IOT

If you own certain Belkin Wemo devices, they'll stop working as soon as Jan. 31. Here's what to know before it happens.

Last year, Belkin announced that it would end support for most of its Wemo smart home products in January 2026. That deadline is now approaching, with customers set to lose access to most Wemo smart home accessory functionality on January 31. The exception, however, is if you use Wemo accessories with HomeKit. more…

2026 will see a 30% rise in eSIM devices, but they're for IoT not smartphones, so a whole new model is being rolled out.

arXiv:2601.18736v1 Announce Type: new Abstract: The rapid expansion of the Internet of Things (IoT) in domains such as smart cities, transportation, and industrial systems has heightened the urgency of addressing their security vulnerabilities. IoT devices often operate under limited computational resources, lack robust physical safeguards, and are deployed in heterogeneous and dynamic networks, making them prime targets for cyberattacks and malware applications. Machine learning (ML) offers a promising approach to automated malware detection and classification, but practical deployment requires models that are both effective and lightweight. The goal of this study is to investigate the effectiveness of four supervised learning models (Random Forest, LightGBM, Logistic Regression, and a Multi-Layer Perceptron) for malware detection and classification using the IoT-23 dataset. We evaluate model performance in both binary and multiclass classification tasks, assess sensitivity to

arXiv:2601.18727v1 Announce Type: new Abstract: Achieving long-range, high-rate, concurrent two-way mmWave communication with power-constrained IoT devices is fundamental to scaling future ubiquitous sensing systems, yet the substantial power demands and high cost of mmWave hardware have long stood in the way of practical deployment. This paper presents the first mmWave full-duplex backscatter tag architecture, charting a genuinely low-cost path toward high-performance mmWave connectivity and localization for ISAC systems. The proposed tag operates at ranges beyond 45m on the uplink and beyond 200m on the downlink, delivering 20x the reach of state-of-the-art systems while being over 100x cheaper than existing mmWave backscatter platforms. Enabling this leap is a novel low-power regenerative amplifier that provides 30 dB of gain while consuming only 30 mW, paired with a regenerative rectifier that achieves state-of-the-art sensitivity down to -60 dBm. We integrate our circuits on a

arXiv:2601.18361v1 Announce Type: new Abstract: This work evaluates the potential of High-Altitude Platform Stations (HAPS) and Low Earth Orbit (LEO) satellites as alternative or complementary systems to enhance Internet of Things (IoT) connectivity. We first analyze the transmission erasure probability under different connectivity configurations, including only HAPS or LEO satellites, as well as hybrid architectures that integrate both aerial/spatial and terrestrial infrastructures. To make the analysis more realistic, we considered movement of LEO satellites regarding a fixed region, elevation angle between gateway and devices, and different fading models for terrestrial and non-terrestrial communication. We also analyze LR-FHSS (Long-Range Frequency Hopping Spread Spectrum) random access uplink technology as a potential use case for IoT connectivity, showing the scalability impact of the scenarios. The simulation results demonstrate that HAPS can effectively complement sparse

arXiv:2601.17817v1 Announce Type: new Abstract: The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges due to dynamic three-dimensional mobility patterns, distributed autonomous operations, and severe resource constraints. Traditional intrusion detection systems designed for static ground-based networks prove inadequate for tackling the unique characteristics of aerial IoT environments, including frequent topology changes, real-time detection requirements, and energy limitations. In this article, we analyze the intrusion detection requirements for LAE-IoT networks, complemented by a comprehensive review of evaluation metrics that cover detection effectiveness, response time, and resource consumption. Then, we investigate transformative potential of agentic artificial intelligence (AI) paradigms and introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT

arXiv:2601.17713v1 Announce Type: new Abstract: With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional

arXiv:2601.17656v1 Announce Type: new Abstract: This paper presents a battery-free and gateway-free water leak detection system capable of direct communication over LTE-M (Cat-M1). The system operates solely on energy harvested through a hydroelectric mechanism driven by an electrochemical sensor, thereby removing the need for conventional batteries. To address the stringent startup and operational power demands of LTE-M transceivers, the architecture incorporates a compartmentalized sensing module and a dedicated power management subsystem, comprising a boost converter, supercapacitor based energy storage, and a hysteresis controlled load isolation circuit. This design enables autonomous, direct to cloud data transmission without reliance on local networking infrastructure. Experimental results demonstrate consistent LTE-M beacon transmissions triggered by water induced energy generation, underscoring the system's potential for sustainable, maintenance free, and globally scalable IoT

