IOT

arXiv:2512.21144v1 Announce Type: new Abstract: The proliferation of Internet-of-things (IoT) infrastructures and the widespread adoption of traffic encryption present significant challenges, particularly in environments characterized by dynamic traffic patterns, constrained computational capabilities, and strict latency constraints. In this paper, we propose DMLITE, a diffusion model and large language model (LLM) integrated traffic embedding framework for network traffic detection within resource-limited IoT environments. The DMLITE overcomes these challenges through a tri-phase architecture including traffic visual preprocessing, diffusion-based multi-level feature extraction, and LLM-guided feature optimization. Specifically, the framework utilizes self-supervised diffusion models to capture both fine-grained and abstract patterns in encrypted traffic through multi-level feature fusion and contrastive learning with representative sample selection, thus enabling rapid adaptation to

arXiv:2512.20997v1 Announce Type: new Abstract: The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has emerged as a promising paradigm, integrating reasoning, planning, and adaptation to enable QoE-aware network management. In this paper, we explore the integration of agentic AI into QoE-aware network slicing for IIoT. We first review the network slicing management architecture, QoE metrics for IIoT applications, and the challenges of dynamically managing heterogeneous network slices, while highlighting the motivations and advantages of adopting agentic AI. We then present the workflow of agentic AI-based slicing management, illustrating the full lifecycle of AI agents from processing

arXiv:2512.20639v1 Announce Type: new Abstract: In the era of digital transformation, the global deployment of internet of things (IoT) networks and wireless sensor networks (WSNs) is critical for applications ranging from environmental monitoring to smart cities. Large-scale monitoring using WSNs incurs high costs due to the deployment of sensor nodes in the target deployment area. In this paper, we address the challenge of prohibitive deployment costs by proposing an integrated mixed-Integer linear programming (MILP) framework that strategically combines static and mobile Zigbee nodes. Our network planning approach introduces three novel formulations, including boundary-optimized static node placement (MILP-Static), mobile path planning for coverage maximization (MILP-Cov), and movement minimization (MILP-Mov) of the mobile nodes. We validated our framework with extensive simulations and experimental measurements of Zigbee power constraints. Our results show that boundary-optimized

arXiv:2512.20623v1 Announce Type: new Abstract: Smart home lighting systems consume 15-20% of residential energy but lack adaptive intelligence to optimize for user comfort and energy efficiency simultaneously. We present BitRL-Light, a novel framework combining 1-bit quantized Large Language Models (LLMs) with Deep Q-Network (DQN) reinforcement learning for real-time smart home lighting control on edge devices. Our approach deploys a 1-bit quantized Llama-3.2-1B model on Raspberry Pi hardware, achieving 71.4 times energy reduction compared to full-precision models while maintaining intelligent control capabilities. Through multi-objective reinforcement learning, BitRL-Light learns optimal lighting policies from user feedback, balancing energy consumption, comfort, and circadian alignment. Experimental results demonstrate 32% energy savings compared to rule-based systems, with inference latency under 200ms on Raspberry Pi 4 and 95% user satisfaction. The system processes natural

Ikea has made some of its Matter-compatible smart home devices even cheaper.  The VergeIkea’s New Matter over Thread Products - Bulb, Contact Sensor, Leak Sensor (video) -  Homekit News and Reviews

Ikea has made some of its Matter-compatible smart home devices even cheaper.  The VergeIkea’s New Matter over Thread Products - Bulb, Contact Sensor, Leak Sensor (video) -  Homekit News and Reviews

arXiv:2512.20004v1 Announce Type: new Abstract: Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings. First, we demonstrate the effectiveness of graph-based classification using a Graph Neural Network (GNN)-based classifier to generate API graph embeddings. The graph embeddings are combined with Permission and Intent features to train multiple machine learning and deep learning models for Android malware detection. The proposed classification approach achieves an accuracy of 98.33 percent on the CICMaldroid dataset and 98.68 percent on the Drebin dataset. However, graph-based deep learning models are vulnerable, as attackers can add fake relationships to evade detection by the classifier. Second, we propose a Generative Adversarial Network

