Shark PowerDetect UV Reveal is SharkNinja's latest robot vacuum and mop. A flagship model with a multifunctional dock that empties the dustbin and refills and washes its mop, the Reveal's signature feature is a UV light designed to "find" stains on your floors. It costs $1,299.99 and is available now. Combined with an RGB camera to detect visible messes and obstacles, the UV light lets the vacuum spot stains that aren't visible under normal lighting, such as pet urine. When it encounters dirt, visible or not, the robot uses onboard AI to identify and decide how to clean it. Its cleaning tools include a vacuum with a single roller brus … Read the full story at The Verge.

As of March 3, save $100 on the Bissell FlexClean FurForce Robot Vacuum and Mop

The Shark UV Reveal can find dried stains that other robovacs miss. But at $1,299.99, its inability to mop large spills may be a dealbreaker.

Shark's new robot vacuum is going to reveal your embarrassing secrets. It uses a UV light to detect old, dried-up stains.

Courtesy of 1X. By Eduardo B. Sandoval, UNSW Sydney Last year, Norwegian-US tech company 1X announced a strange new product: “the world’s first consumer-ready humanoid robot designed to transform life at home”. Standing 168 centimetres tall and weighing in at 30 kilograms, the US$20,000 Neo bot promises to automate common household chores such as folding […]

The Shark UV Reveal is ideal for mostly hard floors and obstacle avoidance. Plus: It's bagless.

As the global population accelerates towards ten billion by mid-century, the urgent demand for more food production collides starkly with the imperative to conserve biodiversity. This growing tension challenges agricultural policies worldwide, urging a shift towards strategies that align food security with environmental sustainability. A pioneering article published in npj Sustainable Agriculture highlights a transformative […]

Google is rolling out Gemini for Home updates that makes commands smarter and more accurate. The post Gemini is getting smarter and a lot less annoying for smart home voice controls appeared first on Digital Trends.

arXiv:2603.02105v1 Announce Type: new Abstract: This paper presents the Distributed Adaptive Multi-Radio Cross-Layer Routing (DAMCR) protocol, designed to enhance reliability, adaptability, and energy efficiency in smart grid and industrial Internet of Things (IoT) communication networks. DAMCR integrates Chaotic Frequency-Hopping Spread Spectrum (C-FHSS) to improve physical-layer security and jamming resilience with Link-Adaptive Quality Power Control (LAQPC) to dynamically regulate transmission power based on instantaneous link quality and residual node energy. To meet heterogeneous traffic requirements, the protocol incorporates priority-aware message classification that differentiates between periodic monitoring data and time-critical fault and protection messages. The proposed framework is implemented and evaluated in MATLAB using a heterogeneous network composed of LoRa, Wi-Fi, and dual-radio nodes operating under AWGN, Rayleigh, and Rician fading environments. Extensive

arXiv:2603.01876v1 Announce Type: new Abstract: The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and

arXiv:2603.01554v1 Announce Type: new Abstract: The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an

Google Home just announced a bunch of Gemini, smart home updates rolling out now  9to5GoogleBig Google Home update lets Gemini describe live camera feeds  The VergeWhat’s New in Google Home: Expanded Triggers and Actions (March 2, 2026)  jetstream.blogGoogle fixes Gemini’s biggest Google Home frustrations  Android PoliceYour Google Home is about to get much better at listening and following orders (finally?!)  Android Authority

Google Home just announced a bunch of Gemini, smart home updates rolling out now  9to5GoogleBig Google Home update lets Gemini describe live camera feeds  The VergeWhat’s New in Google Home: Expanded Triggers and Actions (March 2, 2026)  jetstream.blogGoogle fixes Gemini’s biggest Google Home frustrations  Android PoliceYour Google Home is about to get much better at listening and following orders (finally?!)  Android Authority

Get the best robot vacuum deal. Save 54% on the Shark AV2501AE AI at Amazon.

Samsara Inc. (NYSE:IOT) is one of the stocks with explosive growth potential. On February 26, Samsara announced the launch of its latest-generation Asset Tag and the all-new, ultra-compact Asset Tag XS, designed to track high-value equipment of all sizes. These ruggedized devices are engineered for extreme environments, with the standard Asset Tag offering a six-year […]

Google has announced a number of updates to the Gemini for Home experience that include various fixes and improvements to voice controls for your smart home, addressing quite a few widespread and niche complaints. more…

A digital key standard from the folks that brought you Matter, Aliro introduces a universal system for smart locks. Here's how it will change your smart home.

