We know crawl spaces are creepy, sweaty, and confining but, hear us out…
You need to keep an eye on the humidity level in your crawl space, as it can seriously affect the whole house’s overall health. It’s ideal to be able to do this remotely (given the creepy, sweaty atmosphere of the space), and a Raspberry Pi allows this.
Jamie Bailey took to Medium to share his Raspberry Pi setup that allows him to monitor the humidity of the crawl space in his home from a mobile device and/or laptop. His setup lets you check on the current humidity level and also see the historical data over time. You can also set alarms to be sent to you via text or email whenever the humidity level exceeds a certain threshold.
The hardware you need
Power outlet or extension cord in your crawl space
Raspberry Pi (3 or 4) or Raspberry Pi Zero W (or WH)
The BME280 sensor has four pins you need to connect to your Raspberry Pi. This will send the humidity data to your Raspberry Pi, which you’ll have already set up to let you know what’s happening remotely.
BME280 VIN pin connects to GPIO pin 1 (3.3V)
BME280 GND pin connects to GPIO pin 6 (GND)
BME280 SCL pin connects to GPIO pin 5 (SCL)
BME280 SDA pin connects to GPIO pin 3 (SDA)
You can see the Raspberry Pi in a black case hanging in the centre against a floor joist.
Once you have all your software sorted and your hardware connected, turn your Raspberry Pi off and take it down to your crawl space (monitor, keyboard, and mouse are no longer necessary). Jamie advises hanging your Raspberry Pi from the floor joists instead of letting it touch the ground, to avoid contact with any water. He put a nail in one of the floor joists and draped the power cord over the nail (see above). Turn your tiny computer on, make sure data starts flowing into your dashboard, and you’ve got yourself remote humidity sensor!
PS We’re English so… is a crawl space the same as an attic or what? Asking for a friend!
This is the story of Occu-Pi, or how a magnet, a Raspberry Pi, and a barrel bolt saved an office team from queuing for the toilet.
The toil of toilet queuing
When Brian W. Wolter’s employer moved premises, the staff’s main concern as the dearth of toilets at the new office, and the increased queuing time this would lead to:
Our previous office had long been plagued by unreasonably long bathroom lines. At several high-demand periods throughout the day we’d be forced to wait three, four, five people deep while complaining bitterly to each other until our turn to use the facilities arrived. With even fewer bathrooms in our new office, concern about timely access was naturally high.
Faced with this problem, the in-house engineers decided to find a technological solution.
Occu-Pi
The main thing the engineers had to figure out was just how to determine the difference between a closed door and an occupied stall. Brian explains in his write-up:
There is one notable wrinkle: it’s not enough to know the door is closed, you need to know if the bathroom is actually in use — that is, locked from the inside. After considering and discarding a variety of ‘creative’ solutions (no thank you, motion sensors and facial recognition), we landed on a straightforward and reliable approach.
The team ended up using a magnet attached to the door’s barrel bolt to trigger a notification. Simply shutting the door doesn’t act as a trigger — the bolt needs to lock the door to set off a magnetic switch. That switch then triggers both LED notifications and updates to a dedicated Slack channel.
For the technically-minded, Occu-Pi is a pretty straightforward build. And those wanting to learn more about it can find a full write-up in Brian’s Medium post.
We’ve seen a few different toilet notification projects over the years, for example this project from DIY Tryin’ using a similar trigger plus a website. What’s nice about Occu-Pi, however, is the simplicity of its design and the subtle use of Slack — Pi Tower’s favoured platform for office shenanigans.
One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”
Tadaaaa! The BYO weather station fully assembled.
Our Oracle Weather Station
In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.
The original Raspberry Pi Oracle Weather Station HAT
We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.
Our new BYO weather station guide
We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!
Fun with meteorological experiments!
Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.
Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.
There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.
Who should try this build
We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.
The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.
You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.
For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.
Our plans for the guide
Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!
*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.
The German charity Save Nemo works to protect coral reefs, and they are developing Nemo-Pi, an underwater “weather station” that monitors ocean conditions. Right now, you can vote for Save Nemo in the Google.org Impact Challenge.
Save Nemo
The organisation says there are two major threats to coral reefs: divers, and climate change. To make diving saver for reefs, Save Nemo installs buoy anchor points where diving tour boats can anchor without damaging corals in the process.
In addition, they provide dos and don’ts for how to behave on a reef dive.
The Nemo-Pi
To monitor the effects of climate change, and to help divers decide whether conditions are right at a reef while they’re still on shore, Save Nemo is also in the process of perfecting Nemo-Pi.
This Raspberry Pi-powered device is made up of a buoy, a solar panel, a GPS device, a Pi, and an array of sensors. Nemo-Pi measures water conditions such as current, visibility, temperature, carbon dioxide and nitrogen oxide concentrations, and pH. It also uploads its readings live to a public webserver.
The Save Nemo team is currently doing long-term tests of Nemo-Pi off the coast of Thailand and Indonesia. They are also working on improving the device’s power consumption and durability, and testing prototypes with the Raspberry Pi Zero W.
