IOT

arXiv:2502.01835v1 Announce Type: new Abstract: The proliferation of Internet of Things (IoT) devices has created a pressing need for efficient security solutions, particularly against Denial of Service (DoS) attacks. While existing detection approaches demonstrate high accuracy, they often require substantial computational resources, making them impractical for IoT deployment. This paper introduces a novel lightweight approach to DoS attack detection based on Kolmogorov-Arnold Networks (KANs). By leveraging spline-based transformations instead of traditional weight matrices, our solution achieves state-of-the-art detection performance while maintaining minimal resource requirements. Experimental evaluation on the CICIDS2017 dataset demonstrates 99.0% detection accuracy with only 0.19 MB memory footprint and 2.00 ms inference time per sample. Compared to existing solutions, KAN reduces memory requirements by up to 98% while maintaining competitive detection rates. The model's linear

arXiv:2502.01643v1 Announce Type: new Abstract: Fruits are rich sources of essential vitamins and nutrients that are vital for human health. This study introduces two fully automated devices, FruitPAL and its updated version, FruitPAL 2.0, which aim to promote safe fruit consumption while reducing health risks. Both devices leverage a high-quality dataset of fifteen fruit types and use advanced models- YOLOv8 and YOLOv5 V6.0- to enhance detection accuracy. The original FruitPAL device can identify various fruit types and notify caregivers if an allergic reaction is detected, thanks to YOLOv8's improved accuracy and rapid response time. Notifications are transmitted via the cloud to mobile devices, ensuring real-time updates and immediate accessibility. FruitPAL 2.0 builds upon this by not only detecting fruit but also estimating its nutritional value, thereby encouraging healthy consumption. Trained on the YOLOv5 V6.0 model, FruitPAL 2.0 analyzes fruit intake to provide users with

The Roborock Saros 10 and Saros 10R are equipped with many of the same features, but there are a handful of big differences between the two robot vacuums.

arXiv:2502.00689v1 Announce Type: new Abstract: IoT systems face significant challenges in adapting to user needs, which are often under-specified and evolve with changing environmental contexts. To address these complexities, users should be able to explore possibilities, while IoT systems must learn and support users in the process of providing proper services, e.g., to serve novel experiences. The IoT-Together paradigm aims to meet this demand through the Mixed-Initiative Interaction (MII) paradigm that facilitates a collaborative synergy between users and IoT systems, enabling the co-creation of intelligent and adaptive solutions that are precisely aligned with user-defined goals. This work advances IoT-Together by integrating Large Language Models (LLMs) into its architecture. Our approach enables intelligent goal interpretation through a multi-pass dialogue framework and dynamic service generation at runtime according to user needs. To demonstrate the efficacy of our

arXiv:2502.00347v1 Announce Type: new Abstract: Significant losses in terms of life and property occur from road traffic accidents, which are often caused by drunk and drowsy drivers. Reducing accidents requires effective detection of alcohol impairment and drowsiness as well as real-time driver monitoring. This paper aims to create an Internet of Things (IoT)--enabled Drowsiness Driver Safety Alert System with Real-Time Monitoring Using Integrated Sensors Technology. The system features an alcohol sensor and an IR sensor for detecting alcohol presence and monitoring driver eye movements, respectively. Upon detecting alcohol, alarms and warning lights are activated, the vehicle speed is progressively reduced, and the motor stops within ten to fifteen seconds if the alcohol presence persists. The IR sensor monitors prolonged eye closure, triggering alerts, or automatic vehicle stoppage to prevent accidents caused by drowsiness. Data from the IR sensor is transmitted to a mobile phone

We recently compiled a list of the 10 Trending AI Stocks That Analysts Are Monitoring. In this article, we are going to take a look at where Samsara Inc. (NYSE:IOT) stands against the other trending AI stocks. As the global AI race heated up last month with the launch of DeepSeek R1 and Alibaba’s Qwen […]

Convenience has always been the big promise of robot vacuums. Don’t clean your house yourself — instead, push a button and have a little robot putter around your home, sucking up all the dirt and debris in its path. Indeed, they are convenient, but they haven’t fully replaced a manually operated cordless vacuum. Chances are, you have either a robot vacuum or a cordless vacuum in your house right now. Anker’s home brand Eufy wants us to live in a world where you can have both without spending money on two separate devices that need two separate spots in your home. Announced at CES 2025, Anker’s Eufy E20 is a first-of-its-kind robot vacuum that turns into a cordless stick vacuum and comes with all the necessary attachments to do so, plus a self-emptying base. It even comes in at a midrange price of $600 (or $50 less if you pre-order before it comes out on February 10), which made me believe that it has to be too good to be true. Ultimately, it succeeds at all three of its intended

As of Feb. 3, the Roborock S8 MaxV Ultra is on sale for $1,044.99 at Best Buy. That’s a $755 discount off its regular price of $1,799.99 for today only.

Apple is preparing to release a wave of home technology: Here's how it getting ready.

