IOT

A new KANN-DBSCAN algorithm improves blind channel estimation in NB-IoT links by adapting clustering parameters and bandwidths to complex signal conditions.

arXiv:2611.08118v1 Announce Type: new Abstract: Low Earth Orbit (LEO) satellite-IoT systems increasingly provide connectivity to mobile assets in remote areas, which makes their resilience to radio-frequency jamming a growing concern. Timely jamming detection, i.e., before complete link failure, has been studied for terrestrial networks and Global Navigation Satellite Systems (GNSS), but it remains unclear whether these approaches are effective on LEO links, where propagation varies rapidly, the received signal is weak, and jamming should be detected from the messages that are still correctly received. This paper investigates the extent to which jamming of Iridium LEO satellite-IoT links can be detected from the physical layer of correctly decoded messages. To this aim, we collect and publicly release a real-world dataset of Iridium messages under 47 jamming conditions, spanning two propagation environments, static and mobile receivers, and different jammer distances, powers, and

arXiv:2611.08108v1 Announce Type: new Abstract: Field devices deployed today will still be producing records when quantum computers may break their signatures, so systems that anchor IoT data on blockchains need a post-quantum (PQ) authentication path now. We measure its cost on Ethereum under the current execution rules, including the EIP-7623 calldata floor and the EIP-7825 transaction cap, and under the calldata repricing scheduled for the Glamsterdam upgrade. We combine device-side measurements of ML-DSA, Falcon, SLH-DSA, LMS and XMSS, reproduced costs of open-source Solidity verifiers, a new standard-compliant LMS/HSS verifier for SHA-256 and SHA-256/192, and complete transactions on an Osaka-rule EVM, priced with today's verifiers and with proposed precompiles. Three results stand out. Signing every record costs 8.7 times as much as ECDSA even with an ML-DSA precompile, because the calldata floor rather than verification sets the price. When each device signs a Merkle root over

Researchers have developed IoT ArtNet, an intelligent framework combining deep learning and Internet of Things gesture sensing to dynamically drive adaptive digital media creation.

A new report finds residential proxies exiting the White House network 236 times in the past month.

arXiv:2611.06788v1 Announce Type: new Abstract: Embodied Internet of Things (IoT) systems are promoting the adoption of task-oriented semantic communication (SemCom), in which the trustworthy task decisions require both semantic information reliability and source legitimacy. However, existing trustworthy designs in SemCom mainly focus on semantic content recovery and protection, with limited attention to source legitimacy. Motivated by this limitation, this paper establishes a new information exploitation paradigm for integrated SemCom-identity protection to address active source impersonation and passive identity inference by treating each SemCom signal as an inherent carrier of both task-relevant semantic information and transmitter physical identity information. We first examine these two information resources and then analyze their integration feasibility at the observation, processing, and decision levels. To exploit these resources for trustworthy decision making, we further

arXiv:2611.06186v1 Announce Type: new Abstract: Underwater wireless sensor networks (UWSNs) face critical challenges in energy imbalance, connectivity, and data collection due to harsh environments. To address these issues, this paper focuses on the problem is how to achieve energy-balanced data collection in IoT-enabled UWSNs with a mobility-aware mobile edge computing (MEC), named MECEBDC. Unlike existing approaches, the proposed scheme jointly optimizes energy-aware clustering, cluster head selection, and AUV-assisted data collection. Furthermore, we design a novel metaheuristic-based AUV routing algorithm that incorporates realistic underwater mobility dynamics, explicitly considering gravitational pressure and buoyancy forces acting on the AUV. By leveraging the computing, storage, and mobility capabilities of MEC-enabled AUVs, the proposed approach enables efficient trajectory planning and adaptive data gathering in a stable 3D underwater environment. Extensive simulations and

An outdoor monitoring campaign in Peshawar logged two semi-transparent perovskite building-integrated photovoltaic modules from October 2025 to January 2026, recording a 47.2 percent load-based efficiency decline for one module and 14.8 percent for the other alongside seasonal changes in solar resource.

Apple is developing a tabletop home robot that could cost around $1,000, The Information reported on Monday. It would put a 9-inch screen on a swivelling robotic arm. Apple is aiming for a 2027 launch, but the device may not be ready until 2028, Aaron Tilley wrote. Apple’s codename for the device is J595. Former […] This story continues at The Next Web

Apple robot backed by CEO John Ternus may have hefty price tag and limited uses.

Apple’s HomeView Ultra may be a swiveling, AI-powered robot lamp, but software delays could push it to 2028.

