On the Way to Full-Scale Home Automation

R-Style Lab has prepared a white paper covering home automation challenges and ways to address them successfully. Direct download the PDF below.

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In less than five years, home automation will be a $79 billion industry, while Smart Home penetration rate is projected to reach 20%. As of now, the market is dominated by a handful of companies including Amazon, Apple, and Google. Prominent vendors possess boundless financial resources, state-of-the-art R&D facilities, and the world’s best talent to bring their Home Automation concepts to life, leaving little room for Smart Home startups.

Does that mean startups should abandon their connected home project for good?

On the contrary, smaller companies have an excellent opportunity to fill the functionality gap in modern Home Automation systems!

Smart Homes are Surprisingly Dumb

Today, any gadget that connects to a mobile app is labeled as “smart.” We have smart thermostats, kitchen appliances, wristbands, and toys and speakers with built-in connectivity and voice assistants running on them. Nonetheless, our homes are hardly smarter than they were five years ago.

There are three factors undermining Home Automation as we know it:

  1. Common misconceptions about what a Smart Home really is: The majority of today’s connected home solutions simply enable homeowners to control gadgets remotely via dedicated Android and iOS apps. Even the Nest self-learning thermostat, which remembers users’ preferred settings and manages HVAC system components accordingly, is just an autonomous Wi-Fi-enabled gadget powered by supervised learning algorithms which are incapable of making situation-based decisions.
  2. Market fragmentation: Every company that ventures into Home Automation aims to create an ecosystem of their own. As a result, connected home products produced by different vendors often fail to work in sync and exchange data. Although some companies make their APIs and software libraries publicly available, thus encouraging 3rd-party developers to integrate new products into their solutions, the lack of data communication standards remains a huge barrier to Smart Home development and adoption.
  3. Technology limitations: An Amazon Echo speaker can surely get you a ride with Uber and manage your Spotify lists; still, it won’t understand voice commands with music playing in the background. Family Hub might tell you you’ve run out of milk, but it won’t order groceries from Target unless you intervene. Even Dash buttons—which is the closest we’ve got to automated home replenishment—are set up and operated via the Amazon app.

We now have semi-smart gadgets that require manual configuration and cannot communicate with other devices within a Smart Home system, let alone make decisions autonomously.

How can IoT startups succeed where Amazon and Apple failed?

Obviously, they should focus on building Smart Home solutions with advanced data processing capabilities powered by machine learning algorithms.

Designing Towards Situational Awareness

The failure to identify trigger events and make decisions based on both historical and real-time data is what prevents Smart Home vendors from creating products with mass-market appeal.

And we have to disappoint you: we’re still a few years away from the development and large-scale implementation of elaborate spatial sensing and data analytics solutions that would allow Smart Homes to identify objects and people with 100% accuracy, effectively manage background noise in speech recognition systems and remove friction from connected home set-up and operation.

However, forward-thinking techpreneurs can take a huge step forward by incorporating facial recognition, biometric access control, and Natural Language Processing (NLP) technologies into their connected home products.

#SmartHome techpreneurs should incorporate #FacialRecognition, #Biometric access control, and #NLP technologies into their #ConnectedHome products.|| #IoTForAll #HomeAutomation #Startups #Innovation @RStyleLab @AndrewKlubnikin Click To Tweet

Machine Learning: a Silver Bullet to Solve Home Automation Problems

Smart Home solutions based on machine learning exist in a variety of forms including security systems powered by facial and fingerprint recognition technologies and device management hubs with voice interfaces and connected home solutions with advanced data processing capabilities.

Thanks to the availability of IoT development boards, cheap sensors, open-source NLP tools, and cloud-based Machine Learning-as-a-Service solutions, IoT startups can validate their Smart Home concepts early on and create truly intelligent connected home offerings.

Besides situation-based decision-making, however, Smart Home enthusiasts should focus on device interoperability and fault-free performance. To meet these objectives, developers need to adopt a QA-driven approach to hardware design, ensure effective collaboration between hardware and low-level software development teams, and make use of reliable Smart Home protocols.

R-Style Lab—an IoT development company based in California—offers you this white paper covering Home Automation challenges and ways to successfully address them. Direct download our paper below to discover how machine learning-based connected home systems work under the hood, what technologies exist to assist developers in meeting project goals, and how to create a Smart Home solution users will adore.