IOT

arXiv:2602.20453v1 Announce Type: new Abstract: Construction and operating of buildings is one of the major contributors to global greenhouse emissions. With the inefficient usage of energy due to human behavior and manual operation, the energy consumption of buildings is further increased. These challenges highlight the need for improved Building Energy Management Systems (BEMS) integrated with Internet of Things (IoT) and data driven intelligence to enhance energy-efficiency in a building and contribute to Net-Zero Energy Buildings (NZEB) targets. This paper offers four keys contributions: i) a systematic review of IoT enabled BEMS including components, network architecture and functional capabilities, ii) an evaluation of real-world BEMS datasets to support Artificial Intelligence (AI) based predictive control, iii) an analysis of integration challenges related to interoperability, smart grids and net-zero energy strategies, and iv) a case study highlighting global best practices,

We recently published an article titled 13 Best Internet of Things (IoT) Stocks to Buy Now. On February 17, Truist lowered its price target on Samsara Inc. (NYSE:IOT) to $30 from $39 while maintaining a Hold rating as part of a broader fourth-quarter preview within the security software space. The firm noted that the broader […]

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arXiv:2602.19990v1 Announce Type: new Abstract: Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink

arXiv:2602.18598v1 Announce Type: new Abstract: The Internet of Things (IoT) presents a unique cybersecurity challenge due to its vast network of interconnected, resource-constrained devices. These vulnerabilities not only threaten data integrity but also the overall functionality of IoT systems. This study addresses these challenges by exploring efficient data reduction techniques within a model-based intrusion detection system (IDS) for IoT environments. Specifically, the study explores the efficacy of an autoencoder's latent space combined with three different classification techniques. Utilizing a validated IoT dataset, particularly focusing on the Constrained Application Protocol (CoAP), the study seeks to develop a robust model capable of identifying security breaches targeting this protocol. The research culminates in a comprehensive evaluation, presenting encouraging results that demonstrate the effectiveness of the proposed methodologies in strengthening IoT cybersecurity

In this article, we will discuss the 13 Best Internet of Things (IoT) Stocks to Buy Now. Investing in IoT stocks provides exposure to a rapidly expanding ecosystem powered by industrial automation, 5G connectivity, artificial intelligence, and smart infrastructure. With the global IoT market projected to roughly double in value by 2027, adoption is accelerating […]

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We came across a bullish thesis on Samsara Inc. on T K’s Substack. In this article, we will summarize the bulls’ thesis on IOT. Samsara Inc.’s share was trading at $25.42 as of February 6th. IOT’s forward P/E was 44.25 according to Yahoo Finance. Samsara is the pioneer of the Connected Operations Cloud, providing a platform that allows […]

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arXiv:2602.17619v1 Announce Type: new Abstract: Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA's random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by

arXiv:2602.17114v1 Announce Type: new Abstract: With the availability of automation machinery and its superiority, are being slothful and inviting many diseases to invade them. The world still has so many places where people lack basic health facilities. Due to early detection and intervention, CDV can be cured to an extreme extent. It heavily reduces travel and associated costs. A remote ECG monitoring system enables community health workers to support and empower patients through telemedicine. However, there remains some financial and logistical burden. Heart disease cannot be taken lightly. These patients require regular health check-ups and the attention of health personnel in a short period if their health deteriorates suddenly and rapidly. Chronic diseases are extremely variable in their symptoms and evolution of treatment. Some, if not treated early, will end the patient's life. The trend of the INTERNET OF THINGS, IoT, is spreading massively. This paper focuses on the three

arXiv:2602.16738v1 Announce Type: new Abstract: Industrial IoT predictive maintenance requires systems capable of real-time anomaly detection without sacrificing interpretability or demanding excessive computational resources. Traditional approaches rely on static, offline-trained models that cannot adapt to evolving operational conditions, while LLM-based monolithic systems demand prohibitive memory and latency, rendering them impractical for on-site edge deployment. We introduce SEMAS, a self-evolving hierarchical multi-agent system that distributes specialized agents across Edge, Fog, and Cloud computational tiers. Edge agents perform lightweight feature extraction and pre-filtering; Fog agents execute diversified ensemble detection with dynamic consensus voting; and Cloud agents continuously optimize system policies via Proximal Policy Optimization (PPO) while maintaining asynchronous, non-blocking inference. The framework incorporates LLM-based response generation for

