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Jeremy Rifkin unveils a return to the local in an interconnected future

Internet of Things, local energy sources, and online collaboration underlie the Zero Marginal Cost Society.

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Stratasys’ Education, R&D departments and MIT’s Self-Assembly Lab are researching 4D printing — manufacturing one-off objects that can change their shapes or other physical characteristics in response to their environment. (View the video.)

Jeremy Rifkin is always predicting an avalanche of change: substitutes for human labor in The End of Work, pervasive genetic engineering in Algeny, and so on. Several interlocking themes run through his latest book, The Zero Marginal Cost Society. Behind everything lies the renewed importance of local resources: local energy production, local manufacturing, local governance. And the Internet that ties us all together (evolving into the Internet of Things) will, ironically, bolster local power.
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Materials that make up our world

Digital manufacturing is the future — reusable, composable, and rapid from top to bottom.

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Editor’s note: This is part two of a two-part series reflecting on the O’Reilly Solid Conference from the perspective of a data scientist. Normally we wouldn’t publish takeaways from an event held nearly two months ago, but these insights were so good we thought they needed to be shared.

In mid-May, I was at Solid, O’Reilly’s new conference on the convergence of hardware and software. In Part one of this series, I talked about the falling cost of bringing a hardware start-up to market, about the trends leading to that drop, and a few thoughts on how that relates to the role of a data scientist.

I mentioned two phrases that I’ve heard Jon Bruner say, in one form or another. The first, “merging of hardware and software,” was covered in the last piece. The other is the “exchange between the virtual and actual.” I also mentioned that I think the material future of physical stuff is up for grabs. What does that mean, and how do those two sentiments tie together? Read more…

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Governments can bridge costs and services gaps with sensor networks

Government sensor networks can streamline processes, cut labor costs, and improve services.

Contributing authors: Andre Bierzynski and Kevin Chrapaty.

Screenshot from the Waze app.

What if government agencies followed in the footsteps of Waze, a community-driven mobile phone app that collects location data through GPS and allows its users to report accidents and traffic jams, providing real-time, location-specific traffic alerts?

It’s not news to anyone who works in government that we live in a time of ever-tighter budgets and ever-increasing needs. The 2013 federal shutdown only highlighted this precarious situation: government finds it increasingly difficult to summon the resources and manpower needed to meet its current responsibilities, yet faces new ones after each Congressional session.

Sensor networks are an important emerging technology that some areas of government already are implementing to bridge the widening gap between the demand to reduce costs and the demand to improve services. The Department of Defense, for instance, uses RFID chips to monitor its supply chain more accurately, while the U.S. Geological Survey employs sensors to remotely monitor the bacterial levels of rivers and lakes in real time. Additionally, the General Services Administration has begun using sensors to measure and verify the energy efficiency of “green” buildings (PDF), and the Department of Transportation relies on sensors to monitor traffic and control traffic signals and roadways. All of which is productive, but more needs to be done. Read more…

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Mesh networking extends IoT reach

A Suitable Network Topology for Building Automation

XBee_Series_2_with_Whip_AntennaThis article is part of a series exploring the role of networking in the Internet of Things.

Today we are going to consider the attributes of wireless mesh networking, particularly in the context of our building monitoring and energy application.

A host of new mesh networking technologies came upon the scene in the mid-2000s through start-up ventures such as Millennial Net, Ember, Dust Networks, and others. The mesh network topology is ideally suited to provide broad area coverage for low-power, low-data rate applications found in application areas like industrial automation, home and commercial building automation, medical monitoring, and agriculture.

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Embracing hardware data

Looking at the collision of hardware and software through the eyes of a data scientist.

Raspberry Pi Board. Via Wikimedia Commons

Many aspects of a hardware device can be liberally prototyped. A Raspberry Pi (such as the one seen above) can function as a temporary bridge before ARM circuit boards are put into place.

Editor’s note: This is part one of a two-part series reflecting on the O’Reilly Solid Conference from the perspective of a data scientist. Normally we wouldn’t publish takeaways from an event held nearly two months ago, but these insights were so good we thought they needed to be shared.

In mid-May, I was at Solid, O’Reilly’s new conference on the convergence of hardware and software. I went in as something close to a blank slate on the subject, as someone with (I thought) not very strong opinions about hardware in general.

The talk on the grapevine in my community, data scientists who tend to deal primarily with web data, was that hardware data was the next big challenge, the place that the “alpha geeks” were heading. There are still plenty of big problems left to solve on the web, but I was curious enough to want to go check out Solid to see if I was missing out on the future. I don’t have much experience with hardware — beyond wiring up LEDs as a kid, making bird houses in shop class in high school, and mucking about with an Arduino in college. Read more…

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There are many use cases for graph databases and analytics

Business users are becoming more comfortable with graph analytics.

GraphLab graphThe rise of sensors and connected devices will lead to applications that draw from network/graph data management and analytics. As the number of devices surpasses the number of people — Cisco estimates 50 billion connected devices by 2020 — one can imagine applications that depend on data stored in graphs with many more nodes and edges than the ones currently maintained by social media companies.

This means that researchers and companies will need to produce real-time tools and techniques that scale to much larger graphs (measured in terms of nodes & edges). I previously listed tools for tapping into graph data, and I continue to track improvements in accessibility, scalability, and performance. For example, at the just-concluded Spark Summit, it was apparent that GraphX remains a high-priority project within the Spark1 ecosystem.

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