Apple has reportedly delayed the launch of its rumored smart home robot until next year at the earliest or possibly even 2028, according to The Information. It says the robot's hardware "has been finished for around two years," but that the software isn't ready. The device's software team has reportedly pushed to delay the launch and "attempted to strip back some proposed features and focus the product on a few capabilities related to FaceTime and automating home tasks, like switching on a light." According to The Information, the device is codenamed J595, but former Apple employees predict it will be called the "HomeView Ultra," taking aft … Read the full story at The Verge.

arXiv:2610.40723v1 Announce Type: new Abstract: We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Mat\'ern GMRF posterior, with waypoints selected by entropy pre-screening and Sherman--Morrison lookahead over the reachable set. We show that the likelihood-induced Skew-Normal correction has a sextic entropy effect, explaining its limited influence on waypoint selection. Across 19 retained ray-traced windows,Random and lawnmower sampling cost a median 18.0\% and 14.0\% more residual NMSE than posterior-informed sampling, equivalently reductions of 15.3\% and 12.3\% when referred to those baselines, while differences among informative rules remain unresolved. Widening the prior shadowing standard deviation from 6.0 to 10.025\,dB improves NMSE and credible-interval coverage, with monotone improvement over a 4--16\,dB sweep on this corpus and no observed turning point,

arXiv:2610.40723v1 Announce Type: cross Abstract: We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Mat\'ern GMRF posterior, with waypoints selected by entropy pre-screening and Sherman--Morrison lookahead over the reachable set. We show that the likelihood-induced Skew-Normal correction has a sextic entropy effect, explaining its limited influence on waypoint selection. Across 19 retained ray-traced windows,Random and lawnmower sampling cost a median 18.0\% and 14.0\% more residual NMSE than posterior-informed sampling, equivalently reductions of 15.3\% and 12.3\% when referred to those baselines, while differences among informative rules remain unresolved. Widening the prior shadowing standard deviation from 6.0 to 10.025\,dB improves NMSE and credible-interval coverage, with monotone improvement over a 4--16\,dB sweep on this corpus and no observed turning point,

arXiv:2610.41440v1 Announce Type: new Abstract: Low Earth Orbit (LEO) constellations are a promising way to connect Internet of Things (IoT) devices beyond terrestrial coverage, yet the intermittent visibility of their satellites imposes hard constraints on system and protocol design. Coverage probability and other snapshot statistics, which are often computed without accounting for the terminal's latitude, are insufficient for this purpose. Designers also need the temporal structure of coverage: the rate of coverage gaps, their duration distribution and mean, and quantities such as the probability that a retransmission succeeds after an initial transmission has failed. We develop an ergodic framework for Walker Star constellations in which these quantities reduce to spatial integrals over a two-dimensional torus that can be evaluated efficiently. We validate the framework against a seven-day Simplified General Perturbations 4 (SGP4) simulation of the Iridium NEXT constellation and

arXiv:2610.40723v1 Announce Type: cross Abstract: We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Mat\'ern GMRF posterior, with waypoints selected by entropy pre-screening and Sherman--Morrison lookahead over the reachable set. We show that the likelihood-induced Skew-Normal correction has a sextic entropy effect, explaining its limited influence on waypoint selection. Across 19 retained ray-traced windows,Random and lawnmower sampling cost a median 18.0\% and 14.0\% more residual NMSE than posterior-informed sampling, equivalently reductions of 15.3\% and 12.3\% when referred to those baselines, while differences among informative rules remain unresolved. Widening the prior shadowing standard deviation from 6.0 to 10.025\,dB improves NMSE and credible-interval coverage, with monotone improvement over a 4--16\,dB sweep on this corpus and no observed turning point,

arXiv:2610.42032v1 Announce Type: new Abstract: The Internet of Things (IoT) has expanded beyond consumer smart homes to sectors, such as industrial systems, healthcare, and smart cities, broadening the IoT attack surface. Prior empirical work has focused on studying aggregate IoT vulnerability characteristics or specific protocols, so patching is directed by aggregate security guidance. However, if dominant weaknesses differ substantially across sectors or products, such uniform guidance misdirects the remediation effort. To address this gap, we present a fine-grained, data-driven analysis of IoT vulnerabilities using a dataset of more than 15,000 IoT specific CVEs. We design a pipeline to enrich noisy CPE (Common Platform Enumeration) metadata, normalizing vendors and products, then assigning product type and sector labels using lexical matching, fuzzy clustering, and LLM-assisted classification. We find meaningful differences in the weakness profile of IoT sectors. For example,

arXiv:2610.41440v1 Announce Type: new Abstract: Low Earth Orbit (LEO) constellations are a promising way to connect Internet of Things (IoT) devices beyond terrestrial coverage, yet the intermittent visibility of their satellites imposes hard constraints on system and protocol design. Coverage probability and other snapshot statistics, which are often computed without accounting for the terminal's latitude, are insufficient for this purpose. Designers also need the temporal structure of coverage: the rate of coverage gaps, their duration distribution and mean, and quantities such as the probability that a retransmission succeeds after an initial transmission has failed. We develop an ergodic framework for Walker Star constellations in which these quantities reduce to spatial integrals over a two-dimensional torus that can be evaluated efficiently. We validate the framework against a seven-day Simplified General Perturbations 4 (SGP4) simulation of the Iridium NEXT constellation and

arXiv:2610.41424v1 Announce Type: new Abstract: The widespread deployment of Internet of Things (IoT) devices in critical environments makes anomaly detection essential for identifying abnormal behaviors and supporting timely investigation and response. However, accurate detection alone is insufficient. Practical IoT deployments must also provide explanations, incorporate domain knowledge and reasoning, operate under IoT constraints, and support human decision making. Although recently proposed approaches increasingly incorporate Explainable Artificial Intelligence (XAI) or Neuro-Symbolic Artificial Intelligence (NSAI), XAI and NSAI methods are often considered independently. We argue that they should be considered together to better understand their complementary roles in supporting trustworthy IoT anomaly detection. To address this gap, we propose a unified analytical framework organized around five complementary dimensions. The framework provides a structured basis for

With class-leading navigation and superb cleaning on all floor types, the DJI Romo P is worth picking up ahead of the Black Friday sales rush, thanks to a massive AU$1,003 discount at JB Hi-Fi.