arXiv:2601.17513v1 Announce Type: new Abstract: This paper presents an automated antenna design and optimization framework employing multi-objective genetic algorithms (MOGAs) to investigate various evolutionary optimization approaches, with a primary emphasis on multi-band frequency optimization. Five MOGA variants were implemented and compared: the Pareto genetic algorithm (PGA), non-dominated sorting genetic algorithm with niching (NSGA-I), non-dominated sorting genetic algorithm with elitism (NSGA-II), non-dominated sorting genetic algorithm using reference points (NSGA-III), and strength Pareto evolutionary algorithm (SPEA). These algorithms are employed to design and optimize microstrip patch antennas loaded with complementary split-ring resonators (CSRRs). A weighted-sum scalarization approach was adopted within a single-objective genetic algorithm framework enhanced with domain-specific constraint handling mechanisms. The optimization addresses the conflicting objectives of

arXiv:2601.17414v1 Announce Type: new Abstract: The proliferation of Internet of Things (IoT) devices has created unprecedented opportunities for remote monitoring and control applications across various domains. Traditional monitoring systems often suffer from limitations in real-time data accessibility, remote controllability, and cloud integration. This paper presents a cloud-enabled IoT system that leverages Google's Firebase Realtime Database for synchronized environmental monitoring and device control. The system utilizes an ESP32 microcontroller to interface with a DHT22 temperature/humidity sensor and an HC-SR04 ultrasonic distance sensor, while enabling remote control of two LED indicators through a cloud-based interface. Real-time sensor data is transmitted to Firebase, providing a synchronized platform accessible from multiple devices simultaneously. Experimental results demonstrate reliable data transmission with 99.2\% success rate, real-time control latency under 1.5

arXiv:2601.17373v1 Announce Type: new Abstract: As Smart Home Personal Assistants (SPAs) evolve into social agents, understanding user privacy necessitates interpersonal communication frameworks, such as Privacy Boundary Theory (PBT). To ground our investigation, our three-phase preliminary study (1) identified transmission and sharing ranges as key boundary-related risk factors, (2) categorized relevant SPA functions and data types, and (3) analyzed commercial practices, revealing widespread data sharing and non-transparent safeguards. A subsequent mixed-methods study (N=412 survey, N=40 interviews among the survey participants) assessed users' perceived privacy risks across data types, transmission ranges and sharing ranges. Results demonstrate a significant, non-linear escalation in perceived risk when data crosses two critical boundaries: the `public network' (transmission) and `third parties' (sharing). This boundary effect holds robustly across data types and demographics.

arXiv:2601.17303v1 Announce Type: new Abstract: As Industrial Internet of Things (IIoT) environments expand to include tens of thousands of connected devices. The centralization of security monitoring architectures creates serious latency issues that savvy attackers can exploit to compromise an entire manufacturing ecosystem. This paper outlines a new, decentralized multi-agent swarm (DMAS) architecture that includes autonomous artificial intelligence (AI) agents at each edge gateway, functioning as a distributed digital "immune system" for IIoT networks. Instead of using a traditional static firewall approach, the DMAS agents communicate via a lightweight peer-to-peer protocol to cooperatively detect anomalous behavior across the IIoT network without sending data to a cloud infrastructure. The authors also outline a consensus-based threat validation (CVT) process in which agents vote on the threat level of an identified threat, enabling instant quarantine of a compromised node or

Belkin’s now-ancient Wemo smart home products are set to shut down later this week, years after their launch, in one of the most notable closures of smart home tech. more…

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In the market for a new smart lamp for your table? Philips Hue is updating one of its models.

