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As of June 30, you can get the Roborock Q7 M5+ robot vacuum and mop for $279.99, down from $429.99, at Amazon.

An image posted on X apparently shows the purported DJI Romo cleaner inside its docking station, and the design is quite striking.

Prime Day 2025 begins soon on July 8, but members can already save on a bunch of tech available on Amazon's site. One such early deal is on the Roomba Robot Vacuum and Mop Combo, which is down to $140 for Prime subscribers right now. That's nearly half off its usual price and a far cry from its standard $275 cost. We ranked iRobot's Roomba Robot Vacuum as our overall favorite budget option, but this Prime Day deal features a version that can both vacuum and mop. With the Prime Day price drop, the vacuum and mop combo is cheaper than the vacuum-only model, but it does double the work. The combo Roomba can even be set to only vacuum if you prefer to mop yourself, but you'd be missing out on the four-stage cleaning system that vacuums and mops in the same pass. Since it's a Roomba, it's a straightforward setup process that

As of June 30, the iRobot Roomba 105 is on sale at Amazon for $299.99 ahead of Prime Day. This is 33% off its list price of $449.99.

Save 47% on the Shark Robot Vacuum and Mop Combo at Amazon

The best cordless, upright, and robot vacuum cleaners from top brands like Shark and Dyson.

Nick Flaherty / eeNews Europe: Nordic Semiconductor acquires US startup Memfault, a cloud-based IoT lifecycle management and observability service that supports secure OTA updates, for $120M — Nordic Semiconductor is buying US startup Memfault for $120m for its cloud-based software monitoring platform.

From small obstacle avoidance to strong suction power to a pet camera, the 3i G10+ is a niche yet phenomenal budget robot vacuum.

After the Roborock Saros Z70's arm turned out to be a flop, we need to re-focus on robot vacuum features that actually make life easier.

Trevive, developed by Gurugram teen Abeer Rohan Gosain, is an IoT-powered system that monitors tree health in real time. Featuring soil sensors and a multilingual app, it offers actionable insights to improve tree care, supporting smart agriculture, environmental conservation, and the UN’s Life on Land goals.


Summer is here, and the last thing anyone wants is to think about cleaning floors in the sweltering heat. Fortunately, Roborock robot vacuum and mop solutions are coming to the rescue with groundbreaking innovation and incredible value. Now is the perfect time to plan your next robot cleaner purchase, as the Roborock Prime Day launch is upon us, bringing discounts as significant as $800 off on these top-tier devices. Whether you’re battling pet hair, daily dust, or stubborn spills, Roborock’s latest lineup offers an intelligent, effortless cleaning solution for every home and budget. Let’s explore four standout models poised to make waves during this exclusive sales event. more…

The Eufy 11S Max robot vacuum is on sale for $140 as part of an early Prime Day sale. That's half off, as the typical cost is $280 for this particular model. This is extremely close to a record-low price. This device made our list of the best budget-friendly robot vacuums. Perhaps the coolest feature here is that the 11S Max is extremely thin, so it can slide under short tables and other places typical robovacs are too chonky to reach. It's also extremely quiet during use, so it can be operated at night without waking everyone up. It runs for around 100 minutes per charge and it'll head to the outlet on its own for some juice. The vacuum automatically adapts suction power to suit different floor types. We found the obstacle avoidance here to be excellent, thanks to

arXiv:2506.21078v1 Announce Type: new Abstract: Integrated sensing and communications (ISAC) is considered a key enabler to support application scenarios such as the Internet-of-Things (IoT) in which both communications and sensing play significant roles. Multi-carrier waveforms, such as orthogonal frequency division multiplexing (OFDM), have been considered as good candidates for ISAC due to their high communications data rate and good time bandwidth property for sensing. Nevertheless, their high peak-to-average-power-ratio (PAPR) values lead to either performance degradation or an increase in system complexity. This can make OFDM unsuitable for IoT applications with insufficient resources in terms of power, system complexity, hardware size or cost. This article provides IoT-centric constant modulus waveform designs that leverage the advantage of unit PAPR and thus are more suitable in resource-limited scenarios. More specifically, several single-carrier frequency and/or