We recently published a list of 10 AI News and Ratings Investors Should Take a Look At. In this article, we are going to take a look at where Silicon Laboratories Inc. (NASDAQ:SLAB) stands against other AI news and ratings investors should take a look at. As AI continues to reshape industries, investors are closely […]

We recently compiled a list of the Morgan Stanley’s 15 Best European AI Stocks. In this article, we are going to take a look at where Infineon Technologies AG (OTC:IFNNY) stands against Morgan Stanley’s other European AI stocks. In August last year, investment bank Morgan Stanley released an investor note highlighting that even though the chaos […]

arXiv:2501.18549v1 Announce Type: new Abstract: The rapid integration of the Internet of Things (IoT) and Internet of Medical (IoM) devices in the healthcare industry has markedly improved patient care and hospital operations but has concurrently brought substantial risks. Distributed Denial-of-Service (DDoS) attacks present significant dangers, jeopardizing operational stability and patient safety. This study introduces CryptoDNA, an innovative machine learning detection framework influenced by cryptojacking detection methods, designed to identify and alleviate DDoS attacks in healthcare IoT settings. The proposed approach relies on behavioral analytics, including atypical resource usage and network activity patterns. Key features derived from cryptojacking-inspired methodologies include entropy-based analysis of traffic, time-series monitoring of device performance, and dynamic anomaly detection. A lightweight architecture ensures inter-compatibility with resource-constrained IoT

arXiv:2501.18102v1 Announce Type: new Abstract: There are many challenges for Internet of Things (IoT) sensor networks including the lack of robust standards, diverse wireline and wireless connectivity, interoperability, security, and privacy. Addressing these challenges, the Institute of Electrical and Electronics Engineers (IEEE) P1451.0 standard defines network services, transducer services, transducer electronic data sheets (TEDS) format, and a security framework to achieve sensor data security and interoperability for IoT applications. This paper proposes a security solution for IEEE P1451.1.6-based sensor networks for IoT applications utilizing the security framework defined in IEEE P1451.0. The proposed solution includes an architecture, a security policy with six security levels, security standards, and security TEDS. Further, this paper introduces a new service to update access control lists (ACLs) to regulate the access for topic names by the applications and provides an

We recently compiled a list of the 10 Trending AI Stocks on Investors’ Radar. In this article, we are going to take a look at where Synaptics Incorporated (NASDAQ:SYNA) stands against the other AI stocks. The US is struggling to keep its technology within its borders, a goal that the US government and most of the […]

Forget flowers, a clean home is the real way to their heart! Roborock is $400 off.

arXiv:2501.17164v1 Announce Type: new Abstract: Large models (LMs) have immense potential in Internet of Things (IoT) systems, enabling applications such as intelligent voice assistants, predictive maintenance, and healthcare monitoring. However, training LMs on edge servers raises data privacy concerns, while deploying them directly on IoT devices is constrained by limited computational and memory resources. We analyze the key challenges of training LMs in IoT systems, including energy constraints, latency requirements, and device heterogeneity, and propose potential solutions such as dynamic resource management, adaptive model partitioning, and clustered collaborative training. Furthermore, we propose a split knowledge distillation framework to efficiently distill LMs into smaller, deployable versions for IoT devices while ensuring raw data remains local. This framework integrates knowledge distillation and split learning to minimize energy consumption and meet low model training

The Switchbot K10 Plus Combo is meant to be a clean-anything sort of proposition with both a robot vacuum and a stick vacuum. Here's what it missed.

arXiv:2501.17062v1 Announce Type: new Abstract: This paper introduces EdgeMLOps, a framework leveraging Cumulocity IoT and thin-edge.io for deploying and managing machine learning models on resource-constrained edge devices. We address the challenges of model optimization, deployment, and lifecycle management in edge environments. The framework's efficacy is demonstrated through a visual quality inspection (VQI) use case where images of assets are processed on edge devices, enabling real-time condition updates within an asset management system. Furthermore, we evaluate the performance benefits of different quantization methods, specifically static and dynamic signed-int8, on a Raspberry Pi 4, demonstrating significant inference time reductions compared to FP32 precision. Our results highlight the potential of EdgeMLOps to enable efficient and scalable AI deployments at the edge for industrial applications.

arXiv:2501.16784v1 Announce Type: new Abstract: The rapidly expanding Internet of Things (IoT) landscape is shifting toward cloudless architectures, removing reliance on centralized cloud services but exposing devices directly to the internet and increasing their vulnerability to cyberattacks. Our research revealed an unexpected pattern of substantial Tor network traffic targeting cloudless IoT devices. suggesting that attackers are using Tor to anonymously exploit undisclosed vulnerabilities (possibly obtained from underground markets). To delve deeper into this phenomenon, we developed TORCHLIGHT, a tool designed to detect both known and unknown threats targeting cloudless IoT devices by analyzing Tor traffic. TORCHLIGHT filters traffic via specific IP patterns, strategically deploys virtual private server (VPS) nodes for cost-effective detection, and uses a chain-of-thought (CoT) process with large language models (LLMs) for accurate threat identification. Our results are

arXiv:2501.16368v1 Announce Type: new Abstract: Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models with those derived from data. However, the first generation of ML approaches, which depend on supervised learning with annotated data to create task-specific models, faces significant limitations in scaling to the diverse sensor modalities, deployment configurations, application tasks, and operating dynamics characterizing real-world CPS-IoT systems. The success of task-agnostic foundation models (FMs), including multimodal large language models (LLMs), in addressing similar challenges across natural language, computer vision, and human speech has generated considerable enthusiasm for and exploration of FMs and LLMs as flexible building blocks in CPS-IoT analytics pipelines, promising to reduce the need

We recently published a list of 10 AI News You Should Take a Look At. In this article, we are going to take a look at where Nayax Ltd. (NASDAQ:NYAX) stands against other AI news you should take a look at. DeepSeek has gained attention for creating a high-performing model, R1, at a fraction of […]

We've tested the best smart home devices that can help make your life easier. Here are our top picks.