AI continues to evolve alongside the modern smart home. Take a closer look at the latest ways to use AI at home to improve our quality of life

arXiv:2602.24209v1 Announce Type: new Abstract: Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model. By eliminating the need to transmit raw data, FL inherently preserves privacy. However, the heterogeneous nature of IoT data, stemming from differences in device capabilities, data formats, and communication constraints, poses significant challenges to maintaining both global model performance and privacy. In the context of IoT-based anomaly detection, unsupervised FL offers a promising means to identify abnormal behavior without centralized data aggregation. Nevertheless, feature heterogeneity across devices complicates model training and optimization, hindering effective implementation. In this study we propose an efficient unsupervised FL framework that enhances anomaly detection by leveraging

arXiv:2602.24166v1 Announce Type: new Abstract: Recently, RISC-V has contributed to the development of IoT devices, requiring architectures that balance energy efficiency, compact area, and integrated security. However, most recent RISC-V cores for IoT prioritize either area footprint or energy efficiency, while adding cryptographic support further compromises compactness. As a result, truly integrated architectures that simultaneously optimize efficiency and security remain largely unexplored, leaving constrained IoT environments vulnerable to performance and security trade-offs. In this paper, we introduce SAILOR, an energy-efficient and scalable ultra-lightweight RISC-V core family for cryptographic applications in IoT. Our design is modular and spans 1-, 2-, 4-, 8-, 16-, and 32-bit serialized execution data-paths, prioritizing minimal area. This modular design and adaptable data-path minimizes the overhead of integrating RISC-V cryptography extensions, achieving low hardware cost

arXiv:2602.24047v1 Announce Type: new Abstract: The growth and heterogeneity of IoT devices create security challenges where static identification models can degrade as traffic evolves. This paper presents a two-stage, flow-feature-based pipeline for unsupervised IoT device traffic profiling and incremental model updating, evaluated on selected long-duration captures from the Deakin IoT dataset. For baseline profiling, density-based clustering (DBSCAN) isolates a substantial outlier portion of the data and produces the strongest alignment with ground-truth device labels among tested classical methods (NMI 0.78), outperforming centroid-based clustering on cluster purity. For incremental adaptation, we evaluate stream-oriented clustering approaches and find that BIRCH supports efficient updates (0.13 seconds per update) and forms comparatively coherent clusters for a held-out novel device (purity 0.87), but with limited capture of novel traffic (share 0.72) and a measurable trade-off in

arXiv:2602.23874v1 Announce Type: new Abstract: Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This study evaluates the transferability of three widely used flow-based feature sets (Argus, Zeek and CICFlowMeter) across four widely used datasets representing heterogeneous IoT and Industrial IoT network conditions. Through extensive experiments, we evaluate in- and cross-domain performance across multiple classification models and analyze feature importance using SHapley Additive exPlanations (SHAP). Our results show that models trained on one domain suffer significant performance degradation when applied to a different target domain, reflecting the sensitivity of IoT intrusion detection systems to distribution shifts. Furthermore, the results evidence that the choice of classification algorithm and feature representations significantly impact transferability.

arXiv:2602.23846v1 Announce Type: new Abstract: The rapid expansion of Industrial IoT (IIoT) systems has amplified security challenges, as heterogeneous devices and dynamic traffic patterns increase exposure to sophisticated and previously unseen cyberattacks. Traditional intrusion detection systems often struggle in such environments due to their reliance on extensive labeled data and limited ability to detect new threats. To address these challenges, we propose MI$^2$DAS, a multi-layer intrusion detection framework that integrates anomaly-based hierarchical traffic pooling, open-set recognition to distinguish between known and unknown attacks and incremental learning for adapting to novel attack types with minimal labeling. Experiments conducted on the Edge-IIoTset dataset demonstrate strong performance across all layers. In the first layer, GMM achieves superior normal-attack discrimination (accuracy = 0.953, TPR = 1.000). In open-set recognition, GMM attains a recall of 0.813 for

arXiv:2602.23788v1 Announce Type: new Abstract: The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep durations. Deep-sleep scheduling is the primary lever to reduce energy consumption, since sleeping devices consume only a fraction of their idle power. By choosing its deep-sleep duration online, the sender has to find a trade-off between energy consumption and data quality degradation at the receiver, captured by a weighted sum of costs. We quantify data quality degradation via the recently introduced Goal-Oriented Tensor (GoT) metric, which can take both age and content of delivered data into account. We assume a Markovian observed process and

Upgrading your smart home doesn't have to cost an arm and a leg. In fact, it's easier than you think to find highly-rated smart gadgets under $20 each.

