WELCOME TO The THE INTERNET OF THINGS REPORT
Wireless Communication Standards for the Internet of Things
Incard of Brazil - Joint Venture Results of Interprint, active in the security printing market and management of telecommunications services today...
Article | March 11, 2020
When people think of AI, it's easy to jump to the many possible uses seen in movies -- such as accessing secret areas with biometric data or robots completing human jobs -- but applying AI realistically requires architects and administrators to understand just how flexible AI is in a business setting. Tech leaders have rapidly increased the number of AI and IoT projects in many areas of their businesses, including customer experience, data analysis and security. When organizations apply AI into these different aspects, they can more effectively process the IoT data they create and further improve their operations and products. Popular movies have made customer experience AI one of the better-known examples of AI. Ads may not be as flashy as the personally tailored ones using customer biometrics as seen in The Minority Report, but it's easy to see how organizations will get there from the online ads that use AI to give consumers offers specific to their interests. Businesses use AI that learns from data analytics on customer behavior throughout the IoT customer journey.
As development teams race to build out AI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitly accessing raw user data. Conceived by Google in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Google’s “on-device machine learning” approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was either delivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.
The Internet of Things has given rise to a host of new standards and protocols. Still more protocols that originally existed for other purposes but are well suited to new IoT applications have been adopted by device manufacturers and application creators. Though in some senses IoT devices are the same as any other internet-connected device, the bandwidth, power, and transmission distance constraints inherent in many IoT applications require novel new solutions to the fundamental actions of connectivity, data transfer, device discovery, and communication. This article will serve as a brief glossary of terms related to IoT communication protocols and standards. Click here for a more complete introduction to connectivity options.
In the quickly evolving world of the IoT, multiple standards have developed in a short span of time, each with the goal of allowing smart home devices to communicate with each other and with multiple online services. One solution to this issue is the use of the Dotdot, an application layer developed for IoT devices to easily join networks of other similar devices and to communicate their status and capabilities in a standardized way, combined with Thread. Thread is an IP mesh network implementation designed for IoT device communication, promises to be a widely implemented standard in IoT device manufacture and development.
Keep me plugged in with the best
Join thousands of your peers and receive our weekly newsletter with the latest news, industry events, customer insights, and market intelligence.
Put your news, events, company, and promotional content in front of thousands of your peers and potential customers.
Not a member yet? Not a problem, Sign Up
Sign up to contribute and publish your news, events, brand, and content with the community for FREE