IOT

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

Roborock's offering one of their most intelligent and advanced floor cleaning machines for just $850 through Dec. 1, a huge $650 price drop.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The best robot vacuum to buy on Black Friday won't be a Roomba. iRobot's in trouble this year, and deals on better robot vacuums will be aplenty.

Our pick for the best robot vacuum (or at least one of its many variants) is on sale. Amazon's Black Friday deals include the Shark AI Ultra robot vacuum. This model has a list price of $599, but you can snag one for $250. That's 58 percent off — and a record low. One of this model's standout features is its bagless design. Like many robovacs, it has an auto-empty station. But here, you can remove part of the base, dump its contents in the garbage, and lock it back in place. The base holds up to 60 days of dirt and debris, and you'll never need to order bag refills. The Shark AI Ultra has strong suction and decent obstacle avoidance (via LiDAR). The robovac cleans in a matrix grid. It auto-maps your home and supports Google Assistant and Alexa for voice control. The vacuum has a runtime of about 120

An adaptive LED-based optical wireless power system uses AI image recognition to power multiple devices in any lighting, providing a low-cost, safe option for indoor IoT.

This Home Robot Clears Tables and Loads the Dishwasher All by Itself  WIREDNo Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi  StartupHub.aiSunday Launches Memo, the Robot That Actually Learns Your Home  GlobeNewswireSunday Robotics Unveils Compact Everyday Robot on November 19  x.com

This Home Robot Clears Tables and Loads the Dishwasher All by Itself  WIREDNo Priors Ep. 141 | With Sunday Robotics Co-Founders Tony Zhao and Cheng Chi  StartupHub.aiSunday Launches Memo, the Robot That Actually Learns Your Home  GlobeNewswireSunday Robotics Unveils Compact Everyday Robot on November 19  x.com

Sunday Robotics has a new way to train robots to do common household tasks. The startup plans to put its fully autonomous robots in homes next year.

Dyson is holding its Black Friday sale on vacuums and related products. To that end, the Dyson 360 Vis Nav robot vacuum is a whopping $600 off and down to $400 right now. That's $100 less than its previous all-time low and the cheapest we've seen it. Dyson was pretty late to the robot-vacuum party, but its entry was (and remains) one of the strongest in the category. It doesn't have a lot of bells and whistles like a self-emptying base or mopping capabilities, but it makes up for that by having probably the best suction power of any robovac we've tested. All kinds of debris will fall in its path: dirt, dust, food crumbs, pet hair and more. It also has excellent obstacle avoidance, so you'll rarely — if ever — have to dislodge it from getting stuck on the edge of a carpet or

The agricultural landscape of the 21st century is at a crucial tipping point, with smart technologies poised to revolutionize traditional farming practices. Drip irrigation, long heralded for its efficiency, is undergoing a significant transformation through the integration of Internet of Things (IoT) technologies. This novel approach intersects connectivity with agronomy, promising to enhance both yield […]

If you're planning to put up a lot of lights for Christmas, this device from Harbor Freight can help you control them and save you some electricity.

arXiv:2511.14227v1 Announce Type: new Abstract: Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment information, and device status. This task is crucial for enhancing user satisfaction and corporate profits. Existing recommendation models struggle with complex operation logic, diverse user preferences, and sensitive to suboptimal suggestions, limiting their applicability to IoT device operations. To address these issues, we propose DevPiolt, a LLM-based recommendation model for IoT device operations. Specifically, we first equip the LLM with fundamental domain knowledge of IoT operations via continual pre-training and multi-task fine-tuning. Then, we employ direct preference optimization to align the fine-tuned LLM with specific user preferences. Finally, we design a confidence-based exposure control mechanism to avoid negative user experiences from low-quality

arXiv:2511.14074v1 Announce Type: new Abstract: Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition (HAR). Modern wearable and smart devices feature various built-in Inertial Measurement Unit (IMU) sensors, and such sensor-based measurements can be fed to a machine learning-based model to train and classify human activities. While deep learning-based models have proven successful in classifying human activity and gestures, they pose various security risks. In our paper, we discuss a novel dynamic trigger-generation technique for performing black-box adversarial attacks on sensor data-based IoT systems. Our empirical analysis shows that the attack is successful on various datasets and classifier models with minimal perturbation on the input data. We also provide a detailed comparative analysis of performance and stealthiness to various other poisoning techniques found in backdoor attacks. We

Amazon’s Black Friday sale has seen some premium robot vacuum cleaners drop to their lowest prices yet, including top models from Ecovacs, Roborock and Dreame.

Early Black Friday robot vacuum deals are slow in 2025, but the Dreame Aqua10 Ultra Roller and Narwal Freo Z10 Ultra are at record-low pricing.

The November 18 edition of the AgNet News Hour offered a fascinating look into the future of California agriculture as hosts Nick Papagni and Josh McGill interviewed Taylor Wetli, U.S. Commercial Manager for Solinftec, the global ... Read More The post Solinftec’s Taylor Whetley Talks Solar Robotics and the Future of Smart Farming appeared first on AgNet West.

Just days before Black Friday, Amazon is selling a highly rated Shark robot vacuum for a whopping half off. Here's what you're getting with the purchase.