arXiv:2512.19945v1 Announce Type: new Abstract: Securing Internet of Things (IoT) firmware remains difficult due to proprietary binaries, stripped symbols, heterogeneous architectures, and limited access to executable code. Existing analysis methods, such as static analysis, symbolic execution, and fuzzing, depend on binary visibility and functional emulation, making them unreliable when firmware is encrypted or inaccessible. To address this limitation, we propose a binary-free, architecture-agnostic solution that estimates the likelihood of conceptual zero-day vulnerabilities using only high-level descriptors. The approach integrates a tri-LLM reasoning architecture combining a LLaMA-based configuration interpreter, a DeepSeek-based structural abstraction analyzer, and a GPT-4o semantic fusion model. The solution also incorporates LLM computational signatures, including latency patterns, uncertainty markers, and reasoning depth indicators, as well as an energy-aware symbolic load

All I want for Christmas is a Dyson robovac redemption – can the Spot+Scrub Ai deliver it?

This morning, I asked my Alexa-enabled Bosch coffee machine to make me a coffee. Instead of running my routine, it told me it couldn't do that. Ever since I upgraded to Alexa Plus, Amazon's generative-AI-powered voice assistant, it has failed to reliably run my coffee routine, coming up with a different excuse almost every time I ask. It's 2025, and AI still can't reliably control my smart home. I'm beginning to wonder if it ever will. The potential for generative AI and large language models to take the complexity out of the smart home, making it easier to set up, use, and manage connected devices, is compelling. So is the promise of a " … Read the full story at The Verge.

Resideo sued by Nebraska AG over rebranding footage-leaking Chinese cameras

These feature-packed home security cameras offer crisp 4K footage, solar charging, local storage for your videos, and AI smarts too – with no extra fees.

iRobot, the maker of Roomba, filed for bankruptcy due to intense Chinese competition and failed regulatory approvals. read more

arXiv:2512.19488v1 Announce Type: new Abstract: The widespread deployment of Internet of Things (IoT) devices requires intrusion detection systems (IDS) with high accuracy while operating under strict resource constraints. Conventional deep learning IDS are often too large and computationally intensive for edge deployment. We propose a lightweight IDS that combines SHAP-guided feature pruning with knowledge-distilled Kronecker networks. A high-capacity teacher model identifies the most relevant features through SHAP explanations, and a compressed student leverages Kronecker-structured layers to minimize parameters while preserving discriminative inputs. Knowledge distillation transfers softened decision boundaries from teacher to student, improving generalization under compression. Experiments on the TON\_IoT dataset show that the student is nearly three orders of magnitude smaller than the teacher yet sustains macro-F1 above 0.986 with millisecond-level inference latency. The results

arXiv:2512.19361v1 Announce Type: new Abstract: The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains, where perishable goods are highly susceptible to environmental conditions. Existing methods often lack adaptability to dynamic conditions and fail to optimize decision making in real time. To address these challenges, we propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction. This hybrid architecture captures temporal dependencies within sensor data, enabling robust and adaptive decision making. In alignment with interpretable artificial intelligence principles, a rule-based classifier environment is employed to provide transparent ground truth labeling of spoilage levels based on domain-specific thresholds. This structured design allows the agent to operate within clearly defined semantic boundaries,

arXiv:2512.19131v1 Announce Type: new Abstract: Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against single points of failure. However, statistical heterogeneity arising from non-identically distributed local data creates a fundamental challenge: nodes must learn personalized models adapted to their local distributions while selectively collaborating with compatible peers. Existing approaches either enforce a single global model that fits no one well, or rely on heuristic peer selection mechanisms that cannot distinguish between peers with genuinely incompatible data distributions and those with valuable complementary knowledge. We present Murmura, a framework that leverages evidential deep learning to enable trust-aware model personalization in DFL. Our key insight is that epistemic uncertainty from Dirichlet-based evidential models directly indicates peer compatibility: high

