IOT

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

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

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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

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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.

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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.

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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 […]

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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

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Los Angeles CA (SPX) Jun 03, 2025 Omnispace, known for its leadership in global mobile connectivity, together with Gatehouse Satcom and Nordic Semiconductor, has successfully demonstrated a 5G narrowband Internet of Things (NB-IoT) system operating over an Omnispace S-band non-geostationary orbit (NGSO) satellite. This accomplishment underscores the feasibility of satellite-based 5G NB-IoT connectivity using NGSO satellite archi

arXiv:2505.24806v1 Announce Type: new Abstract: The Internet of Multimedia Things (IoMT) represents a significant advancement in the evolution of IoT technologies, focusing on the transmission and management of multimedia streams. As the volume of data continues to surge and the number of connected devices grows exponentially, internet traffic has reached unprecedented levels, resulting in challenges such as server overloads and deteriorating service quality. Traditional computer network architectures were not designed to accommodate this rapid increase in demand, leading to the necessity for innovative solutions. In response, Software-Defined Networks (SDNs) have emerged as a promising framework, offering enhanced management capabilities by decoupling the control layer from the data layer. This study explores the load balancing of servers within software-defined multimedia IoT networks. The Long Short-Term Memory (LSTM) prediction algorithm is employed to accurately estimate server

arXiv:2505.24140v1 Announce Type: new Abstract: With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link reliability, as evidenced by the measurement results based on our real-world setup. In this paper, we propose a blind coherent combining design named B2LoRa to boost LoRa transmission performance. The intuition behind B2LoRa is to leverage the repeated broadcasting mechanism inherent in satellite-IoT systems to achieve coherent combining under the low-power and low-cost constraints, where each re-transmission at different times is regarded as the same packet transmitted from different antenna elements within an antenna array. Then, the problem is translated into aligning these

arXiv:2505.23939v1 Announce Type: new Abstract: This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -- on the Visual Wake Words dataset --

Sesame cultivation in Kerala and Karnataka thrives with region-specific varieties and scientific farming practices. By adopting these improved techniques, farmers can boost yields, enhance oil quality, and increase income sustainably, meeting rising domestic and global demand for high-quality sesame.

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arXiv:2505.22366v1 Announce Type: new Abstract: The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The key alternative is energy harvesting, but battery-less IoT systems require extensive evaluation to demonstrate that they are sufficiently performant across the full range of expected operating conditions. IoT developers thus need an evaluation platform that (i) ensures that each evaluated application and configuration is exposed to exactly the same energy environment and events, and (ii) provides a detailed account of what the application spends the harvested energy on. We therefore developed the EStacker evaluation platform which (i) provides fair and repeatable evaluation, and (ii) generates energy stacks. Energy stacks break down the total energy consumption of an application across hardware components and application activities, thereby explaining what the

arXiv:2505.21529v1 Announce Type: new Abstract: Large-scale Internet of Things (IoT) applications, such as asset tracking and remote sensing, demand multi-year battery lifetimes to minimize maintenance and operational costs. Traditional wireless protocols often employ duty cycling, introducing a tradeoff between latency and idle consumption - both unsuitable for event-driven and ultra-low power systems. A promising approach to address these issues is the integration of always-on wake-up radios (WuRs). They provide asynchronous, ultra-low power communication to overcome these constraints. This paper presents WakeMod, an open-source wake-up transceiver module for the 868MHz ISM band. Designed for easy integration and ultra-low power consumption, it leverages the -75dBm sensitive FH101RF WuR. WakeMod achieves a low idle power consumption of 6.9uW while maintaining responsiveness with a sensitivity of -72.6dBm. Reception of a wake-up call is possible from up to 130m of distance with a

Nathan Vifflin / Reuters: TSMC plans to open a design center in Munich in Q3 2025 to support European customers in designing chips for automotive, industrial, AI, and IoT applications  —  Taiwan Semiconductor Manufacturing Co (2330.TW), the world's largest contract chipmaker, said on Tuesday it will open a design centre in Munich …

Apple’s plan to overhaul the smart home is taking shape, with a home hub due to launch by the end of 2025.