The web dashboard showing live Nemo-Pi data
Long-term goals
Save Nemo aims to install a network of Nemo-Pis at shallow reefs (up to 60 metres deep) in South East Asia. Then diving tour companies can check the live data online and decide day-to-day whether tours are feasible. This will lower the impact of humans on reefs and help the local flora and fauna survive.
A healthy coral reef
Nemo-Pi data may also be useful for groups lobbying for reef conservation, and for scientists and activists who want to shine a spotlight on the awful effects of climate change on sea life, such as coral bleaching caused by rising water temperatures.
A bleached coral reef
Vote now for Save Nemo
If you want to help Save Nemo in their mission today, vote for them to win the Google.org Impact Challenge:
Click “Abstimmen” in the footer of the page to vote
Click “JA” in the footer to confirm
Voting is open until 6 June. You can also follow Save Nemo on Facebook or Twitter. We think this organisation is doing valuable work, and that their projects could be expanded to reefs across the globe. It’s fantastic to see the Raspberry Pi being used to help protect ocean life.
Warning: a GIF used in today’s blog contains flashing images.
Students at the University of Bremen, Germany, have built a wearable camera that records the seconds of vision lost when you blink. Augenblick uses a Raspberry Pi Zero and Camera Module alongside muscle sensors to record footage whenever you close your eyes, producing a rather disjointed film of the sights you miss out on.
Blink and you’ll miss it
The average person blinks up to five times a minute, with each blink lasting 0.5 to 0.8 seconds. These half-seconds add up to about 30 minutes a day. What sights are we losing during these minutes? That is the question asked by students Manasse Pinsuwan and René Henrich when they set out to design Augenblick.
Blinking is a highly invasive mechanism for our eyesight. Every day we close our eyes thousands of times without noticing it. Our mind manages to never let us wonder what exactly happens in the moments that we miss.
Capturing lost moments
For Augenblick, the wearer sticks MyoWare Muscle Sensor pads to their face, and these detect the electrical impulses that trigger blinking.
Two pads are applied over the orbicularis oculi muscle that forms a ring around the eye socket, while the third pad is attached to the cheek as a neutral point.
Biology fact: there are two muscles responsible for blinking. The orbicularis oculi muscle closes the eye, while the levator palpebrae superioris muscle opens it — and yes, they both sound like the names of Harry Potter spells.
The sensor is read 25 times a second. Whenever it detects that the orbicularis oculi is active, the Camera Module records video footage.
Pressing a button on the side of the Augenblick glasses set the code running. An LED lights up whenever the camera is recording and also serves to confirm the correct placement of the sensor pads.
The Pi Zero saves the footage so that it can be stitched together later to form a continuous, if disjointed, film.
Learn more about the Augenblick blink camera
You can find more information on the conception, design, and build process of Augenblickhere in German, with a shorter explanation including lots of photos here in English.
And if you’re keen to recreate this project, our free project resource for a wearable Pi Zero time-lapse camera will come in handy as a starting point.
It’s a public holiday here today (yes, again). So, while we indulge in the traditional pastime of barbecuing stuff (ourselves, mainly), here’s a little trove of Pi projects that cater for our various furry friends.
Project Floofball
Nicole Horward created Project Floofball for her hamster, Harold. It’s an IoT hamster wheel that uses a Raspberry Pi and a magnetic door sensor to log how far Harold runs.
JaganK3 used to work long hours that meant he couldn’t be there to feed his dog on time. He found that he couldn’t buy an automated feeder in India without paying a lot to import one, so he made one himself. It uses a Raspberry Pi to control a motor that turns a dispensing valve in a hopper full of dry food, giving his dog a portion of food at set times.
He also added a web cam for live video streaming, because he could. Find out more in JaganK3’s Instructable for his pet feeder.
Shark laser cat toy
Sam Storino, meanwhile, is using a Raspberry Pi to control a laser-pointer cat toy with a goshdarned SHARK (which is kind of what I’d expect from the guy who made the steampunk-looking cat feeder a few weeks ago). The idea is to keep his cats interested and active within the confines of a compact city apartment.
Post with 52 votes and 7004 views. Tagged with cat, shark, lasers, austin powers, raspberry pi; Shared by JeorgeLeatherly. Raspberry Pi Automatic Cat Laser Pointer Toy
If I were a cat, I would definitely be entirely happy with this. Find out more on Sam’s website.
All of these makers are generous in acknowledging the tutorials and build logs that helped them with their projects. It’s lovely to see the Raspberry Pi and maker community working like this, and I bet their projects will inspire others too.
Now, if you’ll excuse me. I’m late for a barbecue.
Naturebytes are making their weatherproof Wildlife Cam Case available as a standalone product for the first time, a welcome addition to the Raspberry Pi ecosystem that should take some of the hassle out of your outdoor builds.