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The healthcare supply chain is currently facing a period of intense operational stress and heightened security risk. According

arXiv:2601.16935v1 Announce Type: new Abstract: Energy-harvesting (EH) Internet of Things (IoT) devices operate under intermittent energy availability, which disrupts task execution and makes energy-intensive over-the-air (OTA) updates particularly challenging. Conventional OTA update mechanisms rely on reboots and incur significant overhead, rendering them unsuitable for intermittently powered systems. Recent live OTA update techniques reduce reboot overhead but still lack mechanisms to ensure consistency when updates interact with runtime execution. This paper presents AERO, an Adaptive and Efficient Runtime-Aware OTA update mechanism that integrates update tasks into the device's Directed Acyclic Graph (DAG) and schedules them alongside routine tasks under energy and timing constraints. By identifying update-affected execution regions and dynamically adjusting dependencies, AERO ensures consistent up date integration while adapting to intermittent energy availability. Experiments

The relentless march of climate change is reshaping agricultural practices globally, posing both challenges and opportunities for smallholder farmers. In Nigeria, where agriculture is the backbone of the economy and sustains millions of livelihoods, the adoption of climate-smart agricultural practices is increasingly vital. A recent study has delved into the driving factors behind the uptake […]

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The newest nugget ice maker from GoveeLife features a sleek design and delivers plenty of ice in just minutes. But there's more to it.

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Roborock hasn’t updated its popular, affordable Qrevo Curv line of robovacs for years. Finally, it’s happening!

arXiv:2601.16160v1 Announce Type: new Abstract: The rapid expansion of internet of things (IoT) devices have created a pervasive ecosystem where encrypted wireless communications serve as the primary privacy and security protection mechanism. While encryption effectively protects message content, packet metadata and statistics inadvertently expose device identities and user contexts. Various studies have exploited raw packet statistics and their visual representations for device fingerprinting and identification. However, these approaches remain confined to the spatial domain with limited feature representation. Therefore, this paper presents CONTEX-T, a novel framework that exploits contextual privacy vulnerabilities using spectral representation of encrypted wireless traffic for IoT device characterization. The experiments show that spectral analysis provides new and rich feature representation for covert reconnaissance attacks, revealing a complex and expanding threat landscape

arXiv:2601.15830v1 Announce Type: new Abstract: The increasing global demand for sustainable agriculture necessitates intelligent monitoring systems that optimize resource utilization and plant health management. Traditional farming methods rely on manual observation and periodic watering, often leading to water wastage, inconsistent plant growth, and delayed response to environmental changes. This paper presents a comprehensive IoT-based smart plant monitoring system that integrates multiple environmental sensors with automated irrigation and cloud analytics. The proposed system utilizes an ESP32 microcontroller to collect real-time data from DHT22 (temperature/humidity), HC-SR04 (water level), and soil moisture sensors, with visual feedback through an OLED display and auditory alerts via a buzzer. All sensor data is wirelessly transmitted to the ThingSpeak cloud platform for remote monitoring, historical analysis, and automated alert generation. Experimental results demonstrate the

At CES 2026, energy harvesting and wireless power came closer to becoming a reality.

arXiv:2601.15269v1 Announce Type: new Abstract: The rapid growth of Internet of Things (IoT) devices has increased the scale and diversity of cyberattacks, exposing limitations in traditional intrusion detection systems. Classical machine learning (ML) models such as Random Forest and Support Vector Machine perform well on known attacks but require retraining to detect unseen or zero-day threats. This study investigates lightweight decoder-only Large Language Models (LLMs) for IoT attack detection by integrating structured-to-text conversion, Quantized Low-Rank Adaptation (QLoRA) fine-tuning, and Retrieval-Augmented Generation (RAG). Network traffic features are transformed into compact natural-language prompts, enabling efficient adaptation under constrained hardware. Experiments on the CICIoT2023 dataset show that a QLoRA-tuned LLaMA-1B model achieves an F1-score of 0.7124, comparable to the Random Forest (RF) baseline (0.7159) for known attacks. With RAG, the system attains 42.63%

