IOT

A smarter home doesn't need a huge budget, just the right devices. These picks deliver real utility without the high price tags you might expect.

arXiv:2603.24111v1 Announce Type: new Abstract: The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardware and remaining confined to simulation environments. In this paper, we present the research framework and contributions of our doctoral thesis, which aims to develop a lightweight, Machine Learning (ML)-based security framework for IIoT environments. We first describe our adoption of the Tm-IIoT trust model and the Hybrid IIoT (H-IIoT) architecture as foundational baselines, then introduce the Trust Convergence Acceleration (TCA) approach, our primary contribution that integrates ML to predict and mitigate the impact of degraded network conditions on trust convergence, achieving up to a 28.6% reduction in convergence time while maintaining

In 2025, the two fairs brought together over 2,800 exhibitors from 29 countries and regions and more than 88,000 industry buyers from 148 countries and regions The post Hong Kong’s InnoEX and Electronics Fair to spotlight AI, smart home, future mobility appeared first on Gulf Business.

arXiv:2603.22366v1 Announce Type: cross Abstract: We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.

arXiv:2603.23438v1 Announce Type: new Abstract: The integration of machine learning (ML) algorithms into Internet of Things (IoT) applications has introduced significant advantages alongside vulnerabilities to adversarial attacks, especially within IoT-based intrusion detection systems (IDS). While theoretical adversarial attacks have been extensively studied, practical implementation constraints have often been overlooked. This research addresses this gap by evaluating the feasibility of evasion attacks on IoT network-based IDSs, employing a novel black-box adversarial attack. Our study aims to bridge theoretical vulnerabilities with real-world applicability, enhancing understanding and defense against sophisticated threats in modern IoT ecosystems. Additionally, we propose a defense scheme tailored to mitigate the impact of evasion attacks, thereby reinforcing the resilience of ML-based IDSs. Our findings demonstrate successful evasion attacks against IDSs, underscoring their

arXiv:2603.23084v1 Announce Type: new Abstract: Ambient Internet of Things (A-IoT) devices, as a critical enabler of future green IoT networks, have attracted broad interest from both industry and academia due to their ability to operate without batteries and with low maintenance costs. To accommodate their dynamic and constrained energy budget, an ultra-low-power connectivity protocol is required. Due to the severely limited transmit power of A-IoT devices, multi-hop connectivity is an interesting paradigm to extend their range. However, commonly used protocols for multi-hop communication may not be suitable for A-IoT due to excessive overhead related to channel access procedures, coordinated routing, and tight time synchronization requirements. This paper presents a novel network connectivity protocol based on symbol-synchronous transmissions, which allows battery-less relay nodes to participate in the forwarding process in an ad-hoc manner, without the need for synchronization or

arXiv:2603.22771v1 Announce Type: new Abstract: As the Internet of Things (IoT) continues to expand across critical infrastructure, smart environments, and consumer devices, securing them against cyber threats has become increasingly vital. Traditional intrusion detection models often treat IoT threats as binary classification problems or rely on opaque models, thereby limiting trust. This work studies multiclass threat attribution in IoT environments using the CICIoT2023 dataset, grouping over 30 attack variants into 8 semantically meaningful classes. We utilize a combination of a gradient boosting model and SHAP (SHapley Additive exPlanations) to deliver both global and class-specific explanations, enabling detailed insight into the features driving each attack classification. The results show that the model distinguishes distinct behavioral signatures of the attacks using flow timing, packet size uniformity, TCP flag dynamics, and statistical variance. Additional analysis that

arXiv:2603.22191v1 Announce Type: new Abstract: The use of Internet of Things (IoT) devices is growing at a rapid rate. While much of this growth is consumer devices, IoT devices are also commonly found in corporate and industrial environments, as well. These devices can be organization-owned and managed by an information technology unit, deployed organizationally without the knowledge and involvement of technology staff or brought in to the corporate environment by user-owners. In each case, these devices may have access to corporate networks and data and are, thus, important to consider as part of organizational cybersecurity risk assessment. Despite the prevalence of these devices, there is little literature about how to audit their security. This paper presents a risk-based auditing framework which can be used by both internal and external auditors, of any experience level and in any industry, to assess IoT devices.

