IOT

arXiv:2608.08556v1 Announce Type: new Abstract: The Internet of Things (IoT) increasingly combines sensing, communication, artificial intelligence (AI), decision-making, and actuation. In many domains, sensor observations are processed by edge or cloud intelligence to select actions that configure or control actuators; where actuation changes the environment, later observations may also be affected. Existing explainable AI (XAI) methods can explain model predictions, but they do not by themselves explain the end-to-end path from sensed evidence to physical action. This paper introduces the Internet of Explainable Things (IoXT), a system-level paradigm that makes explainability an architectural property of intelligent IoT. Its novelty is explainability-by-design across the sensing-communication-intelligence-decision-actuation path. IoXT derives requirements and design principles for identity and addressability, temporal fidelity, cross-layer provenance, bidirectional traceability,

In January 2018, major airlines stopped accepting smart bags whose lithium-ion batteries could not be removed. Bluesmart, one

arXiv:2608.06960v1 Announce Type: new Abstract: Internet of Things (IoT) devices handle sensitive privacy-related information such as user audio, video, and authentication data, making it essential to detect vulnerabilities in their firmware. Decompilation, a key detection technique, has recently attracted attention because Large Language Models (LLMs) enable high readability and high recompilation success rates. However, because LLM outputs depend on probabilistic token prediction, they tend to prioritize syntactic correctness and may generate plausible-looking code that is semantically different from the original binary. Vulnerabilities often arise in details that are easily lost in this process, such as error-handling flows and boundary checks. Existing evaluation metrics focus mainly on passing test cases and cannot sufficiently identify code whose internal structure has been altered despite appearing behaviorally valid, so a metric that quantifies the internal structure of

arXiv:2608.06447v1 Announce Type: new Abstract: In modern distributed network environments, particularly in Internet of Things infrastructures and 5G networks, stringent privacy preservation and scalability requirements have created significant challenges for intrusion detection systems. Although federated learning preserves privacy by preventing data centralization, its efficiency and stability is considerably degraded under severe statistical heterogeneity and resource constraints of edge nodes. To address these limitations, this study introduces the FedTransKD-IDS framework, which enhances both system stability and efficiency by integrating robust aggregation based on the geometric mean, federated transfer learning, and knowledge distillation. Within this framework, the collaboratively trained global teacher model transfers its feature extraction component to lightweight student models. Experimental evaluation on heterogeneous datasets demonstrates a peak detection performance,

A compromised SIM card could give attackers far more control over a smartphone, vehicle system, industrial device or router than previously understood, according to research from the University of Birmingham. The study identifies a largely overlooked attack surface in cellular devices: commands sent directly from a SIM to a modem through a telecommunications feature known […]

A home does more than provide shelter, it shapes how we feel from the moment we wake up

OpenAI set to get into the smart home sector with $300 doughnut-shaped puck  nypost.comOpenAI’s New Device Will Be Hockey Puck-Sized and Cost Over $300  Bloomberg.comOpenAI’s new AI smart speaker will reportedly sell for between $300 and $400  TechCrunchOpenAI's first device may be a $300 donut-shaped speaker that'll talk to you  Yahoo TechOpenAI's Ring-Shaped Smart Speaker Will Reportedly Cost Between $300 And $400  Engadget

OpenAI set to get into the smart home sector with $300 doughnut-shaped puck  nypost.comOpenAI’s New Device Will Be Hockey Puck-Sized and Cost Over $300  Bloomberg.comOpenAI’s new AI smart speaker will reportedly sell for between $300 and $400  TechCrunchOpenAI's first device may be a $300 donut-shaped speaker that'll talk to you  Yahoo TechOpenAI's Ring-Shaped Smart Speaker Will Reportedly Cost Between $300 And $400  Engadget

The first device I added to my smart home was a light bulb. It seemed like the safest possible place to start. I could turn it on from anywhere, put it on a schedule, and eventually work it into routines with everything else. Then I tapped the button in the app and waited. The delay […]

arXiv:2608.05495v1 Announce Type: new Abstract: Smart-home assistants increasingly use multimodal large language models (MLLMs) that perceive video and audio directly. This raises a safety question specific to the home: can the agent tell a genuine user command from ambient or externally-sourced content, television speech, on-screen text, or an overheard conversation, that merely looks like a command? We introduce PromptShield-Home, a pilot benchmark of realistic smart-home scenarios spanning addressee ambiguity, screen/audio injection, health-monitor false triggers, mixed occupancy, and a legitimate-command floor, and use it to compare three abstraction layers: traditional detectors (L0), a single MLLM agent (L1; vision, vision+ASR, and audio-visual), and multi-agent mediation (L2; voting, role specialists, cross-model arbitration). Because the label distribution is skewed toward inaction, aggregate accuracy is misleading, a constant always-block predictor scores 82%, so we report

Lutron is expanding its existing Caseta integration with Sonos, adding the ability to control smart-home devices using Sonos Voice Control.

