IOT

arXiv:2505.15376v1 Announce Type: new Abstract: Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure. In this work, we propose a novel Federated Learning-Enhanced Blockchain Framework (FL-BCID) for privacy-preserving intrusion detection tailored for IIoT environments. Our architecture combines federated learning (FL) to ensure decentralized model training with blockchain technology to guarantee data integrity, trust, and tamper resistance across IIoT nodes. We design a lightweight intrusion detection model collaboratively trained using FL across edge devices without exposing sensitive data. A smart contract-enabled blockchain system records model updates and anomaly scores to establish

arXiv:2505.15089v1 Announce Type: new Abstract: The digital transformation of smart cities and workplaces requires effective integration of physical and cyber spaces, yet existing digital twin solutions remain limited in supporting real-time, multi-user collaboration. While metaverse platforms enable shared virtual experiences, they have not supported comprehensive integration of IoT sensors on physical spaces, especially for large-scale smart architectural environments. This paper presents a digital twin environment that integrates Kajima Corp.'s smart building facility "The GEAR" in Singapore with a commercial metaverse platform Cluster. Our system consists of three key components: a standardized IoT sensor platform, a real-time data relay system, and an environmental data visualization framework. Quantitative end-to-end latency measurements confirm the feasibility of our approach for real-world applications in large architectural spaces. The proposed framework enables new forms of

On May 21, Samsara Inc. (NYSE:IOT) announced a partnership with Rivian Automotive Inc. (NASDAQ:RIVN) to streamline electric fleet management. The partnership is aimed at simplifying electric fleet management for commercial customers through integrating Rivian’s vehicle data with Samsara Connected Operations Platform. Rivian Automotive, Inc. (NASDAQ:RIVN) is an American electric vehicle manufacturing company that also sells […]

Eufy features the cheapest robot vacuum combination this year, with a handheld unit built into the robot's body instead of the dock.

arXiv:2505.14659v1 Announce Type: new Abstract: As healthcare systems increasingly adopt advanced wireless networks and connected devices, securing medical applications has become critical. The integration of Internet of Medical Things devices, such as robotic surgical tools, intensive care systems, and wearable monitors has enhanced patient care but introduced serious security risks. Cyberattacks on these devices can lead to life threatening consequences, including surgical errors, equipment failure, and data breaches. While the ITU IMT 2030 vision highlights 6G's transformative role in healthcare through AI and cloud integration, it also raises new security concerns. This paper explores how explainable AI techniques like SHAP, LIME, and DiCE can uncover vulnerabilities, strengthen defenses, and improve trust and transparency in 6G enabled healthcare. We support our approach with experimental analysis and highlight promising results.

arXiv:2505.13764v1 Announce Type: new Abstract: Efficiently supporting remote firmware updates in Internet of Things (IoT) devices remains a significant challenge due to the limitations of many IoT communication protocols, which often make it impractical to transmit full firmware images. Techniques such as firmware partitioning have been introduced to mitigate this issue, but they frequently fall short, especially in battery-powered systems where time and energy constraints are critical. As a result, physical maintenance interventions are still commonly required, which is costly and inconvenient in large-scale deployments. In this work, we present a lightweight and innovative method that addresses this challenge by generating highly compact delta patches, enabling firmware reconstruction directly on the device. Our algorithm is specifically optimized for low-power devices, minimizing both memory usage and computational overhead. Compared to existing solutions, our approach

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At $2,599, the Roborock Saros Z70 should perform nearly perfectly. Though it's good at cleaning, the arm fumbles way too often.

The Saros Z70 likes socks just as much as Gus does. I suspect my dog does not like the Roborock Saros Z70. Unlike the dozens of other robot vacuums that Gus happily lets clean around him while he sleeps, the Z70 keeps stealing his treasures. Not his dog toys - although that could be a future feature - but my family's socks that he loves to collect and carry around the house with him. Since the Z70 arrived, he's had competition. The first robot vacuum with a mechanical arm, the Z70 features a five-axis arm, branded the OmniGrip, that uses onboard sensors and a camera to see, pick up, and tidy away a small list of light items, including the aforementioned socks, footwear such as slippers and sandals, tissues, and paper. In theory, this means I should spend less time picking up after my kids or rummaging in Gus' bed to find the socks he's stolen. The Z70

The Samsung Galaxy Ring is reportedly getting closer to turning you into a smart home cyborg.