Weatherproofing digital making projects
People often use Raspberry Pis and Camera Modules for outdoorprojects, but weatherproofing your set-up can be tricky. You need to keep water — and tiny creatures — out, but you might well need access for wires and cables, whether for power or sensors; if you’re using a camera, it’ll need something clear and cleanable in front of the lens. You can use sealant, but if you need to adjust anything that you’ve applied it to, you’ll have to remove it and redo it. While we’ve seen a few reasonable options available to buy, the choice has never been what you’d call extensive.
The Wildlife Cam Case is ideal for nature camera projects, of course, but it’ll also be useful for anyone who wants to take their Pi outdoors. It has weatherproof lenses that are transparent to visible and IR light, for all your nature observation projects. Its opening is hinged to allow easy access to your hardware, and the case has waterproof access for cables. Inside, there’s a mount for fixing any model of Raspberry Pi and camera, as well as many other components. On top of all that, the case comes with a sturdy nylon strap to make it easy to attach it to a post or a tree.
Order yours now!
At the moment, Naturebytes are producing a limited run of the cases. The first batch of 50 are due to be dispatched next week to arrive just in time for the Bank Holiday weekend in the UK, so get them while they’re hot. It’s the perfect thing for recording a timelapse of exactly how quickly the slugs obliterate your vegetable seedlings, and of lots more heartening things that must surely happen in gardens other than mine.
We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.
I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:
Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.
Camera Control – Tim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.
Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!
Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.
All About AWS IoT 1-Click As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:
Architects can dream up applications for inexpensive, low-powered devices.
Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.
Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.
Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.
I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.
Who’s Got the Button? We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.
The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.
The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.
We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].
AWS IoT 1-Click Concepts I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.
Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.
Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.
Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).
Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”
Getting Started with AWS IoT 1-Click Let’s set up an IoT button using the AWS IoT 1-Click Console:
If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):
The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:
Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:
I am now ready to put them to use. I click on Projects, and then Create a project:
I name and describe my project, and click Next to proceed:
Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):
The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:
I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:
I can inspect my project and see that everything looks good:
I click on the buttons and the SMS messages appear:
I can monitor device activity in the AWS IoT 1-Click Console:
And also in the Lambda Console:
The Lambda function itself is also accessible, and can be used as-is or customized:
As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.
Now Available I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!
Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.
Yesterday, I received an email from someone called Mayank Sinha, showing us the Raspberry Pi home security project he’s been working on. He got in touch particularly because, he writes, the Raspberry Pi community has given him “immense support” with his build, and he wanted to dedicate it to the commmunity as thanks.
Mayank’s project is named Asfaleia, a Greek word that means safety, certainty, or security against threats. It’s part of an honourable tradition dating all the way back to 2012: it’s a prototype housed in a polystyrene box, using breadboards and jumper leads and sticky tape. And it’s working! Take a look.
An IOT based home security system. The link to the code: https://github.com/mayanksinha11/Asfaleia
Home security with Asfaleida
Asfaleia has a PIR (passive infrared) motion sensor, an IR break beam sensor, and a gas sensor. All are connected to a Raspberry Pi 3 Model B, the latter two via a NodeMCU board. Mayank currently has them set up in a box that’s divided into compartments to model different rooms in a house.
All the best prototypes have sticky tape or rubber bands
If the IR sensors detect motion or a broken beam, the webcam takes a photo and emails it to the build’s owner, and the build also calls their phone (I like your ringtone, Mayank). If the gas sensor detects a leak, the system activates an exhaust fan via a small relay board, and again the owner receives a phone call. The build can also authenticate users via face and fingerprint recognition. The software that runs it all is written in Python, and you can see Mayank’s code on GitHub.
Of prototypes and works-in-progess
Reading Mayank’s email made me very happy yesterday. We know that thousands of people in our community give a great deal of time and effort to help others learn and make things, and it is always wonderful to see an example of how that support is helping someone turn their ideas into reality. It’s great, too, to see people sharing works-in-progress, as well as polished projects! After all, the average build is more likely to feature rubber bands and Tupperware boxes than meticulously designed laser-cut parts or expert joinery. Mayank’s YouTube channel shows earlier work on this and another Pi project, and I hope he’ll continue to document his builds.
So here’s to Raspberry Pi projects big, small, beginner, professional, endlessly prototyped, unashamedly bodged, unfinished or fully working, shonky or shiny. Please keep sharing them all!
I was sure that somewhere there must be physically-lightweight sensors with simple power, simple networking, and a lightweight protocol that allowed them to squirt their data down the network with a minimum of overhead. So my interest was piqued when Jan-Piet Mens spoke at FLOSS UK’s Spring Conference on “Small Things for Monitoring”. Once he started passing working demonstration systems around the room without interrupting the demonstration, it was clear that MQTT was what I’d been looking for.
At the moment I’m spending my evenings watching all of Star Trek in order. Yes, I have watched it before (but with some really big gaps). Yes, including the animated series (I’m up to The Terratin Incident). So I’m gratified to find this beautiful The Original Series–style tricorder build.