Once consumers buy your device, they decide whether to keep using it within the first 10 minutes, and

arXiv:2608.04721v1 Announce Type: new Abstract: Warehouse items differ in how urgently they must be moved: perishable goods, pharmaceutical shipments, and just-in-time production materials must be delivered sooner than the rest of the stock. Decentralised robot swarms suit warehouses that cannot justify fixed automation infrastructure, but current swarm controllers treat all items alike or rely on an external scheduler to set priorities, so urgent items wait as long as ordinary ones. This paper presents a swarm logistics system in which each warehouse carrier holds an ultra-low-power Internet-of-Things (IoT) tag that broadcasts the urgency of its item over Bluetooth Low Energy (BLE). Robots read these broadcasts directly and weigh urgency against travel distance when choosing which carrier to serve, so prioritisation happens at the item level without central scheduling. The system is evaluated in simulation and validated on real robots and IoT-tagged carriers against a proximity-only

arXiv:2608.04073v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve local adaptation under highly Non-Independent and Identically Distributed (non-IID) data distributions. However, existing PFL methods often rely on client-side self-adjustment, which may lead to over-personalization and substantial degradation in out-of-distribution (OOD) attack detection. In this paper, we propose Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control. In particular, FBID employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality. Moreover, FBID introduces a trust-based blending mechanism to derive client-specific interpolation coefficients between the global and local

After launching in the US last year, Alexa+ aims to be “smarter, more conversational and more capable” than ever — but at AU$29.99 per month (without Prime), is another agentic AI assistant worth it?

arXiv:2608.00855v1 Announce Type: new Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller

arXiv:2608.00892v1 Announce Type: new Abstract: Latency-critical IoT applications, such as autonomous mobility and industrial automation, require deterministic guarantees to ensure that tasks are completed within strict deadlines. The 6G-enabled IoT-edge-cloud continuum can support such requirements by leveraging communication, computation and intelligence resources across devices, edge, and cloud infrastructures. However, existing task offloading strategies mainly focus on selecting where tasks are executed and typically assume immediate processing upon task arrival. This leads to transient congestion when multiple tasks coincide in time and results in inefficient resource utilization under dynamic workloads. This paper addresses these limitations by introducing an execution timing control strategy for deterministic task offloading that jointly determines where tasks are executed and when their execution starts, while guaranteeing deadline compliance. The key idea is to exploit the

arXiv:2608.00855v1 Announce Type: new Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller

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The Xiaomi Robot Vacuum X20 Max is a capable robot vacuum cleaner, but my unit developed issues after a year of testing.

If you want to avoid purchasing potentially contaminated greens, growing your own indoors is a safer alternative.

arXiv:2607.28585v1 Announce Type: new Abstract: We address the problem of passive localisation of a stationary, ground-level IoT radio emitter using Doppler frequency measurements collected by low-Earth orbit (LEO) satellites during an observation window. The problem is challenging because radio emission from low-cost IoT devices is affected by various compounding sources of measurement error, that collectively render the likelihood function intractable in a closed form. Hence, we apply and investigate the performance of Approximate Bayesian Computation (ABC) methods for this task. Numerical results demonstrate the statistical and computational performance of two ABC methods, rejection sampling ABC and sequential Monte Carlo ABC.

arXiv:2607.28088v1 Announce Type: new Abstract: Many cloud providers for IoT technologies offer access control mechanisms whose proper configuration is critical for security. However, verifying permissions in isolation is insufficient in a setting where devices have different levels of trust or are compartmentalised in various subsystems. This work analyses IoT access control policies to identify potential security vulnerabilities from unwanted information flow between devices. To this end, we formally model AWS IoT Core's components and define an information flow graph to capture the communication among devices permitted by the access control policies. We build a finite representation of the graph by leveraging an SMT solver, thus enabling the verification of information flow between devices. We implement our approach in a tool called IOT:POKER, and assess it on a realistic scenario and several real-world policies.

arXiv:2607.27858v1 Announce Type: new Abstract: Healthcare IoT services increasingly rely on edge gateways to relay routine telemetry and deliver rare but timecritical alarms. Even short traffic bursts can inflate worstcase delay and interfere with urgent messages. We present NANOEDGEGUARD, a kernel-plane closed-loop controller that observes per-source traffic intensity at the edge and enforces an auditable, multi-tier rate policy using in-kernel traffic-control hooks. Unlike static firewall rules or user-space control loops, our design prioritizes fast actuation and explicit recovery through hysteresis, and it records policy transitions for auditability. Using a Raspberry Pi gateway hosting an MQTT broker and two ESP32 endpoints generating vitals, alarms, and a timed burst, we show that adaptive kernel-plane rate control reduces the 99th-percentile alarm RTT by 13.3% compared to a user-space firewall baseline while maintaining no-enforcement-level RTT, and it reduces excess admitted

A new leak reveals wireframe designs for Samsung Ballie's companion app, offering the clearest sign yet that the long-delayed home robot might still be in development.