arXiv:2603.21600v1 Announce Type: new Abstract: Asynchronous messaging is a cornerstone of modern distributed systems, enabling decoupled communication for scalable and resilient applications. Today's message queue (MQ) ecosystem spans a wide range of designs, from high-throughput streaming platforms to lightweight protocols tailored for edge and IoT environments. Despite this diversity, choosing an appropriate MQ system remains difficult. Existing evaluations largely focus on throughput and latency on fixed hardware, while overlooking CPU and memory footprint and the effects of resource constraints, factors that are critical for edge and IoT deployments. In this paper, we present a systematic performance study of eight prominent message brokers: Mosquitto, EMQX, HiveMQ, RabbitMQ, ActiveMQ Artemis, NATS Server, Redis (Pub/Sub), and Zenoh Router. We introduce mq-bench, a unified benchmarking framework to evaluate these systems under identical conditions, scaling up to 10,000 concurrent

arXiv:2603.21596v1 Announce Type: new Abstract: The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed,

Samsung has launched a Car-to-Home integration for Hyundai and Kia vehicles, letting drivers manage smart home devices — from air conditioners to robot vacuums — without ever leaving their seat.

Robot vacuums can claim a ‘hands-free’ experience and still require human interference when they get stuck, bind with hair, or need deeper cleaning because the water wasn’t hot enough. That isn’t the case for Roborock’s Saros 20. more…

arXiv:2603.20037v1 Announce Type: new Abstract: In the Industrial Internet of Things (IIoT) systems, edge devices often operate under strict constraints in memory, compute capability, and wireless bandwidth. These limitations challenge the deployment of advanced data analytics tasks, such as predictive and prescriptive maintenance. In this work, we explore hyperdimensional computing (HDC) as a lightweight learning paradigm for resource-constrained IIoT. Conventional centralized HDC leverages the properties of high-dimensional vector spaces to enable energy-efficient training and inference. We integrate this paradigm into a federated learning (FL) framework where devices exchange only prototype representations, which significantly reduces communication overhead. Our numerical results highlight the potential of federated HDC to support collaborative learning in IIoT with fast convergence speed and communication efficiency. These results indicate that HDC represents a lightweight and

arXiv:2603.19727v1 Announce Type: new Abstract: As the Internet of Things (IoT) becomes an integral part of critical infrastructure, smart cities, and consumer networks, there has been an increase in the number of software attacks on the microcontrollers (MCUs) that constitute such networks. Runtime firmware attestation, i.e., the verification of a firmware's integrity, has become instrumental, and prior work focuses on lightweight IoT MCUs, offloading the verification task to capable remote verifiers. However, modern IoT devices feature large flash and volatile memory, on-device TinyML inference, and Trusted Execution Environments (TEE). Leveraging these capabilities, this paper presents a verifier-less, hybrid Self-Attestation (SA) framework called LiteAtt, which is based on TinyML execution in the Arm TrustZone of an IoT MCU for quick, on-device evaluation of the IoT firmware's SRAM footprint. LiteAtt takes a step towards ubiquitous intelligence and decentralized trust in IoT

arXiv:2603.19583v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems have shown promise for automated software development, but applying them to hardware-in-the-loop (HIL) embedded and Internet-of-Things (IoT) systems remains challenging due to the tight coupling between software logic and physical hardware behavior. Code that compiles successfully may still fail when deployed on real devices because of timing constraints, peripheral initialization requirements, or hardware-specific behaviors. To address this challenge, we introduce a skills-based agentic framework for HIL embedded development together with IoT-SkillsBench, a benchmark designed to systematically evaluate AI agents in real embedded programming environments. IoT-SkillsBench spans three representative embedded platforms, 23 peripherals, and 42 tasks across three difficulty levels, where each task is evaluated under three agent configurations (no-skills, LLM-generated skills, and human-expert