A new framework called Xender+ matches incoming sampled data segments to a cloud-side library of pre-trained models, avoiding costly online retraining while preserving reconstruction accuracy.

A comprehensive review of more than 200 peer-reviewed publications examines how communication protocols, security frameworks, AI, blockchain and quantum technologies could shape the future of the Internet of Medical Things.

A hybrid fuzzy rule-based and reinforcement learning scheduler improves real-time traffic reliability, delay, and energy use in Industrial IoT TSCH networks.

A new systematic review of 22 wake-up radio routing protocols finds major energy savings under low-to-moderate traffic but flags gaps in real-world validation, mobility, security and scalability.

A new review maps how low-cost energy harvesting technologies could replace batteries in smart sensing and Internet of Things devices.

From a new smart alarm clock to an Alexa-powered 3D map of your home, here’s what’s new from Amazon, including prices and release dates.

A new open-access study proposes a communication-allocation framework that ranks edge perception queries by their expected value for maintaining semantic urban digital-twin states under a fixed bandwidth budget.

Two electrification pilots reveal how technology designed to simplify household energy use can add cost, complexity and another hurdle to an all-electric home. The post Six apps to run a smart home? Electrification pilots exposes energy management headache appeared first on Renew Economy.

You decide: Is an Alexa-powered map of your home, inside and out, a good thing?

A replication and extension study in Cluster Computing confirms that LSTM networks maintain high intrusion detection performance in IoT networks, with Taguchi design experiments and Wilcoxon tests revealing how hyperparameters and optimizers affect results.

Amazon has been a major player in the smart home market for years, but fresh competition from Apple may have lit a fire under it. The company has just unveiled a new lineup of Alexa devices, including Echo speakers, a smart alarm clock, a photo frame, and the Echo Show 16. On the software side, […]

On Oct. 7, Wooriga Green Farm, a cherry tomato smart farm in Seobuk District in Cheonan, South Chungcheong Province, was filled with rows of tomato vines stretching nearly to the greenhouse ceiling. Ripe red tomatoes hung in clusters along both sides of the aisles. They were the last fruits of the summer growing season. The farm’s owner, Woo Min-jae, 32, is considered one of the region’s leading young farmers. He worked for a subcontractor of a major company in the Seoul metropolitan area from 2017 until late 2019, but lost his job when the factory relocated to China. Six years after leaving his job, Woo has transformed himself into a full-fledged farmer, operating a greenhouse operation covering 1,900 square meters. His farm generated 150 million won ($110,000) in revenue last year. Young people like Woo are becoming increasingly scarce in rural areas. According to the Ministry of Data and Statistics, the rural farming population plunged from a little over 3 million in 2010 to just

New smart speakers, smart displays, smart photo frames, and smart mapping capabilities have turned up from Amazon.

Amazon has unveiled a new Alexa lineup featuring the Echo Show 16, Echo Time smart clock, and Alexa Photo Frame. Two speakers join them: an upgraded Echo Dot with a built-in smart home hub and the Echo Studio with Home Camera, which lets Alexa+ see its surroundings. Both are available now for $99.99 and $249.99, […]

A simulation study in Cluster Computing reports that a hybrid chaotic fire hawk and pufferfish optimization algorithm lowered energy use and latency in a modeled B5G-enabled IoT network of 100 nodes.

The company is also introducing an interactive home map feature.

A systematic review of 32 peer-reviewed studies finds that AI and IoT technologies show promise for COPD detection, exacerbation prediction and monitoring, but small single-center datasets and limited external validation remain major obstacles.

The FCC's Cyber Trust Mark program will label smart home devices meeting cybersecurity standards, similar to how Energy Star rates energy efficiency.

Researchers report a lightweight, imbalance-aware intrusion detection system achieving up to 0.9998 accuracy on IoT and medical IoT benchmark datasets.

arXiv:2610.37508v1 Announce Type: cross Abstract: This paper investigates dynamic channel tracking for high-rate Internet of things (IoT) in distributed massive multiple-input multiple-output (mMIMO) with low-resolution analog-to-digital converters (ADCs), where pilot and data activities are decoupled, pilots are reused, and mobile channels evolve across access slots. We hereby develop a quantization-aware joint coordinated Kalman filter (Q-JCKF) for known pilot activity and a quantization-aware distributed probabilistic data association filter (Q-DPDAF) for uncoordinated access. For uplink analysis, we derive a closed-form statistical use-and-then-forget (UatF) expression for Q-JCKF and mixture-aware, pilot-conditioned, and long-term statistical UatF formulations for Q-DPDAF, together with the large-N pilot-reuse ceiling and a cross-access point (AP) association term with an O(epsilon) posterior-concentration bound. The proposed framework recovers JC-KF/PDAF in the ideal-ADC