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 Backblaze: Backup you can rely on. Save 20% with code 9to5daily.

Apple is reportedly preparing new smart home devices centered on a revamped Siri, signaling a renewed push into AI-powered home automation. The post Apple Reportedly Plans Biggest Smart Home Push in Years With New Siri appeared first on TechRepublic.

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 Backblaze: Backup you can rely on. Save 20% with code 9to5daily.

As AI continues to transform processes across the IoT, this trajectory marks a fundamental shift.

arXiv:2607.25817v1 Announce Type: new Abstract: Bridging the digital divide is one of the goals of mobile networks in the future, and further building IoT networks in rural areas is a feasible solution. This paper studies the downlink performance of rural wireless networks, where IoT devices we consider are battery-less and powered only by ambient radio-frequency (RF) signals. We model a rural area as a finite area that is far from the city center. The base stations (BSs) in the whole city and the access points (APs) in the finite network both act as sources of wireless RF signals harvested by IoT devices. We assume that BSs follow an inhomogeneous Poisson Point Process (PPP) with a 2D-Gaussian density, and a fixed number of APs are uniformly distributed inside the finite area following a Binomial Point Process (BPP). The IoT devices we consider can harvest energy and receive downlink signals in each time slot, which is divided into two parts: (1) a charging sub-slot, where the RF

arXiv:2607.25325v1 Announce Type: new Abstract: Requirement-to-Code traceability has been widely studied, yet existing research and public benchmarks remain largely centered on functional requirements (FRs). In contrast, traceability for non-functional requirements (NFRs) remains more difficult and underexplored, which hinders the verification of critical quality concerns such as security and reliability.This paper studies NFR-to-Code traceability based on a real-world blockchain-IoT project. We design an annotation protocol for constructing trace links across heterogeneous artifacts and build a manually curated subset containing both FR and NFR links. Using this subset, we examine the characteristics of NFR traceability and further evaluate four representative retrieval baselines: TF-IDF, BM25, LSI, and WMD. The results show that FR-to-Code tracing is consistently easier than NFR-to-Code tracing, while security-related NFRs are the most difficult subset. They further indicate that

Apple is preparing a push into the smart home with a new Apple TV 4K, an updated HomePod mini, and a hub device built around its rebuilt Siri, Bloomberg’s Mark Gurman reported on Tuesday. The hardware for the first two has reportedly been finished for months and is already being tested by Apple employees on […] This story continues at The Next Web

Apple's long-rumored smart home push may finally be nearing launch, with Bloomberg reporting that a new home hub, refreshed Apple TV, and updated HomePod mini could roll out over the coming months.

Apple Set to Make Major Smart Home Push With Siri AI at Center  BloombergApple’s reported ‘HomePad’ may launch as early as October  The VergeApple has three new smart home products ‘nearly ready to launch,’ per report  9to5MacApple's smart home hardware refresh draws closer with Siri AI smarts  AppleInsiderApple's new home device appears similar to products by Amazon and Google: report  Seeking Alpha

Apple Set to Make Major Smart Home Push With Siri AI at Center  BloombergApple’s reported ‘HomePad’ may launch as early as October  The VergeApple has three new smart home products ‘nearly ready to launch,’ per report  9to5MacApple's smart home hardware refresh draws closer with Siri AI smarts  AppleInsiderApple's new home device appears similar to products by Amazon and Google: report  Seeking Alpha

Apple is about to kick off its major new smart home push. A new report from Bloomberg today reveals that Apple has three new smart home devices that are “nearly ready to launch.” The first two are set to come in just a couple of months …

Mark Gurman / Bloomberg: Sources: Apple is preparing to launch a smart home hub with a 7" square display and facial recognition tech, a new Apple TV, and refreshed HomePod mini in 2026  —  Apple Inc., after spending years as a laggard in the smart home market, is preparing a dramatic new push into the category with fresh devices and software.

arXiv:2607.23734v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A

arXiv:2607.23698v1 Announce Type: new Abstract: Remote attestation (RA) is a lightweight security primitive for detecting software compromise on IoT devices. Traditional RA schemes require atomic, non-interruptible memory measurements, making them difficult to deploy alongside real-time workloads. SMARM addresses this limitation by measuring memory in a secret, shuffled block order, reducing the non-interruptibility period to the duration of a single block measurement. However, SMARM was originally designed for microkernel-based systems and has not been studied in RTOS-driven real-time environments. In this work, we present the first systematic study of SMARM in real-time RTOS-based setups. We implement SMARM on commodity ARM TrustZone-M hardware running FreeRTOS and Zephyr, and introduce the Frequency Accuracy Ratio (FAR) to quantify the extent to which attestation can coexist with real-time execution under varying workloads. Our evaluation shows that SMARM's real-time

Between frequent contamination recalls and rising food costs, growing indoors offers a safe and surprisingly simple alternative.