arXiv:2512.18604v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and limited battery capacity of UAVs. To address these issues, we formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it. Furthermore, we propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs and utilizes a mediator Q-network over a double deep Q-network (DDQN) to accelerate and stabilize training and improve performance. Experimental results in both simulated and real-world environments demonstrate the

arXiv:2512.18560v1 Announce Type: new Abstract: Sensor data in IoT (Internet of Things) systems is vulnerable to tampering or falsification when transmitted through untrusted services. This is critical because such data increasingly underpins real-world decisions in domains such as logistics, healthcare, and other critical infrastructure. We propose a general method for secure sensor-data logging in which tamper-evident devices periodically sign readouts, link data using redundant hash chains, and submit cryptographic evidence to a blockchain-based service via Merkle trees to ensure verifiability even under data loss. Our approach enables reliable and cost-effective validation of sensor data across diverse IoT systems, including disaster response and other humanitarian applications, without relying on the integrity of intermediate systems.

arXiv:2512.18155v1 Announce Type: new Abstract: Timely updates are critical for real-time monitoring and control applications powered by the Internet of Things (IoT). As these systems scale, they become increasingly vulnerable to adversarial attacks, where malicious agents interfere with legitimate transmissions to reduce data rates, thereby inflating the age of information (AoI). Existing adversarial AoI models often assume stationary channels and overlook queueing dynamics arising from compromised sensing sources operating under resource constraints. Motivated by the G-queue framework, this paper investigates a two-source M/G/1/1 system in which one source is adversarial and disrupts the update process by injecting negative arrivals according to a Poisson process and inducing i.i.d. service slowdowns, bounded in attack rate and duration. Using moment generating functions, we then derive closed-form expressions for average and peak AoI for an arbitrary number of sources. Moreover, we

Expensive processes and a failure to listen to the customer set iRobot on its downward trajectory, says its CEO.

Should Roomba owners worry about their robot vacuum after iRobot's bankruptcy? Here's what the company says.

Your Roomba is not about to become an expensive doorstop, promises company boss.

Connie Loizos / TechCrunch: Q&A with iRobot co-founder Colin Angle on the company's Chapter 11 bankruptcy, regulatory pressure that killed Amazon's deal, his new robotics startup, and more  —  When iRobot filed for Chapter 11 bankruptcy last Sunday, it marked the end of an era for one of America's most beloved robotics companies.

In the rapidly evolving landscape of the Industrial Internet of Things (IIoT), security concerns loom larger than ever. With industries becoming increasingly reliant on interconnected devices, the attack surface for cybercriminals is broader than in traditional environments. Wushishi, Hussain, and Khalid, in their groundbreaking research presented in the article titled “D3O-IIoT: Deep Reinforcement Learning-Driven Dynamic […]

The Narwal Freo Z10 Ultra could have been great, but erratic performance and 'smart' features that are anything but make it ultra-frustrating.

In a bold step toward securing the Internet of Things (IoT), a team of researchers has proposed a revolutionary approach to malware detection, one that intricately weaves together machine learning models housed within a swarm architecture. This approach not only enhances detection efficiency but also promises to mitigate the increasingly prevalent threat of IoT malware […]

Here's the week's biggest tech news stories from iRobot, Samsung, OnePlus, and more for December 20, 2025.