arXiv:2610.37147v1 Announce Type: new Abstract: Mobile Internet-of-Things (IoT) endpoints such as connected vehicles, wearables, and drones continuously cross heterogeneous network domains, creating handover-driven attack surfaces that static, cloud-anchored detectors cannot follow. However, three obstacles block real-time detection on such roaming endpoints: detection state is lost at every inter-domain handover; transmitting raw telemetry to a fixed detector exhausts the uplink and the device battery; and full-depth language model inference cannot meet millisecond latency budgets on constrained hardware. To tackle these challenges, we propose MAPLE-Sec, a mobility-aware partitioning framework that splits a compact, security-tuned language model across a roaming device and its nearest edge node. First, a trajectory-conditioned partition scheduler recomputes the split point from predicted handover timing, residual battery, and uplink quality. Second, a token-budgeted early-exit

arXiv:2610.35873v1 Announce Type: new Abstract: The integration of the Internet of Things (IoT) into monitoring systems has become increasingly significant across various fields, including energy management, environmental monitoring, and industrial applications. This paper proposes a smart, real-time, energy-aware monitoring system architecture for IoT using a brokerless Data Distribution Service (DDS) middleware. Environmental and electrical metrics are transmitted with high reliability, achieving an average latency of 1.76 milliseconds and a throughput of 57.1 Mbps. These metrics are processed by a multi-head Long Short-Term Memory network. Evaluated on the empirical TDC2.0 data center dataset, the architecture simultaneously performs three tasks: predicting thermal stress, detecting cooling inefficiency, and forecasting multi-step cooling power demand. The multi task network achieved an F1-score of 0.92 for proactive thermal stress prediction and demonstrated the capability to

arXiv:2610.35870v1 Announce Type: new Abstract: Near-field communication (NFC) sensor tags are promising for low-cost, scalable environmental monitoring due to their battery-free, maintenance-free operation. Recent advances in NFC reader garments, integrated with conductive textile coils, allow users to interact seamlessly with tags scattered across their daily environment. However, dynamically identifying and reading out a fluctuating number of tags remains challenging. If multiple tags speak and collide, they must randomly retry until an empty slot is found. Standard NFC identifies tags using time-slotted ALOHA, which assigns tags to fixed time slots. If multiple tags speak and collide, they must retry with a tag filter to avoid collision until all tags are identified. This rigid setup is fragile in dynamic settings; as tags enter and leave the communication range, the heavy overhead of slot re-negotiation and inevitable collisions restricts data throughput. Here, we propose an

My favorite robot vacuum just got quieter, and more expensive  The Verge

The Matic Serene robot vacuum promises quieter cleaning at full suction, operating at just 52 decibels. It costs $1,695, with upgrades coming in 2027.

Researchers combined a low-cost IoT monitoring prototype with a Bayesian-optimized XGBoost model that achieved the highest accuracy and fastest computation time among tested machine learning models for solar panel fault prediction.

A new open-access study reports that an attention graph neural network tuned by adaptive optimization reached 98 percent accuracy in assessing teaching quality on a benchmark dataset of 4,000 classroom records.

An Italian team has built and technically validated OmniTrack, a wearable UWB-based IoT platform that converts indoor distance-to-target into graded visual, auditory, and haptic hot-and-cold feedback for perceptual–motor training.

The Matic Serene looks identical to the current Matic but the company claims its hardware improvements make it much quieter. | Image: Matic Matic, the company behind my favorite robot vacuum, is launching its first new model since its 2023 debut: the Matic Serene. The new robotic floor cleaner has one key upgrade: noise reduction. With a promised output of just 52 decibels at its highest suction level, the Serene claims to be about as quiet as a running refrigerator. It costs $1,695, $200 more than the original Matic's current price, and is available now. The good news is the company is offering an upgrade path for existing Matic owners. The Serene targets people "who are extremely noise sensitive," cofounder and president Mehul Nariyawala tells The Verge. But beyond no … Read the full story at The Verge.

A systematic review of IoT and AI-enabled microclimate regulation in poultry farming demonstrates that predictive climate management can achieve thermal stability of ±0.5 °C, leading to improved welfare and production efficiency.

arXiv:2610.35591v1 Announce Type: new Abstract: Edge-centric IoT systems are increasingly evaluated through sensor values or platform features, yet the telemetry pipeline itself, the sequence of sensing, serialization, message mediation, ingestion, and persistence, is rarely released as a measurable object. This limits reproducible benchmarking of timeliness, provenance, and operational cost, especially in early-stage IoT design where real deployments and high-quality open data are often unavailable. I present a measurement-first edge IoT testbed and dataset that elevate timeliness, provenance, and energy to first-class telemetry dimensions. The testbed operationalizes a role-based event-driven architecture with explicit Source-Broker-Aggregator responsibilities and a schema-governed TelemetryMessage envelope (Avro) that embeds correlation identifiers and a uniform, cross-role vocabulary of stage-level timestamps. To trace mediation effects, I extend an MQTT broker (Eclipse Mosquitto)