And its app is as cluttered as its name.

Welcome to Indie App Spotlight. This is a weekly 9to5Mac series where we showcase the latest apps in the indie app world. If you’re a developer and would like your app featured, get in contact. If you’re someone who spends a lot of time on their Mac, you might often find yourself wanting to control one of your lights, thermostats, or just check on your camera. At the same time, you might not love Apple’s Home app. Itsyhome adds all of the home controls you’ll need to your Mac’s menu bar – and it isn’t even HomeKit exclusive. It also supports Home Assistant, and has loads of useful features under the hood. more…

Robot vacuums are getting a bad rap — let me set the record straight.

Compared to CES 2026 vacuums, Eufy's new roller mop vacuum combo would save you hundreds and space on the floor. But does it clean well enough?

arXiv:2602.22794v1 Announce Type: new Abstract: Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions. Utilization of deep neural networks (DNNs) combined with semantic communication has emerged as a promising paradigm to address these limitations. Deep joint source-channel coding (DJSCC) has recently been proposed to enable semantic communication of images. Building upon the original DJSCC formulation, low-complexity attention-style architectures has been added to the DNNs for further performance enhancement. As a main hurdle, training these DNNs separately for various signal-to-noise ratios (SNRs) will amount to excessive storage or communication overhead, which can not be maintained by small IoT devices. SNR Adaptive DJSCC (ADJSCC), has been proposed to train the DNNs once but feed the current SNR as part of the data to the channel-wise

arXiv:2602.22525v1 Announce Type: new Abstract: Edge deployment of LLM agents on IoT hardware introduces attack surfaces absent from cloud-hosted orchestration. We present an empirical security analysis of three architectures (cloud-hosted, edge-local swarm, and hybrid) using a multi-device home-automation testbed with local MQTT messaging and an Android smartphone as an edge inference node. We identify five systems-level attack surfaces, including two emergent failures observed during live testbed operation: coordination-state divergence and induced trust erosion. We frame core security properties as measurable systems metrics: data egress volume, failover window exposure, sovereignty boundary integrity, and provenance chain completeness. Our measurements show that edge-local deployments eliminate routine cloud data exposure but silently degrade sovereignty when fallback mechanisms trigger, with boundary crossings invisible at the application layer. Provenance chains remain complete

arXiv:2602.22488v1 Announce Type: new Abstract: Distributed denial-of-service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility, and interpretability in operational environments remain insufficiently explored. This study presents an explainability-aware empirical evaluation of seven pre-trained convolutional neural network architectures for multi-class IoT DDoS detection using the CICDDoS2019 dataset and an image-based traffic representation. The analysis integrates performance metrics, reliability-oriented statistics (MCC, Youden Index, confidence intervals), latency and training cost assessment, and interpretability evaluation using Grad-CAM and SHAP. Results indicate that DenseNet and MobileNet-based architectures achieve strong detection performance while

Enterprise access control is what I would consider “legacy” technology. A lot of the systems are Windows-based and look like a fancy Access database. On top of that, you are still dealing with physical cards. It is a fragmented mess for users and an onboarding nightmare for IT departments as well. Today, the Connectivity Standards Alliance is stepping in to fix that with the official release of the Aliro 1.0 specification aiming to create a standard for how mobile devices unlock doors, badge in, etc. more…

As of Feb. 25, get the Shark AI Ultra Voice Control Robot Vacuum for half off at Amazon.

arXiv:2602.20453v1 Announce Type: new Abstract: Construction and operating of buildings is one of the major contributors to global greenhouse emissions. With the inefficient usage of energy due to human behavior and manual operation, the energy consumption of buildings is further increased. These challenges highlight the need for improved Building Energy Management Systems (BEMS) integrated with Internet of Things (IoT) and data driven intelligence to enhance energy-efficiency in a building and contribute to Net-Zero Energy Buildings (NZEB) targets. This paper offers four keys contributions: i) a systematic review of IoT enabled BEMS including components, network architecture and functional capabilities, ii) an evaluation of real-world BEMS datasets to support Artificial Intelligence (AI) based predictive control, iii) an analysis of integration challenges related to interoperability, smart grids and net-zero energy strategies, and iv) a case study highlighting global best practices,

We recently published an article titled 13 Best Internet of Things (IoT) Stocks to Buy Now. On February 17, Truist lowered its price target on Samsara Inc. (NYSE:IOT) to $30 from $39 while maintaining a Hold rating as part of a broader fourth-quarter preview within the security software space. The firm noted that the broader […]

We rated this Roborock bot as one of the very best robot vacuums for carpet.