arXiv:2506.21300v1 Announce Type: new Abstract: Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation of IoT devices leads to the generation of large amounts of IoT data providing a window on the physical context of BPs, which facilitates the discovery of new insights about BPs using process mining (PM) techniques. However, to achieve these benefits, IoT data need to be combined with traditional process (event) data, which is challenging due to the very different characteristics of IoT and process data, for instance in terms of granularity levels. Recently, several data models were proposed to integrate IoT data with process data, each focusing on different aspects of data integration based on different assumptions and requirements. This fragmentation hampers data exchange and collaboration in the

arXiv:2506.21078v1 Announce Type: new Abstract: Integrated sensing and communications (ISAC) is considered a key enabler to support application scenarios such as the Internet-of-Things (IoT) in which both communications and sensing play significant roles. Multi-carrier waveforms, such as orthogonal frequency division multiplexing (OFDM), have been considered as good candidates for ISAC due to their high communications data rate and good time bandwidth property for sensing. Nevertheless, their high peak-to-average-power-ratio (PAPR) values lead to either performance degradation or an increase in system complexity. This can make OFDM unsuitable for IoT applications with insufficient resources in terms of power, system complexity, hardware size or cost. This article provides IoT-centric constant modulus waveform designs that leverage the advantage of unit PAPR and thus are more suitable in resource-limited scenarios. More specifically, several single-carrier frequency and/or

Iridium Communications Inc. (NASDAQ:IRDM) is one of the best telecom stocks to buy according to Wall Street analysts. On June 4, GCT Semiconductor Holding Inc. (NYSE:GCTS) announced a collaboration with Iridium Communications to integrate Iridium NTN Direct service into GCT’s advanced GDM7243SL chipset. The partnership aims to expedite the development of a new Iridium network-enabled Narrowband […]


Early Prime Day robot vacuum deals have started at Amazon. Save hundreds on top Roborock Qrevo and Shark Matrix models.

The Roborock Q7 M5+ is on sale for Prime members for $329.99, down from the usual price of $429.99. That's a 23% discount.

You don't have to do all the cleaning on your own when you can rely on this robot vacuum.

Save 20% on the iRobot Roomba Q0120 at Amazon.

Araza, the Amazon’s golden superfruit, is a tangy, nutrient-rich fruit ideal for tropical farming. Packed with vitamin C, it offers culinary versatility, climate resilience, and market potential- making it a low-maintenance, high-value crop for farmers and a rising star in global superfruit markets.

Amazon Prime Day is just around the corner, and we've gathered the best robot vacuum deals to help you save on automating your house cleaning.

The innovative water recycling system in the 3i S10 Ultra robot vacuum puts it several steps above others - and it's on sale.

If you're building up your smart home setup, introducing door sensors can be great for security, but they can bring a lot more utility around the house, too.

QUALCOMM Incorporated (NASDAQ:QCOM) is also on the list of 10 undervalued blue chip stocks analysts recommend for smart investing. On June 17, Bank of America (BofA) adjusted its price target for Qualcomm from $245 to $200. The firm maintained a “Buy” rating for the stock. According to BofA analysts, the smartphone market has peaked. And […]

While high-tech, automated greenhouses have been used in commercial agriculture for decades, the low-cost…

The innovative water recycling system in the 3i S10 Ultra robot vacuum puts it several steps above others -- and it's on sale.

The Matic Robot is among the most capable vacuums I've tested, offering a standout design and competitive price that set it apart from the rest.