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arXiv:2505.12336v1 Announce Type: cross Abstract: Current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the

arXiv:2505.12336v1 Announce Type: new Abstract: Current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial

arXiv:2505.12147v1 Announce Type: new Abstract: The rapid increase in computing power and the ability to store Big Data in the infrastructure has enabled predictions in a large variety of domains by Machine Learning. However, in many cases, existing Machine Learning tools are considered insufficient or incorrect since they exploit only probabilistic dependencies rather than inference logic. Causal Machine Learning methods seem to close this gap. In this paper, two prevalent tools based on Causal Machine Learning methods are compared, as well as their mathematical underpinning background. The operation of the tools is demonstrated by examining their response to 18 queries, based on the IDEAL Household Energy Dataset, published by the University of Edinburgh. First, it was important to evaluate the causal relations assumption that allowed the use of this approach; this was based on the preexisting scientific knowledge of the domain and was implemented by use of the in-built validation

arXiv:2505.11880v1 Announce Type: new Abstract: The Advanced Encryption Standard (AES) is a widely adopted cryptographic algorithm essential for securing embedded systems and IoT platforms. However, existing AES hardware accelerators often face limitations in performance, energy efficiency, and flexibility. This paper presents AES-RV, a hardware-efficient RISC-V accelerator featuring low-latency AES instruction extensions optimized for real-time processing across all AES modes and key sizes. AES-RV integrates three key innovations: high-bandwidth internal buffers for continuous data processing, a specialized AES unit with custom low-latency instructions, and a pipelined system supported by a ping-pong memory transfer mechanism. Implemented on the Xilinx ZCU102 SoC FPGA, AES-RV achieves up to 255.97 times speedup and up to 453.04 times higher energy efficiency compared to baseline and conventional CPU/GPU platforms. It also demonstrates superior throughput and area efficiency against

arXiv:2505.11845v1 Announce Type: new Abstract: For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficulty, we present ElderFallGuard: A Computer Vision Based IoT Solution for Elderly Fall Detection and Notification, a cutting-edge, non-invasive system intended for quick caregiver alerts and real-time fall detection. Our approach leverages the power of computer vision, utilizing MediaPipe for accurate human pose estimation from standard video streams. We developed a custom dataset comprising 7200 samples across 12 distinct human poses to train and evaluate various machine learning classifiers, with Random Forest ultimately selected for its superior performance. ElderFallGuard employs a specific detection logic, identifying a fall when a designated prone pose ("Pose6") is held for over 3 seconds coupled with a significant drop in motion detected for more than 2 seconds. Upon confirmation, the

arXiv:2505.10600v1 Announce Type: new Abstract: Due to the rapid growth in the number of Internet of Things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learning models tend to struggle in identifying attacks when normal data volume is much higher than the volume of attacks. For example, the dataset used in this study reveals a strong class imbalance with 94,659 instances of the majority class and only 28 instances of the minority class, making it quite challenging to determine rare attacks accurately. The challenges presented in this research are addressed by hybrid sampling techniques designed to improve data imbalance detection accuracy in IoT domains. After applying these techniques, we evaluate the performance of several machine learning models such as Random Forest, Soft Voting, Support Vector

Ecovacs' Deebot N30 Omni is a midrange robot vacuum with high-end features that are worth more than its cost, especially with this deal.

Though not the first three-in-one robot vacuum on the market, the Ecovacs Deebot T30S Combo is one of the most affordable, especially with this deal.

Eufy features the cheapest robot vacuum combination this year, with a handheld unit built into the robot's body instead of the dock.

One of the annoying limitations of Matter is that, often, you’re giving up features if you use a device in something like the Google Home app over another Matter app. But with Aqara’s Advanced Matter Bridging, that doesn’t have to be the case. more…

The new Qualcomm Engineering Center will become a key part of the company’s global network of engineering hubs The post Qualcomm to establish global AI and IoT engineering centre in Abu Dhabi appeared first on Gulf Business.