Chris Metinko / Axios: Cleveland-based Eagle Wireless, which makes cellular modules used in IoT devices, raised a $30M Series B as the US seeks to reduce its reliance on China  —  Eagle Wireless, a cellular module manufacturer, raised a $30 million Series B from Asymmetric Capital Partners and The O.H.I.O. Fund …

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arXiv:2602.15457v1 Announce Type: new Abstract: Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for model selection in practice. We introduce an evaluation protocol with unified event-level augmentations that simulate real-world issues: calibrated sensor dropout, linear and log drift, additive noise, and window shifts. We also perform sensor-level probing via mask-as-missing zeroing with per-channel influence estimation to support root-cause analysis. We evaluate 14 representative models on five public anomaly datasets (SWaT, WADI, SMD, SKAB, TEP) and two industrial datasets (steam turbine, nuclear turbogenerator) using unified splits and event aggregation. There is no universal winner: graph-structured models transfer best under dropout and long events (e.g., on SWaT under additive noise

arXiv:2602.15263v1 Announce Type: new Abstract: An open measurement problem in IoT security is whether scan-observable network configurations encode population-level exposure risk beyond individual devices. An analysis of internet-exposed IoT endpoints using a controlled multi-country sample from Shodan Search and Shodan InternetDB, selecting 100 hosts identified via TCP port 7547 (TR-069/CWMP) and evenly distributed across the ten most represented countries. Hosts are enriched with scan-derived metadata and analyzed using feature-relevance assessment, cross-country comparisons of open and risky port exposure, and supervised classification of higher-risk exposure profiles. The analysis reveals consistent cross-country differences in exposure structure, with mean risky-port counts ranging from 0.4 to 1.0 per host, and achieves balanced accuracy of approximately 0.61 when classifying higher-risk exposure profiles.

What's behind the widespread adoption of extremely high-throughput wireless. The post Wi-Fi 7 Moves To The IoT appeared first on Semiconductor Engineering.

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Berlin, Germany (SPX) Feb 13, 2026 Researchers at Universidad Carlos III de Madrid (UC3M) have developed a new way for an assistive robot to learn how to move its arms autonomously by combining observational learning with communication between its limbs. The approach lets the robot watch how people perform everyday tasks and then adapt those movements so its two arms can work together safely and efficiently in domestic settings s

arXiv:2602.14391v1 Announce Type: new Abstract: Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases

arXiv:2602.13277v1 Announce Type: new Abstract: Mobile data collection using controllable sinks is an effective approach to improve energy efficiency and data freshness in densely deployed wireless sensor networks (WSNs). However, existing path-planning methods are often heuristic-driven and lack the flexibility to adapt to high-level operational objectives under dynamic network conditions. In this paper, we propose ID2P2, a intent-driven diffusion-based path planning framework for jointly addresses rendezvous point selection and mobile data collector (MDC) tour construction in IoT-enabled dense WSNs. High-level intents, such as latency minimization, energy balancing, or coverage prioritization, are explicitly modeled and incorporated into a generative diffusion planning process that produces feasible and adaptive data collection trajectories. The proposed approach learns a trajectory prior that captures spatial node distribution and network characteristics, enabling the MDC to

arXiv:2602.13205v1 Announce Type: new Abstract: Ultra-dense IoT networks require an effective non-orthogonal multiple access (NOMA) scheme, yet they experience intense interference because of fixed code assignment. We suggest a reinforcement learning (RL) model of dynamic Gold code assignment in IoT-NOMA networks. Our Markov Decision Process which is IoT aware is a joint optimization of throughput, energy efficiency, and fairness. Two RL algorithms are created, including Natural Policy Gradient (NPG) to learn stable discrete actions and Deep Deterministic Policy Gradient (DDPG) with continuous code embedding. Under smart city conditions, NPG can attain throughput of 11.6% and energy efficiency of 15.8 likewise superior to its performance with a static allocation. Nonetheless, the performance is worse in organized industrial settings, and the reliability is minimal (0-2%), which points to the fact that dynamic code assignment is not a sufficient measure of ultra-reliable IoT and needs