Summer brings sun, joy and lots of time spent outside. But, it also means a lot of tracking in dirt and debris from outdoors so, if you've been putting off getting a new vacuum, now might be the time. Currently, iRobot is running a Father's Day sale on a few of its Roomba vacuums, including the 105 Vac Robot + AutoEmpty Dock. The 105 Vac Robot is on sale for $280, down from $450 — a 37 percent discount. It's one of the newer, more basic models in Roomba's lineup and offers standard features like smart mapping. It also automatically empties debris for up to 75 days and has a schedule cleaning feature. Plus it has three cleaning stages: power-lifting suction, a multi-surface bristle brush and an edge-sweeping brush. You can compare the iRobot's 105 with other options on our list of

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The Egyptian Food Bank, in collaboration with the Sawiris Foundation for Social Development and the International Food Policy Research Institute (IFPRI), hosted the fifth edition of the “Bridging Evidence and Policy” (BEP) seminar series. This latest session, titled “Climate-Smart Agriculture and Development Practices in Egypt,” convened a wide spectrum of stakeholders to explore the intersection […] The post Egypt pushes climate-smart farming to the forefront appeared first on Egyptian Gazette.

arXiv:2505.18173v1 Announce Type: cross Abstract: This dissertation proposes an electrocardiogram (ECG) tracking device that diagnoses cardiopulmonary problems using the Internet of Things (IoT) desired results. The initiative is built on the internet observing an electrocardiogram with the AD8232 heart rhythm sensor and the ESP32 expansion kit, using an on-premise connected device platform to transform sensing input into meaningful data. That subsequently supervises an ECG signal and delivers it to an intelligent phone via Wi-Fi for data analysis. That is the pace of the circulating. Assessing body temperature, pulse rate, and coronary arteries are vital measures to defend your health. The heartbeat rate may be measured in two ways: there are by palpating the pulse at the wrist or neck directly or other alternative by utilizing a cardiac sensor. Monitoring alcohol levels in cardiac patients is critical for measuring the influence of liquor on their health and the efficacy of therapy.

arXiv:2505.19600v1 Announce Type: new Abstract: This paper presents the design, implementation, and evaluation of an IoT-based robotic system for mapping and monitoring indoor air quality. The primary objective was to develop a mobile robot capable of autonomously mapping a closed environment, detecting concentrations of CO$_2$, volatile organic compounds (VOCs), smoke, temperature, and humidity, and transmitting real-time data to a web interface. The system integrates a set of sensors (SGP30, MQ-2, DHT11, VL53L0X, MPU6050) with an ESP32 microcontroller. It employs a mapping algorithm for spatial data acquisition and utilizes a Mamdani fuzzy logic system for air quality classification. Empirical tests in a model room demonstrated average localization errors below $5\%$, actuator motion errors under $2\%$, and sensor measurement errors within $12\%$ across all modalities. The contributions of this work include: (1) a low-cost, integrated IoT robotic platform for simultaneous mapping

arXiv:2505.19283v1 Announce Type: new Abstract: IoT is a dynamic network of interconnected things that communicate and exchange data, where security is a significant issue. Previous studies have mainly focused on attack classifications and open issues rather than presenting a comprehensive overview on the existing threats and vulnerabilities. This knowledge helps analyzing the network in the early stages even before any attack takes place. In this paper, the researchers have proposed different security aspects and a novel Bayesian Security Aspects Dependency Graph for IoT (BSAGIoT) to illustrate their relations. The proposed BSAGIoT is a generic model applicable to any IoT network and contains aspects from five categories named data, access control, standard, network, and loss. This proposed Bayesian Security Aspect Graph (BSAG) presents an overview of the security aspects in any given IoT network. The purpose of BSAGIoT is to assist security experts in analyzing how a successful

Apple is preparing to launch several new smart home products - GSMArena.com news  GSMArena.comApple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacSiri problems surface again forcing Apple to delay smart display tabletop device  PhoneArenaApple's Rumored Smart Home Hub Has Faced a Disappointing Setback  MacRumors