The Ecovacs Deebot X9 Pro Omni features one of the best mopping systems ever used in a robot vacuum. Here's our full review.

This Alfred smart lock can be continuously charged by infrared lasers from this Wi-Charge transmitter. One morning last month, I walked into my kitchen to get a glass of water, but my smart faucet was out of battery. I went to sit down in my front room, and the shade was still shut - it was out of battery. I walked down the hall and found a beached robot vacuum - out of battery. I headed outside to feed the chickens, unlocking the back door on the way out. The battery-powered smart lock had done what it was supposed to and automatically locked at 8PM. At least something was working. The game changer here is wireless charging. Not wireless like putting your phone on a charging pad, wireless like across the room. For the past year, a Wi-Charge transmitter in my ceiling has been shooting infrared lasers at a photovoltaic panel on the specially modified Alfred

Save 22% on the Eufy X10 Pro Omni robot vacuum at Amazon.

arXiv:2505.10122v1 Announce Type: new Abstract: In unmanned aerial vehicle (UAV)-assisted wake-up radio (WuR)-enabled internet of things (IoT) networks, UAVs can instantly activate the main radios (MRs) of the sensor nodes (SNs) with a wake-up call (WuC) for efficient data collection in mission-driven data collection scenarios. However, the spontaneous response of numerous SNs to the UAV's WuC can lead to significant packet loss and collisions, as WuR does not exhibit its superiority for high-traffic loads. To address this challenge, we propose an innovative receiver-initiated WuR UAV-assisted clustering (RI-WuR-UAC) medium access control (MAC) protocol to achieve low latency and high reliability in ultra-low power consumption applications. We model the proposed protocol using the $M/G/1/2$ queuing framework and derive expressions for key performance metrics, i.e., channel busyness probability, probability of successful clustering, average SN energy consumption, and average

arXiv:2505.09929v1 Announce Type: new Abstract: In recent years, consumer Internet of Things (IoT) devices have become widely used in daily life. With the popularity of devices, related security and privacy risks arise at the same time as they collect user-related data and transmit it to various service providers. Although China accounts for a larger share of the consumer IoT industry, current analyses on consumer IoT device traffic primarily focus on regions such as Europe, the United States, and Australia. Research on China, however, is currently rather rare. This study constructs the first large-scale dataset about consumer IoT device traffic in China. Specifically, we propose a fine-grained traffic collection guidance covering the entire lifecycle of consumer IoT devices, gathering traffic from 70 devices spanning 36 brands and 8 device categories. Based on this dataset, we analyze traffic destinations and encryption practices across different device types during the entire

Your future self (and your spotless floors) will thank you!

We recently published a list of 13 Cheap AI Stocks to Buy According to Analysts. In this article, we are going to take a look at where QUALCOMM Incorporated (NASDAQ:QCOM) stands against other cheap AI stocks to buy according to analysts. As per Morgan Stanley, in 2025, technology companies are expected to maintain their focus on […]

For just a little more than the cost of a regular stick vacuum, you can bring home a great cleaning buddy that will change the way you live.

Dr. Naima Shaikh, founder of eKrishikendra, empowers farmers through AI, IoT, and blockchain-based digital solutions. Her platform offers real-time advice, direct market access, transparent input sourcing, and peer learning—transforming agriculture into a tech-driven, sustainable, and profitable ecosystem for over a million farmers.

Maize is also known as the ‘Queen of Cereals.’ It is becoming increasingly popular among Indian farmers as it is highly profitable, demands less water, and has extensive use. Ranging from food and fodder to industrial uses such as ethanol and poultry feed, maize provides farmers with various sources of income

This Roborock robot vacuum and mop combo can do everything you need it to so you can keep a cleaner house.