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arXiv:2602.13062v1 Announce Type: new Abstract: The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment and replay reinforcement, enabling the implantation of persistent malicious behaviors that endure through updates and deployment cycles. This paper provides a comprehensive analysis of backdoor attacks on CCL within IoT systems. We formalize the objectives of embedding-level attacks, examine

arXiv:2602.12622v1 Announce Type: new Abstract: Internet of things (IoT) networks face increasing security threats due to their distributed nature and resource constraints. Although federated learning (FL) has gained prominence as a privacy-preserving framework for distributed IoT environments, current federated principal component analysis (PCA) methods lack the integration of personalization and robustness, which are critical for effective anomaly detection. To address these limitations, we propose an efficient personalized federated PCA (FedEP) method for anomaly detection in IoT networks. The proposed model achieves personalization through introducing local representations with the $\ell_1$-norm for element-wise sparsity, while maintaining robustness via enforcing local models with the $\ell_{2,1}$-norm for row-wise sparsity. To solve this non-convex problem, we develop a manifold optimization algorithm based on the alternating direction method of multipliers (ADMM) with rigorous

arXiv:2602.12543v1 Announce Type: new Abstract: The rise of heterogeneous Internet of Things (IoT) devices has raised security concerns due to their vulnerability to cyberattacks. Intrusion Detection Systems (IDS) are crucial in addressing these threats. Federated Learning (FL) offers a privacy-preserving solution, but IoT heterogeneity and limited computational resources cause increased latency and reduced performance. This paper introduces a novel approach Cluster-based federated intrusion detection with lightweight networks for heterogeneous IoT designed to address these limitations. The proposed framework utilizes a hierarchical IoT architecture that encompasses edge, fog, and cloud layers. Intrusion detection clients operate at the fog layer, leveraging federated learning to enhance data privacy and distributed processing efficiency. To enhance efficiency, the method employs the lightweight MobileNet model alongside a hybrid loss function that integrates Gumbel-SoftMax and

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In an era where everything from toothbrushes to coffee makers connects to the internet, a recent discovery about a smart sleep mask has exposed just how dangerous our rush toward IoT connectivity can be. What started as a simple attempt to build a better control interface ended up revealing a cybersecurity nightmare that could allow strangers to monitor users' brainwaves and even send electrical impulses to sleeping individuals. The Discovery: From Convenience to Horror The story begins innocuou...

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As part of its acquisition by China-based Picea Robotics, iRobot is creating a new US-based subsidiary called iRobot Safe, which the company says will be "responsible for the protection of US consumer data," similar to the TikTok deal that was completed last month. iRobot customers in the US will be able to continue using their robovacs as usual, but now their data will be handled by iRobot Safe, rather than the company's new owner. In a press release published on January 23rd, iRobot stated that the creation of iRobot Safe is "designed to maintain a clear separation between iRobot's non-US ownership and its US and other global consumer dat … Read the full story at The Verge.

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arXiv:2602.12183v1 Announce Type: new Abstract: The rapid evolution of cyberattacks continues to drive the emergence of unknown (zero-day) threats, posing significant challenges for network intrusion detection systems in Internet of Things (IoT) networks. Existing machine learning and deep learning approaches typically rely on large labeled datasets, payload inspection, or closed-set classification, limiting their effectiveness under data scarcity, encrypted traffic, and distribution shifts. Consequently, detecting unknown attacks in realistic IoT deployments remains difficult. To address these limitations, we propose SiamXBERT, a robust and data-efficient Siamese meta-learning framework empowered by a transformer-based language model for unknown attack detection. The proposed approach constructs a dual-modality feature representation by integrating flow-level and packet-level information, enabling richer behavioral modeling while remaining compatible with encrypted traffic. Through

arXiv:2602.11775v1 Announce Type: new Abstract: Explanations are essential for helping users interpret and trust autonomous smart-home decisions, yet evaluating their quality and impact remains methodologically difficult in this domain. V-SHiNE addresses this gap: a browser-based smarthome simulation framework for scalable and realistic assessment of explanations. It allows researchers to configure environments, simulate behaviors, and plug in custom explanation engines, with flexible delivery modes and rich interaction logging. A study with 159 participants demonstrates its feasibility. V-SHiNE provides a lightweight, reproducible platform for advancing user-centered evaluation of explainable intelligent systems