If your robot vacuum is making a weird noise or just pushing dirt around, it might not be broken. It could just need a hygiene routine.

arXiv:2602.19990v1 Announce Type: new Abstract: Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink

arXiv:2602.18598v1 Announce Type: new Abstract: The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder's latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity

In this article, we will discuss the 13 Best Internet of Things (IoT) Stocks to Buy Now. Investing in IoT stocks provides exposure to a rapidly expanding ecosystem powered by industrial automation, 5G connectivity, artificial intelligence, and smart infrastructure. With the global IoT market projected to roughly double in value by 2027, adoption is accelerating […]

As of Feb. 23, you can get the eufy E25 robot vacuum-mop combo for $599.99, down from $899.99, at Amazon. That's a 33% discount or $300 savings.

We came across a bullish thesis on Samsara Inc. on T K’s Substack. In this article, we will summarize the bulls’ thesis on IOT. Samsara Inc.’s share was trading at $25.42 as of February 6th. IOT’s forward P/E was 44.25 according to Yahoo Finance. Samsara is the pioneer of the Connected Operations Cloud, providing a platform that allows […]

Those temptingly cheap sensors aren't quite such a bargain if you have to buy IKEA's own $109 / £60 hub to make them work.

As of Feb. 20, you can get the eufy Omni C20 robot vacuum-mop for just $349.98 at Amazon, down from $599.99.

arXiv:2602.17619v1 Announce Type: new Abstract: Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA's random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by

arXiv:2602.17114v1 Announce Type: new Abstract: With the availability of automation machinery and its superiority, are being slothful and inviting many diseases to invade them. The world still has so many places where people lack basic health facilities. Due to early detection and intervention, CDV can be cured to an extreme extent. It heavily reduces travel and associated costs. A remote ECG monitoring system enables community health workers to support and empower patients through telemedicine. However, there remains some financial and logistical burden. Heart disease cannot be taken lightly. These patients require regular health check-ups and the attention of health personnel in a short period if their health deteriorates suddenly and rapidly. Chronic diseases are extremely variable in their symptoms and evolution of treatment. Some, if not treated early, will end the patient's life. The trend of the INTERNET OF THINGS, IoT, is spreading massively. This paper focuses on the three

arXiv:2602.16738v1 Announce Type: new Abstract: Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge deployment. We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers. Edge agents perform lightweight feature extraction and pre-filtering; Fog agents execute diversified ensemble detection with dynamic consensus voting; and Cloud agents continuously optimize system policies via Proximal Policy Optimization (PPO) while maintaining asynchronous, non-blocking inference. The framework incorporates LLM-based response generation for

Chris Metinko / Axios: Cleveland-based Eagle Wireless, which makes cellular modules used in IoT devices, raised a $30M Series B as the US seeks to reduce its reliance on China  —  Eagle Wireless, a cellular module manufacturer, raised a $30 million Series B from Asymmetric Capital Partners and The O.H.I.O. Fund …

The Shark PowerDetect Self-Empty Robot Vacuum is on sale at Amazon for $329.99, down from the list price of $549.99. That's a 40% discount.

As of Feb. 18, the Eufy 11S MAX robot vacuum is discounted to $139.99 at Amazon, 50% off its list price of $279.99.

arXiv:2602.15457v1 Announce Type: new Abstract: Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for model selection in practice. We introduce an evaluation protocol with unified event-level augmentations that simulate real-world issues: calibrated sensor dropout, linear and log drift, additive noise, and window shifts. We also perform sensor-level probing via mask-as-missing zeroing with per-channel influence estimation to support root-cause analysis. We evaluate 14 representative models on five public anomaly datasets (SWaT, WADI, SMD, SKAB, TEP) and two industrial datasets (steam turbine, nuclear turbogenerator) using unified splits and event aggregation. There is no universal winner: graph-structured models transfer best under dropout and long events (e.g., on SWaT under additive noise

arXiv:2602.15263v1 Announce Type: new Abstract: An open measurement problem in IoT security is whether scan-observable network configurations encode population-level exposure risk beyond individual devices. An analysis of internet-exposed IoT endpoints using a controlled multi-country sample from Shodan Search and Shodan InternetDB, selecting 100 hosts identified via TCP port 7547 (TR-069/CWMP) and evenly distributed across the ten most represented countries. Hosts are enriched with scan-derived metadata and analyzed using feature-relevance assessment, cross-country comparisons of open and risky port exposure, and supervised classification of higher-risk exposure profiles. The analysis reveals consistent cross-country differences in exposure structure, with mean risky-port counts ranging from 0.4 to 1.0 per host, and achieves balanced accuracy of approximately 0.61 when classifying higher-risk exposure profiles.