Govee's new Neon Rope Light 2 makes it easy to decorate your home and it has quickly become a staple in my household.

Govee's new Neon Rope Light 2 makes it easy to decorate your home and it has quickly become a staple in my household.

arXiv:2501.15931v1 Announce Type: new Abstract: While the integration of IoT devices in virtual spaces is becoming increasingly common, technical barriers to controlling custom devices in multi-user Virtual Reality (VR) environments remain high, particularly limiting new applications in educational and prototyping settings. We propose MetaGadget, a framework for connecting IoT devices to commercial metaverse platforms that implements device control through HTTP-based event triggers without requiring persistent client connections. Through two workshops focused on smart home control and custom device integration, we explored the potential application of IoT connectivity in multi-user metaverse environments. Participants successfully implemented new interactions unique to the metaverse, such as environmental sensing and remote control systems that support simultaneous operation by multiple users, and reported positive feedback on the ease of system development. We verified that our

arXiv:2501.15563v1 Announce Type: new Abstract: The rapid expansion of connected devices has made them prime targets for cyberattacks. To address these threats, deep learning-based, data-driven intrusion detection systems (IDS) have emerged as powerful tools for detecting and mitigating such attacks. These IDSs analyze network traffic to identify unusual patterns and anomalies that may indicate potential security breaches. However, prior research has shown that deep learning models are vulnerable to backdoor attacks, where attackers inject triggers into the model to manipulate its behavior and cause misclassifications of network traffic. In this paper, we explore the susceptibility of deep learning-based IDS systems to backdoor attacks in the context of network traffic analysis. We introduce \texttt{PCAP-Backdoor}, a novel technique that facilitates backdoor poisoning attacks on PCAP datasets. Our experiments on real-world Cyber-Physical Systems (CPS) and Internet of Things (IoT)

arXiv:2501.15395v1 Announce Type: new Abstract: The rapid growth of Internet of Things (IoT) devices has introduced significant challenges to privacy, particularly as network traffic analysis techniques evolve. While encryption protects data content, traffic attributes such as packet size and timing can reveal sensitive information about users and devices. Existing single-technique obfuscation methods, such as packet padding, often fall short in dynamic environments like smart homes due to their predictability, making them vulnerable to machine learning-based attacks. This paper introduces a multi-technique obfuscation framework designed to enhance privacy by disrupting traffic analysis. The framework leverages six techniques-Padding, Padding with XORing, Padding with Shifting, Constant Size Padding, Fragmentation, and Delay Randomization-to obscure traffic patterns effectively. Evaluations on three public datasets demonstrate significant reductions in classifier performance metrics,

arXiv:2501.15365v1 Announce Type: new Abstract: In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service quality, preventing financial losses, and maintaining robust security standards. While machine learning algorithms have shown promise in achieving high accuracy for anomaly detection, their performance is often constrained by the specific conditions of their training data. A persistent challenge in this domain is the scarcity of labeled data for anomaly detection in time-series datasets. This limitation hampers the training efficacy of both traditional machine learning and advanced deep learning models. To address this, unsupervised transfer learning emerges as a viable solution, leveraging unlabeled data from a source domain to identify anomalies in an unlabeled target domain. However, many existing

arXiv:2501.15164v1 Announce Type: new Abstract: Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use unmanned aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider's (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV

arXiv:2501.14754v1 Announce Type: new Abstract: The dynamic environment context necessitates harnessing digital technologies, including artificial intelligence and the Internet of Things, to supply high-resolution, real-time meteorological data to support agricultural decision-making and improve overall farm productivity and sustainability. This study investigates the potential application of various AI-powered, IoT-based, low-cost platforms for local weather forecasting to enable smart farming. Despite the increasing demand for this topic, a few promising studies have explored this area. This paper developed a conceptual research framework based on a systematic review of relevant literature and employed a case study method to validate the framework. The framework comprised five key components: the Data Acquisition Layer, Data Storage Layer, Data Processing Layer, Application Layer, and Decision-Making Layer. This paper contributes to the literature by exploring the integration of

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

You can now control select smart home devices with voice commands using the Gemini app. The update will be rolling out over the next few weeks.

arXiv:2501.14387v1 Announce Type: new Abstract: To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the

Apple's upcoming iOS 18.4 update, expected in April, will introduce smarter Siri functionality, expanded Apple Intelligence language support. read more

A small iPhone upgrade is the first step in a huge Apple smart tech push this year.

These are some of the Raspberry Pi-powered projects you can use to turn your house into a smart home.

Google Gemini adds smart home controls.

