Well, I'm not sure whether they dream or not. But what I can say with certainty is that they do like to nap.
At least this is what we discovered after my daughter and I setup a Raspberry Pi camera module and focused it on her pig Josie's cage.
The idea is that we wanted to have the Pi take pics of Josie throughout the day and send as tweets via her twitter account.
We used a python script using Tweepy along with a Raspberry Pi 2 Model B+ with the standard camera module, but quickly noticed that we were getting a lot of sleeping guinea pig pictures.
We thought it would be better if we only took pictures while she was awake and moving so decided to try to wire up some type of motion detector.
My initial thought was to use a photoresistor (since I had one laying around) and read the light levels near the sensor. The idea being that if the pig is in front of the sensor, the reading should be less bright.
For this we used a Raspberry Pi cobbler board so we could easily access the Pi GPIO header. The cobbler I had was for the A model and not compatible with the B+, but we were able to make it work as the first 24 pins are in the same sequence. We just bent the 25th pin out of the way so that the adapter would connect to the header.
We followed these directions for the photoresistor setup. Here's what the assembled prototype board looked like:
We attached it to the Pi and held the whole thing together using some "helping-hands" with alligator clips. Here's the rig:
So that being done, we put the camera rig near the cage and edited the script so that it calculated the standard deviation of a series of brightness readings. The thought being that a high variance would mean that a pig is on the move...take a pic.
Unfortunately, this did not pan out as expected as we quickly discovered that getting a reading was very slow, and that the analog sensor was prone to inaccuracies.
As a next attempt, I sprung for a $7 PIR sensor.
As this infrared sensor is designed to detect motion, I had little doubt we would be able to get a fast enough reading to take a pic while she was on the move.
We replaced the photoresistor with the PIR sensor and followed a modified version of these instructions to connect to the Pi.
This worked much more reliably, at least until Josie ate the ribbon cable attached to the camera. We still can't figure out how she was able to get to it.
Anyway, we replaced the cable and made another change or two to the code - increased the number of reads before giving up that she's not moving. Also added a random message list so that she tweets some text in addition to the picture. If she's moving, we take a pic, if not we just tweet some text.
This was a great weekend project. If you're interested in the Python code, you can find it here. Also, you can follow Josie on Twitter.
Analyzing the Bitcoin Block Chain with Splunk in 30 Minutes or Less
Interested in analyzing block chain data? In less than 30 minutes you can start indexing Block Chain data and building your own visualizations using Splunk.
Here's how:
Splunk is a platform that makes it easy to search, monitor, and analyze any machine data. By connecting to the blockchain.info API it's possible to index and visualize Bitcoin Block Chain data. Best of all Splunk provides a free license to ingest up to 500MB of data per day, which is more than enough to get started working with the Block Chain.
First step: You're going to need Splunk running on a server. Although Splunk is easy to install, I recommend just standing up a pre-installed server using the AWS Splunk Enterprise AMI
Your lowest cost option would be selecting the m3.medium sized instance. Your performance will be adequate, and your cost will be about $50 per month if running 24x7.
Second step: Install the Bitcoin Observer app on your newly created Splunk server. Log on to your AWS Management Console to get either the IP or public DNS name of your server.
Connect to your Splunk instance by navigating to: http://IP_ADDRESS:8000
Use admin for the login ID and the password is your AWS server instance-id
Once logged in select "Splunk Apps" and then search for "Bitcoin Observer". After the app is found, click on "Install Free".
This will install the app from Splunkbase. You will need an ID/password to be able to download the app, but registration is free.
Last step: Once the app is installed, you will need to run two simple commands from the server itself to be able to start the data collection. From the AWS Management Console, select your server instance and click on "Connect".
Select as a collection method: "A Java SSH Client directly from my browser (Java required)". Make sure to specify the correct path to your private key.
Once logged in to a shell prompt, type the following:
sudo su -
pip install blockchain
This installs the Python module that is needed to connect to the blockchain.info API. Once done Splunk will immediately start collecting data and will check for new blocks on the block chain every 5 minutes.
Within Splunk click on the "Bitcoin Observer" icon and this will bring you to the application.
Once in the application you can click on any of the menu choices to see the different visualizations that are available.
Documentation for Bitcoin Observer can be found here.
These are all of the steps, hopefully you will find that Splunk with the Bitcoin Observer app make it easy to examine the Bitcoin block chain.