What's behind the widespread adoption of extremely high-throughput wireless. The post Wi-Fi 7 Moves To The IoT appeared first on Semiconductor Engineering.

Robot vacuums are only worth it when they actually reduce the number of times you have to think about cleaning. A lot of models still need babysitting: empty the bin, refill the water, wash the mop, dry the pads, repeat. The Dreame L10s Ultra is built to cut that routine down dramatically. It’s $299.99 for […] The post This Dreame robot vacuum does the “hands-off” thing for real, and it’s down to $299.99 appeared first on Digital Trends.

Berlin, Germany (SPX) Feb 13, 2026 Researchers at Universidad Carlos III de Madrid (UC3M) have developed a new way for an assistive robot to learn how to move its arms autonomously by combining observational learning with communication between its limbs. The approach lets the robot watch how people perform everyday tasks and then adapt those movements so its two arms can work together safely and efficiently in domestic settings s

arXiv:2602.14391v1 Announce Type: new Abstract: Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases

arXiv:2602.13277v1 Announce Type: new Abstract: Mobile data collection using controllable sinks is an effective approach to improve energy efficiency and data freshness in densely deployed wireless sensor networks (WSNs). However, existing path-planning methods are often heuristic-driven and lack the flexibility to adapt to high-level operational objectives under dynamic network conditions. In this paper, we propose ID2P2, a intent-driven diffusion-based path planning framework for jointly addresses rendezvous point selection and mobile data collector (MDC) tour construction in IoT-enabled dense WSNs. High-level intents, such as latency minimization, energy balancing, or coverage prioritization, are explicitly modeled and incorporated into a generative diffusion planning process that produces feasible and adaptive data collection trajectories. The proposed approach learns a trajectory prior that captures spatial node distribution and network characteristics, enabling the MDC to

arXiv:2602.13205v1 Announce Type: new Abstract: Ultra-dense IoT networks require an effective non-orthogonal multiple access (NOMA) scheme, yet they experience intense interference because of fixed code assignment. We suggest a reinforcement learning (RL) model of dynamic Gold code assignment in IoT-NOMA networks. Our Markov Decision Process which is IoT aware is a joint optimization of throughput, energy efficiency, and fairness. Two RL algorithms are created, including Natural Policy Gradient (NPG) to learn stable discrete actions and Deep Deterministic Policy Gradient (DDPG) with continuous code embedding. Under smart city conditions, NPG can attain throughput of 11.6% and energy efficiency of 15.8 likewise superior to its performance with a static allocation. Nonetheless, the performance is worse in organized industrial settings, and the reliability is minimal (0-2%), which points to the fact that dynamic code assignment is not a sufficient measure of ultra-reliable IoT and needs

There are several smart home gadgets that can add convenience to your life, but here are five that can solve your everyday problems.

arXiv:2602.13062v1 Announce Type: new Abstract: The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment and replay reinforcement, enabling the implantation of persistent malicious behaviors that endure through updates and deployment cycles. This paper provides a comprehensive analysis of backdoor attacks on CCL within IoT systems. We formalize the objectives of embedding-level attacks, examine

arXiv:2602.12622v1 Announce Type: new Abstract: Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy-preserving framework for distributed IoT environments, current federated principal component analysis (PCA) methods lack the integration of personalization and robustness, which are critical for effective anomaly detection. To address these limitations, we propose an efficient personalized federated PCA (FedEP) method for anomaly detection in IoT networks. The proposed model achieves personalization through introducing local representations with the $\ell_1$-norm for element-wise sparsity, while maintaining robustness via enforcing local models with the $\ell_{2,1}$-norm for row-wise sparsity. To solve this non-convex problem, we develop a manifold optimization algorithm based on the alternating direction method of multipliers (ADMM) with rigorous

arXiv:2602.12543v1 Announce Type: new Abstract: The rise of heterogeneous Internet of Things (IoT) devices has raised security concerns due to their vulnerability to cyberattacks. Intrusion Detection Systems (IDS) are crucial in addressing these threats. Federated Learning (FL) offers a privacy-preserving solution, but IoT heterogeneity and limited computational resources cause increased latency and reduced performance. This paper introduces a novel approach Cluster-based federated intrusion detection with lightweight networks for heterogeneous IoT designed to address these limitations. The proposed framework utilizes a hierarchical IoT architecture that encompasses edge, fog, and cloud layers. Intrusion detection clients operate at the fog layer, leveraging federated learning to enhance data privacy and distributed processing efficiency. To enhance efficiency, the method employs the lightweight MobileNet model alongside a hybrid loss function that integrates Gumbel-SoftMax and

The Mova P50 Ultra is a standout vacuum and mop combo, priced competitively with its latest discount.