Between frequent contamination recalls and rising food costs, growing indoors offers a safe and surprisingly simple alternative.

Minister of Water Resources and Irrigation Hani Sweilem affirmed on Monday that digital transformation and the smart management of water infrastructure are key pillars of the ministry’s second-generation Water System 2.0 strategy, highlighting the importance of drawing on Japan’s advanced expertise in integrated water resources management and the intelligent operation of water facilities. He also […] The post Egypt, Japan deepen co-operation on smart water management appeared first on Egyptian Gazette.

Traditional compressor maintenance often follows fixed calendar dates or run-hour limits. That approach helps with basic planning, but

Between frequent contamination recalls and rising food costs, growing indoors offers a safe and surprisingly simple alternative.

arXiv:2607.21784v1 Announce Type: new Abstract: With the growing demand of Internet of Things (IoT), there is a need for seamless and reliable communication between heterogeneous IoT devices and the cyber-world to ensure autonomous control over any application process. More specifically, seamless communication requires interoperability between heterogeneous devices (actors) having different semantics and data formats (syntaxes), while making it more challenging. In this paper, we propose a middleware solution for unified semantic and syntactic interoperability in the publisher-subscriber framework of IoT network. The proposed framework automatically translates the subscribers (users) compatible syntax and semantics of the receiver message from the publishers (IoT devices). First, we propose a novel method of syntax translation of messages, to solve the syntactic disparities between users and devices, while providing the information in the user requested syntax. Thereafter, a

Between frequent contamination recalls and rising food costs, growing indoors offers a safe and surprisingly simple alternative.

Realbotix is supplying a humanoid robot and at-home AI homework support. The teachers union says students are not lab rats.

Older Google smart home devices are getting Gemini Live, but you'll need a Google Home Premium subscription for the full experience.

A smarter home goes beyond security. Whether you're heading off on a vacation this summer or making the most of longer days outdoors, discover these EZVIZ devices designed to help you stay connected, simplify everyday home management, and enjoy greater peace of mind wherever life takes you.

arXiv:2607.21387v1 Announce Type: new Abstract: Massive Internet of Things (IoT) deployments increasingly share spectrum with incumbent, licensed, and unlicensed systems under uncertain traffic, fading, mobility, and intermittent coordination. Existing mechanisms, including fixed power limits, listen-before-talk procedures, spectrum access databases, and learning-based resource allocation, address important aspects of coexistence, but they do not provide a common control plane to translate a network-wide interference risk budget into lightweight guidance for many autonomous devices. This article introduces Distributed Spectrum Compliance and Orchestration (DISCO), a hierarchical architecture that separates local spectrum learning from edge-level compliance regulation and slower cloud or non-terrestrial-network context adaptation. DISCO is not presented as a new reinforcement-learning optimizer or as a replacement for statutory spectrum rules. Its contribution is a deployable

Echo Show's 21 attached to the adjustable stand, which is sold separately (and also discounted). | Image: Amazon Split between buying a smart calendar, a kitchen TV, a smart home hub, and a smart display? Amazon’s Echo Show 21 is all of those things in one, with a huge 21-inch screen. You can use it to control your smart lights, glance at recipes, watch TV shows, and more. Currently, Best Buy and Home Depot have the product on sale for $319.99 (usually $399.99), matching the lowest price we’ve seen to date. Amazon Echo Show 21 The Echo Show 21 can act as the centerpiece of any burgeoning smart home. The 1080p smart display is a Matter and Thread controller, meaning you can locally control compatible devices using voice commands or a range of widgets. Its built-in auto-framing camera also facilitates video calls. Plus, it has decent speakers for music and other features shared among Alexa displays.