arXiv:2603.19340v1 Announce Type: new Abstract: The migration to post-quantum cryptography is urgent for Internet of Things devices with 10-20 year lifespans, yet no systematic benchmarks exist for the finalised NIST standards on the most constrained 32-bit processor class. This paper presents the first isolated algorithm-level benchmarks of ML-KEM (FIPS 203) and ML-DSA (FIPS 204) on ARM Cortex-M0+, measured on the RP2040 (Raspberry Pi Pico) at 133 MHz with 264 KB SRAM. Using PQClean reference C implementations, we measure all three security levels of ML-KEM (512/768/1024) and ML-DSA (44/65/87) across key generation, encapsulation/signing, and decapsulation/verification. ML-KEM-512 completes a full key exchange in 36.3 ms consuming 2.87 mJ--17x faster and 94% less energy than ECDH P-256 on the same hardware. ML-DSA signing exhibits high latency variance due to rejection sampling (coefficient of variation 61-71%, 99th-percentile up to 1,115 ms for ML-DSA-87). The M0+ incurs only a

Building out your smart home system doesn't have to cost an arm and a leg. Sometimes, older tech you may already have around the house can stand nicely.

Millions of hijacked devices powered traffic floods targeting defense systems and beyond The US government has moved to disrupt a cluster of IoT botnets behind some of the largest DDoS attacks ever recorded, including traffic bursts topping 30 terabits per second.…

arXiv:2603.13570v2 Announce Type: replace Abstract: The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constrained devices. Unlike centralized environments, IoT ecosystems are inherently decentralized, bandwidth-limited, and latency-sensitive, exposing privacy risks across sensing, communication, and distributed training pipelines. These characteristics render conventional anonymization and centralized protection strategies insufficient for practical deployments. This survey presents a comprehensive IoT-centric, cross-paradigm analysis of privacy-preserving machine learning. We introduce a structured taxonomy spanning perturbation-based mechanisms such as differential privacy, distributed paradigms such as federated learning, cryptographic approaches including homomorphic encryption and secure multiparty computation, and

Turning your home into a smarter space can be surprisingly affordable. These under $50 gadgets bring automation, monitoring, and voice control.

arXiv:2603.17665v1 Announce Type: new Abstract: This paper analyzes the physical layer security performance of massive uplink Internet of Things (IoT) networks operating under the finite blocklength (FBL) regime. IoT devices and base stations (BS) are modeled using a stochastic geometry approach, while an eavesdropper is placed at a random location around the transmitting device. This system model captures security risks common in dense IoT deployments. Analytical expressions for the secure success probability, secrecy outage probability and secrecy throughput are derived to characterize how stochastic interference, fading and eavesdropper spatial uncertainty interact with FBL constraints in short packet uplink transmissions. Numerical results illustrate key system behavior under different network and channel conditions.

arXiv:2603.17757v1 Announce Type: new Abstract: RISC-V-based Trusted Execution Environments (TEEs) are gaining traction in the automotive and IoT sectors as a foundation for protecting sensitive computations. However, the supporting infrastructure around these TEEs remains immature. In particular, mechanisms for secure enclave updates and migrations - essential for complete enclave lifecycle management - are largely absent from the evolving RISC-V ecosystem. In this paper, we address this limitation by introducing a novel toolkit that enables RISC-V TEEs to support critical aspects of the software development lifecycle. Our toolkit provides broad compatibility with existing and emerging RISC-V TEE implementations (e.g., Keystone and CURE), which are particularly promising for integration in the automotive industry. It extends the Security Monitor (SM) - the trusted firmware layer of RISC-V TEEs - with three modular extensions that enable secure enclave update, secure migration,

arXiv:2603.17665v1 Announce Type: new Abstract: This paper analyzes the physical layer security performance of massive uplink Internet of Things (IoT) networks operating under the finite blocklength (FBL) regime. IoT devices and base stations (BS) are modeled using a stochastic geometry approach, while an eavesdropper is placed at a random location around the transmitting device. This system model captures security risks common in dense IoT deployments. Analytical expressions for the secure success probability, secrecy outage probability and secrecy throughput are derived to characterize how stochastic interference, fading and eavesdropper spatial uncertainty interact with FBL constraints in short packet uplink transmissions. Numerical results illustrate key system behavior under different network and channel conditions.