arXiv:2610.35344v1 Announce Type: new Abstract: Autonomous IoT systems rely on distributed sensing, but sensor faults and communication loss can leave multichannel evidence incomplete or unreliable. Their agents must therefore classify measurements, assess evidence sufficiency, select a sensor to reacquire, and hand off unsafe decisions. We formulate counterfactual sensing: a degraded window defines the factual state, each candidate restoration defines a controlled alternative, and a pre-request utility model estimates loss reduction. Physics-Guided Invariant-Kinematic Fusion (PGIK-Fusion) constructs class evidence, while calibrated risk and cost-sensitive utility select acceptance, sensor request, or handoff from factual information. On the Logistic Activity Recognition Challenge dataset, PGIK-Fusion achieves 0.768 accuracy and a macro-averaged F1 score (Macro-F1) of 0.696, exceeding extreme gradient boosting (XGBoost)--one-dimensional convolutional neural network (1D-CNN) fusion by

arXiv:2610.34130v1 Announce Type: new Abstract: Indoor IoT cameras can support assisted living and activity-aware spaces, but exporting raw RGB frames raises privacy concerns by exposing facial, clothing, and household details that body-motion tasks often do not require. We present StickFigure, a pose-only IoT camera system that, during real-time monitoring, keeps raw frames local and ephemeral while exporting only compact skeleton streams sufficient for downstream human activity recognition (HAR). While pose estimation has matured on GPUs, extracting accurate pose streams on resource-constrained IoT cameras remains difficult. To overcome these challenges and enable on-device pose streams on (almost) any IoT camera, we present two core designs in StickFigure: an activation-aware pose backbone that allocates channel capacity after spatial downsampling and a lightweight hierarchical MLP refiner that selectively corrects elbow and wrist coordinates using intermediate visual features and

arXiv:2610.33862v1 Announce Type: new Abstract: Distributed Cyber-Physical Systems (CPS) and Edge AI networks increasingly depend on public-key authentication and key-establishment mechanisms that are vulnerable to cryptographically relevant Quantum Computers (QC). Shor's algorithm threatens widely deployed asymmetric primitives, while Grover's algorithm reduces the effective security margins of symmetric and hash-based mechanisms. Although Post-Quantum Cryptography (PQC) provides a migration path, its larger public keys, ciphertexts, and signatures introduce additional computation, memory, communication, storage, and packet-fragmentation costs that can constrain dynamic multi-agent systems. In this article, we introduce rotational secrecy, an architectural objective that limits temporal key exposure through authenticated transitions between cryptographic epochs. We present Private Authentication for Rotational Secrecy (PARS), a reference mechanism that coordinates policy-triggered

A controlled study of 40 smart home app users found that multimodal behavioral analysis guided prototype redesigns associated with lower task times, fewer errors and reduced perceived workload.

arXiv:2610.32661v1 Announce Type: new Abstract: Machine-learning research on water cyber-physical systems (CPS) is constrained by datasets that are either real but chemically poor, or simulated but free of sensing noise and attacks. We present AquaSense-ICS, an open dataset and benchmark that co-registers (i) a 25.4 h recording window (8.0 h of recorded data, 26% coverage) from a Raspberry Pi 5 edge node (TDS probe and BME680 ambient sensor, 1-2 s sampling, 23,688 records), with (ii) a looped network of 13 nodes (10 junctions, 2 reservoirs, 1 tank) simulated in EPANET-MSX for six water-quality species, (iii) a parameterised library of cyber-attacks (false data injection, replay, denial of service, drift) and unintentional sensor faults with sample-level ground truth, and (iv) augmented and TimeGAN-generated sequences, flagged by an origin column. The attack, fault and water-quality labels are traceable to a seeded generator, a documented rule or a simulation input. We show that the

arXiv:2610.31986v1 Announce Type: new Abstract: Vision language models (VLMs) have been increasingly adopted in consumer smart-home camera systems to interpret recorded videos and provide insights. However, video clips captured from smart-home cameras are different from curated web videos dominating existing VLM benchmarks. Smart-home video clips often do not contain subtitles or narration and are captured from different viewing angles, under various lighting conditions, and in diverse scenarios. However, the capabilities of the model in this domain, under sparse visual signals and limited context, are not fully assessed by existing VLM benchmarks. To close this gap, we introduce OpenHome, a visual-only, four-option multiple-choice benchmark for evaluating smart-home video understanding. It contains 2,254 questions on 1,118 real-world video clips and organizes 10 tasks into three capability levels. Perception evaluates visible content, Reasoning evaluates causal, spatial, and temporal

A new protocol generates one-time passwords and one-time keys for IoT devices using elliptic curve scalar multiplication, Montgomery ladder computation, and the ECDHP assumption, with reported performance gains over HOTP and TOTP.

Researchers have introduced CAE-IDS, an intrusion detection framework that pairs a Concrete Autoencoder-based feature selection mechanism with Bayesian hyperparameter optimization, reporting up to 98.93% accuracy and a 98.92% F1-score across binary and multi-class classification tasks on two real-world datasets.

A meerkat-optimized gated recurrent unit model achieved 96.65 percent accuracy and an AUC of 0.97 in classifying table tennis skill levels from IoT training load data, outperforming existing models in a study published in Discover Artificial Intelligence.