The Deebot X9 Pro Omni is a powerful flagship robot vacuum and mop that performs very well on carpet and rugs.

arXiv:2505.07119v1 Announce Type: new Abstract: Visual Anomaly Detection (VAD) is a key task in industrial settings, where minimizing waste and operational costs is essential. Deploying deep learning models within Internet of Things (IoT) environments introduces specific challenges due to the limited computational power and bandwidth of edge devices. This study investigates how to perform VAD effectively under such constraints by leveraging compact and efficient processing strategies. We evaluate several data compression techniques, examining the trade-off between system latency and detection accuracy. Experiments on the MVTec AD benchmark demonstrate that significant compression can be achieved with minimal loss in anomaly detection performance compared to uncompressed data.

arXiv:2505.06822v1 Announce Type: new Abstract: In this paper, we proposes an automatic firmware analysis tool targeting at finding hidden services that may be potentially harmful to the IoT devices. Our approach uses static analysis and symbolic execution to search and filter services that are transparent to normal users but explicit to experienced attackers. A prototype is built and evaluated against a dataset of IoT firmware, and The evaluation shows our tool can find the suspicious hidden services effectively.

arXiv:2505.06517v1 Announce Type: new Abstract: This paper presents a visual-inertial odometry (VIO) method using long-tracked features. Long-tracked features can constrain more visual frames, reducing localization drift. However, they may also lead to accumulated matching errors and drift in feature tracking. Current VIO methods adjust observation weights based on re-projection errors, yet this approach has flaws. Re-projection errors depend on estimated camera poses and map points, so increased errors might come from estimation inaccuracies, not actual feature tracking errors. This can mislead the optimization process and make long-tracked features ineffective for suppressing localization drift. Furthermore, long-tracked features constrain a larger number of frames, which poses a significant challenge to real-time performance of the system. To tackle these issues, we propose an active decoupling mechanism for accumulated errors in long-tracked feature utilization. We introduce a

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Businesses are running old, outdated IoT and aren't adhering to cybersecurity standards.

Smart home controller maker Brilliant NextGen has launched its second generation of touchscreen panels that replace traditional light switches.  The new hardware, which looks like the previous model, has a 4x higher resolution screen and dual-band WiFi (2.4GHz and 5GHz) to help curb interference.  It also has a more powerful processor that Brilliant says is ready “for the next phase of AI-powered smart home evolution,” although the company has not said when it plans to launch such features or what it could do. (As-is, there are currently no new software features from the previous gen.) In an email to The Verge, Brilliant’s head of marketing Erin Wright says the new version also reduces latency with integrations, which sounds promising since the previous model was sluggish with some devices in our

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arXiv:2505.04710v1 Announce Type: new Abstract: With the increasing popularity of IoT applications, end users demand more personalized and intuitive functionality. A major obstacle for this, however, is that custom IoT functionality today still requires at least some coding skills. To address this, no-code development platforms have been proposed as a solution for empowering non-technical users to create applications. However, such platforms still require a certain level of technical expertise for structuring process steps or defining event-action relations. The advent of LLMs can further enhance no-code platforms by enabling natural language-based interaction, automating of complex tasks, and dynamic code generation. By allowing users to describe their requirements in natural language, LLMs can significantly streamline no-code development. As LLMs vary in performance, architecture, training data used, and the use cases they target, it is still unclear which models are best suited and

The growing use of smart home devices is undermining the privacy and safety of domestic workers. New research reveals how surveillance technologies reinforce a sense of constant monitoring and control by domestic workers' employers, increasing their vulnerability and impacting their mental wellbeing.

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I've gone hands-on with dozens of robot vacuums, but Eufy's new Omni E28 actually surprised me when it went to work in my house.

Simplify spring cleaning with the iRobot Roomba j8+, on sale now for $249.99 (reg. $599).

arXiv:2308.11981v2 Announce Type: replace Abstract: Existing FL-based approaches are based on the unrealistic assumption that the data on the client-side is fully annotated with ground truths. Furthermore, it is a great challenge how to improve the training efficiency while ensuring the detection accuracy in the highly heterogeneous and resource-constrained IoT networks. Meanwhile, the communication cost between clients and the server is also a problem that can not be ignored. Therefore, in this paper, we propose a Federated Semi-Supervised and Semi-Asynchronous (FedS3A) learning for anomaly detection in IoT networks. First, we consider a more realistic assumption that labeled data is only available at the server, and pseudo-labeling is utilized to implement federated semi-supervised learning, in which a dynamic weight of supervised learning is exploited to balance the supervised learning at the server and unsupervised learning at clients. Then, we propose a semi-asynchronous model

A defense in depth solution is essential for creating safe and secure ICs for automotive applications. The post IoT Security By Design appeared first on Semiconductor Engineering.