Apple is preparing to launch several new smart home products - GSMArena.com news  GSMArena.comApple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacSiri problems surface again forcing Apple to delay smart display tabletop device  PhoneArenaApple's Rumored Smart Home Hub Has Faced a Disappointing Setback  MacRumors

In the last few years, Apple has been trying to penetrate markets outside its traditional ones, including the automotive industry. Now, according to the latest report from Mark Gurman, Apple is eyeing the smart home market as well. In the past, Apple dipped its toe with the HomePod and HomePod mini, but it wasn't the success the company hoped for. It looks like Apple is taking a second shot at this with several new smart home products that will likely be released by the end of 2025 and in early 2026. One of them is an iPad-like device with a robotic arm. This sounds like a...

Apple's Rumored Smart Home Hub Has Faced a Disappointing Setback  MacRumorsApple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacApple’s upcoming iPad robot loses features to make its launch deadline  PhoneArenaApple's AI smart screen devices will have to wait on Siri revamp  AppleInsider

Apple's Rumored Smart Home Hub Has Faced a Disappointing Setback  MacRumorsApple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacApple’s upcoming iPad robot loses features to make its launch deadline  PhoneArenaApple's AI smart screen devices will have to wait on Siri revamp  AppleInsider

arXiv:2505.17586v1 Announce Type: new Abstract: The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and IoT devices - a mutualistic relationship in which both parties leverage the capabilities of the other. LLMs like OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini/BERT, any many more, all demonstrate powerful capabilities in natural language understanding and generation, enabling more intuitive and context-aware interactions across diverse IoT applications such as smart cities, healthcare systems, industrial automation, and smart home environments. Despite these opportunities, integrating these resource-intensive LLMs into IoT devices that lack the state-of-the-art computational power is a challenging task. The security of these edge

arXiv:2505.17363v1 Announce Type: new Abstract: Due to the exponential rise in IoT-based botnet attacks, researchers have explored various advanced techniques for both dimensionality reduction and attack detection to enhance IoT security. Among these, Variational Autoencoders (VAE), Vision Transformers (ViT), and Graph Neural Networks (GNN), including Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), have garnered significant research attention in the domain of attack detection. This study evaluates the effectiveness of four state-of-the-art deep learning architectures for IoT botnet detection: a VAE encoder with a Multi-Layer Perceptron (MLP), a VAE encoder with a GCN, a VAE encoder with a GAT, and a ViT encoder with an MLP. The evaluation is conducted on a widely studied IoT benchmark dataset--the N-BaIoT dataset for both binary and multiclass tasks. For the binary classification task, all models achieved over 99.93% in accuracy, recall, precision, and F1-score,

Apple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s upcoming iPad robot loses features to make its launch deadline  PhoneArenaApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacApple's AI smart screen devices will have to wait on Siri revamp  AppleInsiderMemorial Day Madness and the Tech Tug-of-War: From Apple ...  BestTechie

Apple’s smart home hub could reportedly make its debut later this year  EngadgetApple’s upcoming iPad robot loses features to make its launch deadline  PhoneArenaApple’s rumored all-new HomePad may launch ‘by the end of this year’, per report  9to5MacApple's AI smart screen devices will have to wait on Siri revamp  AppleInsiderMemorial Day Madness and the Tech Tug-of-War: From Apple ...  BestTechie

Apple's long-awaited smart home hub could be available as soon as the end of this year, according to the latest report from Bloomberg's Mark Gurman. Rumors surrounding Apple's smart home hub began circulating as early as 2022, when the product was first reportedly greenlit. However, the road to its release has been rocky since the product was expected to heavily rely on Apple Intelligence. Gurman previously reported in March that Apple had delayed the announcement of its smart home hub thanks to issues with upgrading Siri. Gurman has since updated his expected timeline for Apple's upcoming product, claiming that a lower-end version will release "by the end of this year at the earliest." Gurman also revealed that a more advanced version that can "move around a person's desk on the end of a robotic arm" should release a year or two after the basic model's launch and is a "major priority at Apple." To meet this release window,

The 3i G10+ has built-in dust compression, and it looks like the perfect solution for small homes.

The Saros Z70 robot vacuum has a mechanical robot arm, with a claw machine-style grabber. We put it to the test in this video review.

