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5 top IoT security questions for manufacturers
| May 18, 2016
DSP Group®, Inc. (NASDAQ: DSPG) is a leading global provider of wireless chipset solutions for converged communications...
Article | April 6, 2020
Scaling Industrial IoT (IIoT) solutions requires a DevOps organization that can manage increased software and hardware complexity in terms of capability, capacity and footprint. DevOps is derived from Development and Operations and is one of the buzz words for ICT companies. Often it is the amalgamation of Software Developers from R&D and senior engineers from Operations into a new organization. Startups are faced with the challenge of how to quickly create a functioning DevOps organization that can scale with rapid growth. In this article, we will deal with the keys for success to scale software solutions with using an example of an Industrial IoT solution. We will look at how DevOps should function and discuss the important principles for software development, tools and operations.
AHR Expo used to be mostly a “mechanical engineering” event, and even in 2017, when I first got there, there were just a few companies who mentioned IoT or connectivity at their stands. Only the most prominent players in the HVACR industry presented their IoT solutions. In my conversations with companies at that time, no one was taking IoT very seriously. And it’s understandable, there already were Modbus, BacNet – well-defined protocols to connect machines to a PC or PLCs to make them work in unison without any Clouds and external access.
In 2018 when Apple unveiled its iconic iPhone X with a feature to unlock the phone with Face ID thereby eliminating the use of the home button, it met a lot of eye-rolls. Fast forward to now, people are in love with the biometrics enabled technologies. While iPhone X had a unimodal authentication system, gadget these days have updated themselves in a better way. Let’s try to have a better understanding of the Biometrics. Biometrics are a way to measure a person’s physical characteristics to verify their identity. It can be physiological traits, like fingerprints and eyes, or behavioral traits, that define the manner an individual respond to stimuli. These characteristics are unique to the person. Once collected the data compared with the pre-existing database to find a match. Accordingly, it then produces an outcome. There are many varieties in which this data is collected. Facial and voice recognition, iris and finger scanner, signature verification, hand geometry, keystroke, gait detectors are some of the examples.
In my previous post, I gave an explanation of basic IoT device management and why it’s insufficient for certain kinds of massive-scale IoT deployments. In addition to basic IoT device management, contextual IoT device management is necessary to ensure success when dealing with IoT solutions involving thousands to millions of devices. In this post, I explore some of the key aspects of contextual IoT device management, with real examples that demonstrate why you need to manage devices contextually if you’re building, buying, and/or implementing massive-scale IoT solutions.
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