Matter update may finally take the tedium out of setting up your smart home  Ars TechnicaMatter’s latest update brings NFC onboarding and multi-device setup  The VergeSetting up HomeKit devices to get easier with tap-to-pair and multi-device QR codes  9to5MacMatter update simplifies setup with multi-device QR codes and NFC tag support  9to5GoogleA big smart home category is still left out of Matter  pcworld.com

Matter update may finally take the tedium out of setting up your smart home  Ars TechnicaMatter’s latest update brings NFC onboarding and multi-device setup  The VergeSetting up HomeKit devices to get easier with tap-to-pair and multi-device QR codes  9to5MacMatter update simplifies setup with multi-device QR codes and NFC tag support  9to5GoogleA big smart home category is still left out of Matter  pcworld.com

Matter is taking care of a few common smart home headaches.

Google's popular casting device has been around for some time, but it still does more than just stream your favorite shows.

The latest update to the Matter smart home protocol shows how it can benefit average users.

Matter gains three new features that will make smart home management less of a headache.

If you don't have a dedicated smart home hub device, your smart TV might be able to help, assuming it's one of the out-of-the-box compatible devices.

arXiv:2505.03139v1 Announce Type: new Abstract: Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and computation resources of geographically distributed edge devices, edge LAMs enable real-time intelligent services at the network edge. Unlike conventional edge AI, which relies on small or moderate-sized models for direct feature-to-prediction mappings, edge LAMs leverage the intricate coordination of modular components to enable context-aware generative tasks and multi-modal inference. We shall propose a collaborative deployment framework for edge LAM by characterizing the LAM intelligent capabilities and limited edge network resources. Specifically, we propose a collaborative training framework over heterogeneous edge networks that adaptively decomposes LAMs according to computation resources, data modalities, and training objectives, reducing communication and

arXiv:2505.03721v1 Announce Type: new Abstract: Solar sensor-based monitoring systems have become a crucial agricultural innovation, advancing farm management and animal welfare through integrating sensor technology, Internet-of-Things, and edge and cloud computing. However, the resilience of these systems to cyber-attacks and their adaptability to dynamic and constrained energy supplies remain largely unexplored. To address these challenges, we propose a sustainable smart farm network designed to maintain high-quality animal monitoring under various cyber and adversarial threats, as well as fluctuating energy conditions. Our approach utilizes deep reinforcement learning (DRL) to devise optimal policies that maximize both monitoring effectiveness and energy efficiency. To overcome DRL's inherent challenge of slow convergence, we integrate transfer learning (TL) and decision theory (DT) to accelerate the learning process. By incorporating DT-guided strategies, we optimize monitoring

arXiv:2505.03409v1 Announce Type: new Abstract: Cardiovascular diseases are a leading cause of fatalities worldwide, often occurring suddenly with limited time for intervention. Current healthcare monitoring systems for cardiac patients rely heavily on hospitalization, which can be impractical for continuous monitoring. This paper presents a novel IoT-based solution for remote, real-time tracking of critical cardiac metrics, addressing the pressing need for accessible and continuous healthcare, particularly for the aging population in Pakistan. The proposed IoT kit measures essential parameters such as body temperature, heart rate (HR), blood pressure (BP), oxygen saturation (SPO2), and electrocardiography (ECG). A key innovation of the system is its integration with a cloud-based application, enabling constant remote monitoring and incorporating an alarm mechanism to alert medical professionals for timely intervention, reducing the risk of catastrophic incidents. The system was

arXiv:2505.03375v1 Announce Type: new Abstract: Wi-Fi sensing is an emerging technology that uses channel state information (CSI) from ambient Wi-Fi signals to monitor human activity without the need for dedicated sensors. Wi-Fi sensing does not only represent a pivotal technology in intelligent Internet of Things (IoT) systems, but it can also provide valuable insights in forensic investigations. However, the high dimensionality of CSI data presents major challenges for storage, transmission, and processing in resource-constrained IoT environments. In this paper, we investigate the impact of lossy compression on the accuracy of Wi-Fi sensing, evaluating both traditional techniques and a deep learning-based approach. Our results reveal that simple, interpretable techniques based on principal component analysis can significantly reduce the CSI data volume while preserving classification performance, making them highly suitable for lightweight IoT forensic scenarios. On the other hand,