At this year’s Replica Prop Forum showcase, we meet up once again wtih Brian Mix, who brought his new Star Trek TOS Tricorder. This beautiful replica captures the weight and finish of the filming hand prop, and Brian has taken it one step further with some modern-day electronics!
A what now?
If you don’t know what a tricorder is, which I guess is faintly possible, the easiest way I can explain is to steal words that Liz wrote when Recanthamade one back in 2013. It’s “a made-up thing used by the crew of the Enterprise to measure stuff, store data, and scout ahead remotely when exploring strange new worlds, seeking out new life and new civilisations, and all that jazz.”
A brief history of Picorders
We’ve seen other Raspberry Pi–based realisations of this iconic device. Recantha’s LEGO-cased tricorder delivered some authentic functionality, including temperature sensors, an ultrasonic distance sensor, a photosensor, and a magnetometer. Michael Hahn’s tricorder for element14’s Sci-Fi Your Pi competition in 2015 packed some similar functions, along with Original Series audio effects, into a neat (albeit non-canon) enclosure.
Brian Mix’s Original Series tricorder
Brian Mix’s tricorder, seen in the video above from Tested at this year’s Replica Prop Forum showcase, is based on a high-quality kit into which, he discovered, a Raspberry Pi just fits. He explains that the kit is the work of the late Steve Horch, a special effects professional who provided props for later Star Trek series, including the classic Deep Space Nine episode Trials and Tribble-ations.
Dax, equipped for time travel
This episode’s plot required sets and props — including tricorders — replicating the USS Enterprise of The Original Series, and Steve Horch provided many of these. Thus, a tricorder kit from him is about as close to authentic as you can possibly find unless you can get your hands on a screen-used prop. The Pi allows Brian to drive a real display and a speaker: “Being the geek that I am,” he explains, “I set it up to run every single Original Series Star Trek episode.”
Even more wonderful hypothetical tricorders that I would like someone to make
This tricorder is beautiful, and it makes me think how amazing it would be to squeeze in some of the sensor functionality of the devices depicted in the show. Space in the case is tight, but it looks like there might be a little bit of depth to spare — enough for an IMU, maybe, or a temperature sensor. I’m certain the future will bring more Pi tricorder builds, and I, for one, can’t wait. Please tell us in the comments if you’re planning something along these lines, and, well, I suppose some other sci-fi franchises have decent Pi project potential too, so we could probably stand to hear about those.
If you’re commenting, no spoilers please past The Animated Series S1 E11. Thanks.
In November 2013, the first commercially available helium-filled hard drive was introduced by HGST, a Western Digital subsidiary. The 6 TB drive was not only unique in being helium-filled, it was for the moment, the highest capacity hard drive available. Fast forward a little over 4 years later and 12 TB helium-filled drives are readily available, 14 TB drives can be found, and 16 TB helium-filled drives are arriving soon.
Backblaze has been purchasing and deploying helium-filled hard drives over the past year and we thought it was time to start looking at their failure rates compared to traditional air-filled drives. This post will provide an overview, then we’ll continue the comparison on a regular basis over the coming months.
The Promise and Challenge of Helium Filled Drives
We all know that helium is lighter than air — that’s why helium-filled balloons float. Inside of an air-filled hard drive there are rapidly spinning disk platters that rotate at a given speed, 7200 rpm for example. The air inside adds an appreciable amount of drag on the platters that in turn requires an appreciable amount of additional energy to spin the platters. Replacing the air inside of a hard drive with helium reduces the amount of drag, thereby reducing the amount of energy needed to spin the platters, typically by 20%.
We also know that after a few days, a helium-filled balloon sinks to the ground. This was one of the key challenges in using helium inside of a hard drive: helium escapes from most containers, even if they are well sealed. It took years for hard drive manufacturers to create containers that could contain helium while still functioning as a hard drive. This container innovation allows helium-filled drives to function at spec over the course of their lifetime.
Checking for Leaks
Three years ago, we identified SMART 22 as the attribute assigned to recording the status of helium inside of a hard drive. We have both HGST and Seagate helium-filled hard drives, but only the HGST drives currently report the SMART 22 attribute. It appears the normalized and raw values for SMART 22 currently report the same value, which starts at 100 and goes down.
To date only one HGST drive has reported a value of less than 100, with multiple readings between 94 and 99. That drive continues to perform fine, with no other errors or any correlating changes in temperature, so we are not sure whether the change in value is trying to tell us something or if it is just a wonky sensor.
Helium versus Air-Filled Hard Drives
There are several different ways to compare these two types of drives. Below we decided to use just our 8, 10, and 12 TB drives in the comparison. We did this since we have helium-filled drives in those sizes. We left out of the comparison all of the drives that are 6 TB and smaller as none of the drive models we use are helium-filled. We are open to trying different comparisons. This just seemed to be the best place to start.
The most obvious observation is that there seems to be little difference in the Annualized Failure Rate (AFR) based on whether they contain helium or air. One conclusion, given this evidence, is that helium doesn’t affect the AFR of hard drives versus air-filled drives. My prediction is that the helium drives will eventually prove to have a lower AFR. Why? Drive Days.