You don’t have to pay a lot for do-it-all robot vacuums like the SwitchBot S10. | Photo by Jennifer Pattison Tuohy / The Verge We’re in an age where you can realistically delegate tasks to smart hunks of metal, whether it’s a self-driving car or a robot that can clean on your behalf. Most of us probably won’t be able to afford the helpful sentient humanoids being developed in our lifetimes, but robot vacuums are an affordable way to experience that promised utopia right now. Today’s floor cleaners are also more advanced than ever. In addition to vacuuming, many of the best models can now mop, allowing you to tackle both carpet and hardwood flooring. Some can automatically dispense of their trash and dirty water, too, and clean their own components without intervention. Soon, we’ll even have models that can pick up dirty laundry and purify the air in your home,

The Dreame L10s Ultra is a robot vacuum and mop combination that is seeing a huge 56% discount for a limited time. But hurry, this Amazon lightning deal ends soon.

This tech-packed bot has convinced me that Dreame is the robovac brand to watch.

arXiv:2501.13508v1 Announce Type: new Abstract: The safe and swift evacuation of passengers from Maritime Vessels, requires an effective Internet of Things(IoT) as well as an information and communication technology(ICT) infrastructure. However, during emergencies, delays in IoT and ICT systems that guide evacuees, can impair the evacuation process. This paper presents explores the impact of the key IoT and ICT elements. The methodology builds upon the deadline-aware adaptive navigation strategy (ANT), which offers the path segment that minimizes the evacuation time for each evacuee at each decision instant. The simulations on a real cruise ship configuration, show that delays in the delivery of correct instructions to evacuees can significantly hinder the effectiveness of the evacuation. Our findings stress the need to design robust and computationally fast IoT and ICT systems to support the evacuation of passengers in ships, and underscores the key role played by the IoT in the

As of Jan. 24, the roborock Qrevo Plus is on sale for $579.99 at Amazon. That's a 36% saving on the list price.

Google’s Nest Learning thermostat. | Photo by Jennifer Pattison Tuohy / The Verge Google is bringing smart home controls in Gemini to everyone. The Google Home extension in the Gemini app is adding a few new features, in addition to letting you adjust your smart lighting, thermostat, speakers, and other compatible devices as long as they’re connected to your Google account. Google first previewed the extension last November. With it, you can use natural language to control your smart home when interacting with Gemini, such as saying “The sun is too bright in the living room” to close your smart blinds. But now, Gemini can also carry out multiple requests, like “Turn the armchair light on too, but dim the kitchen lamp.” You’ll be able to use the Google Home extension to ask Gemini about the status of your devices too, such as whether you’ve left your porch light on. Additionally,

The Eureka E20 Plus is a self-emptying robot vacuum that lacks one feature found in most competitors - but that's what makes it so great.

The Dreame L40 Ultra high-end robot vacuum and mop delivers excellent suction and thorough cleaning capabilities - and it's on sale right now.

Here's how the Google Pixel Tablet performs better (and worse) than stationary displays like the Nest and Echo Hub.

arXiv:2501.12483v1 Announce Type: new Abstract: This study provides a framework that incorporates the Internet of Things (IoT) technology into maize farming activities in Central Uganda as a solution to various challenges including climate change, sub-optimal resource use and low crop yields. Using IoT-based modeling and simulation, the presented solution recommends cost-effective and efficient approaches to irrigation, crop yield improvement enhancement and prevention of drinking water loss while being practical for smallholder farmers. The framework is developed in a manner that is appropriate for low resource use regions by using local strategies that are easily understandable and actionable for the farmers thus solving the issue of technology access and social economic constraints. Research in this area brought to light the promise that the IoT holds for the evolution of agriculture into a more data-informed, climate-smart sector, contributes to the much-needed food in the world,

The new Home AI features will sense when you're working out or sleeping, for example, to suggest different routines for the activity. Here's how that would work.

Bigger, badder DDoSes are flooding the Internet. Dismal IoT security is largely to blame.

Los Angeles CA (SPX) Jan 22, 2025 Rocket Lab USA, Inc. has announced the upcoming launch of its Electron rocket for Kineis, a global Internet-of-Things (IoT) connectivity provider. This mission, titled "IOT 4 You and Me," is scheduled to lift off during a launch window opening on February 4th, NZDT. The daily launch opportunity within this window is at 09:43 am NZDT (20:43 UTC). The launch will take place at Rocket Lab's L

The White House launched a new cybersecurity safety label, the U.S. Cyber Trust Mark, intended to help consumers make informed decisions on smart device safety.