In an era where everything from toothbrushes to coffee makers connects to the internet, a recent discovery about a smart sleep mask has exposed just how dangerous our rush toward IoT connectivity can be. What started as a simple attempt to build a better control interface ended up revealing a cybersecurity nightmare that could allow strangers to monitor users' brainwaves and even send electrical impulses to sleeping individuals. The Discovery: From Convenience to Horror The story begins innocuou...

I've tested dozens of smart home gadgets, and Amazon's Echo Hub is still the most useful - even in 2026.

Building out a smart home setup can add all sorts of conveniences to your life, but even beyond that, it can save you some serious money in the long term.

With these wireless video doorbells you can avoid subscription fees and keep control of your recordings at home.

I’m just about starting to believe that I may be heading into the final stages of an extremely lengthy attempt to sell one apartment and buy another, so I’m currently giving some thought to the smart home technology I’ll want in my new home. This will be some 13 years after I first adopted smart home tech, so I’m expecting to make a few changes – some small, one potentially much larger … more…

As part of its acquisition by China-based Picea Robotics, iRobot is creating a new US-based subsidiary called iRobot Safe, which the company says will be "responsible for the protection of US consumer data," similar to the TikTok deal that was completed last month. iRobot customers in the US will be able to continue using their robovacs as usual, but now their data will be handled by iRobot Safe, rather than the company's new owner. In a press release published on January 23rd, iRobot stated that the creation of iRobot Safe is "designed to maintain a clear separation between iRobot's non-US ownership and its US and other global consumer dat … Read the full story at The Verge.

Find the best robot vacuum deal at Amazon. Save 38% on the roborock Qrevo Series at Amazon.

The Mova Mobius 60 is one of those cool inventions you see at trade shows, and now you can have it clean your floors.

arXiv:2602.12183v1 Announce Type: new Abstract: The rapid evolution of cyberattacks continues to drive the emergence of unknown (zero-day) threats, posing significant challenges for network intrusion detection systems in Internet of Things (IoT) networks. Existing machine learning and deep learning approaches typically rely on large labeled datasets, payload inspection, or closed-set classification, limiting their effectiveness under data scarcity, encrypted traffic, and distribution shifts. Consequently, detecting unknown attacks in realistic IoT deployments remains difficult. To address these limitations, we propose SiamXBERT, a robust and data-efficient Siamese meta-learning framework empowered by a transformer-based language model for unknown attack detection. The proposed approach constructs a dual-modality feature representation by integrating flow-level and packet-level information, enabling richer behavioral modeling while remaining compatible with encrypted traffic. Through

arXiv:2602.11775v1 Announce Type: new Abstract: Explanations are essential for helping users interpret and trust autonomous smart-home decisions, yet evaluating their quality and impact remains methodologically difficult in this domain. V-SHiNE addresses this gap: a browser-based smarthome simulation framework for scalable and realistic assessment of explanations. It allows researchers to configure environments, simulate behaviors, and plug in custom explanation engines, with flexible delivery modes and rich interaction logging. A study with 159 participants demonstrates its feasibility. V-SHiNE provides a lightweight, reproducible platform for advancing user-centered evaluation of explainable intelligent systems

Roomba's parent brand might have a new Chinese owner, but it's still a 'US-based global consumer robotics company'.

arXiv:2602.10762v1 Announce Type: new Abstract: The Internet of Things (IoT) security landscape requires the architectural solutions that can address the technical and operational challenges across the heterogeneous environments. The IoT systems operate in different conditions, and security issues continue to increase. This paper presents the comprehensive security framework for IoT that should integrate the Trusted Execution Environments (TEEs) with the semantic middleware and blockchain technologies. The work provides a systematic analysis of the architectural patterns based on more than twenty recent research works and the existing standards, and it proposes a layered security architecture. The architecture includes the hardware rooted trust at peripheral level, the zero trust principles at network level, and the semantic security mechanisms at application level. The framework focuses on practical implementation aspects such as the performance overhead, interoperability

The Dreame X60 Max Ultra Complete is just 3.13 inches thick, making it great for reaching the dust bunnies hiding under low-clearance furniture. The post Dreame’s latest robot vacuum is slim enough to tackle the dust hiding under your sofa appeared first on Digital Trends.