arXiv:2506.18114v1 Announce Type: new Abstract: The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditional IDS models often overlook the temporal characteristics of network traffic, limiting their effectiveness in early threat detection. We propose a Transformer-based Early Intrusion Detection System (EIDS) that incorporates dynamic temporal positional encodings to enhance detection accuracy while maintaining computational efficiency. By leveraging network flow timestamps, our approach captures both sequence structure and timing irregularities indicative of malicious behaviour. Additionally, we introduce a data augmentation pipeline to improve model robustness. Evaluated on the CICIoT2023 dataset, our method outperforms existing models in both accuracy and earliness. We further demonstrate its real-time feasibility on resource-constrained IoT devices, achieving

arXiv:2506.18100v1 Announce Type: new Abstract: Address Resolution Protocol (ARP) spoofing attacks severely threaten Internet of Things (IoT) networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a multi-layered machine learning-based framework for intelligently detecting ARP spoofing in IoT networks. Our approach utilizes an ensemble of classifiers organized into multiple layers, each layer optimizing detection accuracy and reducing false alarms. Experimental evaluations demonstrate significant improvements in detection accuracy (up to 97.5\%), reduced false positive rates (less than 2\%), and faster detection time compared to existing methods. Our key contributions include introducing multi-layer ensemble classifiers specifically tuned for IoT networks, systematically addressing dataset imbalance problems, introducing a dynamic feedback

arXiv:2506.17295v1 Announce Type: new Abstract: The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil moisture, raindrop detection and obstacle distance. The relay of information occurs between the primary master node (designated as the Green House) to the slave node (the Red House) employing the HC-05 Bluetooth module for information transmission. Each node displays the sensor data on OLED screens and a visual or auditory alert is triggered based on predetermined thresholds. A comparative analysis of STM32 (ARM Cortex-M3) and Arduino (AVR) is

[This is a sponsored article with Tineco.] Most modern households nowadays probably use vacuum cleaners. The advent of this appliance has been instrumental in helping with daily chores, and one brand that has further pushed the envelope with vacuum cleaners’ capabilities is Tineco. A smart home appliance brand, Tineco recently announced that Euromonitor International, a leading global independent provider of strategic market research, recognised the company as the No. 1 global leader in the household wet and dry vacuum cleaner category. This recognition reflects Tineco’s commitment to innovation and quality in the home cleaning sector. If you’ve never heard of Tineco before, now’s the time to get familiar. Who’s Tineco? Founded in 1998, you might recognise its original brand name, TEK, before it adopted the Tineco branding in 2018. Perhaps you’ve also heard of its parent company, Ecovacs Group, an international leading company for in-house robotic

Have you ever seen a robot vacuum with these features at this price?

Google's popular streaming device has been around for over 10 years - and it's still capable of much more than just playing your favorite shows.

The Matic Robot is among the most capable vacuums I've tested, offering a standout design and competitive price that set it apart from the rest.

Early Prime Day robot vacuum deals have started at Amazon. Save hundreds on top Roborock Qrevo and Shark Matrix models.

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

Deciding between a robot vacuum and stick vacuum? The Eufy E20 is both, and I was thoroughly impressed while testing it at home.

arXiv:2506.17063v1 Announce Type: new Abstract: The exponential growth of IoT devices presents critical challenges in bandwidth-constrained wireless networks, particularly regarding efficient data transmission and privacy preservation. This paper presents a novel federated semantic communication (SC) framework that enables collaborative training of bandwidth-efficient models for image reconstruction across heterogeneous IoT devices. By leveraging SC principles to transmit only semantic features, our approach dramatically reduces communication overhead while preserving reconstruction quality. We address the fundamental challenge of client selection in federated learning environments where devices exhibit significant disparities in dataset sizes and data distributions. Our framework implements three distinct client selection strategies that explore different trade-offs between system performance and fairness in resource allocation. The system employs an end-to-end SC architecture with

arXiv:2506.16647v1 Announce Type: new Abstract: The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency.

Brian Flood / Fox News: Docs: the FCC is probing USCTM, launched by the Biden administration as a voluntary safety label for IoT devices, over its administrators' alleged ties to China — Internal document reveals national security concerns over alleged Chinese ties to U.S. Cyber Trust Mark program administrators

The Matic Robot is one of the most competent vacuums I've tested, with a refreshing design and price point that outdoes its competitors.