As of Jan. 22, 2025, Narwal Freo Z Ultra robot vacuum and mop is $200 off at Amazon, priced at $1,299.99. Get smarter, cleaner floors now.

arXiv:2310.08822v2 Announce Type: replace Abstract: We propose a new coded blockchain scheme suitable for the Internet-of-Things (IoT) network. In contrast to existing works for coded blockchains, especially blockchain-of-things, the proposed scheme is more realistic, practical, and secure while achieving high throughput. This is accomplished by: 1) modeling the variety of transactions using a reward model, based on which an optimization problem is solved to select transactions that are more accessible and cheaper computational-wise to be processed together; 2) a transaction-based and lightweight consensus algorithm that emphasizes on using the minimum possible number of miners for processing the transactions; and 3) employing the raptor codes with linear-time encoding and decoding which results in requiring lower storage to maintain the blockchain and having a higher throughput. We provide detailed analysis and simulation results on the proposed scheme and compare it with the

arXiv:2209.13793v2 Announce Type: replace Abstract: The concepts of Internet of Things (IoT) and Cyber Physical Systems (CPS) are closely related to each other. IoT is often used to refer to small interconnected devices like those in smart home while CPS often refers to large interconnected devices like industry machines and smart cars. In this paper, we present a unified view of IoT and CPS: from the perspective of network architecture, IoT and CPS are similar given that they are based on either the OSI model or TCP/IP model. In both IoT and CPS, networking/communication modules are attached to original things so that isolated things can be integrated into cyber space. If needed, actuators can also be integrated with a thing so as to control the thing. With this unified view, we can perform risk assessment of an IoT/CPS system from six factors, hardware, networking, operating system (OS), software, data and human. To illustrate the use of such risk analysis framework, we analyze an

arXiv:2501.12169v1 Announce Type: new Abstract: With the advancement of Internet of Things (IoT) technology, underwater target detection and tracking have become increasingly important for ocean monitoring and resource management. Existing methods often fall short in handling high-noise and low-contrast images in complex underwater environments, lacking precision and robustness. This paper introduces a novel SVGS-DSGAT model that combines GraphSage, SVAM, and DSGAT modules, enhancing feature extraction and target detection capabilities through graph neural networks and attention mechanisms. The model integrates IoT technology to facilitate real-time data collection and processing, optimizing resource allocation and model responsiveness. Experimental results demonstrate that the SVGS-DSGAT model achieves an mAP of 40.8% on the URPC 2020 dataset and 41.5% on the SeaDronesSee dataset, significantly outperforming existing mainstream models. This IoT-enhanced approach not only excels in

arXiv:2501.11984v1 Announce Type: new Abstract: Long-range frequency-hopping spread spectrum (LR-FHSS) promises to enhance network capacity by integrating frequency hopping into existing Long Range Wide Area Networks (LoRaWANs). Due to its simplicity and scalability, LR-FHSS has generated significant interest as a potential candidate for direct-to-satellite IoT (D2S-IoT) applications. This paper explores methods to improve the reliability of data transfer on the uplink (i.e., from terrestrial IoT nodes to satellite) of LR-FHSS D2S-IoT networks. Because D2S-IoT networks are expected to support large numbers of potentially uncoordinated IoT devices per satellite, acknowledgment-cum-retransmission-aided reliability mechanisms are not suitable due to their lack of scalability. We therefore leverage message-replication, wherein every application-layer message is transmitted multiple times to improve the probability of reception without the use of receiver acknowledgments. We propose two

arXiv:2501.11618v1 Announce Type: new Abstract: To address the critical need for secure IoT networks, this study presents a scalable and lightweight curriculum learning framework enhanced with Explainable AI (XAI) techniques, including LIME, to ensure transparency and adaptability. The proposed model employs novel neural network architecture utilized at every stage of Curriculum Learning to efficiently capture and focus on both short- and long-term temporal dependencies, improve learning stability, and enhance accuracy while remaining lightweight and robust against noise in sequential IoT data. Robustness is achieved through staged learning, where the model iteratively refines itself by removing low-relevance features and optimizing performance. The workflow includes edge-optimized quantization and pruning to ensure portability that could easily be deployed in the edge-IoT devices. An ensemble model incorporating Random Forest, XGBoost, and the staged learning base further enhances

arXiv:2501.11574v1 Announce Type: new Abstract: Co-existence of 5G New Radio (5G-NR) with IoT devices is considered as a promising technique to enhance the spectral usage and efficiency of future cellular networks. In this paper, a unified framework has been proposed for allocating in-band resource blocks (RBs), i.e., within a multi-cell network, to 5G-NR users in co-existence with NB-IoT and LTE-M devices. First, a benchmark (upper-bound) scheduler has been designed for joint sub-carrier (SC) and modulation and coding scheme (MCS) allocation that maximizes instantaneous throughput and fairness among users/devices, while considering synchronous RB allocation in the neighboring cells. A series of numerical simulations with realistic ICI in an urban scenario have been used to compute benchmark upper-bound solutions for characterizing performance in terms of throughput, fairness, and delay. Next, an edge learning based multi-agent deep reinforcement learning (DRL) framework has been

arXiv:2501.11250v1 Announce Type: new Abstract: Integrating Internet of Things (IoT) devices in healthcare has revolutionized patient care, offering improved monitoring, diagnostics, and treatment. However, the proliferation of these devices has also introduced significant cybersecurity challenges. This paper reviews the current landscape of cybersecurity threats targeting IoT devices in healthcare, discusses the underlying issues contributing to these vulnerabilities, and explores potential solutions. Additionally, this study offers solutions and suggestions for researchers, agencies, and security specialists to overcome these IoT in healthcare cybersecurity vulnerabilities. A comprehensive literature survey highlights the nature and frequency of cyber attacks, their impact on healthcare systems, and emerging strategies to mitigate these risks.