The headlines might be gloomy, but CEO Gary Cohen argues the outlook for iRobot is sunny.

arXiv:2512.16348v1 Announce Type: new Abstract: Identifying devices such as cameras, printers, voice assistants, or health monitoring sensors, collectively known as the Internet of Things (IoT), within a network is a critical operational task, particularly to manage the cyber risks they introduce. While behavioral fingerprinting based on network traffic analysis has shown promise, most existing approaches rely on machine learning (ML) techniques applied to fine-grained features of short-lived traffic units (packets and/or flows). These methods tend to be computationally expensive, sensitive to traffic measurement errors, and often produce opaque inferences. In this paper, we propose a macroscopic, lightweight, and explainable alternative to behavioral fingerprinting focusing on the network services (e.g., TCP/80, UDP/53) that IoT devices use to perform their intended functions over extended periods. Our contributions are threefold. (1) We demonstrate that IoT devices exhibit stable

Badger Meter, Inc. (NYSE:BMI) is included among the 12 Best Long Term US Stocks to Buy Now. On December 12, Jefferies initiated coverage of Badger Meter, Inc. (NYSE:BMI) with a Buy rating and a $220 price target. The firm described Badger Meter as a leader in smart water metering and said the recent slowdown in growth […]

In an era defined by rapid technological advancements, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) represents a formidable evolution. Research conducted by Jian Sheng, titled “Intelligent integration of AI and IoT big data using QDCN for scalable smart manufacturing,” explores this intersection and its implications for the manufacturing industry. The […]

MADISON, Wis. — If you closed your eyes — and maybe if David Bartling wasn’t trying to shout over the roar of harvesting machinery — you might guess he was talking about his software business or a chemistry lab. Not the weather on the farm. “The more data, the better,” says Bartling, co-owner of Bartling’s […] The post Smart farming appeared first on Morning Ag Clips.

Todd Bishop / GeekWire: Amazon rolls out Alexa+ on the web to early access users, with chat functionality, smart home controls, file management, and cross-device conversations  —  Amazon is quietly rolling out the last big pillar of its AI-powered Alexa+ vision: the Alexa.com website, bridging the gap between its Echo devices …

The Shark AI Ultra robot vacuum is on sale at Amazon for $249.99, down from the normal price of $549.99. That's a 55% discount.

The Roomba maker will continue to operate but will be owned by Picea Robotics. The news that the maker of the Roomba robot vacuum, iRobot, filed for bankruptcy this week came as no surprise. Its CEO, Gary Cohen, had been warning investors all year that the company could run out of cash unless a buyer was found. When its last potential deal fell through, bankruptcy became inevitable. But Cohen says this is not the end for iRobot; instead, he sees it as the beginning of a new chapter for the company, one he hopes will turn it back into a competitive market leader and potentially take it into new, greener territory. Cohen was brought on in early 2024 to turn the company around when cofounder and CEO Colin Angle stepped d … Read the full story at The Verge.

arXiv:2512.15558v1 Announce Type: cross Abstract: In the evolving landscape of the Internet of Things (IoT), integrating cognitive radio (CR) has become a practical solution to address the challenge of spectrum scarcity, leading to the development of cognitive IoT (CIoT). However, the vulnerability of radio communications makes radio jamming attacks a key concern in CIoT networks. In this paper, we introduce a novel deep reinforcement learning (DRL) approach designed to optimize throughput and extend network lifetime of an energy-constrained CIoT system under jamming attacks. This DRL framework equips a CIoT device with the autonomy to manage energy harvesting (EH) and data transmission, while also regulating its transmit power to respect spectrum-sharing constraints. We formulate the optimization problem under various constraints, and we model the CIoT device's interactions within the channel as a model-free Markov decision process (MDP). The MDP serves as a foundation to develop a

arXiv:2512.15206v1 Announce Type: new Abstract: In real-world IoT applications, sensor data is usually collected under diverse and dynamic contextual conditions where factors such as sensor placements or ambient environments can significantly affect data patterns and downstream performance. Traditional domain adaptation or generalization methods often ignore such context information or use simplistic integration strategies, making them ineffective in handling unseen context shifts after deployment. In this paper, we propose Chorus, a context-aware, data-free model customization approach that adapts models to unseen deployment conditions without requiring target-domain data. The key idea is to learn effective context representations that capture their influence on sensor data patterns and to adaptively integrate them based on the degree of context shift. Specifically, Chorus first performs unsupervised cross-modal reconstruction between unlabeled sensor data and language-based context