A new federated framework called FedAE-KMeans trains autoencoders locally on each device and clusters compressed latent representations on a central server, cutting communication overhead while preserving privacy.

arXiv:2610.30856v1 Announce Type: new Abstract: Ambient Internet of Things (A-IoT) devices operate under stringent energy constraints, relying on harvested power in the $\mu$W-to-mW range and limited energy storage. The A-IoT air interface for active devices is still under development; we therefore use Narrowband Internet of Things (NB-IoT) as an implementable reference for evaluating autonomous Uplink (UL) access under harvested energy. Under these conditions, the standard NB-IoT contention-based Random Access (RA) procedure incurs substantial per-packet overhead, making UL access in dense A-IoT networks increasingly energy-expensive. Context-retaining UL access schemes reduce this overhead, but their energy-latency trade-offs are insufficiently studied in literature under realistic harvesting and protocol constraints. To address this gap, we extend the LENA-NB ns-3 module with solar energy harvesting, supercapacitor storage, and a hysteretic brown-out mechanism, and propose Hybrid

arXiv:2610.30222v1 Announce Type: new Abstract: Language models have shown strong potential for intrusion detection, but their high memory and computational demands make them difficult to deploy in resource-constrained IoT environments. In this work, we propose a compact Transformer-based architecture that compresses the input into a small latent space before processing it with a minimal Transformer core. We analyze nine compression configurations, compare parameter sharing with layer stacking, and evaluate our approach against SecurityBERT on two IoT intrusion detection datasets, Edge-IIoTset and CICIoT2023. With only 306k non-embedding parameters and 1.6M parameters in total (6MB), our model is about 7x smaller than SecurityBERT (11.2M parameters, 45MB). With the same privacy-preserving encoding, it outperforms SecurityBERT by about 2 points of macro F1-score on Edge-IIoTset and is within 0.7 points on CICIoT2023. It is also about 89x faster on a simulated edge device. We further

arXiv:2610.30015v1 Announce Type: new Abstract: Antenna diversity in Internet of Things (IoT) devices is explored to address challenges associated with fixed device orientation and interference from nearby metallic objects, which can degrade wireless network performance. Integrating multiple antennas with different polarizations can significantly enhance signal quality and network reliability. Experiments demonstrated significant improvements in link quality, with gains exceeding 30 dB in received signal strength for specific scenarios. Already the addition of a second PCB-based antenna alone, resulted in a 17 dB improvement. These findings suggest that the added complexity in hardware and software to incorporate antenna diversity could well be justified by the resulting enhancements in network stability for real-world applications.

arXiv:2610.29236v1 Announce Type: new Abstract: Collecting relevant data from Internet of Things devices under freshness and energy constraints is a key challenge in 6G edge networks. A lightweight neural network on the device filters irrelevant observations, but storing it in dynamic random access memory consumes refresh energy during standby. This letter proposes a pull-based framework where an edge server remotely activates and approximates memory and jointly selects the activation timing, refresh period, model size, and transmission threshold. We derive the expected freshness and energy over erasure channels in closed form and show that the scheme consumes less energy than an always-on baseline under the same freshness constraint.

arXiv:2610.28702v1 Announce Type: new Abstract: LLM applications are increasingly built as systems of cooperating components rather than as single prompt-response pipelines. IoT applications add constraints of their own, because sensing, reasoning, control, and actuation are spread across devices, edge nodes, and cloud services to meet latency, privacy, and bandwidth requirements. How multi-agent LLM systems are organized and deployed across this cloud-edge-IoT continuum has not been mapped systematically. We therefore conducted a systematic mapping study. From 831 records retrieved up to March 2026, we selected 45 primary studies and classified each one by architecture family, deployment pattern, application domain, and evaluation practice. We also recorded how each system was evaluated and whether its code or data are public. Five architecture families recur in the corpus. Specialized agent teams run next to sensors, planners delegate subtasks to workers, and small local models

An IoT-integrated hydroponic maize system completed three sequential validation cycles and produced fresh-biomass conversion indices of 2.82:1 to 4.03:1 using municipal tap water without chemical adjustment.

arXiv:2610.27351v1 Announce Type: new Abstract: Accurate IoT device identification is essential for network security, yet many existing approaches rely on payload inspection, feature engineering, or long observation windows that limit scalability and often assume stable network conditions despite the inherent variability of real-world IoT traffic. We propose a lightweight IoT device identification approach based on packet headers and locality-sensitive hashing (LSH), generating compact fingerprint representations at packet- and window-level granularities. To evaluate robustness, we consider packet-level traffic anomalies including drop, perturbation, reordering, duplication, and packet injection. Experimental results show that the proposed approach achieves up to 99% accuracy while maintaining robustness under realistic traffic distortions using short bounded observation windows. Anomalies affecting global traffic structure produce the largest performance degradation, whereas

A mixed-methods study of 506 older adults in China identified differences in smart home care requirements associated with age, living arrangement, education and stakeholder perspectives.

An international model estimates that smart-farming technologies are already profitable on 42% of global cropland and could pay off on 50% within 20 years.