Let’s go back in time to Q1 2017 when the air-filled drives listed in the table above had a similar number of Drive Days to the current number of Drive Days for the helium drives. We find that the failure rate for the air-filled drives at the time (Q1 2017) was 1.61%. In other words, when the drives were in use a similar number of hours, the helium drives had a failure rate of 1.06% while the failure rate of the air-filled drives was 1.61%.
Helium or Air?
My hypothesis is that after normalizing the data so that the helium and air-filled drives have the same (or similar) usage (Drive Days), the helium-filled drives we use will continue to have a lower Annualized Failure Rate versus the air-filled drives we use. I expect this trend to continue for the next year at least. What side do you come down on? Will the Annualized Failure Rate for helium-filled drives be better than air-filled drives or vice-versa? Or do you think the two technologies will be eventually produce the same AFR over time? Pick a side and we’ll document the results over the next year and see where the data takes us.
In this article from The MagPi issue 69, David Crookes explains how Daniel Berrangé took an old Kodak Brownie from the 1950s and turned it into a quirky digital camera. Get your copy of The MagPi magazine in stores now, or download it as a free PDF here.
The Kodak Box Brownie
When Kodak unveiled its Box Brownie in 1900, it did so with the slogan ‘You press the button, we do the rest.’ The words referred to the ease-of-use of what was the world’s first mass-produced camera. But it could equally apply to Daniel Berrangé’s philosophy when modifying it for the 21st century. “I wanted to use the Box Brownie’s shutter button to trigger image capture, and make it simple to use,” he tells us.
Daniel’s project grew from a previous effort in which he placed a pinhole webcam inside a ladies’ powder compact case. “The Box Brownie project is essentially a repeat of that design but with a normal lens instead of a pinhole, a real camera case, and improved software to enable a shutter button. Ideally, it would look unchanged from when it was shooting film.”
Webcam woes
At first, Daniel looked for a cheap webcam, intending to spend no more than the price of a Pi Zero. This didn’t work out too well. “The low-light performance of the webcam was not sufficient to make a pinhole camera so I just decided to make a ‘normal’ digital camera instead,” he reveals. To that end, he began removing some internal components from the Box Brownie. “With the original lens removed, the task was to position the webcam’s electronic light sensor (the CCD) and lens as close to the front of the camera as possible,” Daniel explains. “In the end, the CCD was about 15 mm away from the front aperture of the camera, giving a field of view that was approximately the same as the unmodified camera would achieve.”
It was then time for him to insert the Raspberry Pi, upon which was a custom ‘init’ binary that loads a couple of kernel modules to run the webcam, mount the microSD file system, and launch the application binary. Here, Daniel found he was in luck. “I’d noticed that the size of a 620 film spool (63 mm) was effectively the same as the width of a Raspberry Pi Zero (65 mm), so it could be held in place between the film spool grips,” he recalls. “It was almost as if it was designed with this in mind.”
Shutter success
In order to operate the camera, Daniel had to work on the shutter button. “The Box Brownie’s shutter button is entirely mechanical, driven by a handful of levers and springs,” Daniel explains. “First, the Pi Zero needs to know when the shutter button is pressed and second, the physical shutter has to be open while the webcam is capturing the image. Rather than try to synchronise image capture with the fraction of a second that the physical shutter is open, a bit of electrical tape was used on the shutter mechanism to keep it permanently open.”
Daniel made use of the Pi Zero’s GPIO pins to detect the pressing of the shutter button. It determines if each pin is at 0 or 5 volts. “My thought was that I could set a GPIO pin high to 5 V, and then use the action of the shutter button to short it to ground, and detect this change in level from software.”
This initially involved using a pair of bare wires and some conductive paint, although the paint was later replaced by a piece of tinfoil. But with the button pressed, the GPIO pin level goes to zero and the device constantly captures still images until the button is released. All that’s left to do is smile and take the perfect snap.
Creating these defenses is the goal of NIST’s lightweight cryptography initiative, which aims to develop cryptographic algorithm standards that can work within the confines of a simple electronic device. Many of the sensors, actuators and other micromachines that will function as eyes, ears and hands in IoT networks will work on scant electrical power and use circuitry far more limited than the chips found in even the simplest cell phone. Similar small electronics exist in the keyless entry fobs to newer-model cars and the Radio Frequency Identification (RFID) tags used to locate boxes in vast warehouses.
All of these gadgets are inexpensive to make and will fit nearly anywhere, but common encryption methods may demand more electronic resources than they possess.
The Internet of Things (IoT) has precipitated to an influx of connected devices and data that can be mined to gain useful business insights. If you own an IoT device, you might want the data to be uploaded seamlessly from your connected devices to the cloud so that you can make use of cloud storage and the processing power to perform sophisticated analysis of data. To upload the data to the AWS Cloud, devices must pass authentication and authorization checks performed by the respective AWS services. The standard way of authenticating AWS requests is the Signature Version 4 algorithm that requires the caller to have an access key ID and secret access key. Consequently, you need to hardcode the access key ID and the secret access key on your devices. Alternatively, you can use the built-in X.509 certificate as the unique device identity to authenticate AWS requests.
