Here's how I cut down my family's reliance on Alexa. Until recently, my smart home setup was in chaos. After years of testing, buying, and upgrading to the latest smart home gadgets in an attempt to make my life easier, it became a bloated mess that was actually making it more complicated. My Alexa, Google Home, and Apple Home apps were awash with dead devices, duplicates, and automations that simply didn't work. My Hue Bridge, trying desperately to tie it all together, was creaking at the seams.

When you purchase through links in our articles, we may earn a small commission. Your home is only as smart as the hub that orchestrates everything behind the scenes. We'll help you pick the right system for your home-automation needs. It's never been easier-or less expensive-to build out a state-of-the art smart home. We have other roundups that name the best smart home components-everything from the best smart bulbs to the best smart speakers, but in this story, we name the best hubs-the central controllers-that make home living more convenient. While the lines are becoming increasingly blurred, we see two basic types of smart home systems: Those focused on convenience first-the hubs listed here-and those focused on home security first (and here are our top DIY home security system picks).

Maybe it was the sight of Sengled users literally left in the dark by their useless Wi-Fi bulbs, maybe it was another price hike, or just an overall sense that my smart devices weren't truly under my control. Whatever the reason, I'd developed a growing desire to build a smart home setup that wasn't a hostage to the cloud. Specifically, I'm talking about a locally hosted smart home setup, and I'm currently in the process of building one. And while I'm a smart home expert thanks to my six years' experience here at TechHive, I'm quickly realizing how much I still don't know as I tackle the steep learning curve of a DIY smart home. This isn't a step-by-step guide of how to build your own smart home system--that might come later--but more of a journal about where I am in my self-hosted smart home journey, where I started, and what I'm hoping to achieve.

Vintage-style LED smart bulbs have been a thing for a while, but I haven't seen one quite like this new one from Savant's GE Lighting division. The all-new Cync Clear Full Color Direct Connect A19 Smart Bulb--now there's a mouthful--features a spiral LED filament in a clear glass globe. The Edison-style smart bulb can be programmed to glow from a palette of millions of colors as well as a variety of white color temperatures (from a warm candlelight-like 2,000 Kelvin to an energizingly cool 7,000K). You'll want to install the Cync Clear Full Direct Connect in a clear luminaire that will show off its spiral LED filament. The new bulb connects directly to your Wi-Fi network, and it supports Matter, rendering it compatible with Amazon Alexa, Apple Home, Google Assistant, and Samsung SmartThings.

Requirements Engineering (RE) is essential for developing complex and regulated software projects. Given the challenges in transforming stakeholder inputs into consistent software designs, Qualitative Data Analysis (QDA) provides a systematic approach to handling free-form data. However, traditional QDA methods are time-consuming and heavily reliant on manual effort. In this paper, we explore the use of Large Language Models (LLMs), including GPT-4, Mistral, and LLaMA-2, to improve QDA tasks in RE. Our study evaluates LLMs' performance in inductive (zero-shot) and deductive (one-shot, few-shot) annotation tasks, revealing that GPT-4 achieves substantial agreement with human analysts in deductive settings, with Cohen's Kappa scores exceeding 0.7, while zero-shot performance remains limited. Detailed, context-rich prompts significantly improve annotation accuracy and consistency, particularly in deductive scenarios, and GPT-4 demonstrates high reliability across repeated runs. These findings highlight the potential of LLMs to support QDA in RE by reducing manual effort while maintaining annotation quality. The structured labels automatically provide traceability of requirements and can be directly utilized as classes in domain models, facilitating systematic software design.

This empirical study evaluates the effectiveness of Large Language Models (LLMs) in predicting fixes for configuration bugs in smart home systems. The research analyzes three prominent LLMs - GPT-4, GPT-4o (GPT-4 Turbo), and Claude 3.5 Sonnet - using four distinct prompt designs to assess their ability to identify appropriate fix strategies and generate correct solutions. The study utilized a dataset of 129 debugging issues from the Home Assistant Community, focusing on 21 randomly selected cases for in-depth analysis. Results demonstrate that GPT-4 and Claude 3.5 Sonnet achieved 80\% accuracy in strategy prediction when provided with both bug descriptions and original scripts. GPT-4 exhibited consistent performance across different prompt types, while GPT-4o showed advantages in speed and cost-effectiveness despite slightly lower accuracy. The findings reveal that prompt design significantly impacts model performance, with comprehensive prompts containing both description and original script yielding the best results. This research provides valuable insights for improving automated bug fixing in smart home system configurations and demonstrates the potential of LLMs in addressing configuration-related challenges.

Using rules for home automation presents several challenges, especially when considering multiple stakeholders in addition to residents, such as homeowners, local authorities, energy suppliers, and system providers, who will wish to contribute rules to safeguard their interests. Managing rules from various sources requires a structured procedure, a relevant policy, and a designated authority to ensure authorized and correct contributions and address potential conflicts. In addition, the smart home rule language needs to express conditions and decisions at a high level of abstraction without specifying implementation details such as interfaces, access protocols, and room layout. Decoupling high-level decisions from these details supports the transferability and adaptability of rules to similar homes. This separation also has important implications for structuring the smart home system and the security architecture. Our proposed approach and system implementation introduce a rule management process, a rule administrator, and a domain-specific rule language to address these challenges. In addition, the system provides a learning process that observes residents, detects behavior patterns, and derives rules which are then presented as recommendations to the system.

Designing smart home services is a complex task when multiple services with a large number of sensors and actuators are deployed simultaneously. It may rely on knowledge-based or data-driven approaches. The former can use rule-based methods to design services statically, and the latter can use learning methods to discover inhabitants' preferences dynamically. However, neither of these approaches is entirely satisfactory because rules cannot cover all possible situations that may change, and learning methods may make decisions that are sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic Based Rule Extractor) is proposed to extract rules from learning methods to realize dynamic rule generation for smart home systems. The expected advantage is that both the explainability of rule-based methods and the dynamicity of learning methods are adopted. We compare PBRE with an existing rule extraction method, and the results show better performance of PBRE. We also apply PBRE to extract rules from a smart home service represented by an NRL (Neural Network-based Reinforcement Learning). The results show that PBRE can help the NRL-simulated service to make understandable suggestions to the inhabitant.

Ring Alarm has been our favorite home-security-focused smart home system since its launch, and the second-generation system is even better. That said, Ring hasn't yet delivered on its implied promise to make the Ring Alarm the unifying core of a complete smart home system. Fulfilling that promise--which Ring Solutions president Mike Harris spoke of in 2018--would have bumped up our bottom-line score by a half point. I'll assume, however, that your primary interest in reading this review is to learn about Ring Alarm as a home security system. So, I'll focus on that aspect first and summarize its shortcomings as a smart home system later. This is an in-depth review of a complex system, written after living with the product for a couple of months with the professional monitoring option enabled.

Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.