During battery design discussions with IoT product teams, one question appears repeatedly: why does a low-power device still

arXiv:2607.19590v1 Announce Type: new Abstract: Communication is the dominant source of energy consumption in Internet-of-Things (IoT) networks, yet many sensed measurements exhibit strong temporal correlations and provide little new information to the receiver. This paper introduces \textsc{ADAPTIVEML}, a volatility-aware predictive communication framework that enables IoT devices to intelligently decide when communication is necessary. Each sensor maintains a lightweight machine learning predictor and transmits only when the prediction residual exceeds an adaptive threshold proportional to the local signal volatility. By normalizing prediction errors using a rolling estimate of signal variability, the proposed transmission policy automatically adapts to changing environmental conditions, seasonal variations, and deployment-specific dynamics without manual threshold tuning. To address long-term non-stationarity, we further propose \textsc{ADAPTIVEML-RLS}, an online learning extension

arXiv:2607.19590v1 Announce Type: new Abstract: Communication is the dominant source of energy consumption in Internet-of-Things (IoT) networks, yet many sensed measurements exhibit strong temporal correlations and provide little new information to the receiver. This paper introduces \textsc{ADAPTIVEML}, a volatility-aware predictive communication framework that enables IoT devices to intelligently decide when communication is necessary. Each sensor maintains a lightweight machine learning predictor and transmits only when the prediction residual exceeds an adaptive threshold proportional to the local signal volatility. By normalizing prediction errors using a rolling estimate of signal variability, the proposed transmission policy automatically adapts to changing environmental conditions, seasonal variations, and deployment-specific dynamics without manual threshold tuning. To address long-term non-stationarity, we further propose \textsc{ADAPTIVEML-RLS}, an online learning extension

The Narwal Flow 2 promises to be one of the best robot vacuum cleaners for obstacle avoidance and mopping - and it is.

These locks, lights, and other smart home upgrades let you add automation without messing up your home’s vibe.

arXiv:2607.18034v1 Announce Type: new Abstract: Smart home assistants interpret a wide range of user commands, from explicit device control to underspecified and preference dependent requests. While recent systems based on Large Language Models (LLMs) improve this capability, they often rely on heavyweight reasoning pipelines and cloud-based deployment, limiting their efficiency and suitability for resource-constrained environments, and raising privacy concerns. In addition, existing approaches provide limited support for stable long-term personalization. To address these issues, we present AdaHome, an adaptive smart home assistant designed for locally deployed small language models in smart home environments. Rather than applying complex reasoning uniformly, AdaHome introduces an intent-aware planning framework that dynamically routes commands either to straightforward prompt-based or lightweight reasoning-based components. For commands requiring interpretation, we adopt a

arXiv:2607.17035v1 Announce Type: new Abstract: While the Internet of Things (IoT) has become essential, they introduced serious security and privacy challenges, especially for mission-critical environments. Legacy devices are vulnerable to viruses, data breaches, and unauthorized access, and updating these devices would be infeasibly costly. As a solution, this paper presents a Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipeline. Unlike existing solutions, our framework enforces Zero Trust at every communication layer via mutual TLS (mTLS) over MQTT, ensuring no device or message is implicitly trusted. Our experiments with Raspberry Pis and various sensors achieve an F1 score of 0.9091 and an ROC-AUC of 1.0, highlighting the effectiveness of the proposed framework in

A newly disclosed SharkNinja vulnerability could let attackers access robot vacuum cameras, home maps, and Wi-Fi passwords through an unpatched cloud security flaw affecting millions of devices.

Financial Times: How smart home devices have given abusive partners a new tool to remotely manipulate, intimidate, and disturb their victims, in a form of tech-assisted abuse  —  Remote access to domestic devices is increasingly being used to manipulate, intimidate and disturb.  Policymakers and tech companies are grappling with safeguards

Clap on. Clap off. Well, more like, Clap, pause for half a beat but no longer because otherwise it'll stop hearing you, clap again because you waited too long, clap louder and faster, that didn't work, clap two more times, and suddenly: on. The Clapper didn't always work - and even when it did, it might fry your gadgets in the process - but it managed to become a big hit anyway. On this episode of Version History, we tell the story of The Clapper, from its beginnings as The Great American Turn-On to its debut alongside one of the great advertising campaigns of all time. The Verge's David Pierce and Victoria Song are joined by Allison Marsh, … Read the full story at The Verge.

Always-on devices can quietly boost your energy use. Here are some settings to change -- and some devices you might want to unplug -- to avoid wasting energy and money.

Sejong University and Dongguk University have signed an agreement to launch a joint degree program aimed at nurturing convergence skills in the field of intelligent Internet of Things (IoT). The agreement was signed by Sejong University President Eom Jong-hwa and Dongguk University President Yoon Jae-woong at the Sejong University campus in Seoul Tuesday. Sejong University said Wednesday that the agreement is part of the interuniversity joint degree initiative promoted by the Internet of Things Convergence and Open Sharing System (IoT COSS) Consortium. The IoT COSS initiative is funded by the Ministry of Education and supported by the National Research Foundation of Korea. Building on the Standard Curriculum for the Department of Intelligent IoT jointly developed by the consortium, the two universities will establish the joint degree program. Both institutions have developed the curriculum in cooperation with three other member universities of the consortium. Led by Sejong University,