AWS IoT has introduced the credentials provider feature that allows a caller to authenticate AWS requests by having an X.509 certificate. The credentials provider authenticates a caller using an X.509 certificate, and vends a temporary, limited-privilege security token. The token can be used to sign and authenticate any AWS request. Thus, the credentials provider relieves you from having to manage and periodically refresh the access key ID and secret access key remotely on your devices.
In the process of retrieving a security token, you use AWS IoT to create a thing (a representation of a specific device or logical entity), register a certificate, and create AWS IoT policies. You also configure an AWS Identity and Access Management (IAM) role and attach appropriate IAM policies to the role so that the credentials provider can assume the role on your behalf. You also make an HTTP-over-Transport Layer Security (TLS) mutual authentication request to the credentials provider that uses your preconfigured thing, certificate, policies, and IAM role to authenticate and authorize the request, and obtain a security token on your behalf. You can then use the token to sign any AWS request using Signature Version 4.
In this blog post, I explain the AWS IoT credentials provider design and then demonstrate the end-to-end process of retrieving a security token from AWS IoT and using the token to write a temperature and humidity record to a specific Amazon DynamoDB table.
Note: This post assumes you are familiar with AWS IoT and IAM to perform steps using the AWS CLI and OpenSSL. Make sure you are running the latest version of the AWS CLI.
Overview of the credentials provider workflow
The following numbered diagram illustrates the credentials provider workflow. The diagram is followed by explanations of the steps.
To explain the steps of the workflow as illustrated in the preceding diagram:
The AWS IoT device uses the AWS SDK or custom client to make an HTTPS request to the credentials provider for a security token. The request includes the device X.509 certificate for authentication.
The credentials provider forwards the request to the AWS IoT authentication and authorization module to verify the certificate and the permission to request the security token.
If the certificate is valid and has permission to request a security token, the AWS IoT authentication and authorization module returns success. Otherwise, it returns failure, which goes back to the device with the appropriate exception.
If assuming the role succeeds, AWS STS returns a temporary, limited-privilege security token to the credentials provider.
The credentials provider returns the security token to the device.
The AWS SDK on the device uses the security token to sign an AWS request with AWS Signature Version 4.
The requested service invokes IAM to validate the signature and authorize the request against access policies attached to the preconfigured IAM role.
If IAM validates the signature successfully and authorizes the request, the request goes through.
In another solution, you could configure an AWS Lambda rule that ingests your device data and sends it to another AWS service. However, in applications that require the uploading of large files such as videos or aggregated telemetry to the AWS Cloud, you may want your devices to be able to authenticate and send data directly to the AWS service of your choice. The credentials provider enables you to do that.
Outline of the steps to retrieve and use security token
Perform the following steps as part of this solution:
Create an AWS IoT thing: Start by creating a thing that corresponds to your home thermostat in the AWS IoT thing registry database. This allows you to authenticate the request as a thing and use thing attributes as policy variables in AWS IoT and IAM policies.
Register a certificate: Create and register a certificate with AWS IoT, and attach it to the thing for successful device authentication.
Create and configure an IAM role: Create an IAM role to be assumed by the service on behalf of your device. I illustrate how to configure a trust policy and an access policy so that AWS IoT has permission to assume the role, and the token has necessary permission to make requests to DynamoDB.
Create a role alias: Create a role alias in AWS IoT. A role alias is an alternate data model pointing to an IAM role. The credentials provider request must include a role alias name to indicate which IAM role to assume for obtaining a security token from AWS STS. You may update the role alias on the server to point to a different IAM role and thus make your device obtain a security token with different permissions.
Attach a policy: Create an authorization policy with AWS IoT and attach it to the certificate to control which device can assume which role aliases.
Request a security token: Make an HTTPS request to the credentials provider and retrieve a security token and use it to sign a DynamoDB request with Signature Version 4.
Use the security token to sign a request: Use the retrieved token to sign a request to DynamoDB and successfully write a temperature and humidity record from your home thermostat in a specific table. Thus, starting with an X.509 certificate on your home thermostat, you can successfully upload your thermostat record to DynamoDB and use it for further analysis. Before the availability of the credentials provider, you could not do this.
Deploy the solution
1. Create an AWS IoT thing
Register your home thermostat in the AWS IoT thing registry database by creating a thing type and a thing. You can use the AWS CLI with the following command to create a thing type. The thing type allows you to store description and configuration information that is common to a set of things.
Now, you need to have a Certificate Authority (CA) certificate, sign a device certificate using the CA certificate, and register both certificates with AWS IoT before your device can authenticate to AWS IoT. If you do not already have a CA certificate, you can use OpenSSL to create a CA certificate, as described in Use Your Own Certificate. To register your CA certificate with AWS IoT, follow the steps on Registering Your CA Certificate.