arXiv:2505.03139v1 Announce Type: new Abstract: Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and computation resources of geographically distributed edge devices, edge LAMs enable real-time intelligent services at the network edge. Unlike conventional edge AI, which relies on small or moderate-sized models for direct feature-to-prediction mappings, edge LAMs leverage the intricate coordination of modular components to enable context-aware generative tasks and multi-modal inference. We shall propose a collaborative deployment framework for edge LAM by characterizing the LAM intelligent capabilities and limited edge network resources. Specifically, we propose a collaborative training framework over heterogeneous edge networks that adaptively decomposes LAMs according to computation resources, data modalities, and training objectives, reducing communication and

arXiv:2505.03036v1 Announce Type: new Abstract: The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating

When the dust bunnies in your home are getting large enough to adopt as pets (and yes, I’m talking about my own living quarters at this point), maybe it’s time to stop promising yourself you’ll clean up “one day” and get yourself a robot vacuum. Admittedly, they’re not inexpensive, so it’s a good idea to take advantage of sales when they come along. Luckily, there’s currently a great one on iRobot’s Roomba j7, which is available at Woot for $169.99 ($430 off) until May 12th at 1AM ET, or while supplies last. The Roomba j7 — previously our No. 1 robot vacuum pick — is basically a simpler version of the j7 Plus (that adds an auto-empty base) and the Combo j7 Plus (which includes some new sensors, a bigger battery, and the ability to mop). That being said, if you’re looking for something at a reasonable price to clean up your space, this is a good gadget to consider. What is

The Eufy Auto-Empty C10 robot vacuum is on sale at Amazon for $269.99, down from the normal price of $479.99. That's a 44% discount.

arXiv:2505.02806v1 Announce Type: new Abstract: This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization

arXiv:2505.02806v1 Announce Type: new Abstract: This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization

arXiv:2505.02640v1 Announce Type: new Abstract: Internet of Things (IoT) systems increasingly operate in environments where devices must respond in real time while managing fluctuating resource constraints, including energy and bandwidth. Yet, current approaches often fall short in addressing scenarios where operational constraints evolve over time. To address these limitations, we propose a novel Budgeted Multi-Armed Bandit framework tailored for IoT applications with dynamic operational limits. Our model introduces a decaying violation budget, which permits limited constraint violations early in the learning process and gradually enforces stricter compliance over time. We present the Budgeted Upper Confidence Bound (UCB) algorithm, which adaptively balances performance optimization and compliance with time-varying constraints. We provide theoretical guarantees showing that Budgeted UCB achieves sublinear regret and logarithmic constraint violations over the learning horizon.

arXiv:2505.02543v1 Announce Type: new Abstract: The widespread adoption of IoT has driven the development of cyber-physical systems (CPS) in industrial environments, leveraging Industrial IoTs (IIoTs) to automate manufacturing processes and enhance productivity. The transition to autonomous systems introduces significant operational costs, particularly in terms of energy consumption. Accurate modeling and prediction of IIoT energy requirements are critical, but traditional physics- and engineering-based approaches often fall short in addressing these challenges comprehensively. In this paper, we propose a novel methodology for benchmarking and analyzing IIoT devices and applications to uncover insights into their power demands, energy consumption, and performance. To demonstrate this methodology, we develop a comprehensive framework and apply it to study an industrial CPS comprising an educational robotic arm, a conveyor belt, a smart camera, and a compute node. By creating

arXiv:2505.01437v1 Announce Type: new Abstract: The Internet of Things (IoT) technology has rapidly gained popularity with applications widespread across a variety of industries. However, IoT devices have been recently serving as a porous layer for many malicious attacks to both personal and enterprise information systems with the most famous attacks being botnet-related attacks. The work in this study leveraged Variational Auto-encoder (VAE) and cost-sensitive learning to develop lightweight, yet effective, models for IoT-botnet detection. The aim is to enhance the detection of minority class attack traffic instances which are often missed by machine learning models. The proposed approach is evaluated on a multi-class problem setting for the detection of traffic categories on highly imbalanced datasets. The performance of two deep learning models including the standard feed forward deep neural network (DNN), and Bidirectional-LSTM (BLSTM) was evaluated and both recorded commendable