A new Super Bowl ad is raising questions about the power of doorbell cameras.

Updated Home app is required for Matter support and some types of accessories.

Samsara Inc. (NYSE:IOT) is one of the 11 Best Beaten Down Growth Stocks to Buy Now. Piper Sandler, on February 6, cut its target price on Samsara by 24.4% to $37 (from $49) but kept its Overweight call on the shares. The firm noted that while overall investor sentiment for software stocks was at an all-time […]

SAN FRANCISCO, Calif. — America’s farm families are aging alongside their operations. The average U.S. farm producer is now 58.1 years old—part of a decades-long trend that has one in three farmers over 65, according to the CDC’s National Institute for Occupational Safety and Health. That experience brings invaluable knowledge, but physical realities shift too. […] The post Still Farming Strong: How Smart Home Mobility Keeps Seniors Active on the Land appeared first on Morning Ag Clips.

arXiv:2602.09970v1 Announce Type: cross Abstract: Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable continuous in situ audio collection at scale. While recent self-supervised learning (SSL)-based audio encoders, such as BEATs and AVES, have shown strong performance in bioacoustic tasks, their computational cost and limited robustness to unseen environments hinder deployment on resource-constrained platforms. In this work, we introduce BioME, a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model, enabling strong representational transfer while reducing the parameter count by 75%. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the

arXiv:2602.09515v1 Announce Type: new Abstract: This paper presents an Internet of Things (IoT) application that utilizes an AI classifier for fast-object detection using the frame difference method. This method, with its shorter duration, is the most efficient and suitable for fast-object detection in IoT systems, which require energy-efficient applications compared to end-to-end methods. We have implemented this technique on three edge devices: AMD AlveoT M U50, Jetson Orin Nano, and Hailo-8T M AI Accelerator, and four models with artificial neural networks and transformer models. We examined various classes, including birds, cars, trains, and airplanes. Using the frame difference method, the MobileNet model consistently has high accuracy, low latency, and is highly energy-efficient. YOLOX consistently shows the lowest accuracy, lowest latency, and lowest efficiency. The experimental results show that the proposed algorithm has improved the average accuracy gain by 28.314%, the

arXiv:2602.09263v1 Announce Type: new Abstract: Cloud-mediated IoT architectures fragment authentication across vendor silos and create latency and availability bottlenecks for cross-vendor device-to-device (D2D) interactions. We present Atlas, a framework that extends the Web public-key infrastructure to IoT by issuing X.509 certificates to devices via vendor-operated ACME clients and vendor-controlled DNS namespaces. Devices obtain globally verifiable identities without hardware changes and establish mutual TLS channels directly across administrative domains, decoupling runtime authentication from cloud reachability. We prototype Atlas on ESP32 and Raspberry Pi, integrate it with an MQTT-based IoT stack and an Atlas-aware cloud, and evaluate it in smart-home and smart-city workloads. Certificate provisioning completes in under 6s per device, mTLS adds only about 17ms of latency and modest CPU overhead, and Atlas-based applications sustain low, predictable latency compared to

arXiv:2602.09254v1 Announce Type: new Abstract: Bystander privacy in smart homes has been widely studied in Western contexts, yet it remains underexplored in non-Western countries such as China. In this study, we analyze 49 Chinese smart home apps using a mixed-methods approach, including privacy policy review, UX/UI evaluation, and assessment of Apple App Store privacy labels. While most apps nominally comply with national regulations, we identify significant gaps between written policies and actual implementation. Our traceability analysis highlights inconsistencies in data controls and a lack of transparency in data-sharing practices. Crucially, bystander privacy -- particularly for visitors and non-user individuals -- is largely absent from both policy documents and interface design. Additionally, discrepancies between privacy labels and actual data practices threaten user trust and undermine informed consent. We provide design recommendations to strengthen bystander protections,

arXiv:2602.09239v1 Announce Type: new Abstract: The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers' homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing how AI analytics, residual data logs, and cross-household data flows shaped the privacy risks faced by participants. In employer-controlled homes, AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance and constrained participants' ability to negotiate privacy boundaries. In their own homes, participants had greater control as device owners but still faced challenges, including

As of Feb. 10, get $500 off the Roborock Saros 10R robot vacuum.