arXiv:2501.11198v1 Announce Type: new Abstract: Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL)

arXiv:2501.10743v1 Announce Type: new Abstract: We study an internet of things (IoT) network where devices harvest energy from transmitter power. IoT devices use this harvested energy to operate and decode data packets. We propose a slot division scheme based on a parameter $\xi$, where the first phase is for energy harvesting (EH) and the second phase is for data transmission. We define the joint success probability (JSP) metric as the probability of the event that both the harvested energy and the received signal-to-interference ratio (SIR) exceed their respective thresholds. We provide lower and upper bounds of (JSP), as obtaining an exact JSP expression is challenging. Then, the peak age-of-information (PAoI) of data packets is determined using this framework. Higher slot intervals for EH reduce data transmission time, requiring higher link rates. In contrast, a lower EH slot interval will leave IoT devices without enough energy to decode the packets. We demonstrate that both

arXiv:2501.10547v1 Announce Type: new Abstract: We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyperdimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform using off-the-shelf hardware and demonstrate that HyperCam can achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST, Fashion-MNIST, Face Detection, and Face Identification tasks, respectively, while significantly outperforming other classifiers in resource efficiency. Specifically, it delivers inference latency of 0.08-0.27s while using 42.91-63.00KB flash memory and 22.25KB RAM at peak. Among other machine learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy while maintaining competitive memory footprint and inference

arXiv:2501.10514v1 Announce Type: new Abstract: Bus transit plays a vital role in urban public transportation but often struggles to provide accurate and reliable departure times. This leads to delays, passenger dissatisfaction, and decreased ridership, particularly in transit-dependent areas. A major challenge lies in the discrepancy between actual and scheduled bus departure times, which disrupts timetables and impacts overall operational efficiency. To address these challenges, this paper presents a neural network-based approach for real-time bus departure time prediction tailored for smart IoT public transit applications. We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. In our case study analyzing bus data from Boston, we observed an average deviation of nearly 4 minutes

arXiv:2501.10430v1 Announce Type: new Abstract: Aquaculture involves cultivating marine and freshwater organisms, with real-time monitoring of aquatic parameters being crucial in fish farming. This thesis proposes an IoT-based framework using sensors and Arduino for efficient monitoring and control of water quality. Different sensors including pH, temperature, and turbidity are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a CSV file in an IoT cloud named Thingspeak through the Arduino Microcontroller. In the experimental part, we collected data from 5 ponds with various sizes and environments. After getting the real-time data, we compared these with the standard reference values. As a result, we can make the decision about which ponds are satisfactory for cultivating fish and what is not. After that, we labeled the data with 11 fish categories including

arXiv:2501.10743v1 Announce Type: new Abstract: We study an internet of things (IoT) network where devices harvest energy from transmitter power. IoT devices use this harvested energy to operate and decode data packets. We propose a slot division scheme based on a parameter $\xi$, where the first phase is for energy harvesting (EH) and the second phase is for data transmission. We define the joint success probability (JSP) metric as the probability of the event that both the harvested energy and the received signal-to-interference ratio (SIR) exceed their respective thresholds. We provide lower and upper bounds of (JSP), as obtaining an exact JSP expression is challenging. Then, the peak age-of-information (PAoI) of data packets is determined using this framework. Higher slot intervals for EH reduce data transmission time, requiring higher link rates. In contrast, a lower EH slot interval will leave IoT devices without enough energy to decode the packets. We demonstrate that both

Zigbang, the operator of Korea’s leading real estate brokerage app, is expanding its portfolio with smart home appliances, releasing a digital door lock without passcodes in the global market.

I’ve previously argued that Apple Intelligence could be the biggest reason to buy an Apple smart home camera. Longer term, I think it also has the potential to create a whole new era of truly smart homes. How long it will take before we can trust Apple Intelligence to run our homes is a whole other question! There’s no arguing with the fact that there’s a long way to go before the AI tech will be more than a public beta. But the longer-term potential does excite me … more…

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If it feels like every piece of home tech is now “smart,” you’re not far off. The smart home space has grown exponentially in the past few years to include speakers, cameras, locks, lights and even kitchen appliances. There are also different voice assistants and IoT standards to consider, all of which can make it confusing (to say the least) to build your smart home ecosystem from the ground up.Allow us at Engadget to help with that. We’ve tested dozens of smart home gadgets over the years and continue to test the latest offerings to see which work well and are worth your money. We recommend, before you even dive in, to resist the urge to outfit your whole home in one go. Not only can this be quite expensive, but also we think it’s generally best to buy just one or two items first to see if you like them. You should also pick a preferred voice assistant and stick with it. If you’re at the point where you’re ready to invest in a few new IoT gadgets, below are the best smart home

Amazon's 30% off sale on the small-yet-mighty speaker will fill your home with amazing sound and control your smart devices.

arXiv:2501.09926v1 Announce Type: new Abstract: Early detection of forest fires is crucial to minimizing the environmental and socioeconomic damage they cause. Indeed, a fire's duration directly correlates with the difficulty and cost of extinguishing it. For instance, a fire burning for 1 minute might require 1 liter of water to extinguish, while a 2-minute fire could demand 100 liters, and a 10-minute fire might necessitate 1,000 liters. On the other hand, existing fire detection systems based on novel technologies (e.g., remote sensing, PTZ cameras, UAVs) are often expensive and require human intervention, making continuous monitoring of large areas impractical. To address this challenge, this work proposes a low-cost forest fire detection system that utilizes a central gateway device with computer vision capabilities to monitor a 360{\deg} field of view for smoke at long distances. A deep reinforcement learning agent enhances surveillance by dynamically controlling the camera's

Long before the era of Bluetooth and Wi-Fi, an inventor in Michigan had rigged his house with technology that simulated much of what today's smart homes can do.