The use of robots, drones and AI tools in agriculture is already paying off on 42% of the world's cropland. This could increase to 50% in the next 20 years and to as much as 75% if every country tightened environmental regulations. If every country loosened its regulations, it would fall to 31%.

arXiv:2610.23597v1 Announce Type: new Abstract: Non-terrestrial networks (NTNs), such as Low Earth Orbit (LEO) satellites, can provide consistent coverage for Internet of Things (IoT) nodes, particularly in rural areas where cellular coverage is lacking. These IoT nodes are used to monitor and record their surroundings, supporting various applications, including real-time applications. In such cases, transmission of the most up-to-date data is essential, making the Age of Information (AoI) a critical metric to consider. The AoI measures the elapsed time from the data generation at the source node to its reception at the destination. Moreover, the Peak AoI (PAoI), which denotes the maximum AoI in the system before an update is received, provides valuable insights into the network's performance. Therefore, this encourages us to focus on the PAoI within a large-scale and fully loaded IoT network, which is covered by LEOSATs. Using tools from stochastic geometry, this paper derives the

arXiv:2610.23597v1 Announce Type: new Abstract: Non-terrestrial networks (NTNs), such as Low Earth Orbit (LEO) satellites, can provide consistent coverage for Internet of Things (IoT) nodes, particularly in rural areas where cellular coverage is lacking. These IoT nodes are used to monitor and record their surroundings, supporting various applications, including real-time applications. In such cases, transmission of the most up-to-date data is essential, making the Age of Information (AoI) a critical metric to consider. The AoI measures the elapsed time from the data generation at the source node to its reception at the destination. Moreover, the Peak AoI (PAoI), which denotes the maximum AoI in the system before an update is received, provides valuable insights into the network's performance. Therefore, this encourages us to focus on the PAoI within a large-scale and fully loaded IoT network, which is covered by LEOSATs. Using tools from stochastic geometry, this paper derives the

A new review argues that AI-IoT dairy welfare systems must be designed as complete cyber–physical architectures, with digital twins still an emerging rather than established reality.

Jeff and Fernando discuss Apple’s smart home reboot, including the new $299 Apple HomeView, HomePod mini 2, Apple TV 4K, and Apple Home Collection lineup. Sponsored by Rokform: Get your iPhone 18 Pro and iPhone Duo cases today for 20% off when using code MAC2026. more…

... ... 8-10 paragraphs, 1400-1600 words total. Need to write a science news article based on the source material. Facts available: FL-IDS for IoT, CNN-LSTM hybrid, FedAvg, adaptive global decision-threshold optimization post-training on pooled predictions, class imbalance, N-BaIoT benchmark with

Apple’s Home Collection is part of the company’s first major push into the smart home category. | Photo by Jennifer Pattison Tuohy / The Verge For more than a decade, Apple has told other companies to build smart home devices for its platform. This week, Apple launched its own. The Apple Home Collection is a lineup of cameras, thermostats, sensors, a smart door lock, and a video doorbell, all codeveloped with LG Electronics and Schneider Electric. They arrive alongside HomeView, Apple's first smart display. Even after 12 years of HomeKit, Apple's smart home ecosystem still feels underdeveloped, especially compared to the competition. Amazon's and Google's ecosystems support more products, and both have popular in-house brands: Ring and Nest. Apple had nothing of its own to point c … Read the full story at The Verge.

arXiv:2610.21064v1 Announce Type: new Abstract: Selecting a firmware stack for a memory-constrained microcontroller is usually guided by aggregate binary sizes, which report how much SRAM a build consumes but not where it goes or why. We address that gap with a per-symbol attribution method that decomposes static RAM into architectural categories - scheduler heap, network buffers and pools, task stacks, and linker-reserved regions - by bounding sized ELF symbols to the .data, .bss and .stack section ranges. The method is validated by reconciling every category sum exactly against the size-based totals for all sixteen builds. We apply it to eight cumulative firmware milestones, from a bare task scaffold to a full MQTT-over-Ethernet client, implemented twice, in FreeRTOS-C and in Embassy-Rust, on the CH32V203RBT6, a RISC-V microcontroller with 64 KB SRAM and an on-chip Ethernet MAC and PHY, with functional parity verified on hardware at every milestone. The decomposition shows that both

A systematic review of 88 studies shows radar and camera-based IoT systems dominate contactless vital-sign monitoring, while blood pressure, oxygen saturation, and temperature remain largely out of reach.

A dense IoT sensor network exposed striking vertical and longitudinal gradients of temperature, humidity, and carbon dioxide inside two commercial multi-tier layer hen houses in southern Brazil.

Eyeing Apple’s new Home Collection products? Renters should check their lease agreement before installing.

Good morning! Today I’m watching this epic discussion between Thom Yorke and Harry Styles, reading everything I can about the new Ocarina of Time remake, playing MMO Solitaire with 400 other people, and jamming to Remi Wolf’s new album, Mud. As a reminder, Inbox is the tech newsletter that goes beyond the news cycle. We cover the most important 9to5Google stories from the last couple of days along with a few highlights from around the web. We publish every Tuesday and Thursday, and if you like what you read, please subscribe. more…

arXiv:2610.20047v1 Announce Type: new Abstract: As Internet of Things (IoT) devices collect and process data, security is essential. IoT devices often combine with other emerging technologies, such as artificial intelligence (AI) and fifth-generation (5G) communications, to support applications such as smart grids and internet-of-vehicles (IoV). Because IoT devices are deployed remotely, remote attestation (RA) effectively verifies each device's state. However, the scale of IoT deployments makes device management challenging for owners. Therefore, cloud-assisted IoT management services often provide RA solutions. Although cloud-based IoT services can securely manage IoT devices, passive malicious administrators or hacked IoT device owners' accounts can control IoT devices without cloud IoT service authorization. Therefore, this work proposes a cloud-assisted RA system that addresses three main challenges of IoT device RA: 1) lightweight RA design for resource-limited IoT devices, 2)