arXiv:2601.14505v1 Announce Type: new Abstract: In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial attacks in machine learning-based NIDSs have been viewed as attack packets misclassified as benign. Due to this prevailing belief, the possibility of (maliciously) perturbed benign packets being misclassified as attack has been largely ignored. In this paper, we demonstrate that this is not only theoretically possible, but also a particular threat to NIDS. In particular, we uncover a practical cyberattack, FPR manipulation attack (FPA), especially targeting industrial IoT networks, where domain-specific knowledge of the widely used MQTT protocol is exploited and a systematic simple packet-level perturbation is performed to alter the labels of benign traffic samples without employing traditional gradient-based or non-gradient-based methods. The experimental evaluations demonstrate that this novel

arXiv:2601.14343v1 Announce Type: new Abstract: The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by increasingly sophisticated patterns. Leveraging Generative AI through On-Device Large Language Models (ODLLMs) provides a viable solution for real-time threat detection at the network edge, though limited computational resources present challenges for smaller ODLLMs. This paper introduces a novel detection framework that integrates Chain-of-Thought (CoT) reasoning with Retrieval-Augmented Generation (RAG), tailored specifically for IoT edge environments. We systematically evaluate compact ODLLMs, including LLaMA 3.2 (1B, 3B) and Gemma 3 (1B, 4B), using structured prompting and exemplar-driven reasoning strategies. Experimental results demonstrate substantial performance improvements with few-shot prompting, achieving macro-average F1 scores as high as 0.85. Our findings highlight the

arXiv:2601.14305v1 Announce Type: new Abstract: The increase in the number of Internet of Things (IoT) devices has tremendously increased the attack surface of cyber threats thus making a strong intrusion detection system (IDS) with a clear explanation of the process essential towards resource-constrained environments. Nevertheless, current IoT IDS systems are usually traded off with detection quality, model elucidability, and computational effectiveness, thus the deployment on IoT devices. The present paper counteracts these difficulties by suggesting an explainable AI (XAI) framework based on an optimized Decision Tree classifier with both local and global importance methods: SHAP values that estimate feature attribution using local explanations, and Morris sensitivity analysis that identifies the feature importance in a global view. The proposed system attains the state of art on the test performance with 99.91% accuracy, F1-score of 99.51% and Cohen Kappa of 0.9960 and high

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Dmitry Kaminskiy Presents at the UAE’s Largest Real Estate Conference, IPS 2025, on the Near-Future of Longevity Architecture at the...

We recently published 10 Stocks Investors Are Dumping. Samsara Inc. (NYSE:IOT) was one of the worst performers on Tuesday. Samsara dropped its share prices by 8.31 percent on Tuesday to close at $31.99—a near 52-week low—tracking a broader market pessimism after President Donald Trump’s announcement of fresh tariff threats on European countries. During the session, […]

arXiv:2601.14092v1 Announce Type: new Abstract: Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attention for addressing UAV path planning tasks in large and complex environments, bridging the gap with real-world deployments. However, many existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments. Moreover, they often overlook the inherently multi-objective nature of the task, treating it in an overly simplistic manner. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Our

arXiv:2601.13423v1 Announce Type: new Abstract: Post-quantum cryptography (PQC) introduces significant computational and communication overhead, which poses challenges for resource-constrained computer systems, Internet of Things (IoT), and Industrial IoT (IIoT) devices. This paper presents an experimental evaluation of the Quantum Encryption Resilience Score (QERS) applied to MQTT, HTTP, and HTTPS communication protocols operating under PQC. Using an ESP32-C6 client and an ARM-based Raspberry Pi CM4 server, latency, CPU utilization, RSSI, energy consumption, key size, and TLS handshake overhead are measured under realistic operating conditions. QERS integrates these heterogeneous metrics into normalized Basic, Tuned, and Fusion scores, enabling systematic comparison of protocol efficiency and security resilience. Experimental results show that MQTT provides the highest efficiency under PQC constraints, while HTTPS achieves the highest security-weighted resilience at the cost of