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arXiv:2602.10762v1 Announce Type: new Abstract: The Internet of Things (IoT) security landscape requires the architectural solutions that can address the technical and operational challenges across the heterogeneous environments. The IoT systems operate in different conditions, and security issues continue to increase. This paper presents the comprehensive security framework for IoT that should integrate the Trusted Execution Environments (TEEs) with the semantic middleware and blockchain technologies. The work provides a systematic analysis of the architectural patterns based on more than twenty recent research works and the existing standards, and it proposes a layered security architecture. The architecture includes the hardware rooted trust at peripheral level, the zero trust principles at network level, and the semantic security mechanisms at application level. The framework focuses on practical implementation aspects such as the performance overhead, interoperability

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Samsara Inc. (NYSE:IOT) is one of the 11 Best Beaten Down Growth Stocks to Buy Now. Piper Sandler, on February 6, cut its target price on Samsara by 24.4% to $37 (from $49) but kept its Overweight call on the shares. The firm noted that while overall investor sentiment for software stocks was at an all-time […]

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arXiv:2602.09970v1 Announce Type: cross Abstract: Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable continuous in situ audio collection at scale. While recent self-supervised learning (SSL)-based audio encoders, such as BEATs and AVES, have shown strong performance in bioacoustic tasks, their computational cost and limited robustness to unseen environments hinder deployment on resource-constrained platforms. In this work, we introduce BioME, a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model, enabling strong representational transfer while reducing the parameter count by 75%. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the

arXiv:2602.09515v1 Announce Type: new Abstract: This paper presents an Internet of Things (IoT) application that utilizes an AI classifier for fast-object detection using the frame difference method. This method, with its shorter duration, is the most efficient and suitable for fast-object detection in IoT systems, which require energy-efficient applications compared to end-to-end methods. We have implemented this technique on three edge devices: AMD AlveoT M U50, Jetson Orin Nano, and Hailo-8T M AI Accelerator, and four models with artificial neural networks and transformer models. We examined various classes, including birds, cars, trains, and airplanes. Using the frame difference method, the MobileNet model consistently has high accuracy, low latency, and is highly energy-efficient. YOLOX consistently shows the lowest accuracy, lowest latency, and lowest efficiency. The experimental results show that the proposed algorithm has improved the average accuracy gain by 28.314%, the

arXiv:2602.09263v1 Announce Type: new Abstract: Cloud-mediated IoT architectures fragment authentication across vendor silos and create latency and availability bottlenecks for cross-vendor device-to-device (D2D) interactions. We present Atlas, a framework that extends the Web public-key infrastructure to IoT by issuing X.509 certificates to devices via vendor-operated ACME clients and vendor-controlled DNS namespaces. Devices obtain globally verifiable identities without hardware changes and establish mutual TLS channels directly across administrative domains, decoupling runtime authentication from cloud reachability. We prototype Atlas on ESP32 and Raspberry Pi, integrate it with an MQTT-based IoT stack and an Atlas-aware cloud, and evaluate it in smart-home and smart-city workloads. Certificate provisioning completes in under 6s per device, mTLS adds only about 17ms of latency and modest CPU overhead, and Atlas-based applications sustain low, predictable latency compared to

arXiv:2602.09254v1 Announce Type: new Abstract: Bystander privacy in smart homes has been widely studied in Western contexts, yet it remains underexplored in non-Western countries such as China. In this study, we analyze 49 Chinese smart home apps using a mixed-methods approach, including privacy policy review, UX/UI evaluation, and assessment of Apple App Store privacy labels. While most apps nominally comply with national regulations, we identify significant gaps between written policies and actual implementation. Our traceability analysis highlights inconsistencies in data controls and a lack of transparency in data-sharing practices. Crucially, bystander privacy -- particularly for visitors and non-user individuals -- is largely absent from both policy documents and interface design. Additionally, discrepancies between privacy labels and actual data practices threaten user trust and undermine informed consent. We provide design recommendations to strengthen bystander protections,

arXiv:2602.09239v1 Announce Type: new Abstract: The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers' homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing how AI analytics, residual data logs, and cross-household data flows shaped the privacy risks faced by participants. In employer-controlled homes, AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance and constrained participants' ability to negotiate privacy boundaries. In their own homes, participants had greater control as device owners but still faced challenges, including