You then have to create a device certificate signed by the CA certificate and register it with AWS IoT, which you can do by following the steps on Creating a Device Certificate Using Your CA Certificate. Save the certificate and the corresponding key pair; you will use them when you request a security token later. Also, remember the password you provide when you create the certificate.
Run the following command in the AWS CLI to attach the device certificate to your thing so that you can use thing attributes in policy variables.
If the attach-thing-principal command succeeds, the output is empty.
3. Configure an IAM role
Next, configure an IAM role in your AWS account that will be assumed by the credentials provider on behalf of your device. You are required to associate two policies with the role: a trust policy that controls who can assume the role, and an access policy that controls which actions can be performed on which resources by assuming the role.
The following trust policy grants the credentials provider permission to assume the role. Put it in a text document and save the document with the name, trustpolicyforiot.json.
The following access policy allows DynamoDB operations on the table that has the same name as the thing name that you created in Step 1, MyHomeThermostat, by using credentials-iot:ThingName as a policy variable. I explain after Step 5 about using thing attributes as policy variables. Put the following policy in a text document and save the document with the name, accesspolicyfordynamodb.json.
Finally, run the following command in the AWS CLI to attach the access policy to your role.
aws iam attach-role-policy --role-name dynamodb-access-role --policy-arn arn:aws:iam::<your_aws_account_id>:policy/accesspolicyfordynamodb
If the attach-role-policy command succeeds, the output is empty.
Configure the PassRole permissions
The IAM role that you have created must be passed to AWS IoT to create a role alias, as described in Step 4. The user who performs the operation requires iam:PassRole permission to authorize this action. You also should add permission for the iam:GetRole action to allow the user to retrieve information about the specified role. Create the following policy to grant iam:PassRole and iam:GetRole permissions. Name this policy, passrolepermission.json.
Now, run the following command to attach the policy to the user.
aws iam attach-user-policy --policy-arn arn:aws:iam::<your_aws_account_id>:policy/passrolepermission --user-name <user_name>
If the attach-user-policy command succeeds, the output is empty.
4. Create a role alias
Now that you have configured the IAM role, you will create a role alias with AWS IoT. You must provide the following pieces of information when creating a role alias:
RoleAlias: This is the primary key of the role alias data model and hence a mandatory attribute. It is a string; the minimum length is 1 character, and the maximum length is 128 characters.
RoleArn: This is the Amazon Resource Name (ARN) of the IAM role you have created. This is also a mandatory attribute.
CredentialDurationSeconds: This is an optional attribute specifying the validity (in seconds) of the security token. The minimum value is 900 seconds (15 minutes), and the maximum value is 3,600 seconds (60 minutes); the default value is 3,600 seconds, if not specified.
Run the following command in the AWS CLI to create a role alias. Use the credentials of the user to whom you have given the iam:PassRole permission.
You created and registered a certificate with AWS IoT earlier for successful authentication of your device. Now, you need to create and attach a policy to the certificate to authorize the request for the security token.
Let’s say you want to allow a thing to get credentials for the role alias, Thermostat-dynamodb-access-role-alias, with thing owner Alice, thing type thermostat, and the thing attached to a principal. The following policy, with thing attributes as policy variables, achieves these requirements. After this step, I explain more about using thing attributes as policy variables. Put the policy in a text document, and save it with the name, alicethermostatpolicy.json.
If the attach-policy command succeeds, the output is empty.
You have completed all the necessary steps to request an AWS security token from the credentials provider!
Using thing attributes as policy variables
Before I show how to request a security token, I want to explain more about how to use thing attributes as policy variables and the advantage of using them. As a prerequisite, a device must provide a thing name in the credentials provider request.
Thing substitution variables in AWS IoT policies
AWS IoT Simplified Permission Management allows you to associate a connection with a specific thing, and allow the thing name, thing type, and other thing attributes to be available as substitution variables in AWS IoT policies. You can write a generic AWS IoT policy as in alicethermostatpolicy.json in Step 5, attach it to multiple certificates, and authorize the connection as a thing. For example, you could attach alicethermostatpolicy.json to certificates corresponding to each of the thermostats you have that you want to assume the role alias, Thermostat-dynamodb-access-role-alias, and allow operations only on the table with the name that matches the thing name. For more information, see the full list of thing policy variables.
Thing substitution variables in IAM policies
You also can use the following three substitution variables in the IAM role’s access policy (I used credentials-iot:ThingName in accesspolicyfordynamodb.json in Step 3):
credentials-iot:ThingName
credentials-iot:ThingTypeName
credentials-iot:AwsCertificateId
When the device provides the thing name in the request, the credentials provider fetches these three variables from the database and adds them as context variables to the security token. When the device uses the token to access DynamoDB, the variables in the role’s access policy are replaced with the corresponding values in the security token. Note that you also can use credentials-iot:AwsCertificateId as a policy variable; AWS IoT returns certificateId during registration.