The problem is an over-permissive AWS IoT policy.

arXiv:2607.14903v1 Announce Type: new Abstract: Firmware rehosting executes firmware images in emulated environments such as QEMU to enable scalable dynamic analysis of Internet of Things (IoT) devices. In practice, rehosting pipelines remain fragile across diverse real-world firmware images, as reaching an externally observable execution state depends on tightly coupled artifacts spanning boot scripts, persistent configuration (e.g., NVRAM-like key-value state), and network setup. Template-driven frameworks often fail to accommodate long-tail vendor conventions, while unconstrained use of large language models (LLMs) risks unsupported modifications and irreproducible executions. We introduce FirmPilot, an evidence-guided multi-agent framework for environment recovery in firmware rehosting. FirmPilot reformulates rehosting as iterative environment reconstruction in which a search agent grounds decisions through similarity-based retrieval, a planner coordinates execution-accepted

Robovacs were flying off the shelves at Amazon during Prime Day, but there are still plenty of discounts of up to 69% from Dreame, Roborock and more if you missed out.

arXiv:2607.13462v1 Announce Type: new Abstract: Integrated sensing and communication (ISAC) has become a promising technical framework for Marine Internet of Things (MIoT) systems. Nevertheless, all devices rely on battery power, so energy efficiency becomes a core bottleneck limiting practical deployment. This paper investigates the energy consumption minimization problem of MIoT-oriented ISAC systems. In this system, an uncrewed aerial vehicle (UAV) uses non-orthogonal multiple access (NOMA) to simultaneously perform target sensing and collect data from uncrewed surface vehicles (USVs), then forwards processed sensing information and USV data to a shore-based base station (SBS). Subject to latency limits and sensing performance requirements, total system energy consumption can be minimized via joint optimization of multiple variables, UAV transmit beamforming, dedicated sensing signal, USV transmit power, UAV computation power, and time resource allocation for sensing and

These smart home gadgets elevate my home and routine. Here's why you may want them too.

arXiv:2607.12662v1 Announce Type: new Abstract: The paper introduces the Internet of Agentic Things (IoAT), an architectural framework that integrates agentic AI, IoT, cyber-physical systems, Physical AI, edge computing, and digital twins into a unified closed-loop orchestration framework. The proposed architecture consists of cloud, edge/fog, and physical IoT layers connected through autonomous AI agents that perceive, reason, coordinate, and actuate across distributed cyber-physical environments. The paper formalizes IoAT as a coupled workflow-control problem with nested strategic and tactical decision making using a hylomorphic dynamic programming framework that links agentic planning with physical execution. Smart-building orchestration is presented as a representative use case, and key research challenges related to safety, security, governance, resilience, and trustworthy deployment are discussed.

I've tested dozens of smart home devices, but only a select few truly stand out. Here are the smart home gadgets I wholeheartedly recommend.

When people discuss smart homes, the conversation usually revolves around voice assistants, connected security infrastructure, smart lighting grids,

arXiv:2607.11488v1 Announce Type: new Abstract: Long range-frequency hopping spread spectrum (LR-FHSS) is a promising uplink physical layer for massive low Earth orbit satellite Internet of Things, where low power terminals report short packets from wide area regions with limited terrestrial infrastructure. However, satellite IoT links are exposed to external interference, and the coexistence of multiple interference components can severely degrade receiver reliability and complicate interference mitigation. Existing recognition methods either focus on single interference scenarios or treat each compound interference combination as an independent class, leading to limited generalization or poor scalability. To address this problem, this paper formulates LR-FHSS uplink compound interference recognition as a multi-instance multi-label learning problem and proposes a multi-domain instance fusion method. The proposed method fuses local instances from the time-frequency and frequency

arXiv:2607.11649v1 Announce Type: new Abstract: Network-based anomaly detection for IoT devices has matured to the point of reporting strong detection accuracy, yet most published systems stop at raising an alert and leave the question of automated enforcement to future work or to a programmable data plane that few real networks operate. This paper presents an access-control architecture that closes that loop using only standard, already-deployed protocols. Devices authenticate via IEEE 802.1X with EAP-TLS, and a RADIUS server acts as a continuous policy decision point capable of evicting an active session via a Change-of-Authorization Disconnect-Request and permanently excluding a device through certificate revocation. A central, contextual access policy engine continuously consumes the anomaly detector's output and actuates this response over a narrowly restricted channel to the RADIUS server; the same engine is designed to be extensible to other access types, though this paper