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arXiv:2602.07630v1 Announce Type: new Abstract: Blockchain offers a decentralized trust framework for the Internet of Things (IoT), yet deploying consensus in spectrum-congested and dynamic wireless edge IoT networks faces fundamental obstacles: traditional BFT protocols are spectrum-ignorant, leading to inefficient resource utilization and fragile progress under time-varying interference. This paper presents \textit{Wireless Streamlet}, a spectrum-aware and cognitive consensus protocol tailored for wireless edge IoT. Building on Streamlet's streamlined structure, we introduce a \textit{Channel-Aware Leader Election (CALE)} mechanism. CALE serves as a verifiable cross-layer cognitive engine that leverages receiver-measured channel state information (CSI) piggybacked in signed votes to derive Byzantine-robust connectivity scores from notarization certificates, and deterministically selects a unique weighted leader per epoch from finalized history, thereby improving proposal

arXiv:2602.08989v1 Announce Type: new Abstract: The proliferation of Multi-Radio Access Technology, Internet of Things devices, particularly Unmanned Aerial Vehicles operating across LoRaWAN, 5G/4G cellular, Meshtastic mesh, proprietary protocols such as DJI OcuSync, MAVLink telemetry links, Wi-Fi, and satellite, creates a fundamental and hitherto unexamined challenge for Zero Trust Architecture adoption. Each transition between radio access technologies constitutes a trust boundary crossing: the device exits one network trust domain and enters another, potentially invalidating authentication state, device attestation, and contextual trust signals. Current ZTA frameworks assume relatively stable network environments and do not address the trust implications of frequent, dynamic RAT switching in mobile IoT deployments.

arXiv:2602.08593v1 Announce Type: new Abstract: We present Kissan-Dost, a multilingual, sensor-grounded conversational system that turns live on-farm measurements and weather into plain-language guidance delivered over WhatsApp text or voice. The system couples commodity soil and climate sensors with retrieval-augmented generation, then enforces grounding, traceability, and proactive alerts through a modular pipeline. In a 90-day, two-site pilot with five participants, we ran three phases (baseline, dashboard only, chatbot only). Dashboard engagement was sporadic and faded, while the chatbot was used nearly daily and informed concrete actions. Controlled tests on 99 sensor-grounded crop queries achieved over 90 percent correctness with subsecond end-to-end latency, alongside high-quality translation outputs. Results show that careful last-mile integration, not novel circuitry, unlocks the latent value of existing Agri-IoT for smallholders.

arXiv:2602.08446v1 Announce Type: new Abstract: Federated learning (FL) is a decentralized learning paradigm widely adopted in resource-constrained Internet of Things (IoT) environments. These devices, typically relying on TinyML models, collaboratively train global models by sharing gradients with a central server while preserving data privacy. However, as data heterogeneity and task complexity increase, TinyML models often become insufficient to capture intricate patterns, especially under extreme non-IID (non-independent and identically distributed) conditions. Moreover, ensuring robustness against malicious clients and poisoned updates remains a major challenge. Accordingly, this paper introduces RIFLE - a Robust, distillation-based Federated Learning framework that replaces gradient sharing with logit-based knowledge transfer. By leveraging a knowledge distillation aggregation scheme, RIFLE enables the training of deep models such as VGG-19 and Resnet18 within constrained IoT

arXiv:2602.08170v1 Announce Type: new Abstract: The Internet of Things (IoT) has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has recently emerged as a promising technique for detecting malware activity based on device power consumption patterns. However, the resilience of such detection systems under adversarial manipulation remains underexplored. This work presents a novel adversarial strategy against power side-channel-based malware detection. By injecting structured dummy code into the scanning phase of the Mirai botnet, we dynamically perturb power signatures to evade AI/ML-based anomaly detection without disrupting core functionality. Our approach systematically analyzes the trade-offs between stealthiness, execution overhead, and evasion effectiveness across multiple

arXiv:2602.07630v1 Announce Type: new Abstract: Blockchain offers a decentralized trust framework for the Internet of Things (IoT), yet deploying consensus in spectrum-congested and dynamic wireless edge IoT networks faces fundamental obstacles: traditional BFT protocols are spectrum-ignorant, leading to inefficient resource utilization and fragile progress under time-varying interference. This paper presents \textit{Wireless Streamlet}, a spectrum-aware and cognitive consensus protocol tailored for wireless edge IoT. Building on Streamlet's streamlined structure, we introduce a \textit{Channel-Aware Leader Election (CALE)} mechanism. CALE serves as a verifiable cross-layer cognitive engine that leverages receiver-measured channel state information (CSI) piggybacked in signed votes to derive Byzantine-robust connectivity scores from notarization certificates, and deterministically selects a unique weighted leader per epoch from finalized history, thereby improving proposal

