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.

In recent years, as smart home systems have become more widespread, security concerns within these environments have become a growing threat. Currently, most smart home security solutions, such as anomaly detection and behavior prediction models, are trained using fixed datasets that are precollected. However, the process of dataset collection is time-consuming and lacks the flexibility needed to adapt to the constantly evolving smart home environment. Additionally, the collection of personal data raises significant privacy concerns for users. Lately, large language models (LLMs) have emerged as a powerful tool for a wide range of tasks across diverse application domains, thanks to their strong capabilities in natural language processing, reasoning, and problem-solving. In this paper, we propose an LLM-based synthetic dataset generation IoTGen framework to enhance the generalization of downstream smart home intelligent models. By generating new synthetic datasets that reflect changes in the environment, smart home intelligent models can be retrained to overcome the limitations of fixed and outdated data, allowing them to better align with the dynamic nature of real-world home environments. Specifically, we first propose a Structure Pattern Perception Compression (SPPC) method tailored for IoT behavior data, which preserves the most informative content in the data while significantly reducing token consumption. Then, we propose a systematic approach to create prompts and implement data generation to automatically generate IoT synthetic data with normative and reasonable properties, assisting task models in adaptive training to improve generalization and real-world performance.

In the last few years, the smart home has been built up as a kind of domestic nirvana. Consumers simply need to purchase multiple smart devices, such as televisions or appliances, and, presto!--they all work together to create a personalized and ultra-connected home. Recent research shows four major barriers impacting the adoption of smart homes. Let's take a closer look at each. The cost of a home automation system typically ranges from $404 to $1,830, with a national average of $1,045, according to HomeAdvisor.

Some predictions say that by the end of this decade, more than 500 million people will have to completely forget their current skillset and start learning new ones if they want to stay employed. "Astounding" Artificial Intelligence statistics for 2020 show that this industry is on a constant rise, and everyone who implements it in their line of business, sees a tremendous profit. In the following, you'll be able to read what are the industries that will mostly be affected by it, so read on if you want to know more! At the moment, we see some Tesla and Google cars driving on their own. The AI software that is used now is still in the phase of testing, and some problems must be resolved.

We are bringing smart speakers into our homes with a passion not seen since the Trojans pulled a huge wooden horse into their city as a victory trophy. A new scientific article has inadvertently highlighted where this tech could take us. And it is not to The Good Place. The article naively suggests AI add-ons to smart home systems that snoop on users and make'moral decisions' about whether or not to report them to the authorities. Its selling example is about catching teenagers smoking cannabis in their bedrooms.

Your message has been sent. There was an error emailing this page. From smart light bulbs and thermostats that think for themselves to Bluetooth door locks, wireless security cameras, and all manner of sensors, today's home technology can sound awfully sophisticated while actually being a messy hodgepodge of gizmos and apps. Whether you call it home automation or the connected home, installing all this stuff in your house is one thing. Getting it to work together smoothly and with a single user interface can be something entirely different.

During last night's Super Bowl, Google's advert for its Home device was one of the most talked-about of the event. But viewers with their own Google Home were left annoyed by the advert, as their home system thought the TV was trying to communicate with them. Users took to Twitter to complain, with one person saying the advert had made his Google Home go'bonkers.' Google Home is continually listening for commands. Google says nothing gets passed back to them until the speakers hear the keywords -- 'Hey Google' or'OK, Google'.

Google used the Super Bowl to plug its Google Home connectivity service, but the TV commercial apparently confused the systems in homes of those who already have it. For them, Google Home went whacko. Those who already have Google Home took to Twitter to complain that it interfered with their units. Apparently, the home systems heard the TV broadcasts calling its name, and it became befuddled. "The Google commercial had my Google Home going haywire," complained a Twitter user with the handle CheezusPrice.

Hey Siri, turn off the kitchen light. The first "smart" home gadgets that can be controlled by Apple's voice-activated digital assistant are going on sale this week, just days after rival tech giant Google announced it's building its own software for Internet-connected home appliances and other gadgets. The new products could be an important step forward for the emerging industry of "smart" or "connected" homes, where appliances, thermostats and even door locks contain computer chips that communicate wirelessly. While a number of companies are working on similar products, analysts say Apple could persuade more consumers to try them by making it easy to control different products from a familiar device, such as the iPhone. Apple announced its "HomeKit" software project a year ago, but isn't making the new products.