Apple is mandating users upgrade to its new Home architecture by February 10, 2026, or risk losing control of HomeKit-connected smart devices and automations. PCWorld reports the updated system no longer supports iPads as home hubs, requiring an Apple TV 4K ($129+) or HomePod ($99) instead. Despite initial rollout problems in 2022, the re-released architecture since iOS 16.4 promises improved reliability and efficiency for smart home management. Well, this is it: After a series of delays, Apple is finally nixing support for its old Home architecture, meaning those still relying on the previous version of Apple's Home framework have some decisions to make--quickly. The moment of truth arrives February 10, 2026, less than a week away.

Google Home now supports smart button triggers for routines, filling a significant automation gap that Alexa and HomeKit have long offered users. PCWorld reports the update includes additional triggers like humidity levels, robot vacuum status, and contact sensor alerts for enhanced home monitoring.

The Meross smart plug mini boasts excellent compatibility and slim construction. On the hunt for new smart plugs to upgrade your home automation? One of our favorite picks, the Meross MSS110 Smart Plug Mini, is currently marked down on Amazon. The two pack is discounted from $34 to $27, and the four pack is down to $34 from its usual price of $52 . These plugs help add smart functionality to otherwise dumb devices around your home, like lamps or fans, so they can be included in your routines.

With grid operators confronting rising uncertainty from renewable integration and a broader push toward electrification, Demand-Side Management (DSM) -- particularly Demand Response (DR) -- has attracted significant attention as a cost-effective mechanism for balancing modern electricity systems. Unprecedented volumes of consumption data from a continuing global deployment of smart meters enable consumer segmentation based on real usage behaviours, promising to inform the design of more effective DSM and DR programs. However, existing clustering-based segmentation methods insufficiently reflect the behavioural diversity of consumers, often relying on rigid temporal alignment, and faltering in the presence of anomalies, missing data, or large-scale deployments. To address these challenges, we propose a novel two-stage clustering framework -- Clustered Representations Optimising Consumer Segmentation (CROCS). In the first stage, each consumer's daily load profiles are clustered independently to form a Representative Load Set (RLS), providing a compact summary of their typical diurnal consumption behaviours. In the second stage, consumers are clustered using the Weighted Sum of Minimum Distances (WSMD), a novel set-to-set measure that compares RLSs by accounting for both the prevalence and similarity of those behaviours. Finally, community detection on the WSMD-induced graph reveals higher-order prototypes that embody the shared diurnal behaviours defining consumer groups, enhancing the interpretability of the resulting clusters. Extensive experiments on both synthetic and real Australian smart meter datasets demonstrate that CROCS captures intra-consumer variability, uncovers both synchronous and asynchronous behavioural similarities, and remains robust to anomalies and missing data, while scaling efficiently through natural parallelisation. These results...

Who doesn't want a camera in their toilet? Image of the Throne Toilet Computer perched on the side of a toilet. Throne has rocked up to CES 2026 to show off its forthcoming toilet computer which uses computer vision to study your poop. It hangs from the side of the bowl and has a camera and microphone to track bowel motions and urination and offer feedback. It was co-founded by (activity tracker) Whoop co-founder John Capodilupo, who explained the hardware is designed to understand what your base state is to be able to identify when you fall out of that pattern.

When you purchase through links in our articles, we may earn a small commission. These appliances don't depend on smart speakers for voice control Emerson Smart's new appliances respond to voice commands, but they don't need a smart speaker--or even a broadband connection--to pull off the trick. Smart appliances that can be controlled with voice commands are nothing new, but IAI Smart is showing a new line of Emerson Smart appliances at CES that respond to voice commands. They don't need a smart speaker in the middle, and they don't rely on a broadband connection, an app, or anything other infrastructure--everything is processed locally. If you're leery of the privacy and security vulnerabilities of IoT devices, this could be the answer.

Ugreen makes plenty of things, but you're probably familiar with the name in the context of its NAS systems (should that be NASes? Naturally, the company has turned up to CES 2026 with the former, but it's also branching out into home security. It's announcing SynCare, an AI infused all-in-one surveillance platform which, it rather boldly claims, will become an "attentive, integrated guardian" of your home. Leading the pack is the SynCare Video Doorbell with head-to-toe 4K video, intelligent detection and 24/7 recording -- especially if you've got it hooked up to your Ugreen NAS. That works in tandem with SynCare cameras offering 4K video on a pan-tilt base and, of course, AI to recognise "people, pets and key events."

Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage. Previous works re-design a small network for IoT devices, and then compress the network size by mixed-precision quantization.

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching (MT) algorithms for online personalized education on home devices. This direction becomes more promising during the COVID-19 pandemic when in-person education becomes infeasible. However, as one of the most influential and practical MT paradigms, iterative machine teaching (IMT) is prohibited on IoT devices due to its inefficient and unscalable algorithms. IMT is a paradigm where a teacher feeds examples iteratively and intelligently based on the learner's status. In each iteration, current IMT algorithms greedily traverse the whole training set to find an example for the learner, which is computationally expensive in practice. We propose a novel teaching framework, Locality Sensitive Teaching (LST), based on locality sensitive sampling, to overcome these challenges. LST has provable near-constant time complexity, which is exponentially better than the existing baseline.

In the rapidly evolving landscape of smart home automation, the potential of IoT devices is vast. In this realm, rules are the main tool utilized for this automation, which are predefined conditions or triggers that establish connections between devices, enabling seamless automation of specific processes. However, one significant challenge researchers face is the lack of comprehensive datasets to explore and advance the field of smart home rule recommendations. These datasets are essential for developing and evaluating intelligent algorithms that can effectively recommend rules for automating processes while preserving the privacy of the users, as it involves personal information about users' daily lives. To bridge this gap, we present the Wyze Rule Dataset, a large-scale dataset designed specifically for smart home rule recommendation research. Wyze Rule encompasses over 1 million rules gathered from a diverse user base of 300,000 individuals from Wyze Labs, offering an extensive and varied collection of real-world data. With a focus on federated learning, our dataset is tailored to address the unique challenges of a cross-device federated learning setting in the recommendation domain, featuring a large-scale number of clients with widely heterogeneous data. To establish a benchmark for comparison and evaluation, we have meticulously implemented multiple baselines in both centralized and federated settings. Researchers can leverage these baselines to gauge the performance and effectiveness of their rule recommendation systems, driving advancements in the domain.