ENTRIES TAGGED "Industrial Internet"
Strata Week: The power of the Internet, wielded by machines and things
Jon Bruner's industrial Internet report; IBM, Belkin, and the Internet of Things; cars as software platforms; and coding is the job of the future.
Soon, everything will be an Internet platform
Ben Schiller at Fast Company took a look this week at a recent report by Jon Bruner on the industrial Internet. “According to Jon Bruner [the industrial Internet] is ‘machines becoming nodes on pervasive networks that use open protocols,’” writes Schiller. “And, to many others, it is as a big a deal as the Internet itself: essentially completing a job that’s only half-finished with web sites, email, Twitter, and so on.”
Shiller pulls some highlights from Bruner’s report, especially noting how the industrial Internet will effect various industries, such as energy, health care, and transport. Read more…
Strata Week: Political data mining “bait-and-switch”
Inaugural 2013 app has plans for your data, the "unprecedented" security issues of the Internet of Things, and optical switches speed up data centers.
Here are a few stories from the data space that caught my attention this week.
Inaugural 2013 app takes as much as it gives
The Presidential Inaugural Committee (PIC) launched the first official inaugural smartphone app, Inaugural 2013 (for iOS and for Android), Monday. Daniel Strauss reports in a post at The Hill that inauguration attendees can use the app to locate and RSVP to events, watch events via livestream, and navigate the event with an interactive map.
What isn’t front and center in the pomp and circumstance of the shiny new app are the terms of service and the privacy statement. Steve Friess at Politico points out that in the fine print, users are giving the PIC permission to share their data — phone numbers, email, home addresses, and GPS location data, for instance — “with candidates, organizations, groups or causes that [the PIC] believe have similar political viewpoints, principles or objectives.”
Gregory Ferenstein reports at TechCrunch that “privacy advocates find it troubling that the fine-print on the PIC’s website says it can use activity data ‘without limitation in advertising, fundraising and other communications in support of PIC and the principles of the Democratic party, without any right of compensation or attribution.’”
Software that keeps an eye on Grandma
Networked sensors and machine learning make it easy to see when things are out of the ordinary.
Much of health care — particularly for the elderly — is about detecting change, and, as the mobile health movement would have it, computers are very good at that. Given enough sensors, software can model an individual’s behavior patterns and then figure out when things are out of the ordinary — when gait slows, posture stoops or bedtime moves earlier.
Technology already exists that lets users set parameters for households they’re monitoring. Systems are available that send an alert if someone leaves the house in the middle of the night or sleeps past a preset time. Those systems involve context-specific hardware (i.e., a bed-pressure sensor) and conscientious modeling (you have to know what time your grandmother usually wakes up).
The next step would be a generic system. One that, following simple setup, would learn the habits of the people it monitors and then detect the sorts of problems that beset elderly people living alone — falls, disorientation, and so forth — as well as more subtle changes in behavior that could signal other health problems.
A group of researchers from Austria and Turkey has developed just such a system, which they presented at the IEEE’s Industrial Electronics Society meeting in Montreal in October.*
Activity as surmised in different rooms by the researchers’ machine-learning algorithms. Source: “Activity Recognition Using a Hierarchical Model.”
In their approach, the researchers train a machine-learning algorithm with several days of routine household activity using door and motion sensors distributed through the living space. The sensors aren’t associated with any particular room at the outset: their software algorithmically determines the relative positions of the sensors, then classifies the rooms that they’re in based on activity patterns over the course of the day. Read more…