arXiv:2607.11488v1 Announce Type: new Abstract: Long range-frequency hopping spread spectrum (LR-FHSS) is a promising uplink physical layer for massive low Earth orbit satellite Internet of Things, where low power terminals report short packets from wide area regions with limited terrestrial infrastructure. However, satellite IoT links are exposed to external interference, and the coexistence of multiple interference components can severely degrade receiver reliability and complicate interference mitigation. Existing recognition methods either focus on single interference scenarios or treat each compound interference combination as an independent class, leading to limited generalization or poor scalability. To address this problem, this paper formulates LR-FHSS uplink compound interference recognition as a multi-instance multi-label learning problem and proposes a multi-domain instance fusion method. The proposed method fuses local instances from the time-frequency and frequency

arXiv:2607.10438v1 Announce Type: new Abstract: Autonomous driving planning is a key component of IoT-enabled intelligent transportation systems, requiring vehicles to generate safe, efficient, and executable trajectories in complex urban environments from multi-source contextual information. While imitation learning (IL) has shown promise on large-scale datasets, IL-based planners still suffer from limited coverage of complex long-tail interactions, weak consistency with downstream constrained refinement, and insufficient use of high level scene semantics under real time constraints. To address these issues, this paper proposes a large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving. Specifically, we introduce a surrounding agent centric data augmentation strategy to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training distribution without collecting additional raw

Smart home hubs are useful, but are they essential? Not really, not anymore. In fact, you might already have everything you need at home right now.

arXiv:2607.09653v1 Announce Type: new Abstract: Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments, their application to IoT specific vulnerabilities remains unexplored. This paper presents an autonomous multi-agent framework, referred to as Vulnerability EXploitation using AI Agents (VEXAIoT), for vulnerability discovery and exploitation in IoT environments using LLM-based reasoning and offensive security tools. The framework combines a vulnerability detection agent and an attack execution agent to perform reconnaissance, plan attack sequences, and execute exploits against vulnerable IoT services. The system is evaluated in IoTGoat and Metasploitable environments across ten

arXiv:2607.09259v1 Announce Type: new Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests, and its main result is that the QLoRA-tuned LLM branch becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ensemble. The framework maps each synthetic deployment record to a managed request, blocks explicit out-of-boundary cases through a deterministic hard-fraud gate, scores non-hard requests using centralized ML (M1), federated meta-learning (M2), or LLM-family risk sources (M3), and resolves actions through a shared five-state policy, two-zone refinement mechanism, and local Ethereum-compatible audit layer. Evaluation uses separate synthetic training data

arXiv:2607.08978v1 Announce Type: new Abstract: Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms. Detecting abnormal temporal behavior is critical for fault diagnosis, predictive maintenance, and security. However, practical IoT anomaly detection is hindered by decentralized and non-IID data, limited bandwidth, and the constrained computation and memory of edge devices. This paper proposes FedKAD, a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series. Unlike deep-learning-based anomaly detectors that require training and communicating large neural models, FedKAD learns normal temporal dynamics through lightweight sliding-window Koopman representations. Federated training is formulated as a low-rank consensus problem, where raw sensor streams and local reduced dynamics remain on device while only compact subspace variables are exchanged with

A photo of a lightbulb glowing purple. | Photo: Amelia Holowaty Krales / The Verge The state of the smart home can be frustrating, because it is just so obvious how things ought to work. You should be able to control everything from everywhere. Your spaces should adapt to what you're doing and how you're feeling. Making your home smart shouldn't require renovating, and the smarts should be mostly invisible. All of this is, of course, incredibly hard to pull off - but the goal is pretty clear. Until now, maybe no product has come closer to nailing the smart home than Philips Hue. And on this episode of Version History, we dig into all the things Hue got right. The Verge's David Pierce and Jennifer Pattison Tuohy are joined … Read the full story at The Verge.

Humanoid robots learned to walk years ago. The thing still tripping them up is the hand. 1X has given its NEO home robot new hands, and they are the most interesting thing about it. A robot can stride across a stage and still be useless in a kitchen. Lifting a wet glass takes precision, fast […] This story continues at The Next Web

I think if we are honest with ourselves, most of us would admit that smart home tech falls into one of two categories. First, there is the genuinely useful. Second, there’s the stuff we do just because we can, and it is to some degree cool. I think I’ve now discovered a third category: things that really aren’t worth all of the time and effort they take …

1X’s NEO home robot has tendon-driven hands with tactile sensing, force control, and water resistance, but impressive hardware still needs reliable autonomy before it can handle everyday chores.

