Ikea's Smart Home Reset Goes Back to Basics Ikea's new 21-product series of bulbs, sensors, and remotes is dirt cheap, idiot-proof, Matter-ready, and designed to work with everything. But it's still years from the promised house of the future. That's what you might be thinking if you've been following smart-home tech for the past decade or, indeed, building out your own fortress of missed connections. The first Nest thermostat launched in 2011, Philips Hue in 2012, the Amazon Echo in 2014. But for anyone who has spent long nights scrolling through IoT troubleshooting forums since then, here's the latest: It's finally time for a do-over.

It's often more'smash and grab' than'Mission: Impossible.' French police officers stand next to a furniture elevator used by robbers to enter the Louvre Museum, on Quai Francois Mitterrand, in Paris on October 19, 2025. Robbers broke in to the Louvre and fled with jewellery on October 19, 2025 morning, a source close to the case said, adding that its value was still being evaluated. A police source said an unknown number of thieves arrived on a scooter armed with small chainsaws and used a goods lift to reach the room they were targeting. Breakthroughs, discoveries, and DIY tips sent every weekday. A heist at a world famous museum likely evokes images of stealthy cat burglars skulking at night armed with state-of-the-art gadgets, possibly even soundtracked with a cool, jazzy instrumental.

Philips Hue appears to be teeing up a new, more powerful hub that can turn Hue bulbs into motion sensors, according to leaked details and images that briefly appeared on Philips Hue's own website. The unannounced products, which have since been yanked from the "New on Hue" page, included the "faster" Hue Bridge Pro as well as a wired video doorbell, a refreshed and more efficient A19 bulb, permanent and globe-style versions of Hue's Festavia outdoor string lights, a gradient light strip, and the ability to control your Hue lights with the Sonos voice assistant. No pricing details were included in the leaked details, which were live on the Hue website for several hours Wednesday. The leaked products were initially spotted by users on Reddit. Reached by TechHive, a Phillips Hue spokesperson declined to comment.

Abode makes some of our favorite home security systems, and the Abode Security Kit is a great value made even better by Amazon's Prime Day sale. For just 60 bucks, you get the foundation of a robust security system that you can expand over time. This is an easy DIY product consisting of a central hub, one door/window sensor, and a keyfob for arming and disarming. Once you have it set up, you can add Abode's reasonably priced motion sensors, smart lock, security camera, video doorbell, and keypad (for arming/disarming) as you need them. You can also arm/disarm the system with the keyfob or the Abode app on your phone.

The Aqara Camera Protect Kit Y100 is one of the easiest to install and set up tech products I've tested, and it does an outstanding job of monitoring a relatively small space. But steer clear if you're looking for a professional monitoring option, as that's not on offer. But before I get too deep into this review, be aware that Aqara does not offer any professional monitoring service, where someone in a central office monitors your security system and can dispatch first responders in the event of a break-in, fire, or medical emergency. While such plans are always paid subscriptions, its absence here will be a deal-breaker for some (Aqara does manufacture a Zigbee smart smoke detector if self-monitoring is all you're looking for). The Matter-compatible Aqara Camera Hub G3 includes a Zigbee radio and a dual-band Wi-Fi adapter.

The Eufy Wired Wall Light Cam S100 isn't as powerful as many of its floodlight competitors, but it will look a whole lot more attractive mounted next to your door. Floodlight cameras almost always have one thing in common: They expect you to tolerate their industrial appearance for a large pool of light. And that's fine if you need a lot of light and you're willing to accept their beastly looks to get it. But if you want something more understated, the Eufy Wired Wall Light Cam S100 might be for you. Like the 270 Netatmo Smart Outdoor Camera I reviewed way back in 2016, the far more affordable Eufy Wired Wall Light Cam S100 isn't a true floodlight; it's more of a porch light with an onboard camera.

The proportion of elderly people is increasing worldwide, particularly those living alone in Japan. As elderly people get older, their risks of physical disabilities and health issues increase. To automatically discover these issues at a low cost in daily life, sensor-based detection in a smart home is promising. As part of the effort towards early detection of abnormal behaviors, we propose a simulator-based detection systems for six typical anomalies: being semi-bedridden, being housebound, forgetting, wandering, fall while walking and fall while standing. Our detection system can be customized for various room layout, sensor arrangement and resident's characteristics by training detection classifiers using the simulator with the parameters fitted to individual cases. Considering that the six anomalies that our system detects have various occurrence durations, such as being housebound for weeks or lying still for seconds after a fall, the detection classifiers of our system produce anomaly labels depending on each anomaly's occurrence duration, e.g., housebound per day and falls per second. We propose a method that standardizes the processing of sensor data, and uses a simple detection approach. Although the validity depends on the realism of the simulation, numerical evaluations using sensor data that includes a variety of resident behavior patterns over nine years as test data show that (1) the methods for detecting wandering and falls are comparable to previous methods, and (2) the methods for detecting being semi-bedridden, being housebound, and forgetting achieve a sensitivity of over 0.9 with fewer than one false alarm every 50 days.

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model's accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.

Human activity recognition (HAR) using ambient sensors in smart homes has numerous applications for human healthcare and wellness. However, building general-purpose HAR models that can be deployed to new smart home environments requires a significant amount of annotated sensor data and training overhead. Most smart homes vary significantly in their layouts, i.e., floor plans and the specifics of sensors embedded, resulting in low generalizability of HAR models trained for specific homes. We address this limitation by introducing a novel, layout-agnostic modeling approach for HAR systems in smart homes that utilizes the transferrable representational capacity of natural language descriptions of raw sensor data. To this end, we generate Textual Descriptions Of Sensor Triggers (TDOST) that encapsulate the surrounding trigger conditions and provide cues for underlying activities to the activity recognition models. Leveraging textual embeddings, rather than raw sensor data, we create activity recognition systems that predict standard activities across homes without either (re-)training or adaptation on target homes. Through an extensive evaluation, we demonstrate the effectiveness of TDOST-based models in unseen smart homes through experiments on benchmarked CASAS datasets. Furthermore, we conduct a detailed analysis of how the individual components of our approach affect downstream activity recognition performance.

This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear Temporal Logic (LTL) from demonstrations. Both LLMs and optimization-based methods can extract LTL specifications from demonstrations; however, they have distinct limitations. LLMs can quickly generate solutions and incorporate human explanations, but their lack of consistency and reliability hampers their applicability in safety-critical domains. On the other hand, optimization-based methods do provide formal guarantees but cannot process natural language explanations and face scalability challenges. We present a principled approach to combining LLMs and optimization-based methods to faithfully translate human explanations and demonstrations into LTL specifications. We have implemented a tool called Janaka based on our approach. Our experiments demonstrate the effectiveness of combining explanations with demonstrations in learning LTL specifications through several case studies.