arXiv:2601.13399v1 Announce Type: new Abstract: Post-quantum cryptography (PQC) is becoming essential for securing Internet of Things (IoT) and Industrial IoT (IIoT) systems against quantum-enabled adversaries. However, existing evaluation approaches primarily focus on isolated performance metrics, offering limited support for holistic security and deployment decisions. This paper introduces QERS (Quantum Encryption Resilience Score), a universal measurement framework that integrates cryptographic performance, system constraints, and multi-criteria decision analysis to assess PQC readiness in computer, IoT, and IIoT environments. QERS combines normalized metrics, weighted aggregation, and machine learning-assisted analysis to produce interpretable resilience scores across heterogeneous devices and communication protocols. Experimental results demonstrate how the framework enables comparative evaluation of post-quantum schemes under realistic resource constraints, supporting informed

arXiv:2601.13054v1 Announce Type: new Abstract: Small-scale farming communities are disproportionately affected by water scarcity, erratic climate patterns, and a lack of access to advanced, affordable agricultural technologies. To address these challenges, this paper presents a novel, edge-first IoT framework that integrates Tiny Machine Learning (TinyML) for intelligent, offline-capable precision irrigation. The proposed four-layer architecture leverages low-cost hardware, an ESP32 microcontroller as an edge inference node, and a Raspberry Pi as a local edge server to enable autonomous decision-making without cloud dependency. The system utilizes capacitive soil moisture, temperature, humidity, pH, and ambient light sensors for environmental monitoring. A rigorous comparative analysis of ensemble models identified gradient boosting as superior, achieving an R^2 score of 0.9973 and a Mean Absolute Percentage Error (MAPE) of 0.99%, outperforming a random forest model (R^2 = 0.9916,

arXiv:2601.12305v1 Announce Type: new Abstract: We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than ten thousand MLaaS service instances and construct a large-scale benchmark dataset suitable for downstream evaluation. We also implement a built-in composition mechanism that models how services interact under varied Internet of Things conditions. Experiments demonstrate that datasets generated by MDG enhance selection accuracy and composition quality compared to existing

Fewer open entry points mean a more secure smart home. Here's how to keep yours as protected as possible.

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arXiv:2601.10743v1 Announce Type: cross Abstract: Sensor nodes localization in wireless Internet of Things (IoT) sensor networks is crucial for the effective operation of diverse applications, such as smart cities and smart agriculture. Existing sensor nodes localization approaches heavily rely on anchor nodes within wireless sensor networks (WSNs). Anchor nodes are sensor nodes equipped with global positioning system (GPS) receivers and thus, have known locations. These anchor nodes operate as references to localize other sensor nodes. However, the presence of anchor nodes may not always be feasible in real-world IoT scenarios. Additionally, localization accuracy can be compromised by fluctuations in Received Signal Strength Indicator (RSSI), particularly under non-line-of-sight (NLOS) conditions. To address these challenges, we propose UBiGTLoc, a Unified Bidirectional Long Short-Term Memory (BiLSTM)-Graph Transformer Localization framework. The proposed UBiGTLoc framework

arXiv:2601.11326v1 Announce Type: new Abstract: The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it

SwitchBot’s new video doorbell shines in a couple of ways, but fails hard in too many key smart home camera ways.

Water leaks and water damage are one of my biggest fears as a homeowner. With hardwood floors throughout my house, even a small water leak could turn into a massive repair bill and a huge inconvenience to get them repaired. A slow drip from a sink can quickly cause thousands of dollars (or tens of thousands) in damage to your floors and cabinets. If I had to choose one smart home category that actually gives me peace of mind, it would be the ones that prevent disasters. In 2026, the water leak sensor is still the most critical piece of hardware in my HomeKit setup. more…

Solid vacuuming and a handy deep clean module almost make the Omni E28 a solid choice, if not for a noisy dock and mediocre mopping.

The Dreame X50 Ultra works well on a range of floor types, conquers obstacles up to 2.36 inches and makes a great cleaning companion.