AI is redefining how organizations protect and manage connected devices.

arXiv:2607.08231v1 Announce Type: new Abstract: Smart homes have emerged as an important domain for HCI research, including work on usable security and privacy. Ideally, studies in these areas draw on datasets collected in real homes with real residents, capturing authentic device interactions, network traffic, and daily routines. However, creating such datasets is slow, expensive, and raises significant privacy concerns, as it requires long-term observation of people in their most private spaces. We propose using LLMs to generate diverse resident personas that interact with a simulated smart home, producing behaviorally grounded interaction schedules that can be executed on physical testbeds. We present (1) a design framework configuring simulated households across five socio-technical dimensions, (2) a multi-stage LLM pipeline that produces structured, executable device interaction schedules, and (3) a proof of concept demonstrating feasibility. As a work in progress, we aim to

arXiv:2607.07635v1 Announce Type: new Abstract: Botnets pose a significant cybersecurity threat, enabling attacks such as DDoS, data theft, and service disruptions on IoT devices. These devices often lack built-in botnet traffic filtering, leaving them highly exposed. Existing AI-based solutions improve detection capabilities but have limitations: (i) they are too heavy for IoT deployment, and (ii) they lack unlearning capabilities to forget sensitive or outdated features without retraining. To address these challenges, we propose DiRLU, a lightweight, reinforcement learning driven framework, while ensuring privacy by selectively unlearning sensitive or outdated features without requiring retraining. The framework leverages knowledge distillation to transfer knowledge from a teacher model into a lightweight student model, with both models trained using A2C. A post-hoc unlearning mechanism modifies weights to remove targeted features, while restored features show negligible performance

arXiv:2607.06784v1 Announce Type: new Abstract: In the era of digital revolution many contemporary events that changed the world were shaped through the internet. Nowadays, the emergence of internet of things (IoT), combining physical objects with virtual networks is expected to have even more influence. This new 'decentralised' structure in the world raises questions such as power, governance and the notion of democracy online. The aim of this paper is to investigate these notions. We have taken the examples of Bitcoin and Wikipedia and examined their decision-making process. Our analysis has found some inconsistencies in their policies, that are in contradiction with democracy and consensus principles of governance. Starting from our findings, we present further improvements that can be used to achieve more democracy and equity in the digital context.

arXiv:2607.06748v1 Announce Type: new Abstract: Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks. Existing YAML validators, static analysis tools, and general-purpose large language models offer limited support for end-to-end diagnosis and repair because they lack domain-specific understanding and validated correction workflows. This paper presents SmartHomeSecure, a prototype for automated detection and repair of Home Assistant configuration errors using lightweight program analysis and constraint-guided large language model generation. SmartHomeSecure parses YAML files, detects syntactic and common semantic errors, normalizes error context, applies deterministic auto-fixes for routine defects, and constructs constrained prompts that guide LLMs toward minimal and structurally valid

Faced with heightened geopolitical instability and the persistent threat of maritime disruptions in the Strait of Hormuz, Persian Gulf nations are moving aggressively to secure their domestic food supply chains. Now, Korea’s advanced agricultural sector is positioning itself as a key technological ally in that effort. The Ministry of Agriculture, Food and Rural Affairs, alongside the Korea Trade-Investment Promotion Agency (KOTRA), said Thursday that AgroSolution Korea finalized a $2.6 million contract with United Arab Emirates buyer Alfafa to export a factory-style vertical smart farm. The agreement represents a major breakthrough for Korea's agricultural export ambitions in the Middle East. Under the UAE’s "National Food Security Strategy 2051," the country currently relies on imports for more than 90 percent of its food requirements. While Abu Dhabi had prioritized smart agriculture as a strategic state industry long before the latest regional unrest, commercial experts note that

Faced with heightened geopolitical instability and the persistent threat of maritime disruptions in the Strait of Hormuz, Persian Gulf nations are moving aggressively to secure their domestic food supply chains. Now, Korea’s advanced agricultural sector is positioning itself as a key technological ally in that effort. The Ministry of Agriculture, Food and Rural Affairs, alongside the Korea Trade-Investment Promotion Agency (KOTRA), said Thursday that AgroSolution Korea finalized a $2.6 million contract with United Arab Emirates buyer Alfafa to export a factory-style vertical smart farm. The agreement represents a major breakthrough for Korea's agricultural export ambitions in the Middle East. Under the UAE’s "National Food Security Strategy 2051," the country currently relies on imports for more than 90 percent of its food requirements. While Abu Dhabi had prioritized smart agriculture as a strategic state industry long before the latest regional unrest, commercial experts note that

arXiv:2607.06349v1 Announce Type: new Abstract: Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed covariate sets. We present TopoBrick, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting. TopoBrick uses building knowledge graphs to construct a compact structural skeleton and employs an agentic topology sampler to select target-specific exogenous variables. The selected variables are organized by deployment-time availability, separating past-known sensor states from future-known calendar, schedule, and meteorological exogenous variables. Across three real-world buildings, TopoBrick outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models. Ablations show that topology-aware sampling is more reliable than random, ontology-only, or fixed-hop selection,