arXiv:2602.07456v1 Announce Type: new Abstract: The burgeoning and ubiquitous deployment of the Internet of Things (IoT) landscape struggles with ultra-low latency demands for computation-intensive tasks in massive connectivity scenarios. In this paper, we propose an innovative uplink non-orthogonal multiple access (NOMA)-assisted multi-base station (BS) mobile edge computing (BS-MEC) network tailored for massive IoT connectivity. To fulfill the quality-of-service (QoS) requirements of delay-sensitive and computation-intensive IoT applications, we formulate a joint task offloading, user grouping, and power allocation optimization problem with the overarching objective of minimizing the system's total delay, aiming to address issues of unbalanced subchannel access, inter-group interference, computational load disparities, and device heterogeneity. To effectively tackle this problem, we first reformulate task offloading and user grouping into a non-cooperative game model and propose an

arXiv:2602.07139v1 Announce Type: new Abstract: Millimeter-Wave (mmWave) radar enables camera-free gesture recognition for Internet of Things (IoT) interfaces, with robustness to lighting variations and partial occlusions. However, recent studies reveal that its data can inadvertently encode biometric signatures, raising critical privacy challenges for IoT applications. In particular, we demonstrate that mmWave radar point cloud data can leak identity-related information in the absence of explicit identity labels. To address this risk, we propose {ImmCOGNITO}, a graph-based autoencoder that transforms radar gesture point clouds to preserve gesture-relevant structure while suppressing identity cues. The encoder first constructs a directed graph for each sequence using Temporal Graph KNN. Edges are defined to capture inter-frame temporal dynamics. A message-passing neural network with multi-head self-attention then aggregates local and global spatio-temporal context, and the global

arXiv:2602.06819v1 Announce Type: cross Abstract: This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer's optimization tasks through an iteratively process. By leveraging the naturally available closed-loop feedback inherent in wireless communication systems, PE-RTFV enables real-time physical-layer optimization without requiring model retraining. The proposed framework employs an optimization LLM (O-LLM) to generate task-specific structured prompts, which are provided to an agent LLM (A-LLM) to produce task-specific solutions. Utilizing real-time system feedback, the O-LLM iteratively refines the prompts to guide the A-LLM toward improved solutions in a gradient-descent-like optimization process. We test PE-RTFV approach on wireless-powered IoT testbed case study on user-goal-driven

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We recently published an article titled 13 High Growth Cloud Stocks to Buy.  On January 15, BNP Paribas upgraded Samsara Inc. (NYSE:IOT) to Outperform from Neutral while maintaining its $40 price target, citing a more attractive risk/reward profile following what the firm described as a “tough year” for the stock. The upgrade reflects growing confidence […]

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arXiv:2602.05706v1 Announce Type: new Abstract: Recently, the use of smart cameras in outdoor settings has grown to improve surveillance and security. Nonetheless, these systems are susceptible to tampering, whether from deliberate vandalism or harsh environmental conditions, which can undermine their monitoring effectiveness. In this context, detecting camera tampering is more challenging when a camera is capturing still images rather than video as there is no sequence of continuous frames over time. In this study, we propose two approaches for detecting tampered images: a rule-based method and a deep-learning-based method. The aim is to evaluate how each method performs in terms of accuracy, computational demands, and the data required for training when applied to real-world scenarios. Our results show that the deep-learning model provides higher accuracy, while the rule-based method is more appropriate for scenarios where resources are limited and a prolonged calibration phase is

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Reliable and ultra‑low power 20 MHz Wi‑Fi performance. The post Wi-Fi 7 for IoT appeared first on Semiconductor Engineering.