CEO of Roomba maker iRobot says previous management was ‘in denial’  Financial TimesThe Roomba Was a Disappointment  The AtlanticWelcome to home robotics limbo  CNNiRobot’s Cofounder Weighs In on Company’s Bankruptcy  IEEE SpectrumiRobot filed for bankruptcy: How the Roomba maker got here  Business Insider

CEO of Roomba maker iRobot says previous management was ‘in denial’  Financial TimesThe Roomba Was a Disappointment  The AtlanticWelcome to home robotics limbo  CNNiRobot’s Cofounder Weighs In on Company’s Bankruptcy  IEEE SpectrumiRobot filed for bankruptcy: How the Roomba maker got here  Business Insider

The Ecovacs Deebot T80 robot vacuum is on sale at Amazon for $499.99, down from the list price of $999.99. That's a 50% discount.

Chinese robotics manufacturers are set to cement their dominance in the global smart vacuum market with Picea Robotics’ acquisition of Roomba maker iRobot, after the US firm’s proposed sale to Amazon.com last year fell through due to regulatory hurdles. China-based suppliers held the top five spots for worldwide smart robotic vacuum shipments in the first three quarters of 2025, led by Roborock with a 21.7 per cent market share, equal to 3.8 million units, according to data from research firm...

As of Dec. 17, get $220 off the Narwal Freo Pro robot vacuum and mop at Amazon.

New York  —  Tech historians may look back on 2025 as a kind of trough in consumer robotics. Two decades ago, Roombas blew everyone’s mind. The company that made them, iRobot, was so flush by 2015 it started its own venture capital arm. But after years of struggling to compete against cheaper competitors from China … The post Welcome to home robotics limbo appeared first on Egypt Independent.

arXiv:2512.14488v1 Announce Type: cross Abstract: This paper proposes a hierarchical deep reinforcement learning (DRL) framework based on the soft actor-critic (SAC) algorithm for hybrid underlay-overlay cognitive Internet of Things (CIoT) networks with simultaneous wireless information and power transfer (SWIPT)-energy harvesting (EH) and cooperative caching. Unlike prior hierarchical DRL approaches that focus primarily on spectrum access or power control, our work jointly optimizes EH, hybrid access coordination, power allocation, and caching in a unified framework. The joint optimization problem is formulated as a weighted-sum multi-objective task, designed to maximize throughput and cache hit ratio while simultaneously minimizing transmission delay. In the proposed model, CIoT agents jointly optimize EH and data transmission using a learnable time switching (TS) factor. They also coordinate spectrum access under hybrid overlay-underlay paradigms and make power control and cache

arXiv:2512.14029v1 Announce Type: cross Abstract: In cognitive Internet of Things (CIoT) networks, efficient spectrum sharing is essential to address increasing wireless demands. This paper presents a novel deep reinforcement learning (DRL)-based approach for joint cooperative caching and spectrum access coordination in CIoT networks, enabling the CIoT agents to collaborate with primary users (PUs) by caching PU content and serving their requests, fostering mutual benefits. The proposed DRL framework jointly optimizes caching policy and spectrum access under challenging conditions. Unlike traditional cognitive radio (CR) methods, where CIoT agents vacate the spectrum for PUs, or relaying techniques, which merely support spectrum sharing, caching brings data closer to the edge, reducing latency by minimizing retrieval distance. Simulations demonstrate that our approach outperforms others in lowering latency, increasing CIoT and PU cache hit rates, and enhancing network throughput. This