By mashing Splunk with the blockchain.info API, it possible to build some interesting visualizations from Bitcoin block chain data.
A common misnomer is that Bitcoin transactions are anonymous, but that's not entirely true. The Bitcoin network doesn't actively conceal the IP addresses from which transactions were initiated.
As detailed in this cointext.com link, blockchain.info includes a "relayed_by" IP address with transaction information. This address represents the first IP that blockchain.info sees on the network relaying a transaction. In many cases the first IP equates to the actual client making the transaction.
Splunk has a built in maps widget and a function that can lookup IP addresses to determine geographic location. As you can see below, this makes it possible to get very specific when pinpointing a Bitcoin transaction location.
As bad as this might seem from a privacy perspective, there are ways to hide this info. Mobile wallets and clients which are not full Bitcoin nodes do not broadcast their IP addresses.
Also, if you do wish to run a full node and broadcast transactions it is easy to avoid a node that is collecting and displaying IP addresses. Simply place a list of nodes you want to connect to and Bitcoin won’t connect to any other nodes.
Check out the new Linino One developed by Dog Hunter. An Arduino board that supports OpenWRT linux. The board has built in wifi capabilities, providing a capable and inexpensive linux platform for IoT projects.
Being dumb in the existing educational system is bad enough. Failing to create a new way of learning adapted to contemporary circumstances might be a national disaster.
Learning through discovery is a better educational model for today's world.
The time is now to support the role of learning in the pursuit of discovery and to embrace the powerful agency of culture.
Handling Enterprise IoT Data - Splunk vs VMware vRealize
Is it possible to manage Internet of Things data with enterprise tools like Splunk and VMware vRealize Insight? #internetofthings #splunk #vmware
Two popular data analytics tools both claim that they are well positioned in the IoT space - to ingest and make sense of the tidal wave of data generated by IoT devices and sensors, but do their real world capabilities live up to the hype?
VMware recently released a white paper titled "Keeping Your Cloud Infrastructure Healthy With the Internet of Things" highlighting how their vRealize Insight suite could help not only with the analytics needed understand IoT data, but also understand the performance of the underlying cloud infrastructure needed to host IoT data.
For those who might be unaware, vRealize Insight is the result of a recent rebranding of VMware's VC Operations Manager (VCOPs) suite. It's a performance analytics platform that can easily ingest any time series data, with self learning capabilities that automatically discover trends and abnormalities. There's a dashboarding engine and the application allows users to model applications by tying disparate data sets together.
Splunk also has IoT marketing message with a page and video devoted to how their product is positioned to ingest and gain insight from large volumes of industrial and machine data.
For those unfamiliar with Splunk, it's a big data platform capable of easily indexing both structured and unstructured machine data from virtually any source. The software optimizes how the data is stored to drive scalability and ease of search. With a sophisticated search query language users can perform complex statistical analysis and create data visualizations with a built in dashboard engine.
On the surface both applications seem well poised for the challenges associated with massive IoT data streams, but what are the differentiators?
One of the biggest challenges with handling IoT data is scaling. IoT devices send out constant signals and all of that data needs to be captured, stored and analyzed. Both the Splunk and VMware products are architected to process very large data sets. Splunk uses a MapReduce model, which allows for storage and computation to be distributed throughout a cluster of machines. Splunk scales out linearly based on the number of machines in the cluster.
VMWare VCOPs scalability is much improved with their newest pending release. Their redesigned backend will use GemFire - a distributed data management platform which provides dynamic scalability, high performance and database-like persistence. Like Splunk, capacity is added linearly by adding nodes to a cluster.
Given that both platforms can potentially handle the amount of data associated with IoT, what about the data format?
This is one area where there's a clear distinction between the two products. Splunk can handle any type of machine data - both structured and unstructured, while VCOPs ingests mainly time series data. This is an important distinction as IoT can represent many different data formats depending on the implementation.
Another consideration is the frequency of updates. In some cases sensors can send near continual data streams at sub second update intervals, and the ability to handle this highlights another major difference between the two products.
I have personal experience integrating Splunk with market data feeds, and can say with confidence that Splunk can index data on sub-second intervals. VCOPs however has some limitations in this area. Out of the box VCOPs expects data to arrive on 5 minute intervals. Although it's possible to change the update frequency, VMWare strongly discourages configuring for sub-minute updates. In fact, VMWare suggests only changing the 5 minute default for individual metrics and only sparingly, to reduce the potential impact on system performance.