Semtech Corporation (NASDAQ:SMTC) is one of the 11 Best Tech Stocks to Buy On the Dip. On May 28, Analyst Christopher Rolland of Susquehanna reiterated a Buy rating on Semtech Corporation (NASDAQ:SMTC) with a price target of $60. The rating comes after the company released its fiscal first-quarter results for 2026. The company reported net sales […]

Baron Funds, an investment management company, released its “Baron Fifth Avenue Growth Fund” first quarter 2025 investor letter. A copy of the letter can be downloaded here. The fund declined 13.4% (Institutional Shares) in the first quarter compared to a 10.0% decline for the Russell 1000 Growth Index and a 4.3% decrease for the S&P […]

STIHL Water Pumps are compact, petrol-powered solutions ideal for irrigation in remote and hilly areas. With EURO V engine tech, they offer high head capacity, fuel efficiency, and key features like low-oil safety, anti-vibration design, and durable cast iron build, perfect for modern farming needs.

Amazon Prime Day is just around the corner, and we've gathered the best robot vacuum deals to help you save on automating your house cleaning.

This robot means the end of cleaning chores for life.

As of June 18, get the Ecovacs Deebot T50 Pro Omni Robot Vacuum and Mop for $120 off at Amazon.

Say goodbye to traditional vacuuming and hello to a cleaner home.

Samsung's sleep-conscious good'sleep mode on its air conditioners is getting supercharged if you also own a Samsung wearable. Here's how the feature works.

If setting up smart devices feels daunting, here are three simple steps I’ve learned to build a functional smart home.

arXiv:2506.12263v1 Announce Type: new Abstract: Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based

There's big news this week if you have Philips Hue lights, with a free AI upgrade in the app, plus a new Wall Washer light

The Environment Agency warns England faces needs a 'continued and sustained effort' to cut water demand.

This robot can tidy up around your home even when you're too tired to handle it.

arXiv:2506.11892v1 Announce Type: new Abstract: Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in

arXiv:2506.11835v1 Announce Type: new Abstract: Agricultural irrigation ensures that the water required for plant growth is delivered to the soil in a controlled manner. However, uncontrolled management can lead to water waste while reducing agricultural productivity. Drip irrigation systems, which have been one of the most efficient methods since the 1970s, are modernised with IoT and artificial intelligence in this study, aiming to both increase efficiency and prevent water waste. The developed system is designed to be applicable to different agricultural production areas and tested with a prototype consisting of 3 rows and 3 columns. The project will commence with the transmission of environmental data from the ESP32 microcontroller to a computer via USB connection, where it will be processed using an LSTM model to perform learning and prediction. The user will be able to control the system manually or delegate it to artificial intelligence through the Blynk application. The system

arXiv:2506.11054v1 Announce Type: new Abstract: The dynamic nature of Internet of Things (IoT) environments challenges the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions. The uncertainty and variability of IoT environments lead to fluctuations in data distribution, e.g., concept drift and data heterogeneity, and evolving system requirements, e.g., scalability demands and resource limitations. This paper proposes an adaptive MLaaS composition framework to ensure a seamless, efficient, and scalable MLaaS composition. The framework integrates a service assessment model to identify underperforming MLaaS services and a candidate selection model to filter optimal replacements. An adaptive composition mechanism is developed that incrementally updates MLaaS compositions using a contextual multi-armed bandit optimization strategy. By continuously adapting to evolving IoT constraints, the approach maintains Quality of Service (QoS) while reducing the computational

As of June 13, get 43% off the Eufy Omni C20 robot vacuum and mop at Amazon.

Deciding between a robot vacuum and stick vacuum? The Eufy E20 is both, and I was thoroughly impressed while testing it at home.

arXiv:2401.01343v2 Announce Type: replace Abstract: Previous research on behavior-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated. This paper presents IoTGeM, an approach for modeling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. We first introduce an improved rolling window approach for feature extraction. To reduce overfitting, we then apply a multi-step feature selection process where a Genetic Algorithm (GA) is uniquely guided by exogenous feedback from a separate, independent dataset. To prevent common data leaks that have limited previous models, we build and test our models using strictly isolated train and test datasets. The resulting models are rigorously evaluated using a diverse portfolio of machine learning algorithms and datasets. Our window-based models demonstrate superior generalization

