IOT

arXiv:2603.10776v1 Announce Type: new Abstract: The expansion of Internet of Things (IoT) devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled us to train models from network intrusion data collected from clients in a privacy preserving manner. However, the effectiveness of these systems can degrade over time due to concept drift, where patterns in data evolve as attackers develop new techniques. Realistic detection models should be non-stationary, so they can be continuously updated with new intrusion data while maintaining their detection capability for older data. As IoT environments are resource constrained, updates should consume minimal computational resources. This study provides a comprehensive performance analysis of incremental federated learning in enhancing the long term

arXiv:2603.10038v1 Announce Type: new Abstract: Smart-home IoT systems rely on heterogeneous sensor networks whose correctness shapes application behavior and the physical environment. However, these low-cost, resource-constrained sensors are highly prone to failure under real-world stressors. Prior methods often assume single-failure, single-resident settings, offer only failure detection rather than sensor-level localization, cover limited fault types and sensor modalities, require labels and human intervention, or impose overheads hindering edge deployment. To overcome these limitations, we propose Tureis, a self-supervised, context-aware method for failure detection and faulty-sensor localization in smart homes, designed for multi-failure, multi-resident edge settings. Tureis encodes heterogeneous binary and numeric sensor streams into compact bit-level features. It then trains a lightweight BERT-style Transformer with sensor-wise masked reconstruction over short-horizon windows,

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arXiv:2603.09311v1 Announce Type: new Abstract: Entropy--a measure of randomness--is compulsory for the generation of secure cryptographic keys; however, Internet of Things (IoT) devices that are small or constrained often struggle to collect suf ficient entropy. In this article, we solve the entropy provisioning problem for a fleet of IoT devices that can generate a limited amount of entropy. We employ a Trusted Execution Environment (TEE) based on RISC-V to create an external entropy service for a fleet of IoT devices. A small measure of true entropy or pre-installed keys can establish initial secure communication. Once connected, devices can request cryptographically strong entropy from a TEE-backed server. RISC-V offers True Random Number Generators (TRNGs) and a TEE for devices to attest that they are receiving reliable entropy. In addition, this solution can be expanded by adding IoT devices with sensors that produce high-quality entropy as additional entropy sources for the

arXiv:2603.08828v1 Announce Type: new Abstract: Wide-area IoT sensor networks require efficient data collection mechanisms when sensors are dispersed over large regions with limited communication infrastructure. Unmanned aerial vehicle (UAV)-mounted Mobile Base Stations (MBSs) provide a flexible solution; however, their limited onboard energy and the strict energy budgets of sensors necessitate carefully optimized tour planning. In this paper, we introduce the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint. The tour also avoids restricted areas. We formally model the MOT problem as a combinatorial optimization problem, which is NP-complete. Owing to its computational intractability, we develop a polynomial-time greedy heuristic that jointly considers travel cost and incremental

It has been nearly a year and a half since the company announced the AI-powered product.

The Roomba Mini is half the size of other Roombas, and according to iRobot it’s perfect for cleaning smaller homes.

Listen to a recap of the top stories of the day from 9to5Mac. 9to5Mac Daily is available on iTunes and Apple’s Podcasts app, Stitcher, TuneIn, Google Play, or through our dedicated RSS feed for Overcast and other podcast players. Sponsored by BenQ: Check out BenQ’s smarter displays made for how Mac users actually work. Sign up for the giveaway here.  more…

The device was originally planned for spring 2025 but keeps getting pushed back due to AI issues.

iRobot has announced its first new robot since the company filed for bankruptcy last December and was later acquired by China's Picea Robotics. At just 9.5-inches in diameter, the new Roomba Mini is half the size of iRobot's entry-level 105 series robovacs that launched last March, allowing the vacuum to access and clean spaces that are too narrow for larger robots. The Roomba Mini was originally developed for smaller Japanese homes, but iRobot is expanding its availability to the United Kingdom where it's now available for £379, and the rest of Europe for €399. Color options include back, pink, white, and mint. The company also plans to m … Read the full story at The Verge.

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arXiv:2603.07906v1 Announce Type: new Abstract: Integrating Internet of Things (IoT) data with business process event logs is crucial for analysing IoT-enhanced processes, yet remains challenging due to differences in abstraction levels and the separation of data sources. Simply incorporating raw IoT data increases the size and complexity of the resulting log, often requiring additional processing before process analysis can be performed. While tools for generating IoT-enriched event logs exist, they either rely on specialised schemas or focus on extracting event logs from sensor data, offering limited support for integrating process-relevant IoT data into existing event logs. To address this gap, we present IOTEL, a tool for systematically generating IoT-enriched object-centric event logs (OCEL). By building on the OCEL schema, IOTEL enables structured IoT data integration compatible with existing process mining tools. It support practitioners and researchers in analysing

arXiv:2603.07507v1 Announce Type: new Abstract: In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed data change with time, the on-device inference model becomes obsolete, which necessitates strategic model updating. OCLADS keeps track of data distribution shifts to timely update the on-device IoT AD model. To do so, OCLADS introduces two mechanisms during the interaction between the resource-constrained IoT device and an edge server (ES): i) an intelligent sample selection mechanism at the device for data transmission, and ii) a distribution-shift detection mechanism at the ES for model updating. Experimental results with TinyML demonstrate that our proposed framework achieves high inference accuracy while realizing a significantly smaller number of model updates compared to the baseline schemes.