Cointelegraph Research delves into Chirp’s DePIN and how it addresses the problem of the fragmented IoT industry.

A robot vacuum can cost a pretty penny, so it's important to follow these steps and get the best performance and longest battery life possible.

arXiv:2501.09394v1 Announce Type: cross Abstract: The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments. Traditional machine learning methods often struggle to generalize effectively under such conditions. To address this, we introduce Q-ASC, a novel Quantum-Inspired Acoustic Scene Classifier that leverages the power of quantum-inspired transformers. By integrating quantum concepts like superposition and entanglement, Q-ASC achieves superior feature learning and enhanced noise resilience compared to classical models. Furthermore, we introduce a Quantum Variational Autoencoder (QVAE) based data augmentation technique to mitigate the challenge of limited labeled data in IoT deployments. Extensive evaluations on the Tampere University of Technology (TUT) Acoustic Scenes 2016 benchmark dataset demonstrate that Q-ASC achieves remarkable accuracy

arXiv:2501.09216v1 Announce Type: new Abstract: Prior research yielded many techniques to mitigate software compromise for low-end Internet of Things (IoT) devices. Some of them detect software modifications via remote attestation and similar services, while others preventatively ensure software (static) integrity. However, achieving run-time (dynamic) security, e.g., control-flow integrity (CFI), remains a challenge. Control-flow attestation (CFA) is one approach that minimizes the burden on devices. However, CFA is not a real-time countermeasure against run-time attacks since it requires communication with a verifying entity. This poses significant risks if safety- or time-critical tasks have memory vulnerabilities. To address this issue, we construct EILID - a hybrid architecture that ensures software execution integrity by actively monitoring control-flow violations on low-end devices. EILID is built atop CASU, a prevention-based (i.e., active) hybrid Root-of-Trust (RoT) that

The Dreame X40 Ultra is a high-end robot vacuum priced for the stars, but is it worth its hefty price tag? At $900 off, the answer is a definite yes.

The Roborock Saros Z70 is making waves in the robot vacuum world, and its robotic arm is poised to bring added versatility to your home. Here's why I'm excited.

arXiv:2501.08990v1 Announce Type: new Abstract: Ambient internet of things (A-IoT) paradigm is under study in 3GPP with the intention to provide a sustainable solution for the IoT market without any need to replace the batteries and operate in harsh environments where it is difficult to replenish batteries. This article provides insight on 3rd Generation Partnership Project (3GPP) discussions in Release 18 and 19 with the focus on network architecture aspects. 3GPP has recently decided to start normative work in its Radio Access Network (RAN) Working Group (WG) and discussions are ongoing to start a work item in other WGs with more focus on architecture aspects. We explore and analyze various aspects of system design related to architecture requirements to support A-IoT service, different architecture options to consider, security and authentication mechanisms for A-IoT devices as well as key challenges for standardization of A-IoT service.

arXiv:2501.08840v1 Announce Type: new Abstract: Binary Static Code Analysis (BSCA) is a pivotal area in software vulnerability research, focusing on the precise localization of vulnerabilities within binary executables. Despite advancements in BSCA techniques, there is a notable scarcity of comprehensive and readily usable vulnerability datasets tailored for diverse environments such as IoT, UEFI, and MCU firmware. To address this gap, we present CveBinarySheet, a meticulously curated database containing 1033 CVE entries spanning from 1999 to 2024. Our dataset encompasses 16 essential third-party components, including busybox and curl, and supports five CPU architectures: x86-64, i386, MIPS, ARMv7, and RISC-V64. Each precompiled binary is available at two compiler optimization levels (O0 and O3), facilitating comprehensive vulnerability analysis under different compilation scenarios. By providing detailed metadata and diverse binary samples, CveBinarySheet aims to accelerate the

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Are you a frequent user of the Google Home app? Two new features are rolling out now to upgrade your smart home in a couple of key ways.

arXiv:2501.08229v1 Announce Type: new Abstract: This research proposes a system as a solution for the challenges faced by Sri Lanka' s historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS based real time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure.

arXiv:2501.07895v1 Announce Type: new Abstract: This paper highlights the significance of resource-constrained Internet of Things (RCD-IoT) systems in addressing the challenges faced by industries with limited resources. This paper presents an energy-efficient solution for industries to monitor and control their utilities remotely. Integrating intelligent sensors and IoT technologies, the proposed RCD-IoT system aims to revolutionize industrial monitoring and control processes, enabling efficient utilization of resources.The proposed system utilized the IEEE 802.15.4 WiFi Protocol for seamless data exchange between Sensor Nodes. This seamless exchange of information was analyzed through Packet Tracer. The system was equipped with a prototyped, depicting analytical chemical process to analyze the significant performance metrics. System achieved average Round trip time (RTT) of just 12ms outperforming the already existing solutions presented even with higher Quality of Service (QoS)

The Eufy Security E340 dual-camera video doorbell can help protect deliveries from porch pirates with no subscription fees required.