The Ecovacs Deebot N30 Omni is a midrange robot vacuum with high-end features that are worth more than its cost, especially with this Memorial Day sale price.

The Roborock Saros Z70 is one of the most exciting robot vacuums ever made, but its robotic arm isn’t quite ready for the spotlight.

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 Memorial Day deal.

The Dreame L40 Ultra high-end robot vacuum and mop delivers excellent suction and thorough cleaning capabilities. You can get it for 60% off right now.

This Memorial Day deal sucks… in a good way. The Dyson 360 Vis Nav may have the best suction of any robot vacuum. This purple dirt eater usually retails for $1,000. But you can get it for a mere $800 this holiday weekend. That's a record low. The deal is featured on Dyson's website and Amazon. Even the world-famous Ginsu knife Mecca, QVC, got in on the action. Dyson says the 360 Vis Nav sucks debris as well as its cordless stick vacuums. The company claims it's twice as powerful as any of its competitors. For the technically minded, it offers 22,000 Pa/pascals of suction pressure. In short, that's a lot. Your dirty, pet-hair-infested floor won't stand a chance. The D-shaped robovac has a little actuator protruding from its sides. It's an alternative to the side sweepers on competing models.

The innovative water recycling system in the 3i S10 Ultra robot vacuum puts it several steps above others.

The high-end iRobot Roomba Combo 10 Max is available for $779 via Wellbots as part of a Memorial Day promotion. That's a giant discount of $620. This is exclusive to Engadget readers, so enter the code ENGD620 at checkout to secure the deal. The Roomba Combo 10 Max is one of the company's most advanced robovacs. It comes with all kinds of bells and whistles, including a mop, a self-emptying bin and an autowash dock. That's right. It'll wash and dry the mop pads all on its own. At the time of its release last year, this was the first robot vacuum on the market that could do that. The software is also advanced enough to know which areas of the home get dirtiest fastest, adjusting cleaning power accordingly. It also boosts suction power when rolling over a carpet, which is something pet

arXiv:2505.16936v1 Announce Type: new Abstract: This work develops the underpinnings of self-supervised placement-aware representation learning given spatially-distributed (multi-view and multimodal) sensor observations, motivated by the need to represent external environmental state in multi-sensor IoT systems in a manner that correctly distills spatial phenomena from the distributed multi-vantage observations. The objective of sensing in IoT systems is, in general, to collectively represent an externally observed environment given multiple vantage points from which sensory observations occur. Pretraining of models that help interpret sensor data must therefore encode the relation between signals observed by sensors and the observers' vantage points in order to attain a representation that encodes the observed spatial phenomena in a manner informed by the specific placement of the measuring instruments, while allowing arbitrary placement. The work significantly advances

arXiv:2505.16872v1 Announce Type: new Abstract: The rapid expansion of Internet of Things (IoT) devices has introduced critical security challenges, underscoring the need for accurate anomaly detection. Although numerous studies have proposed machine learning (ML) methods for this purpose, limited research systematically examines how different preprocessing steps--normalization, transformation, and feature selection--interact with distinct model architectures. To address this gap, this paper presents a multi-step evaluation framework assessing the combined impact of preprocessing choices on three ML algorithms: RNN-LSTM, autoencoder neural networks (ANN), and Gradient Boosting (GBoosting). Experiments on the IoTID20 dataset shows that GBoosting consistently delivers superior accuracy across preprocessing configurations, while RNN-LSTM shows notable gains with z-score normalization and autoencoders excel in recall, making them well-suited for unsupervised scenarios. By offering a

On May 22, Silicon Laboratories Inc. (NASDAQ:SLAB) announced the launch of its Series 3 portfolio of wireless SoCs. Built on the advanced 22 nm process node, the Series 3 will power breakthroughs in computer power, energy efficiency, integration, and security. The Series 3, which includes SiXG301 and SiXG302 wireless SoC families, will address the growing […]

The 3i G10 Plus is an affordable robot vacuum that can compact dust in its dustbin like a Roomba, letting you go 60 days without emptying.

Save 44% on the Roborock Qrevo Master at Amazon.

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