We recently published a list of the 11 Hidden AI Stocks to Buy Right Now. In this article, we are going to take a look at where Samsara Inc. (NYSE:IOT) stands against other hidden AI stocks. David Grain, Founder & CEO of Grain Management, joined CNBC on May 1 to discuss the AI-driven demand for […]

arXiv:2505.01196v1 Announce Type: new Abstract: To improve crop forecasting and provide farmers with actionable data-driven insights, we propose a novel approach integrating IoT, machine learning, and blockchain technologies. Using IoT, real-time data from sensor networks continuously monitor environmental conditions and soil nutrient levels, significantly improving our understanding of crop growth dynamics. Our study demonstrates the exceptional accuracy of the Random Forest model, achieving a 99.45\% accuracy rate in predicting optimal crop types and yields, thereby offering precise crop projections and customized recommendations. To ensure the security and integrity of the sensor data used for these forecasts, we integrate the Ethereum blockchain, which provides a robust and secure platform. This ensures that the forecasted data remain tamper-proof and reliable. Stakeholders can access real-time and historical crop projections through an intuitive online interface, enhancing

arXiv:2505.00918v1 Announce Type: new Abstract: The last few decades have witnessed a rapid increase in IoT devices owing to their wide range of applications, such as smart healthcare monitoring systems, smart cities, and environmental monitoring. A critical task in IoT networks is sensing and transmitting information over the network. The IoT nodes gather data by sensing the environment and then transmit this data to a destination node via multi-hop communication, following some routing protocols. These protocols are usually designed to optimize possibly contradictory objectives, such as maximizing packet delivery ratio and energy efficiency. While most literature has focused on optimizing a static objective that remains unchanged, many real-world IoT applications require adapting to rapidly shifting priorities. For example, in monitoring systems, some transmissions are time-critical and require a high priority on low latency, while other transmissions are less urgent and instead

After months of testing in a preview program, the Home Panel layout is finally rolling out to Chromecast and Google TV devices.

Punjab Agricultural University (PAU) is advancing agricultural innovation by introducing two new tech-focused academic programmes. These initiatives aim to equip students with the skills needed to revolutionize farming through artificial intelligence, robotics, and data science.

Looking to invest in a smart home hub to transform your living space? These nine smart home hubs should be enough to get you started.

The world's first mass-produced robot vacuum with an intelligent robotic arm is bringing home cleaning appliances into the next generation.

This is the best robot vacuum we’ve tested, and it scored a rare 10 out of 10.

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Los Angeles CA (SPX) May 01, 2025 Spire Global has announced an expanded agreement with Australian IoT company Myriota to design, build, and operate 16 new satellites carrying advanced second-generation payloads. The deal strengthens Myriota's partnership with Spire Space Services and extends its total satellite fleet to over 40 spacecraft. The collaboration, which began in 2021, has enabled Myriota to scale its Internet o

arXiv:2505.00593v1 Announce Type: new Abstract: The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and ineffective for large-volume image data, leading to inefficiencies in privacy-preserving distributed learning applications. To address these concerns, this paper proposes a novel Feature-Aware Chaotic Image Encryption scheme that integrates Feature-Aware Pixel Segmentation (FAPS) with Chaotic Chain Permutation and Confusion mechanisms to enhance security while maintaining efficiency. The proposed scheme consists of three stages: (1) FAPS, which extracts and reorganizes pixels based on high and low edge intensity features for correlation disruption; (2) Chaotic Chain Permutation, which employs a logistic chaotic map with SHA-256-based

arXiv:2505.00240v1 Announce Type: new Abstract: The increasing complexity and scale of the Internet of Things (IoT) have made security a critical concern. This paper presents a novel Large Language Model (LLM)-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on IoT-specific datasets (IoT-23, TON_IoT) for real-time anomaly detection and automated, context-aware mitigation strategies optimized for resource-constrained devices. A modular Docker-based deployment enables scalable and reproducible evaluation across diverse network conditions. Experimental results in simulated IoT environments demonstrate significant improvements in detection accuracy, response latency, and resource efficiency over traditional security methods. The proposed framework highlights the potential of LLM-driven, autonomous security solutions for future IoT ecosystems.