arXiv:2603.06654v1 Announce Type: new Abstract: The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies and Graph Neural Networks (GNNs) to capture relationships between data instances. Since GNNs require graph-structured input, tabular NetFlow data must be transformed accordingly. This study evaluates how the choice of the method for constructing the graph-structured dataset impacts the classification performance of a GNN model. Five methods--k-Nearest Neighbors, Mutual Nearest Neighbors, Shared Nearest Neighbor, Gabriel Graph, and epsilon-radius Graph--were evaluated in this research. To reduce the computational burden associated with high-dimensional data, a Variational Autoencoder (VAE) is employed to project the original features into a lower-dimensional latent space prior to graph generation. Subsequently, a Graph

Samsara Inc. (NYSE:IOT) is one of the 10 best low-priced AI stocks to buy now. Trading under $50 and boasting strong analyst and hedge fund interest, Samsara Inc. (NYSE:IOT) secures a spot on our list of the 10 best low-priced AI stocks to buy now. On March 4, 2026, BofA noted that the company’s strong […]

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Apple Postpones Smart Home Display Launch as It Waits for New AI and Siri  Bloomberg.comApple's 'HomePad' Now Rumored to Launch Even Later Than Expected  MacRumorsMy patience did not pay off – no new Apple TV 4K announced at Apple's "Special Experience"  What Hi-Fi?Apple reportedly targeting smart home display release around iOS 27  9to5MacApple smart home display rumors now point to a fall launch with iOS 27  The Verge

Apple Postpones Smart Home Display Launch as It Waits for New AI and Siri  Bloomberg.comApple's 'HomePad' Now Rumored to Launch Even Later Than Expected  MacRumorsMy patience did not pay off – no new Apple TV 4K announced at Apple's "Special Experience"  What Hi-Fi?Apple reportedly targeting smart home display release around iOS 27  9to5MacApple smart home display rumors now point to a fall launch with iOS 27  The Verge

The rumored "HomePod with a screen" we've heard so much about was reportedly lined up for launch in 2025, and then this spring, and now, according to the latest updates, it's on the shelf until this fall. Leaker Kosutami posted as much on X last week, and today, Bloomberg reporter Mark Gurman followed up with similar information, saying its robot arm-equipped cousin is now planned for launch in 2027. According to Gurman, the J490 smart home display / HomePad is waiting for Apple to finish work on its chatbot-style AI update for Siri. That was supposed to be ready by now, but it is now predicted to arrive later this year, along with the iP … Read the full story at The Verge.

Apple's smart home display, which reportedly features an iPad-inspired screen and attaches to a wall, has been pushed to the latter half of 2026 due to AI-related delays for the next-gen Siri assistant. The post Apple’s smart home display is apparently delayed, and Siri’s late AI rebirth is to blame appeared first on Digital Trends.

Apple has been developing a smart home display for years, but the product relies the promised Siri upgrade to ship first. A new report corroborates a recent leak that claims Apple is targeting a smart home display release around iOS 27. more…

Mark Gurman / Bloomberg: Sources: Apple delayed the release of its smart home display, planned for this month, until later this year to let the company finish work on the new Siri  —  Apple Inc.'s artificial intelligence struggles are rippling through its product plans, forcing the company to delay a long …

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Samsara Inc. (NYSE:IOT) is one of the 10 Stocks Investors Are Watching Closely This Week. Samsara saw its share prices jump by 22 percent week-on-week as investors took heart from the company’s path to profitability, having nearly wiped out its losses last fiscal year. In an updated report, Samsara Inc. (NYSE:IOT) said that it narrowed […]

A software engineer gets access to 7,000 DJI Romo robot vacuum cleaners while tinkering with an idea to use a gamepad to control his robotic hoover. Gets $30,000 from DJI for an undisclosed discovery.

Samsara Inc. (NYSE:IOT) Q4 2026 Earnings Call Transcript March 5, 2026 Samsara Inc. beats earnings expectations. Reported EPS is $0.18, expectations were $0.13. Mike Chang: [Presentation] Good afternoon and welcome to Samsara’s Fourth Quarter Fiscal 2026 Earnings Call. I’m Mike Chang, Samsara’s Senior Vice President of Finance. Joining me today are Samsara’s Chief Executive Officer […]

Samsara Inc. (NYSE:IOT) is one of the 10 Stocks to Watch Right Now. Samsara soared by 19.54 percent on Friday to finish at $35.36 apiece, as investor sentiment was bolstered by a stellar earnings performance, having nearly wiped out its losses in the last fiscal year. In an updated report, Samsara Inc. (NYSE:IOT) said that […]