arXiv:2506.10699v1 Announce Type: cross Abstract: Sensor-based local inference at IoT devices faces severe computational limitations, often requiring data transmission over noisy wireless channels for server-side processing. To address this, split-network Deep Neural Network (DNN) based Joint Source-Channel Coding (JSCC) schemes are used to extract and transmit relevant features instead of raw data. However, most existing methods rely on fixed network splits and static configurations, lacking adaptability to varying computational budgets and channel conditions. In this paper, we propose a novel SNR- and computation-adaptive distributed CNN framework for wireless image classification across IoT devices and edge servers. We introduce a learning-assisted intelligent Genetic Algorithm (LAIGA) that efficiently explores the CNN hyperparameter space to optimize network configuration under given FLOPs constraints and given SNR. LAIGA intelligently discards the infeasible network

Eufy features the cheapest robot vacuum combination this year, with a handheld unit built into the robot's body instead of the dock. Right now, it's available for the lowest price we've ever seen.

A new Apple framework makes it easy for developers of smart home apps to help cut your electricity bills. While EnergyKit is currently limited to thermostats and EV chargers, it’s the first step toward optimizing energy usage throughout your entire home. Some smart home devices can already help you reduce power usage and costs, like the and Nest thermostats, but EnergyKit takes this much further … more…

arXiv:2506.09186v1 Announce Type: cross Abstract: Sensors provide a vital source of data that link digital systems with the physical world. However, as sensors age, the relationship between what they measure and what they output changes. This is known as sensor drift and poses a significant challenge that, combined with limited opportunity for re-calibration, can severely limit data quality over time. Previous approaches to drift correction typically require large volumes of ground truth data and do not consider measurement or prediction uncertainty. In this paper, we propose a probabilistic sensor drift correction method that takes a fundamental approach to modelling the sensor response using Gaussian Process Regression. Tested using dissolved oxygen sensors, our method delivers mean squared error (MSE) reductions of up to 90% and more than 20% on average. We also propose a novel uncertainty-driven calibration schedule optimisation approach that builds on top of drift correction and

arXiv:2506.09066v1 Announce Type: new Abstract: With the rapid development of deep learning, a growing number of pre-trained models have been publicly available. However, deploying these fixed models in real-world IoT applications is challenging because different devices possess heterogeneous computational and memory resources, making it impossible to deploy a single model across all platforms. Although traditional compression methods, such as pruning, quantization, and knowledge distillation, can improve efficiency, they become inflexible once applied and cannot adapt to changing resource constraints. To address these issues, we propose ReStNet, a Reusable and Stitchable Network that dynamically constructs a hybrid network by stitching two pre-trained models together. Implementing ReStNet requires addressing several key challenges, including how to select the optimal stitching points, determine the stitching order of the two pre-trained models, and choose an effective fine-tuning

Black beans are a nutritious, soil-enriching legume ideal for small farmers. With low input needs, resilience, and growing demand for plant protein, they offer high market potential. Their health benefits and adaptability make them a sustainable, profitable crop suited for diverse climates and regenerative farming systems.

Tools that offer early and accurate insight into plant health—and allow individual plant interventions—are key to increasing crop yields as environmental pressures increasingly impact horticulture and agriculture.

As of June 11, get 42% off the Eufy E20 3-in-1 Robot Vacuum with Stick and Handheld Vacuum Combo at Amazon.

Robot vacuums are undeniably a big convenience, saving you so much time while keeping your house clean. But sometimes there's a small mess you want to quickly remove or a little corner the robovac can't reach, requiring a traditional vacuum. While you could purchase one of each, there's another option available to you. Eufy released the E20 3-in-1 robot vacuum earlier this year and it comes with a robot vacuum and a cordless option. Plus, right now, it's on sale for $380, down from $650 — a 42 percent discount. It's one of our favorite robot vacuums on the market, especially with such a steep price cut. We gave Eufy's E20 3-in-1 robot vacuum an 80 in our review largely thanks to its versatility. We also found that the robot vacuum performed well and that

