When you purchase through links in our articles, we may earn a small commission. These are the smart devices I can't live without My household would fall apart without these smart gadgets up and running. I've been covering smart home and security products for years, and I've written about and reviewed scores of smart devices. Yet I can count on one hand (plus an extra finger or two) the number of smart devices that my family and I actually depend on. Sure, I have plenty of smart gadgets in my house that are nice-to-haves.

Over the past several months, the question surrounding Google's next smart devices hasn't been when they will arrive, but if they will arrive. After all, Google's been slowly but steadily discontinuing older smart products (the Nest Protect, the Nest x Yale Lock) while leaving their replacements to third parties. At time same time, its aging line of Nest smart speakers and displays has been languishing. But Google has previously hinted that new Google Home smart devices are on tap for later this year, and during the company's big Made by Google event today, we may have gotten a glimpse of one. During some pre-recorded banter between Milwaukee Bucks player Giannis Antetokounmpo and F1 driver Lando Norris, the camera panned over to reveal a small, slightly squished sphere with a gray exterior and a telltale light ring encircling its narrow base.

This paper introduces Intelligence of Things (INOT), a novel spatial context-aware control system that enhances smart home automation through intuitive spatial reasoning. Current smart home systems largely rely on device-specific identifiers, limiting user interaction to explicit naming conventions rather than natural spatial references. INOT addresses this limitation through a modular architecture that integrates Vision Language Models with IoT control systems to enable natural language commands with spatial context (e.g., "turn on the light near the window"). The system comprises key components including an Onboarding Inference Engine, Zero-Shot Device Detection, Spatial Topology Inference, and Intent-Based Command Synthesis. A comprehensive user study with 15 participants demonstrated INOT's significant advantages over conventional systems like Google Home Assistant, with users reporting reduced cognitive workload (NASA-TLX scores decreased by an average of 13.17 points), higher ease-of-use ratings, and stronger preference (14 out of 15 participants). By eliminating the need to memorize device identifiers and enabling context-aware spatial commands, INOT represents a significant advancement in creating more intuitive and accessible smart home control systems.

Time is running out for Google Assistant as Gemini prepares to take its place on mobile and--eventually--smart devices. Now Google is announcing another round of features that Google Assistant is soon to lose. None of the about-to-be-yanked features are all that critical, but the move is yet another sign that Google Assistant is going by the wayside. The nixed features were spotted by 9to5Google on a support page that lists other deprecated Google Assistant features, including more than a dozen that were dropped early last year. Among the chopped Google Assistant features that owners of Nest smart speakers and displays might miss is Family Bell, which allowed users to create reminder bells for family events such as breakfast or dinner time.

The writing was already on the wall, but now it's official: The Google Assistant era is over. In a blog post Friday, Google announced plans for Google Assistant's final phase-out, starting on mobile and continuing with tablets, cars, and mobile-connected devices such as headphones and tablets. Finally, Google Assistant will be going away on Nest smart speakers and displays as well as on Google TV devices. Google Assistant's replacement will, of course, be Gemini, Google's entry in the generative AI race. Gemini itself will become the new assistant on Google mobile devices such as phones and tablets, while a "new experience powered by Gemini" is coming to smart speakers and displays.

Shop Amazon's winter sale and get serious discounts on all your electronics. Amazon is running its yearly winter sale, which runs now through January 17. If you missed any Black Friday sales, don't worry, you can get up to 40% off on everything from laptops to Amazon devices, headphones and more. So, spend your gift cards and get major discounts on all those electronics you didn't get during the holiday season. Make sure your items are delivered ASAP by signing up for a Prime membership. The benefits include fast, free delivery, access to invite-only deals and the option to Buy With Prime.

This paper presents a new Large Language Model (LLM)-based Smart Device Management framework, a pioneering approach designed to address the intricate challenges of managing intelligent devices within public facilities, with a particular emphasis on applications to libraries. Our framework leverages state-of-the-art LLMs to analyze and predict device failures, thereby enhancing operational efficiency and reliability. Through prototype validation in real-world library settings, we demonstrate the framework's practical applicability and its capacity to significantly reduce budgetary constraints on public facilities. The advanced and innovative nature of our model is evident from its successful implementation in prototype testing. We plan to extend the framework's scope to include a wider array of public facilities and to integrate it with cutting-edge cybersecurity technologies, such as Internet of Things (IoT) security and machine learning algorithms for threat detection and response. This will result in a comprehensive and proactive maintenance system that not only bolsters the security of intelligent devices but also utilizes machine learning for automated analysis and real-time threat mitigation. By incorporating these advanced cybersecurity elements, our framework will be well-positioned to tackle the dynamic challenges of modern public infrastructure, ensuring robust protection against potential threats and enabling facilities to anticipate and prevent failures, leading to substantial cost savings and enhanced service quality.

Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors. However, current approaches rely on patient's subjective descriptions of symptoms, causing increased misdiagnosis. Recognizing the value of daily data from smart devices, we introduce a novel LLM-based multi-turn consultation virtual doctor system, DrHouse, which incorporates three significant contributions: 1) It utilizes sensor data from smart devices in the diagnosis process, enhancing accuracy and reliability. 2) DrHouse leverages continuously updating medical databases such as Up-to-Date and PubMed to ensure our model remains at diagnostic standard's forefront. 3) DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments. Through multi-turn interactions, DrHouse determines the next steps, such as accessing daily data from smart devices or requesting in-lab tests, and progressively refines its diagnoses. Evaluations on three public datasets and our self-collected datasets show that DrHouse can achieve up to an 18.8% increase in diagnosis accuracy over the state-of-the-art baselines. The results of a 32-participant user study show that 75% medical experts and 91.7% patients are willing to use DrHouse.

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in federated learning by considering the trust needs of both the client and the server. Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers, (3) Assigning credibility scores to recommending devices for better server trustworthiness measurement, (4) Developing a trust assessment mechanism for smart devices using a statistical Interquartile Range (IQR) method, (5) Designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing non-trustworthy clients in the system.

Owning a pet can be a rewarding experience, but it can also come with challenges. In celebration of National Pet Day on 4/11, here are 10 home pet products that can help make dog (or cat) parenting smarter, not harder. A growing market of innovative products can help you level up your pet care. Pet parents can select gadgets and devices that make caring for their furry friends easier. From products that help you take care of indoor messes with the push of a button to devices that toss your pet a treat to keep things interesting or feed your pet while alone in the home – these smart devices make pet parenting more manageable and more enjoyable.