arXiv:2512.14013v1 Announce Type: cross Abstract: In this paper, we address the challenge of dynamic spectrum access in a cognitive Internet of Things (CIoT) network where a secondary user (SU) operates under both energy constraints and adversarial interference from a smart jammer. The SU coexists with primary users (PUs) and must ensure that its transmissions do not exceed a predefined interference threshold on licensed channels. At each time slot, the SU must jointly determine whether to transmit or harvest energy, which channel to access, and the appropriate transmit power while satisfying energy and interference constraints. Meanwhile, a smart jammer actively selects a channel to disrupt, aiming to degrade the SU's communication performance. This setting presents a significant challenge due to its multi-level decision structure and hybrid action space, which combines both discrete and continuous decisions. To tackle this, we propose a novel Hierarchical Deep Deterministic Policy

arXiv:2512.13709v1 Announce Type: new Abstract: The proliferation of Internet of Things (IoT) devices has grown exponentially in recent years, introducing significant security challenges. Accurate identification of the types of IoT devices and their associated actions through network traffic analysis is essential to mitigate potential threats. By monitoring and analysing packet flows between IoT devices and connected networks, anomalous or malicious behaviours can be detected. Existing research focuses primarily on device identification within local networks using methods such as protocol fingerprinting and wireless frequency scanning. However, these approaches are limited in their ability to monitor or classify IoT devices externally. To address this gap, we investigate the use of machine learning (ML) techniques, specifically Random Forest (RF), Multilayer Perceptron (MLP), and K-Nearest Neighbours (KNN), in conjunction with targeted network traffic monitoring to classify IoT device

Luxembourg-based OQ Technology said Dec. 17 it has connected a commercial IoT chipset directly to one of its LEO satellites, using internally developed software based on 3GPP mobile standards. The post OQ Technology links commercial IoT chipset to LEO satellite appeared first on SpaceNews.

The fewer entry points you leave open, the more secure your smart home will be. Here's how to accomplish that.

Busy dog owners, rejoice. This smart door makes for easy transport.

As of Dec. 16, get the Shark Robot Vacuum and Mop Combo for $100 off.

The company which popularized robot vacuum cleaners around the world has filed for Chapter 11 bankruptcy. iRobot, makers of the Roomba, has been synonymous with the category since its inception, but its star had dulled in recent years. The company plans to sell its assets to its primary supplier, China’s Picea Robotics, in the hope of maintaining its business. Everyone’s got a strident opinion as to why iRobot fell from grace. The rugged individualists blame limp regulators on both sides of the pond (and their hatred for big tech) for blocking Amazon’s attempted purchase in 2023. Those on the hardware side of the fence say iRobot’s refusal to embrace LiDAR for navigation until this year left it behind rivals. Then there’s the geopolitical experts, who can point at China’s industrial policy, subsidies and favorable regulatory environment compared to the US approach. After all, iRobot’s US gear is made in Vietnam, which is now subject to a 46 percent import levy. As BBC News

'Who's Home' turns Vodafone's Ultra Hub 7 into a home security tool to check who's connected to the network.

iRobot, the company that brought robotic vacuum cleaners to homes and popular culture, has filed for bankruptcy. It plans to sell all assets to its primary supplier, the Chinese company Picea Robotics. Investors “will experience a total loss and not receive recovery on their investment” if the deal is approved, iRobot said. The company didn’t discuss how the move might affect its employees in the US or elsewhere. Amazon dropped its $1.7 billion acquisition of the company last year after a veto threat from European regulators, leaving the Roomba maker with no other option. Political scrutiny came on two fronts: The company was also reportedly hit hard by Trump’s tariffs in Vietnam, where it manufactures products for the US market. iRobot launched its first Roomba in 2002, arguably inventing the world of robot vacuums — and the first robots to enter many of our homes. Competition from rivals has chipped away at its dominance, with other companies coming in at both lower and