There are flashy smart home tools headed to store shelves this year, but those might not be the products that make the biggest splash in 2025.

A cutting-edge AI tool can now predict how well seed potatoes will grow into healthy potato plants. Developed by biologists from Utrecht University in collaboration with the Delft University of Technology and plant breeders, the tool uses DNA data from bacteria and fungi found on seed potatoes and drone images of potato fields. "This marks the beginning of a new era in farming, where microbiology and AI come together to enhance agriculture."

Is the enormous Echo Show 21 Amazon's best smart display or its biggest missed opportunity? Here's my verdict.

arXiv:2501.07154v1 Announce Type: new Abstract: Data from Internet of Things (IoT) sensors has emerged as a key contributor to decision-making processes in various domains. However, the quality of the data is crucial to the effectiveness of applications built on it, and assessment of the data quality is heavily context-dependent. Further, preserving the privacy of the data during quality assessment is critical in domains where sensitive data is prevalent. This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document to achieve objectivity. Additionally, we utilise a trusted execution environment to create a "data-blind" model that ensures individual privacy, eliminates assessee bias, and enhances adaptability across data types. This

arXiv:2501.07326v1 Announce Type: new Abstract: There is an expectation that users of home IoT devices will be able to secure those devices, but they may lack information about what they need to do. In February 2022, we launched a web service that scans users' IoT devices to determine how secure they are. The service aims to diagnose and remediate vulnerabilities and malware infections of IoT devices of Japanese users. This paper reports on findings from operating this service drawn from three studies: (1) the engagement of 114,747 users between February, 2022 - May, 2024; (2) a large-scale evaluation survey among service users (n=4,103), and; (3) an investigation and targeted survey (n=90) around the remediation actions of users of non-secure devices. During the operation, we notified 417 (0.36%) users that one or more of their devices were detected as vulnerable, and 171 (0.15%) users that one of their devices was infected with malware. The service found no issues for 99% of users.

arXiv:2501.07154v1 Announce Type: new Abstract: Data from Internet of Things (IoT) sensors has emerged as a key contributor to decision-making processes in various domains. However, the quality of the data is crucial to the effectiveness of applications built on it, and assessment of the data quality is heavily context-dependent. Further, preserving the privacy of the data during quality assessment is critical in domains where sensitive data is prevalent. This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document to achieve objectivity. Additionally, we utilise a trusted execution environment to create a "data-blind" model that ensures individual privacy, eliminates assessee bias, and enhances adaptability across data types. This

arXiv:2501.07039v1 Announce Type: new Abstract: The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing medical-related human activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions that are critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method for MRHA detection, leveraging multi-stage deep learning techniques integrated with IoT. The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions (MBConv) blocks, followed by

arXiv:2501.06464v1 Announce Type: new Abstract: An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal

Apple plans iPhone revamp, push into smart home, catching up with AI: report  Seeking AlphaApple’s 2025 Plan: iPhone Overhaul, Smart Home Push and AI Catch-Up  BloombergApple’s packed 2025 iPhone and iPad roadmap has just leaked  TechRadarApple's roadmap for 2025: Bloomberg analyst names Apple Watch SE redesign, iPhone Air, M4 Mac Studio and more  Notebookcheck.netApple's 2025 product roadmap: an insider's glimpse into what's in store this year  PhoneArena

Apple plans iPhone revamp, push into smart home, catching up with AI: report  Seeking AlphaApple’s 2025 Plan: iPhone Overhaul, Smart Home Push and AI Catch-Up  BloombergApple’s packed 2025 iPhone and iPad roadmap has just leaked  TechRadarApple's roadmap for 2025: Bloomberg analyst names Apple Watch SE redesign, iPhone Air, M4 Mac Studio and more  Notebookcheck.netApple's 2025 product roadmap: an insider's glimpse into what's in store this year  PhoneArena

iRobot shares drop after revealing preliminary Q4 results, with projected revenue of $171 million and expected GAAP operating loss of $59 million. read more

arXiv:2501.06033v1 Announce Type: new Abstract: As the Internet of Things (IoT) becomes more embedded within our daily lives, there is growing concern about the risk `smart' devices pose to network security. To address this, one avenue of research has focused on automated IoT device identification. Research has however largely neglected the identification of IoT device firmware versions. There is strong evidence that IoT security relies on devices being on the latest version patched for known vulnerabilities. Identifying when a device has updated (has changed version) or not (is on a stable version) is therefore useful for IoT security. Version identification involves challenges beyond those for identifying the model, type, and manufacturer of IoT devices, and traditional machine learning algorithms are ill-suited for effective version identification due to being limited by the availability of data for training. In this paper, we introduce an effective technique for identifying IoT

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