arXiv:2602.04415v1 Announce Type: new Abstract: Cryptographic operations are critical for securing IoT, edge computing, and autonomous systems. However, current RISC-V platforms lack efficient hardware support for comprehensive cryptographic algorithm families and post-quantum cryptography. This paper presents Crypto-RV, a RISC-V co-processor architecture that unifies support for SHA-256, SHA-512, SM3, SHA3-256, SHAKE-128, SHAKE-256 AES-128, HARAKA-256, and HARAKA-512 within a single 64-bit datapath. Crypto-RV introduces three key architectural innovations: a high-bandwidth internal buffer (128x64-bit), cryptography-specialized execution units with four-stage pipelined datapaths, and a double-buffering mechanism with adaptive scheduling optimized for large-hash. Implemented on Xilinx ZCU102 FPGA at 160 MHz with 0.851 W dynamic power, Crypto-RV achieves 165 times to 1,061 times speedup over baseline RISC-V cores, 5.8 times to 17.4 times better energy efficiency compared to powerful

Exclusive: 'In the long term we don't really see smart products as a segment at all.'

Agtech expert Dave Swain examines agriculture’s ongoing connectivity and IoT saga, explaining progress, pitfalls, and why there’s no easy button. The post The Ongoing Saga of Connectivity and IoT in Agriculture appeared first on CropLife.

Let’s be honest: trying to get an entire household to agree on a fitness routine is usually a recipe for disaster. It often feels like trying to solve a puzzle where the pieces keep changing shape. You have adults balancing the stress of work and the exhaustion of parenting, kids who are glued to their […] The post AEKE K1 is a smart home gym that evolves with your family appeared first on Digital Trends.

Qualcomm Inc. (NASDAQ:QCOM) is one of the best cheap stocks to buy for 2026. On January 26, Mizuho trimmed its price target on Qualcomm Inc. (NASDAQ:QCOM) to $160 from $175 and kept a Neutral rating on the shares. The investment bank tied the update directly to a weaker outlook for global handset demand and related […]

The Internet of Things (IoT) is revolutionizing various sectors worldwide, and agriculture is no exception. Recent studies reveal that the integration of IoT into farming practices ushers in a new era of efficiency, productivity, and sustainability. By harnessing the power of sensors, connectivity, and data analytics, farmers can now monitor crop health, manage resources intelligently, […]

With the X60 Max Ultra, Dreame ties with Roborock for the most powerful robot vacuum right now. I got to try it on my own floors early.

arXiv:2602.01932v1 Announce Type: new Abstract: Matter is the most recent application-layer standard for the Internet of Things (IoT). As one of its major selling points, Matter's design imposes particular attention to security and privacy: it provides validated secure session establishment protocols, and it uses robust security algorithms to secure communications between IoT devices and Matter controllers. However, to our knowledge, there is no systematic analysis investigating the extent to which a passive attacker, in possession of lower layer keys or exploiting security misconfiguration at those layers, could infer information by passively analyzing encrypted Matter traffic. In this paper, we fill this gap by analyzing the robustness of the Matter IoT standard to encrypted traffic analysis performed by a passive eavesdropper. By using various datasets collected from real-world testbeds and simulated setups, we identify patterns in metadata of the encrypted Matter traffic that

arXiv:2602.01910v1 Announce Type: new Abstract: Smart-home sensor data holds significant potential for several applications, including healthcare monitoring and assistive technologies. Existing approaches, however, face critical limitations. Supervised models require impractical amounts of labeled data. Foundation models for activity recognition focus only on inertial sensors, failing to address the unique characteristics of smart-home binary sensor events: their sparse, discrete nature combined with rich semantic associations. LLM-based approaches, while tested in this domain, still raise several issues regarding the need for natural language descriptions or prompting, and reliance on either external services or expensive hardware, making them infeasible in real-life scenarios due to privacy and cost concerns. We introduce DomusFM, the first foundation model specifically designed and pretrained for smart-home sensor data. DomusFM employs a self-supervised dual contrastive learning

IKEA has offered a few smart devices here and there in recent years, but a revamp of its offerings is bringing a lot of new gadgets to the company's stores.