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arXiv:2601.10544v1 Announce Type: new Abstract: Mobile Ad Hoc Networks (MANETs) and Internet of Things (IoT) networks operate in decentralized and dynamic environments, making them ideal for scenarios lacking traditional infrastructure. However, these networks face challenges such as inefficient routing, limited scalability, and security vulnerabilities due to their decentralized nature and resource constraints. This paper explores the integration of Software-Defined Networking (SDN) as a unified solution that leverages its centralized control and network programmability to improve routing, resource management, and security. A mathematical model evaluates the impact of SDN integration on Capital Expenditure (CAPEX), Operational Expenditure (OPEX), and performance metrics. Results demonstrate that SDN-enhanced MANETs and IoT networks offer superior scalability, reduced latency, increased throughput, and lower packet loss, especially in dynamic and large-scale environments. While SDN

arXiv:2601.09744v1 Announce Type: new Abstract: Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale. These challenges are not rooted in tooling limitations but in the absence of a governance model that aligns with the realities of distributed operational ownership, heterogeneous source systems, and continuous change at the edge. This paper presents a federated governance model that emphasizes contract-driven interoperability, policy-as-code enforcement, and asset-centric accountability across global manufacturing organizations. The model addresses governance enforcement at architectural boundaries, enabling semantic consistency, quality assurance, and regulatory compliance without requiring centralized control of operational

LI-COR announced today that its Carbon Node is the recipient of the "Internet of Environment Solution of the Year" award in the 10th annual IoT Breakthrough Awards program conducted by IoT Breakthrough.

These 7 Smart Home Gadgets From CES Can Actually Improve Your Daily Routine  PCMagI Can't Stop Thinking About These Home Security Game-Changers at CES  CNETFive Smart-Home Highlights From CES 2026  Mansion GlobalThese are the smart home gadgets that impressed me at CES 2026  The VergeTech finally grows up: What CES 2026 means for your home  Fernandina Observer

These 7 Smart Home Gadgets From CES Can Actually Improve Your Daily Routine  PCMagI Can't Stop Thinking About These Home Security Game-Changers at CES  CNETFive Smart-Home Highlights From CES 2026  Mansion GlobalThese are the smart home gadgets that impressed me at CES 2026  The VergeTech finally grows up: What CES 2026 means for your home  Fernandina Observer

arXiv:2601.09422v1 Announce Type: new Abstract: The unprecedented surge of massive Internet of things (mIoT) traffic in beyond fifth generation (B5G) communication systems calls for transformative approaches for multiple access and data transmission. While classical model-based tools have been proven to be powerful and precise, an imminent trend for resource management in B5G networks is promoting solutions towards data-driven design. Considering an IoT network with devices spread in clusters covered by a base station, we present in this paper a novel model-free multiple access and data transmission framework empowered by reinforcement learning, designed for power-domain non-orthogonal multiple access networks to facilitate uplink traffic of small data packets. The framework supports two access modes referred to as contention-based and semi-contention-free, with its core component being a policy gradient algorithm executed at the base station. The base station performs access control

Unveiled at CES, the new Copilot Star platform is designed to take smart device interoperability to the next level.

arXiv:2601.08484v1 Announce Type: new Abstract: Maintaining optimal water quality in aquariums is critical for aquatic health but remains challenging due to the need for continuous monitoring of multiple parameters. Traditional manual methods are inefficient, labor-intensive, and prone to human error, often leading to suboptimal aquatic conditions. This paper presents an IoT-based smart aquarium system that addresses these limitations by integrating an ESP32 microcontroller with multiple sensors (pH, TDS, temperature, turbidity) and actuators (servo feeder, water pump) for comprehensive real-time water quality monitoring and automated control. The system architecture incorporates edge processing capabilities, cloud connectivity via Blynk IoT platform, and an intelligent alert mechanism with configurable cooldown periods to prevent notification fatigue. Experimental evaluation in a 10-liter aquarium environment demonstrated the system's effectiveness, achieving 96\% average sensor

arXiv:2601.08394v1 Announce Type: new Abstract: Pet ownership is increasingly common in modern households, yet maintaining a consistent feeding schedule remains challenging for the owners particularly those who live in cities and have busy lifestyles. This paper presents the design, development, and validation of a low-cost, scalable GSM-IoT smart pet feeder that enables remote monitoring and control through cellular communication. The device combines with an Arduino microcontroller, a SIM800L GSM module for communication, an ultrasonic sensor for real-time food-level assessment, and a servo mechanism for accurate portion dispensing. A dedicated mobile application was developed using MIT App Inventor which allows owners to send feeding commands and receive real-time status updates. Experimental results demonstrate a 98\% SMS command success rate, consistent portion dispensing with $\pm 2.67$\% variance, and reliable autonomous operation. Its modular, energy-efficient design makes it