Roomba maker iRobot goes bankrupt; now owned by former supplier Picea

The Internet of Things has expanded far beyond regular smart devices. Now, IoT powers global logistics fleets, industrial

arXiv:2512.13460v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters, degrading convergence. We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions during FL aggregation. DP-EMAR estimates corruption patterns and applies adaptive correction before privacy noise is added, enabling reliable in network repair without violating confidentiality. By integrating Differential Privacy (DP) with Secure Aggregation (SA), the framework distinguishes DP noise from genuine transmission errors. Experiments on heterogeneous IoT sensor and graph datasets show that DP-EMAR preserves convergence stability and

arXiv:2512.13460v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized model training without sharing raw data, but model weight distortion remains a major challenge in resource constrained IoT networks. In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters, degrading convergence. We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions during FL aggregation. DP-EMAR estimates corruption patterns and applies adaptive correction before privacy noise is added, enabling reliable in network repair without violating confidentiality. By integrating Differential Privacy (DP) with Secure Aggregation (SA), the framework distinguishes DP noise from genuine transmission errors. Experiments on heterogeneous IoT sensor and graph datasets show that DP-EMAR preserves convergence stability and

arXiv:2512.13340v1 Announce Type: new Abstract: The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates due to the non-stationarity in the IoT environment and limited initial training data. To counteract this, the deployed models can be updated occasionally with new observed data samples. However, this approach consumes additional energy, which is undesirable for energy constrained IoT devices. This letter introduces an event-driven communication framework that strategically integrates continual learning (CL) in IoT networks for energy-efficient fault detection. Our framework enables the IoT device and the edge server (ES) to collaboratively update the lightweight ML model by adapting to the wireless link conditions for communication and the available energy budget. Evaluation on real-world datasets

arXiv:2512.12778v1 Announce Type: new Abstract: FPGA-based SmartNICs and IoT devices integrating soft-processors for network function execution have emerged to address the limited hardware reconfigurability of DPUs and MCUs. However, existing FPGA-based solutions lack a highly configurable many-core architecture specialized for network packet processing. This work presents VeBPF many-core architecture, a resource-optimized and highly configurable many-core architecture composed of custom VeBPF (Verilog eBPF) CPU cores designed for FPGA-based packet processing. The VeBPF cores are eBPF ISA compliant and implemented in Verilog HDL for seamless integration with existing FPGA IP blocks and subsystems. The proposed many-core architecture enables parallel execution of multiple eBPF rules across multiple VeBPF cores, achieving low-latency packet processing. The architecture is fully parameterizable, allowing the number of VeBPF cores and eBPF rules to scale according to application

Roomba maker iRobot set for buyout by manufacturer after filing for bankruptcy  Yahoo Finance UKWhat happens to Roombas now that the company has declared bankruptcy?  Los Angeles TimesRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesHow Roomba invented the home robot — and lost the future  The VergeRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC News

Roomba maker iRobot set for buyout by manufacturer after filing for bankruptcy  Yahoo Finance UKWhat happens to Roombas now that the company has declared bankruptcy?  Los Angeles TimesRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesHow Roomba invented the home robot — and lost the future  The VergeRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC News

How Roomba invented the home robot — and lost the future  The VergeRobot Vacuum Roomba Maker Files for Bankruptcy After 35 Years  Bloomberg.comRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC NewsiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR Newswire

How Roomba invented the home robot — and lost the future  The VergeRobot Vacuum Roomba Maker Files for Bankruptcy After 35 Years  Bloomberg.comRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC NewsiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR Newswire

Major U.S. benchmarks finished Monday lower, with the Dow Jones Industrial Average edging down 0.09% to 48,416.56. read more

Roomba Robot Vacuums Face a Shakeup as iRobot Files for Bankruptcy  CNETRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR NewswireRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC News

Roomba Robot Vacuums Face a Shakeup as iRobot Files for Bankruptcy  CNETRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR NewswireRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesRoomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC News

Biggest Loser Monday: iRobot Stock Collapses After Chapter 11 Filing  Yahoo FinanceRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRHow Roomba invented the home robot — and lost the future  The VergeiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR NewswireRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York Times