arXiv:2603.17507v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are increasingly available on edge devices, their potential to reduce the energy overhead of fine-tuning remains underexplored. In this work, we propose QuantFL, a sustainable FL framework that leverages pre-trained initialisation to enable aggressive, computationally lightweight quantisation. We demonstrate that pre-training naturally concentrates update statistics, allowing us to use memory-efficient bucket quantisation without the energy-intensive overhead of complex error-feedback mechanisms. On MNIST and CIFAR-100, QuantFL reduces total communication by 40\% ($\simeq40\%$ total-bit reduction with full-precision downlink; $\geq80\%$ on uplink or when downlink is quantised) while matching or exceeding

arXiv:2603.17054v1 Announce Type: new Abstract: Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivity in disaster-affected areas. Distributed IoT sensors collect critical environmental data and forward it to nearby gateways via short-range links, while the HAPS-RIS system provides backhaul to these gateways. To overcome the severe double path loss of passive RIS at high altitudes, we propose a dynamically adjustable sub-connected active RIS architecture that can reconfigure the number of elements connected to each power amplifier through switching mechanisms. Simulation results demonstrate substantial gains in downlink and uplink data rates, as well as system energy efficiency,

The Matic robot vacuum finally adds smart home control. | Photo by Jennifer Pattison Tuohy / The Verge Matic, my current top pick for the best robot vacuum, just got a big update. The unique-looking floor bot now works with Google Home and Apple Home, with support coming through the smart home standard Matter. This enables voice control and smart home integrations, features that have been missing from the robot since launch. Now you can set up automations such as "Run Matic when everyone leaves home," "Dock the robot when I arrive home," or "Stop Matic when the door unlocks." The Matic now works with Apple Home, Google Home, and Home Assistant. The pairing code to connect through Matter appears on its screen when initiated in the app. | Photo by Jennifer Pattison Tuohy / The Verge" data-portal-copyright="Photo by Jennifer Pattison Tuohy / The Verge">

Ikea smart home products have long been the preferred choice of those who want to begin or expand a smart home at an affordable price. The company’s use of physical remotes also simplified the process for those new to the technology. But when the company expanded its lineup with some budget Matter-over-Thread devices, things rather quickly headed south – with Apple Home users among those affected … more…

arXiv:2603.16719v1 Announce Type: new Abstract: This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotional and engagement patterns in real time. The system was evaluated using the Classroom Emotion Dataset, consisting of 1,500 labeled images and 300 classroom detection videos. Tailored for IoT devices, the system addresses load balancing and latency challenges through efficient real-time processing. Field testing was conducted across three educational institutions in a large metropolitan area: a primary school (hereafter school A), a secondary school (school B), and a high school (school C). The system demonstrated robust performance, detecting up to 50 faces at 25 FPS and achieving 88%

Ikea’s new smart bulbs, sensors, and remotes use Matter-over-Thread to connect to any compatible ecosystem — including Apple Home and Amazon Alexa. In theory. | Photo by Jennifer Pattison Tuohy / The Verge Ikea's new Matter-over-Thread products were supposed to prove that the smart home could be cheap, accessible, and reliable. The highly anticipated line - which includes sensors, remotes, smart plugs, air-quality monitors, and smart bulbs - has most everything you need to build a smart home, with prices starting at $6. It's an exciting idea, but it's still not ready for primetime. When I first got the Ikea devices in January, I had a lot of problems connecting them to my main platform, Apple Home. And it turned out I was not alone. Reddit forums and user reviews were full of reports of onboarding and connectivity issues. Many people were str … Read the