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arXiv:2504.21571v1 Announce Type: new Abstract: To improve sustainability, Internet-of-Things (IoT) is increasingly adopting battery-free devices powered by ambient energy scavenged from the environment. The unpredictable availability of ambient energy leads to device intermittency, bringing critical challenges to device communication and related fundamental operations like data aggregation. We propose FreeBeacon, a novel scheme for efficient communication and data aggregation in battery-free IoT. We argue that the communication challenge between battery-free devices originates from the complete uncertainty of the environment. FreeBeacon is built on the insight that by introducing just a small degree of certainty into the system, the communication problem can be largely simplified. To this end, FreeBeacon first introduces a small number of battery-powered devices as beacons for battery-free devices. Then, FreeBeacon features protocols for battery-free devices to achieve interaction

arXiv:2504.20275v1 Announce Type: new Abstract: Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and

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arXiv:2504.18781v1 Announce Type: new Abstract: Despite the demonstrated effectiveness of transformer models in NLP, and image and video classification, the available tools for extracting features from captured IoT network flow packets fail to capture sequential patterns in addition to the absence of spatial patterns consequently limiting transformer model application. This work introduces a novel preprocessing method to adapt transformer models, the vision transformer (ViT) in particular, for IoT botnet attack detection using network flow packets. The approach involves feature extraction from .pcap files and transforming each instance into a 1-channel 2D image shape, enabling ViT-based classification. Also, the ViT model was enhanced to allow use any classifier besides Multilayer Perceptron (MLP) that was deployed in the initial ViT paper. Models including the conventional feed forward Deep Neural Network (DNN), LSTM and Bidirectional-LSTM (BLSTM) demonstrated competitive performance

arXiv:2504.18571v1 Announce Type: new Abstract: The rapid expansion of Internet of Things (IoT) devices, particularly in smart home environments, has introduced considerable security and privacy concerns due to their persistent connectivity and interaction with cloud services. Despite advancements in IoT security, effective privacy measures remain uncovered, with existing solutions often relying on cloud-based threat detection that exposes sensitive data or outdated allow-lists that inadequately restrict non-essential network traffic. This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic (i.e., not influencing the device operations) by analyzing network behavior at the edge, leveraging Machine Learning to classify network destinations. Our approach includes building a labeled dataset based on IoT device behavior and employing a feature-extraction pipeline to enable a binary classification of essential vs. non-essential network destinations. We

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arXiv:2504.18451v1 Announce Type: new Abstract: Due to rapid population growth globally, digitally-enabled agricultural sectors are crucial for sustainable food production and making informed decisions about resource management for farmers and various stakeholders. The deployment of Internet of Things (IoT) technologies that collect real-time observations of various environmental (e.g., temperature, humidity, etc.) and operational factors (e.g., irrigation) influencing production is often seen as a critical step to enable additional novel downstream tasks, such as AI-based yield forecasting. However, since AI models require large amounts of data, this creates practical challenges in a real-world dynamic farm setting where IoT observations would need to be collected over a number of seasons. In this study, we deployed IoT sensors in strawberry production polytunnels for two growing seasons to collect environmental data, including water usage, external and internal temperature, external

arXiv:2504.18222v1 Announce Type: new Abstract: Agricultural field operations are generally tracked as work records (WR), incorporating data points such as; work type, machine type, timestamped trajectories and field information. WR data which is automatically recorded by modern machinery equipped with Information and Communication Technologies (ICT) can enable efficient farm management decision making. Globally, farmers often rely on aged or legacy farming machinery and manual data recording, which introduces significant labor costs and increases the risk of inaccurate data input. To address this challenge, a field study in Central Japan was conducted to showcase automated data collection by retrofitting legacy farming machinery with low-cost Internet of Things (IoT) devices. For single-purpose vehicles (SPV), which only carry out single work types such as planting, LTE (Long Term Evolution) and Global Navigation Satellite System (GNSS) units were installed to record trajectory data.

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