Majority of exposures located in the US, including datacenters, healthcare facilities, factories, and more Security researchers managed to access the live feeds of 40,000 internet-connected cameras worldwide and they may have only scratched the surface of what's possible.…

arXiv:2506.07836v1 Announce Type: new Abstract: Nowadays, the Internet of Things (IoT) is widely employed, and its usage is growing exponentially because it facilitates remote monitoring, predictive maintenance, and data-driven decision making, especially in the healthcare and industrial sectors. However, IoT devices remain vulnerable due to their resource constraints and difficulty in applying security patches. Consequently, various cybersecurity attacks are reported daily, such as Denial of Service, particularly in IoT-driven solutions. Most attack detection methodologies are based on Machine Learning (ML) techniques, which can detect attack patterns. However, the focus is more on identification rather than considering the impact of ML algorithms on computational resources. This paper proposes a green methodology to identify IoT malware networking attacks based on flow privacy-preserving statistical features. In particular, the hyperparameters of three tree-based models -- Decision

arXiv:2506.07494v1 Announce Type: new Abstract: The smart home systems, based on AI speech recognition and IoT technology, enable people to control devices through verbal commands and make people's lives more efficient. However, existing AI speech recognition services are primarily deployed on cloud platforms on the Internet. When users issue a command, speech recognition devices like ``Amazon Echo'' will post a recording through numerous network nodes, reach multiple servers, and then receive responses through the Internet. This mechanism presents several issues, including unnecessary energy consumption, communication latency, and the risk of a single-point failure. In this position paper, we propose a smart home concept based on offline speech recognition and IoT technology: 1) integrating offline keyword spotting (KWS) technologies into household appliances with limited resource hardware to enable them to understand user voice commands; 2) designing a local IoT network with

arXiv:2506.06591v1 Announce Type: new Abstract: Previous research has explored the privacy needs and concerns of device owners, primary users, and different bystander groups with regard to smart home devices like security cameras, smart speakers, and hubs, but little is known about the privacy views and practices of smart home product teams, particularly those in non-Western contexts. This paper presents findings from 27 semi-structured interviews with Chinese smart home product team members, including product/project managers, software/hardware engineers, user experience (UX) designers, legal/privacy experts, and marketers/operation specialists. We examine their privacy perspectives, practices, and risk mitigation strategies. Our results show that participants emphasized compliance with Chinese data privacy laws, which typically prioritized national security over individual privacy rights. China-specific cultural, social, and legal factors also influenced participants' ethical

We recently published a list of 10 Stocks Took a Shocking Nosedive. In this article, we are going to take a look at where Samsara Inc. (NYSE:IOT) stands against other Friday’s worst-performing stocks. Samsara ended a two-day rally on Friday, shedding 4.55 percent to close at $45.10 apiece as investors repositioned portfolios following the disposition […]

Samsara Inc. (NYSE:IOT) Q1 2026 Earnings Call Transcript June 5, 2025 Samsara Inc. beats earnings expectations. Reported EPS is $0.11, expectations were $0.05794. Mike Chang: Good afternoon, and welcome to Samsara’s First Quarter Fiscal 2026 Earnings Call. I’m Mike Chang, Samsara’s Vice President of Corporate Development and Investor Relations. Joining me today are Samsara Chief […]

With many people building smart homes, the concern is that we need a steady internet connection to keep everything up and running, or do we?

Sands Capital, an investment management company, released its “Sands Capital Global Growth Fund” first-quarter 2025 investor letter. Global Growth adopts a flexible approach to identify the most promising growth companies across the globe. Global equities fell in the first quarter, as per the MSCI ACWI. The strategy underperformed the benchmark amid the weakness. You can […]

arXiv:2506.04804v1 Announce Type: new Abstract: The widespread adoption of age of information (AoI) as a meaningful and analytically tractable information freshness metric has led to a wide body of work on the timing performance of Internet of things (IoT) systems. However, the spatial correlation inherent to environmental monitoring has been mostly neglected in the recent literature, due to the significant modeling complexity it introduces. In this work, we address this gap by presenting a model of spatio-temporal information freshness, considering the conditional entropy of the system state in a remote monitoring scenario, such as a low-orbit satellite collecting information from a wide geographical area. Our analytical results show that purely age-oriented schemes tend to select an overly broad communication range, leading to inaccurate estimates and energy inefficiency, both of which can be mitigated by adopting a spatio-temporal approach.