arXiv:2602.07630v1 Announce Type: new Abstract: Blockchain offers a decentralized trust framework for the Internet of Things (IoT), yet deploying consensus in spectrum-congested and dynamic wireless edge IoT networks faces fundamental obstacles: traditional BFT protocols are spectrum-ignorant, leading to inefficient resource utilization and fragile progress under time-varying interference. This paper presents \textit{Wireless Streamlet}, a spectrum-aware and cognitive consensus protocol tailored for wireless edge IoT. Building on Streamlet's streamlined structure, we introduce a \textit{Channel-Aware Leader Election (CALE)} mechanism. CALE serves as a verifiable cross-layer cognitive engine that leverages receiver-measured channel state information (CSI) piggybacked in signed votes to derive Byzantine-robust connectivity scores from notarization certificates, and deterministically selects a unique weighted leader per epoch from finalized history, thereby improving proposal

arXiv:2602.08989v1 Announce Type: new Abstract: The proliferation of Multi-Radio Access Technology, Internet of Things devices, particularly Unmanned Aerial Vehicles operating across LoRaWAN, 5G/4G cellular, Meshtastic mesh, proprietary protocols such as DJI OcuSync, MAVLink telemetry links, Wi-Fi, and satellite, creates a fundamental and hitherto unexamined challenge for Zero Trust Architecture adoption. Each transition between radio access technologies constitutes a trust boundary crossing: the device exits one network trust domain and enters another, potentially invalidating authentication state, device attestation, and contextual trust signals. Current ZTA frameworks assume relatively stable network environments and do not address the trust implications of frequent, dynamic RAT switching in mobile IoT deployments.

arXiv:2602.08593v1 Announce Type: new Abstract: We present Kissan-Dost, a multilingual, sensor-grounded conversational system that turns live on-farm measurements and weather into plain-language guidance delivered over WhatsApp text or voice. The system couples commodity soil and climate sensors with retrieval-augmented generation, then enforces grounding, traceability, and proactive alerts through a modular pipeline. In a 90-day, two-site pilot with five participants, we ran three phases (baseline, dashboard only, chatbot only). Dashboard engagement was sporadic and faded, while the chatbot was used nearly daily and informed concrete actions. Controlled tests on 99 sensor-grounded crop queries achieved over 90 percent correctness with subsecond end-to-end latency, alongside high-quality translation outputs. Results show that careful last-mile integration, not novel circuitry, unlocks the latent value of existing Agri-IoT for smallholders.

arXiv:2602.08446v1 Announce Type: new Abstract: Federated learning (FL) is a decentralized learning paradigm widely adopted in resource-constrained Internet of Things (IoT) environments. These devices, typically relying on TinyML models, collaboratively train global models by sharing gradients with a central server while preserving data privacy. However, as data heterogeneity and task complexity increase, TinyML models often become insufficient to capture intricate patterns, especially under extreme non-IID (non-independent and identically distributed) conditions. Moreover, ensuring robustness against malicious clients and poisoned updates remains a major challenge. Accordingly, this paper introduces RIFLE - a Robust, distillation-based Federated Learning framework that replaces gradient sharing with logit-based knowledge transfer. By leveraging a knowledge distillation aggregation scheme, RIFLE enables the training of deep models such as VGG-19 and Resnet18 within constrained IoT

arXiv:2602.08170v1 Announce Type: new Abstract: The Internet of Things (IoT) has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has recently emerged as a promising technique for detecting malware activity based on device power consumption patterns. However, the resilience of such detection systems under adversarial manipulation remains underexplored. This work presents a novel adversarial strategy against power side-channel-based malware detection. By injecting structured dummy code into the scanning phase of the Mirai botnet, we dynamically perturb power signatures to evade AI/ML-based anomaly detection without disrupting core functionality. Our approach systematically analyzes the trade-offs between stealthiness, execution overhead, and evasion effectiveness across multiple

arXiv:2602.07630v1 Announce Type: new Abstract: Blockchain offers a decentralized trust framework for the Internet of Things (IoT), yet deploying consensus in spectrum-congested and dynamic wireless edge IoT networks faces fundamental obstacles: traditional BFT protocols are spectrum-ignorant, leading to inefficient resource utilization and fragile progress under time-varying interference. This paper presents \textit{Wireless Streamlet}, a spectrum-aware and cognitive consensus protocol tailored for wireless edge IoT. Building on Streamlet's streamlined structure, we introduce a \textit{Channel-Aware Leader Election (CALE)} mechanism. CALE serves as a verifiable cross-layer cognitive engine that leverages receiver-measured channel state information (CSI) piggybacked in signed votes to derive Byzantine-robust connectivity scores from notarization certificates, and deterministically selects a unique weighted leader per epoch from finalized history, thereby improving proposal