arXiv:2610.19491v1 Announce Type: new Abstract: The rise of quantum computing presents substantial challenges to the security of Peer-to-Peer (P2P) energy trading systems. This paper introduces an innovative mutual authenticated session key agreement protocol aimed at protecting edge-IoT P2P energy trading communications in the quantum era. Each peer is an IoT-enabled smart metering device that is registered by an edge registration authority and acts as both producer and consumer. The proposed protocol incorporates CRYSTALS-Kyber's quantum-resistant cryptographic suite to guarantee the authenticity and confidentiality of negotiated session keys in the presence of quantum threats. Furthermore, it leverages the Hedera Hashgraph public blockchain, which is recognized for its quantum-resilient design, to ensure transparency and support efficient revocation mechanisms. Extensive comparative analyses have been conducted to demonstrate the protocol's superiority over existing alternatives,

A bug in the Google Home app has left device lists messy for some users, with no obvious way to fix it for the time being. more…

I think it’s sometimes easy for geeks to forget that we live in a tech bubble with our fellow geeks. Many of us here have been using smart home technology for more than a decade now, some even for many decades. I’ve seen a lot of bemused comments on the Apple Home Collection, with people asking why anyone would want to pay significantly higher prices for devices which don’t seem to offer any added benefit. But I think this is absolutely a bubble effect … more…

"Small weight-based replicators" could usher in a new generation of computer worms.

I’m not easily impressed when it comes to smart home hubs, here’s why the HomeView is different.

Researchers have developed a revocable, time-based attribute-based encryption scheme for cloud-assisted IoT that cuts encryption and decryption times while saving over 50 percent of ciphertext storage.

A new multi-dimensional evaluation framework shows that the most accurate intrusion detection model is often not the best choice for industrial edge networks once security coverage and five-year costs are counted.

Apple's Oct. 13 smart home event introduced HomeView, HomePod mini 2, Apple TV 4K, and new connected-home accessories. Here's what each product costs and when it arrives. The post Apple October 13 Smart Home Event: HomeView, HomePod Mini 2, Apple TV 4K, and More appeared first on TechRepublic.

Starting this December, Apple’s unleashing its own version of the smart home. We saw what’s in store with our own eyes.

Apple releases HomeView smart home hub with screen as showcase for Siri AI  CNBCMeet Apple HomeView, a new way to enjoy entertainment, stay connected, and manage the home  AppleApple Introduces HomeView, a $299 Tablet for the Home  The New York TimesThe Apple HomeView Is Like a Giant Apple Watch for the Home  WIREDApple October 2026 Event: Apple Debuts Its HomeView Smart Hub  Forbes

Apple makes another push into the smart home market with HomeView  TechCrunchMeet Apple HomeView, a new way to enjoy entertainment, stay connected, and manage the home  AppleI tried out Apple’s HomeView smart display  The VergeApple Unveils $299 HomeView Hub as Part of New Smart Home Push  Bloomberg.comApple just announced a new gadget that could be key to its AI push  CNN

Apple has launched HomeView, a $299 smart display built around Siri AI, with shipping from 13 November in eight markets. The European Union is not one of them, because Siri AI is still blocked on iOS there. Apple has launched HomeView, a $299 smart display built around the new Siri, with pre-orders open and shipping […] This story continues at The Next Web

Anthropic said on Monday that small AI models could be months away from self-replicating across internet-connected devices, raising the possibility of AI botnets spreading quickly, fighting takedown attempts and even breaking into data centers to turbocharge their growth. Last month, Anthropic CEO Dario Amodei first floated the idea that within the next year or even sooner, AI might be able to hijack "the entire internet" with "a persistent botnet," a warning that raised eyebrows in the cybersecurity community. Botnets — vast networks of devices controlled by malicious operators — have been around since the early days of the internet, and are still around. The network typically includes poorly secured Internet of Things (IoT) devices, such as Android TV boxes, smart appliances and security cameras, and is used by hackers to send spam, seed malware, or sabotage target companies using floods of bogus traffic. Anthropic's Monday blog post appeared to be the first attempt to articulate how

Apple’s “Welcome Home” event featured all the new smart home hardware coming to the Apple lineup.

Apple releases HomeView smart home hub with screen as showcase for Siri AI  CNBCMeet Apple HomeView, a new way to enjoy entertainment, stay connected, and manage the home  AppleApple Unveils $299 HomeView Hub as Part of New Smart Home Push  Bloomberg.comEverything Announced At Apple's 'Welcome Home' Event  EngadgetApple Introduces HomeView, a $299 Tablet for the Home  The New York Times

Researchers at Jerash University have developed an energy-aware federated deep learning framework that cuts per-round energy use by about 43 percent while keeping image recognition accuracy within 2.1 percentage points of centralized training on resource-constrained IoT devices.

The iPhone maker also announced the new Apple TV 4K and HomePod Mini.