arXiv:2506.05138v1 Announce Type: new Abstract: Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low-resource, unsupervised method well-suited for edge deployment and continuous learning. In this paper, we present an application of Isolation Forest-based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 96% accuracy in distinguishing normal from abnormal readings and above 78% precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 160 KB

arXiv:2506.04804v1 Announce Type: new Abstract: The widespread adoption of age of information (AoI) as a meaningful and analytically tractable information freshness metric has led to a wide body of work on the timing performance of Internet of things (IoT) systems. However, the spatial correlation inherent to environmental monitoring has been mostly neglected in the recent literature, due to the significant modeling complexity it introduces. In this work, we address this gap by presenting a model of spatio-temporal information freshness, considering the conditional entropy of the system state in a remote monitoring scenario, such as a low-orbit satellite collecting information from a wide geographical area. Our analytical results show that purely age-oriented schemes tend to select an overly broad communication range, leading to inaccurate estimates and energy inefficiency, both of which can be mitigated by adopting a spatio-temporal approach.

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.

As of June 5, get the Roborock Q10 X5+ robot vacuum and mop for $180 off at Amazon.

Context-aware computing enables ultra-low-power operation while maintaining high-performance AI capabilities when needed. The post Multimodal AI For IoT Devices Requires A New Class Of MCU appeared first on Semiconductor Engineering.

arXiv:2506.03168v1 Announce Type: new Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing. While smart agriculture utilizes artificial intelligence (AI) technologies to enable precise control, it still encounters significant challenges, including excessive reliance on agricultural expert knowledge, difficulties in fusing multimodal data, poor adaptability to dynamic environments, and bottlenecks in real-time decision-making at the edge. Large language models (LLMs), with their exceptional capabilities in knowledge acquisition and semantic understanding, provide a promising solution to address these challenges. To this end, we propose Farm-LightSeek, an edge-centric multimodal agricultural IoT data analytics framework that integrates LLMs with edge computing. This framework collects real-time

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On June 2, BMO Capital increased its price target for Samsara Inc. (NYSE:IOT) to $54 from $48, while maintaining an Outperform rating on the stock. This outlook comes as Samsara prepares to release its FQ1 2026 financial results on June 5. BMO Capital’s positive stance was initially reinforced in March after a post-FQ4 2025 pullback […]

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

arXiv:2506.01767v1 Announce Type: new Abstract: Fragmentation is a routine part of communication in 6LoWPAN-based IoT networks, designed to accommodate small frame sizes on constrained wireless links. However, this process introduces a critical vulnerability fragments are typically stored and processed before their legitimacy is confirmed, allowing attackers to exploit this gap with minimal effort. In this work, we explore a defense strategy that takes a more adaptive, behavior-aware approach to this problem. Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms. The first tracks how each node behaves over time, rewarding consistent and successful interactions while quickly penalizing suspicious or failing patterns. The second checks the integrity of packet fragments using a chained hash, allowing incomplete or manipulated sequences to be caught early, before they can occupy memory or waste processing time. We put this system to the

arXiv:2506.00898v1 Announce Type: new Abstract: This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transitions generated by a reinforcement learning agent policy, while simultaneously updating the agent policy that can produce transitions to confuse the discriminator. The proposed HMPC-AIRL method eliminates the need for explicit thermal dynamics models, prior or predictive knowledge of uncertain parameters, or manually designed reward functions. Simulation results based on real-world traces demonstrate the effectiveness and data efficiency of the

arXiv:2506.00377v1 Announce Type: new Abstract: The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources, which are crucial issues in IoT networks considering the recent trends of decentralized data processing and computing systems. Consequently, many optimization techniques have been incorporated with these ML models. Specifically, a special category of optimizer adopted from the behavior of living creatures and different aspects of natural phenomena, known as metaheuristic algorithms, has been a central focus in recent years and brought about