The relatively slow data intervals are a tradeoff however, for the benefit gained: a superior analytics engine that examines each individual data point to automatically discover performance anomalies based on learned behavior.
In summary, this post covers three considerations for handling Internet of Things data:
Scaling - handling storage and retrieval of the volume of data associated with "connected everything".
Data formatting - ability to easily consume and gain insight from data in different formats.
Data intervals - how quickly is the data arriving to your analytics engine.
Given these three characteristics, my opinion is that Splunk would be the better option to gain insight from the IoT data itself. However, given the strength of VMware's performance data analytics with vRealize Insight, I would suggest leveraging this product to manage the health of the underlying cloud infrastructure that actually hosts the IoT data.
A noticeable increase in IoT exhibitors at this year's Maker Faire. #MakerFaire #IoT
It's interesting how the exhibitor makeup tends to map to the hyped trends of the year.
In 2012 it was the coming of age of the 3D printer - 3D printer kits and niche manufacturers seemed to be everywhere. In 2013 it was the arrival of DIY drone kits and UAVs. This year there was still plenty of the former, but intermixed with product extensions like really large 3D printers, 3D printing services, and micro drones.
This year seems to mark the rise of IoT or "connected everything" awareness and products. There were a few particularly rememberable exhibits, including Littlebits, who offers "user based hardware" for easy modular prototyping and is featured in the header image for this post.
The Google trend chart below does a nice job showing which year each technology gained traction in overall public awareness. 3D printers by far were the most hyped as evident by the relative size of the search volume. Notice how each subsequent year each technology gained traction in terms of public awareness (in terms of search).
Contrast the Google trend chart with this year's Gartner Hype Cycle for Emerging Technologies (below). For those who are not aware, the Gartner Hype Cycle is supposed to be a measure of the maturity of emerging technologies. Typically the ones that are the most hyped are on the top of the curve.
Both 3D printing and Internet of Things are on the top of the hype curve while drones/UAVs are not listed - perhaps because drones aren't a fit for the scope of the report.
Notice how "Wearable User Interfaces" are also at the top of the curve. Perhaps this will be the category for 2015?
Solid Conference San Francisco 2014: Video Compilation Review
My review of O’Reilly's Solid Conference San Francisco 2014: Complete Video Compilation. #OReillySolid @OreillySolid
If you're an entrepreneur and interested in new opportunities created by the convergence of hardware and software, O'Reilly's video compilation of the 2014 Solid Conference offers a great introduction to the current landscape of the Internet of Things (or Internet of Everything depending on your euphemism of choice).
The videos are divided into six categories providing a well-rounded set of viewpoints:
"Keynotes" which include thought provoking commentaries from leaders in the space.
"Companies" which include lessons learned from entrepreneurs who have launched successful IOT businesses. (These are a set of must see videos if you're looking to avoid costly startup mistakes. )
"Machines" which provide useful advice from moving from prototypes to scalable manufacturing.
"Society" (perhaps the oddly named category) which includes videos related to practical use cases for intelligent hardware.
"Foundations" which is the "deep thought" presentation track which includes topics that are intended to expand the view of what’s possible with IoT technologies.
The last category, "Tools" is the most technical and provides specific implementation details.
A particularly solid discourse (excuse the pun) was the short Keynote clip from Jim Stogdill explaining the overall concept of Solid. Balanced with high-level videos like this one are clips that are intended to provide specific and actionable implementation advice like Rob Coneybeer's tutorial on hardware branding.
Overall the series includes over 100 videos encompassing 36 hours of content. If you are in the process of launching a hardware startup, purchasing this library is a no brainer, given the costly mistakes that can be avoided just by watching the Companies track.
If you're an industry outsider who is simply interested in gaining a thorough understanding about the possibilities promised by a converged hardware and software world, the $499 price might be considered somewhat steep, but in my opinion is still a buy, especially if you consider the depth of education being offered on a single topic.
In a recent Ted talk Rachel Botsman makes the case that trust is the new currency of the shared economy.
Is it possible to come up with a system to objectively evaluate the trustworthiness of someone prior to engaging in a transaction in the shared economy? An @innocentive challenge is looking for people who believe they can answer exactly that.
I believe such a system would require a social proof mechanism that can evaluate an individual across multiple social networks similar to Klout. Perhaps this could be coupled with a shared public ledger system to prove identity?
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