The Narwal Freo Z10 robot vacuum and mop combo is on sale at Amazon for $649.99, down from the normal price of $1,099.99. That's a 41% discount.

Microsoft's Azure cloud has mitigated the largest DDoS attack in history at close to 16 Tbps from the Aisuru botnet. At its peak, the attack used over 500,000 connected devices to hit the Azure servers with over 3.6 million packets per second to target a single cloud endpoint in Australia.

The Connectivity Standards Alliance has released Zigbee 4.0, an updated version of its wireless mesh networking standard with better security, improved battery life, and batch setup of new devices like smart lights, switches, and plugs. It’s also announcing a new feature called Suzi, which is short for Sub-GHz and Zigbee.  While Zigbee smart home devices typically operate on the 2.4GHz band, which can experience signal loss caused by obstacles like thick walls, Suzi-branded devices will be able to work on the European 800 MHz and North American 900 MHz frequency bands. That way, the group says they’ll offer coverage for smart devices installed outside and farther away from a home, without the need for additional hardware to extend the networks. Suzi is similar to other branded features that have been added to the 802.15.4-based

These nifty robovacs will take care of cleaning while you relax – and right now there are some major deals to be had.

arXiv:2511.12841v1 Announce Type: new Abstract: Pervasive data collection by Smart Home Devices (SHDs) demands robust Privacy Protection Mechanisms (PPMs). The effectiveness of many PPMs, particularly user-facing controls, depends on user awareness and adoption, which are shaped by manufacturers' public documentations. However, the landscape of academic proposals and commercial disclosures remains underexplored. To address this gap, we investigate: (1) What PPMs have academics proposed, and how are these PPMs evaluated? (2) What PPMs do manufacturers document and what factors affect these documentation? To address these questions, we conduct a two-phase study, synthesizing a systematic review of 117 academic papers with an empirical analysis of 86 SHDs' publicly disclosed documentations. Our review of academic literature reveals a strong focus on novel system- and algorithm-based PPMs. However, these proposals neglect deployment barriers (e.g., cost, interoperability), and lack

arXiv:2511.12175v1 Announce Type: new Abstract: This study presents an AI enhanced IoT framework for predictive maintenance and affordability optimization in smart microgrids using a Digital Twin modeling approach. The proposed system integrates real time sensor data, machine learning based fault prediction, and cost aware operational analytics to improve reliability and energy efficiency in distributed microgrid environments. By synchronizing physical microgrid components with a virtual Digital Twin, the framework enables early detection of component degradation, dynamic load management, and optimized maintenance scheduling. Experimental evaluations demonstrate improved predictive accuracy, reduced operational downtime, and measurable cost savings compared to baseline microgrid management methods. The findings highlight the potential of Digital Twin driven IoT architectures as a scalable solution for next generation intelligent and affordable energy systems.

arXiv:2511.11885v1 Announce Type: new Abstract: Smart cities and pervasive IoT deployments have generated interest in IoT data analysis across transportation and urban planning. At the same time, Large Language Models offer a new interface for exploring IoT data - particularly through natural language. Users today face two key challenges when working with IoT data using LLMs: (1) data collection infrastructure is expensive, producing terabytes of low-level sensor readings that are too granular for direct use, and (2) data analysis is slow, requiring iterative effort and technical expertise. Directly feeding all IoT telemetry to LLMs is impractical due to finite context windows, prohibitive token costs at scale, and non-interactive latencies. What is missing is a system that first parses a user's query to identify the analytical task, then selects the relevant data slices, and finally chooses the right representation before invoking an LLM. We present Flash-Fusion, an end-to-end

arXiv:2511.11598v1 Announce Type: new Abstract: Efficient routing in IoT sensor networks is critical for minimizing energy consumption and latency. Traditional centralized algorithms, such as Dijkstra's, are computationally intensive and ill-suited for dynamic, distributed IoT environments. We propose a novel distributed Q-learning framework for constructing shortest-path trees (SPTs), enabling sensor nodes to independently learn optimal next-hop decisions using only local information. States are defined based on node positions and routing history, with a reward function that incentivizes progression toward the sink while penalizing inefficient paths. Trained on diverse network topologies, the framework generalizes effectively to unseen networks. Simulations across 100 to 500 nodes demonstrate near-optimal routing accuracy (over 99% for networks with more than 300 nodes), with minor deviations (1-2 extra hops) in smaller networks having negligible impact on performance. Compared to

You can already get a solid deal on a Roomba robot vacuum before Black Friday kicks off in earnest. iRobot's entry-level Roomba 104 Vac robot vacuum is available for 40 percent off right now, taking its normal $250 price to a record low of $150. A number of other Roombas are on sale for Black Friday, too. In iRobot's lineup of robot vacuums, the Roomba 104 sits on the low end, adept at vacuuming up dust and hair, but without the mopping ability of its more expensive Max, Plus or Combo counterparts. The Roomba 104 Vac makes for a great first robot vacuum all the same, though, because of its four levels of powerful suction, and easy-to-use app. Like iRobot's other vacuums, the Roomba 104 maps and navigates your home with LiDAR, which helps it avoid obstacles. And using the Roomba Home app, you can schedule it to clean specific rooms, and even spot-clean particularly dirty spots.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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