Smart home company Aqara has launched what it says is the first camera certified for Matter, the open source standard that enables interoperability across brands, like Google and Amazon. The Aqara G350 is an indoor security cam that also functions as a Zigbee and Matter hub in the Aqara Home app, which means the camera will enable you to control various devices across smart home protocols from different brands within one location. The camera itself comes with a 4K wide-angle and a 2.5K telephoto lens, providing both panoramic and closeup views. It also has 9x hybrid zoom and a pan-tilt mechanism that can give you 360-degree coverage of the room it’s in. The camera uses AI-powered tracking to keep people and pets in frame, as well as to determine which events and sounds are truly meaningful before sending you an alert. The Camera Hub G350 is now available via Aqara’s website, Amazon and other retailers for $140. Aqara has also introduced the G400 wired doorbell camera that can

arXiv:2603.14729v1 Announce Type: new Abstract: Next-generation IoT applications increasingly span across autonomous administrative entities, necessitating silo-cooperative scheduling to leverage diverse computational resources while preserving data privacy. However, realizing efficient cooperation faces significant challenges arising from infrastructure heterogeneity, Non-IID workload shifts, and the inherent risks of adversarial environments. Existing approaches, relying predominantly on centralized coordination or independent learning, fail to address the incompatibility of state-action spaces across heterogeneous silos and lack robustness against malicious attacks. This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling. DeFRiS integrates three synergistic innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer

iRobot is a pioneering brand reinventing itself to lead the next generation of innovation

Apple Inc. (NASDAQ:AAPL) earns a place on our list of the 13 unrivaled stocks of the next 10 years, though recent developments suggest the company’s artificial intelligence rollout is still evolving. According to TheFly’s report citing Bloomberg’s Mark Gurman’s comments on March 9, 2026, Apple Inc. (NASDAQ:AAPL) has decided to delay the release of its smart home […]

Long-running SocksEscort proxy network brought down by US-EU operation

As of March 13, the Dreame X60 Max Ultra Complete robot vacuum and mop is on sale for $1,499.99 exclusively for Prime members at Amazon. This is 12% off its list price of $1,699.99.

These safes impressed us the most, not only with their locks but with useful smart features.

Dyson first ever wet and dry robot vacuum is out, and I got to get an early look at it at the Dyson Soho Store.

Paayal Zaveri / Bloomberg: Sunday, which is building autonomous home robots, raised a $165M Series B led by Coatue at a valuation of $1.15B, and aims to begin testing in homes this year  —  Sunday Inc. has raised $165 million to build a dream robot for any household: a friendly looking machine capable of performing tasks …

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,

As of March 11, you can get the Shark AV2501AE AI robot vacuum for $299.99, down from $649.99. That's a 54% discount.

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.

© seekingalpha.com. Use of this feed is limited to personal, non-commercial use and is governed by Seeking Alpha's Terms of Use (https://about.seekingalpha.com/terms). Publishing this feed for public or commercial use and/or misrepresentation by a third party is prohibited.

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 […]

Tidy up without putting in the elbow grease thanks to this Shark Matrix Plus 2-in-1 Self-Empty Robot Vacuum and Mop, now only $134.99 (reg. $299.99)

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 …

Find the best robot vacuum deal. Save 39% on the Ecovacs Deebot X9 Pro Omni at Amazon.

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 […]

The latest news and headlines from Yahoo! News. Get breaking news stories and in-depth coverage with videos and photos.

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.

Get the best robot vacuum deal on Amazon. Save 39% on the Roborock Qrevo S5V at Amazon right now.

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

As of March 4, get $400 off the Mova Z60 Ultra Roller Complete Robot Vacuum and Mop.

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 […]

As of March 4, the Shark Matrix Plus 2-in-1 robot vacuum-mop is down to $299.99 at Amazon. That's a 57% discount or $400 in savings.

Get the best robot vacuum deal at Amazon. Save 12% on the Dreame L10s Ultra at Amazon.

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

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

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

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

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

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

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