The Spot+Scrub Ai isn't quite the robovac redemption Dyson must have been hoping for.

arXiv:2601.23147v1 Announce Type: new Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as smart grids and microgrids. However, IoT systems are vulnerable to clock drift, time-synchronization manipulation, and timestamp discontinuities, such as the Year 2038 (Y2K38) Unix overflow, all of which disrupt temporal ordering. Conventional anomaly-detection models, which assume reliable timestamps, fail to capture temporal inconsistencies. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a framework that models both temporal distortion and inter-device consistency in energy IoT systems. STGAT combines drift-aware temporal embeddings and temporal self-attention to capture corrupted time evolution at individual devices, and uses graph attention to model spatial propagation of timing errors. A curvature-regularized latent representation geometrically separates normal

IKEA's smart home lineup is finally moving to Matter, with affordable sensors and remotes that fix old limits and play nicer with other platforms.

Li Xia suffers from an incurable genetic disease that has left him paralysed from the neck down. Yet the 36-year-old has overcome the odds by setting up a mini farm that he cultivates from inside his home in China.

In case you missed it, we recently overhauled our guides to the best budget robot vacuums and the best mopping vacs, both of which feature a slate of new picks from Roborock, Narwal, Dreame, and others. Dyson’s 360 Vis Nav Robot Vacuum didn’t technically make the cut, though it was once one of our top picks for carpets. And now through the rest of today, January 30th, it’s on sale at Woot with a two-year warranty for an all-time low of $279.99 ($720 off). Dyson 360 Vis Nav robot vacuum Where to Buy: $999.99 $349.99 at Amazon $999.99 $279.99 at Woot $999.99 $399.99 at Dyson If you have a relatively simple floor plan with lots of high-pile carpet, Dyson’s D-shaped robovac is worth considering. It can traverse obstacles up to 21 millimeters high and offers a whopping 65 air watts of suction — twice that of many robovacs

arXiv:2601.21595v1 Announce Type: new Abstract: The global water crisis necessitates affordable, accurate, and real-time water quality monitoring solutions. Traditional approaches relying on manual sampling or expensive commercial systems fail to address accessibility challenges in resource-constrained environments. This paper presents HydroSense, an innovative Internet of Things framework that integrates six critical water quality parameters including pH, dissolved oxygen (DO), temperature, total dissolved solids (TDS), estimated nitrogen, and water level into a unified monitoring system. HydroSense employs a novel dual-microcontroller architecture, utilizing Arduino Uno for precision analog measurements with five-point calibration algorithms and ESP32 for wireless connectivity, edge processing, and cloud integration. The system implements advanced signal processing techniques including median filtering for TDS measurement, temperature compensation algorithms, and robust error

As of January 31, Belkin is shutting down a large number of Wemo smart home devices, including wiping its app and cloud features.

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arXiv:2601.20548v1 Announce Type: new Abstract: This paper critically examines the device identification process using machine learning, addressing common pitfalls in existing literature. We analyze the trade-offs between identification methods (unique vs. class based), data heterogeneity, feature extraction challenges, and evaluation metrics. By highlighting specific errors, such as improper data augmentation and misleading session identifiers, we provide a robust guideline for researchers to enhance the reproducibility and generalizability of IoT security models.

arXiv:2601.20366v1 Announce Type: new Abstract: The integration of physical security systems with environmental safety monitoring represents a critical advancement in smart infrastructure management. Traditional approaches maintain these systems as independent silos, creating operational inefficiencies, delayed emergency responses, and increased management complexity. This paper presents a comprehensive dual-modality Internet of Things framework that seamlessly integrates RFID-based access control with multi-sensor environmental safety monitoring through a unified cloud architecture. The system comprises two coordinated subsystems: Subsystem 1 implements RFID authentication with servo-actuated gate control and real-time Google Sheets logging, while Subsystem 2 provides comprehensive safety monitoring incorporating flame detection, water flow measurement, LCD status display, and personnel identification. Both subsystems utilize ESP32 microcontrollers for edge processing and wireless

arXiv:2601.20183v1 Announce Type: new Abstract: Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer-coded Hybrid ARQ (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade-off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the Cross-layer Age of Error Information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the

I am not, by any definition, a coder, but when I started seeing people's vibe-coded smart home projects all over my social feeds this month, I was intrigued. From a "master command center" built on a Lutron system to AI controlling a smart oven, people were unleashing AI in their smart homes, using Claude Code to build tools that would normally take weeks to create by hand. The barrier between "I wish this existed" and "I made it" suddenly looked remarkably thin. So, what did I wish existed in my home? A decent smart home dashboard. I've been reviewing smart home devices for over a decade, and the constant switching and swapping of lights, … Read the full story at The Verge.