arXiv:2601.08044v1 Announce Type: new Abstract: Denial-of-Service (DoS) attacks pose a critical threat to Internet of Things (IoT) ecosystems, yet deploying effective intrusion detection on resource-constrained edge devices remains challenging. Kolmogorov-Arnold Networks (KANs) offer a compact alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate spline functions on edges rather than fixed activations on nodes, achieving competitive accuracy with fewer parameters. However, runtime B-spline evaluation introduces significant computational overhead unsuitable for latency-critical IoT applications. We propose a lookup table (LUT) compilation pipeline that replaces expensive spline computations with precomputed quantized tables and linear interpolation, dramatically reducing inference latency while preserving detection quality. Our lightweight KAN model (50K parameters, 0.19~MB) achieves 99.0\% accuracy on the CICIDS2017 DoS dataset. After LUT compilation with

Devices equipped with Emerson's SmartVoice don't require Wi-Fi or a hub; instead, they utilize on-device voice control.

For selection and affordability, Ikea will be hard to beat for Matter-compatible smart home sensors and lighting in 2026.

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arXiv:2601.07536v1 Announce Type: new Abstract: MQTT is the dominant lightweight publish--subscribe protocol for IoT deployments, yet edge security remains inadequate. Cloud-based intrusion detection systems add latency that is unsuitable for real-time control, while CPU-bound firewalls and generic SDN controllers lack MQTT awareness to enforce session validation, topic-based authorization, and behavioral anomaly detection. We propose a P4-based data-plane enforcement scheme for protocol-aware MQTT security and anomaly detection at the network edge. The design combines parser-safe MQTT header extraction with session-order validation, byte-level topic-prefix authorization with per-client rate limiting and soft-cap enforcement, and lightweight anomaly detection based on KeepAlive and Remaining Length screening with clone-to-CPU diagnostics. The scheme leverages stateful primitives in BMv2 (registers, meters, direct counters) to enable runtime policy adaptation with minimal per-packet

arXiv:2601.06612v1 Announce Type: new Abstract: The reliance of Large Language Models and Internet of Things systems on massive, globally distributed data flows creates systemic security and privacy challenges. When data traverses borders, it becomes subject to conflicting legal regimes, such as the EU's General Data Protection Regulation and China's Personal Information Protection Law, compounded by technical vulnerabilities like model memorization. Current static encryption and data localization methods are fragmented and reactive, failing to provide adequate, policy-aligned safeguards. This research proposes a Jurisdiction-Aware, Privacy-by-Design architecture that dynamically integrates localized encryption, adaptive differential privacy, and real-time compliance assertion via cryptographic proofs. Empirical validation in a multi-jurisdictional simulation demonstrates this architecture reduced unauthorized data exposure to below five percent and achieved zero compliance

arXiv:2601.06466v1 Announce Type: new Abstract: The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service (DDoS), malware injection, and data exfiltration. Conventional intrusion detec- tion systems (IDS) face critical challenges like privacy, scala- bility, and robustness when applied in such heterogeneous IoT environments. To address these issues, we propose SecureDyn- FL, a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks. SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS: (1) poisoning detection through dynamic temporal gradient auditing, (2) privacy protection against inference and eavesdrop- ping attacks through secure aggregation, and (3) adaptation

Samsara Inc. (NYSE:IOT) is one of the promising stocks to buy under $50. On January 5, RBC Capital lowered the firm’s price target on Samsara to $46 from $50 with an Outperform rating on the shares. The firm predicted that 2026 would be a year of divergence for the software industry. Companies prepared for enterprise […]

I can't stop thinking about IKEA's new Varmblixt smart lamp, and these are the 5 reasons I seriously want to upgrade.

Devices equipped with Emerson's SmartVoice don't require Wi-Fi or a hub; instead, they utilize on-device voice control. You can buy these appliances now.

There are several smart home gadgets that can make your home life easier and safer. However, here are 14 you may have never known existed.