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arXiv:2603.05027v1 Announce Type: new Abstract: The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home ecosystems expand with heterogeneous IoT protocols, diverse devices, and evolving threats, autonomous systems must manage comfort, security, energy, and safety for residents. Such autonomous decision-making requires a trust anchor, making blockchain a preferred foundation for transparent and accountable smart home governance. However, realizing this vision requires blockchain-governed smart homes to simultaneously address adaptive consensus, intelligent multi-agent coordination, and resident-controlled governance aligned with the principles of Society 5.0. Existing frameworks rely solely on rigid smart contracts with fixed consensus protocols, employ at most a single AI model without multi-agent coordination, and offer no governance mechanism for residents to control automation behaviour. To address these limitations, this

arXiv:2603.04626v1 Announce Type: new Abstract: The rapid growth of the Internet of Things (IoT) devices in the sixth-generation (6G) wireless networks raises significant generality and scalability challenges due to energy consumption, deployment complexity, and environmental impact. Ambient IoT (A-IoT), leveraging ambient energy harvesting (EH) for batteryless device operation, has emerged as a promising solution to address these challenges.Among various EH and communication techniques, visible light communication (VLC) integrated with ambient backscatter communication (AmBC) offers remarkable advantages, including energy neutrality, high reliability, and enhanced security. In this paper, we propose a joint VLC-AmBC architecture, emphasizing fundamental concepts, system designs, and practical implementations. We explore potential applications in environmental monitoring, healthcare, smart logistics, and secure communications. We present proof-of-concept demonstrations for three

Amazon and Google think that artificially intelligent assistants like Alexa+ and Gemini will speed up the process of setting up a smart home, but many problems remain unsolved.

Amazon and Google think that artificially intelligent assistants like Alexa+ and Gemini will speed up the process of setting up a smart home, but many problems remain unsolved.

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arXiv:2603.03802v1 Announce Type: new Abstract: Design of antenna structures for Internet of Things (IoT) applications is a challenging problem. Contemporary radiators are often subject to a number of electric and/or radiation-related requirements, but also constraints imposed by specifics of IoT systems and/or intended operational environments. Conventional approaches to antenna design typically involve manual development of topology intertwined with its tuning. Although proved useful, the approach is prone to errors and engineering bias. Alternatively, geometries can be generated and optimized without supervision of the designer. The process can be controlled by suitable algorithms to determine and then adjust the antenna geometry according to the specifications. Unfortunately, automatic design of IoT radiators is associated with challenges such as determination of desirable geometries or high optimization cost. In this work, a variable-fidelity framework for performance-oriented

arXiv:2603.04221v1 Announce Type: new Abstract: The widespread adoption of the Internet of Things (IoT) has positioned smart homes as paradigmatic examples of distributed automation systems, where reliability, efficiency, and interoperability depend critically on the underlying communication protocol. Among the low-power wireless technologies available for this scenario, Zigbee and Matter over Thread have emerged as leading contenders. While Zigbee represents a mature, non-IP mesh networking solution, Matter over Thread introduces an IP-based architecture designed to unify device interoperability across different ecosystems. However, despite extensive documentation of their design principles, there is a lack of empirical, comparative performance data under realistic network conditions. This paper presents a comprehensive experimental comparison between the two protocols, conducted on a testbed built from commercially available hardware. The proposed methodology focuses on different

arXiv:2603.03804v1 Announce Type: new Abstract: Central Bank Digital Currency (CBDCs) are becoming a new digital financial tool aimed at financial inclusion, increased monetary stability, and improved efficiency of payment systems, as they are issued by central banks. One of the most important aspects is that the CBDC must offer secure offline payment methods to users, allowing them to retain cash-like access without violating Anti-Money Laundering and Counter-terrorism Financing (AML/CFT) rules. The offline CBDC ecosystems will provide financial inclusion, empower underserved communities, and ensure equitable access to digital payments, even in connectivity-poor remote locations. With the rapid growth of Internet of Things (IoT) devices in our everyday lives, they are capable of performing secure digital transactions. Integrating offline CBDC payment with IoT devices enables seamless, automated payment without internet connectivity. However, IoT devices face special challenges due to

arXiv:2603.03802v1 Announce Type: new Abstract: Design of antenna structures for Internet of Things (IoT) applications is a challenging problem. Contemporary radiators are often subject to a number of electric and/or radiation-related requirements, but also constraints imposed by specifics of IoT systems and/or intended operational environments. Conventional approaches to antenna design typically involve manual development of topology intertwined with its tuning. Although proved useful, the approach is prone to errors and engineering bias. Alternatively, geometries can be generated and optimized without supervision of the designer. The process can be controlled by suitable algorithms to determine and then adjust the antenna geometry according to the specifications. Unfortunately, automatic design of IoT radiators is associated with challenges such as determination of desirable geometries or high optimization cost. In this work, a variable-fidelity framework for performance-oriented

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Samsara Inc. (NYSE:IOT) is one of the Best Up and Coming AI Stocks to Buy. On March 3, Craig-Hallum reiterated a Buy rating on the stock without disclosing any price targets. On the same day, Jason Celino from KeyBanc reiterated a Buy rating on Samsara Inc. (NYSE:IOT) but lowered the price target from $55 to […]

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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.

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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 […]

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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

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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

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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…

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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.

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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