Biggest Loser Monday: iRobot Stock Collapses After Chapter 11 Filing  Yahoo FinanceRoomba maker files for bankruptcy, weighed down by debt and tariffs  NPRHow Roomba invented the home robot — and lost the future  The VergeiRobot Announces Strategic Transaction to Drive Long-Term Growth Plan  PR NewswireRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York Times

Roomba is way past code red. For many, iRobot's Roomba robot vacuum was their first experience with a home robot. When I got my Roomba in 2005, I felt like I was a step closer to my dream of living in a Jetsons-style home where a robot did my chores for me. It was expensive, around $300 for a small black disc on wheels that sucked up dirt, but its promise to do one of my most-hated chores while I was at work was compelling. The reality back then was that I spent more time babysitting it than it did actually cleaning my floors, but it was an exciting glimpse into the future of today's excellent robot vacuums. I wasn't alone in my excitement. Robot vacuums quickly became … Read the full story at The Verge.

Smart gadgets are capable of much more than reporting the weather and playing music. These selections of time-saving tech could return hours to your busy day.

iRobot going bankrupt doesn't mean that Roombas will stop working, or that they'll stop selling Roombas. But the future is still iffy.

IRobot, which became well known for its robotic vacuums, has struggled of late, dealing with increased competition, layoffs and a declining stock price. The post Roomba maker iRobot files for bankruptcy protection; will be taken private under restructuring appeared first on Boston.com.

iRobot Corporation (NASDAQ: IRBT) stock is getting hit on Monday after the company filed for Chapter 11 bankruptcy. read more

iRobot says it will continue to offer its robots and smart home devices​ to consumers.

While iRobot is selling itself off as scrap, let's remember what actually makes a good robovac.

Roomba maker iRobot gets cleaned out in Chapter 11  theregister.comRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesiRobot Files for Chapter 11 Amid Rising Competition and Tariff Pressures  Yahoo FinanceRoomba vacuum cleaner firm files for bankruptcy  BBCiRobot, Tesla, ServiceNow, Texas Instruments, Newmont, Tilray, and More Market Movers  Barron's

Roomba maker iRobot gets cleaned out in Chapter 11  theregister.comRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesiRobot Files for Chapter 11 Amid Rising Competition and Tariff Pressures  Yahoo FinanceRoomba vacuum cleaner firm files for bankruptcy  BBCiRobot, Tesla, ServiceNow, Texas Instruments, Newmont, Tilray, and More Market Movers  Barron's

Shenzhen-based Picea Robotics, its lender and primary supplier, will acquire all of iRobot’s shares.

iRobot has filed for bankruptcy and may be taken over by its primary supplier  EngadgetRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesiRobot Files for Chapter 11 Amid Rising Competition and Tariff Pressures  Yahoo FinanceRoomba vacuum cleaner firm files for bankruptcy  BBCiRobot, Tesla, ServiceNow, Texas Instruments, Newmont, Tilray, and More Market Movers  Barron's

iRobot has filed for bankruptcy and may be taken over by its primary supplier  EngadgetRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesiRobot Files for Chapter 11 Amid Rising Competition and Tariff Pressures  Yahoo FinanceRoomba vacuum cleaner firm files for bankruptcy  BBCiRobot, Tesla, ServiceNow, Texas Instruments, Newmont, Tilray, and More Market Movers  Barron's

The company that pioneered the US robot vacuum market is no longer setting the pace in it. Here's what's to come.

Founded in 1990 by three M.I.T. researchers, iRobot introduced its vacuum in 2002. Its restructuring will turn the company over to its largest creditor.

Roomba maker iRobot files for bankruptcy protection; will be taken private under restructuring  ABC NewsRoomba vacuum cleaner firm files for bankruptcy  BBCRoomba Maker iRobot Files for Bankruptcy, With Chinese Supplier Taking Control  The New York TimesRoomba maker iRobot gets cleaned out in Chapter 11  theregister.comRoomba Maker Declares Bankruptcy, but Tries to Ease ‘Bricking’ Fears  The Wall Street Journal