As of Jan. 12, the Eufy Omni C20 robot vacuum and mop has dropped to $349.99 at Amazon, 42% off its list price of $599.99.

arXiv:2601.05984v1 Announce Type: new Abstract: The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Communities of Interest (CoIs) provide a promising paradigm for organising heterogeneous IoT sensor networks by grouping devices with similar operational and environmental characteristics. This work presents an anomaly detection framework based on the CoI paradigm by grouping sensors into communities using a fused similarity matrix that incorporates temporal correlations via Spearman coefficients, spatial proximity using Gaussian distance decay, and elevation similarities. For each community, representative stations based on the best silhouette are selected and three autoencoder architectures (BiLSTM, LSTM, and MLP) are trained using Bayesian hyperparameter optimization with expanding window cross-validation and tested on stations from the same cluster and the best representative

A giant version of Lockin’s wirelessly charged V7 smart lock was a showstopper on the CES show floor. I picked Aqara's Smart Lock U400 and Roborock's Saros Rover robot vacuum as the overall best smart home gadgets from CES 2026, but there were gazillions of other great gadgets on the show floor. It was a banner year for smart home products, but the big trends I saw weren't about new product categories; they were about bringing better features and lower prices to smart home staples such as smart lighting, smart locks, cameras, and TVs. This is what I expected the launch of the interoperability protocol Matter would bring. Once companies could stop spending time and money on working on integrating with half a dozen platforms, they could fo … Read the full story at The Verge.

Ikea's 2026 leap into the smart home world is full of promise. Very affordable promise.

I spent the week on the CES show floor, searching for compelling smart home devices. Here's what I found.

Matter really Mattered, this year.

Just because you're renting doesn't mean you can't have some smart devices in your home, and these devices are perfectly suited for apartments.

As of Jan. 9, get the Eufy 11S Max robot vacuum for 50% off at Amazon.

Investing in a smart home gym? These are our favorite systems to get you in shape.

The app-free, Wi-Fi-free, hub-free smart home is here, and Emerson is leading the charge.

arXiv:2601.04761v1 Announce Type: new Abstract: Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources for the manual observation and monitoring of cow health is a complex and economically demanding task. It necessitates the involvement of skilled personnel, thereby contributing to elevated farm maintenance costs and operational inefficiencies. Therefore, the development of an automated, low-cost, and reliable smart system is essential to address these

arXiv:2601.04680v1 Announce Type: new Abstract: The proliferation of smart home devices has increased the complexity of controlling and managing them, leading to user fatigue. In this context, large language models (LLMs) offer a promising solution by enabling natural-language interfaces for Internet of Things (IoT) control. However, existing LLM-based approaches suffer from unreliable and inefficient device control due to the non-deterministic nature of LLMs, high inference latency and cost, and limited personalization. To address these challenges, we present IoTGPT, an LLM-based smart home agent designed to execute IoT commands in a reliable, efficient, and personalized manner. Inspired by how humans manage complex tasks, IoTGPT decomposes user instructions into subtasks and memorizes them. By reusing learned subtasks, subsequent instructions can be processed more efficiently with fewer LLM calls, improving reliability and reducing both latency and cost. IoTGPT also supports

Chinese household appliances maker Dreame Technology unveiled more than 20 new artificial intelligence-embedded products at this year’s CES trade show in Las Vegas, showing a glimpse of the future when smart devices would run almost all chores in a house. Those formed part of more than 150 products that Dreame, one of the world’s top five vendors of robot vacuum cleaners, exhibited at the four-day trade show that concludes this Friday. Dreame’s new “AI-powered whole-home smart ecosystem”...

It was only a matter of time before someone strapped a robot vacuum to a drone and tried to fly it up the stairs. I saw Mova's attempt at CES 2026.

The pivot doesn’t look out of place at CES, where Chinese electronics companies are increasingly applying their manufacturing prowess to new industries.

IKEA took to CES for the first time ever this year in an effort to show off its new smart home tech, which continues to get better and better, but there’s one big asterisk for Google Home users based on early user impressions. more…

From folding laundry to light baking, LG's latest CES reveal wants to handle your chores. Find out why experts are impressed by its brain but wary of its speed.