6. Request a security token
Make an HTTPS request to the credentials provider to fetch a security token. You have to supply the following information:
Certificate and key pair: Because this is an HTTP request over TLS mutual authentication, you have to provide the certificate and the corresponding key pair to your client while making the request. Use the same certificate and key pair that you used during certificate registration with AWS IoT.
RoleAlias: Provide the role alias (in this example, Thermostat-dynamodb-access-role-alias) to be assumed in the request.
ThingName: Provide the thing name that you created earlier in the AWS IoT thing registry database. This is passed as a header with the name, x-amzn-iot-thingname. Note that the thing name is mandatory only if you have thing attributes as policy variables in AWS IoT or IAM policies.
Run the following command in the AWS CLI to obtain your AWS account-specific endpoint for the credentials provider. See the DescribeEndpoint API documentation for further details.
Note that if you are on Mac OS X, you need to export your certificate to a .pfx or .p12 file before you can pass it in the https request. Use OpenSSL with the following command to convert the device certificate from .pem to .pfx format. Remember the password because you will need it subsequently in a curl command.
Now, make an HTTPS request to the credentials provider to fetch a security token. You may use your preferred HTTP client for the request. I use curl in the following examples.
This command returns a security token object that has an accessKeyId, a secretAccessKey, a sessionToken, and an expiration. The following is sample output of the curl command.
Create a DynamoDB table called MyHomeThermostat in your AWS account. You will have to choose the hash (partition key) and the range (sort key) while creating the table to uniquely identify a record. Make the hash the serial_number of the thermostat and the range the timestamp of the record. Create a text file with the following JSON to put a temperature and humidity record in the table. Name the file, item.json.
You can use the accessKeyId, secretAccessKey, and sessionToken retrieved from the output of the curl command to sign a request that writes the temperature and humidity record to the DynamoDB table. Use the following commands to accomplish this.
In this blog post, I demonstrated how to retrieve a security token by using an X.509 certificate and then writing an item to a DynamoDB table by using the security token. Similarly, you could run applications on surveillance cameras or sensor devices that exchange the X.509 certificate for an AWS security token and use the token to upload video streams to Amazon Kinesis or telemetry data to Amazon CloudWatch.
If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the AWS IoT forum.
If you’re not already familiar with building visualizations for quick access to business insights using Amazon QuickSight, consider this your introduction. In this post, we’ll walk through some common scenarios with sample datasets to provide an overview of how you can connect yuor data, perform advanced analysis and access the results from any web browser or mobile device.
The following visualizations are built from the public datasets available in the links below. Before we jump into that, let’s take a look at the supported data sources, file formats and a typical QuickSight workflow to build any visualization.
Which data sources does Amazon QuickSight support?
At the time of publication, you can use the following data methods:
Connect to AWS data sources, including:
Amazon RDS
Amazon Aurora
Amazon Redshift
Amazon Athena
Amazon S3
Upload Excel spreadsheets or flat files (CSV, TSV, CLF, and ELF)
Connect to on-premises databases like Teradata, SQL Server, MySQL, and PostgreSQL
Import data from SaaS applications like Salesforce and Snowflake
Use big data processing engines like Spark and Presto
SPICE is the Amazon QuickSight super-fast, parallel, in-memory calculation engine, designed specifically for ad hoc data visualization. SPICE stores your data in a system architected for high availability, where it is saved until you choose to delete it. Improve the performance of database datasets by importing the data into SPICE instead of using a direct database query. To calculate how much SPICE capacity your dataset needs, see Managing SPICE Capacity.
Typical Amazon QuickSight workflow
When you create an analysis, the typical workflow is as follows:
Connect to a data source, and then create a new dataset or choose an existing dataset.
(Optional) If you created a new dataset, prepare the data (for example, by changing field names or data types).
Create a new analysis.
Add a visual to the analysis by choosing the fields to visualize. Choose a specific visual type, or use AutoGraph and let Amazon QuickSight choose the most appropriate visual type, based on the number and data types of the fields that you select.
(Optional) Modify the visual to meet your requirements (for example, by adding a filter or changing the visual type).
(Optional) Add more visuals to the analysis.
(Optional) Add scenes to the default story to provide a narrative about some aspect of the analysis data.
(Optional) Publish the analysis as a dashboard to share insights with other users.
The following graphic illustrates a typical Amazon QuickSight workflow.
Visualizations created in Amazon QuickSight with sample datasets
Data catalog: The World Bank invests into multiple development projects at the national, regional, and global levels. It’s a great source of information for data analysts.
The following graph shows the percentage of the population that has access to electricity (rural and urban) during 2000 in Asia, Africa, the Middle East, and Latin America.
The following graph shows the share of healthcare costs that are paid out-of-pocket (private vs. public). Also, you can maneuver over the graph to get detailed statistics at a glance.
Data catalog: The DBG PDS project makes real-time data derived from Deutsche Börse’s trading market systems available to the public for free. This is the first time that such detailed financial market data has been shared freely and continually from the source provider.
The following graph shows the market trend of max trade volume for different EU banks. It builds on the data available on XETRA engines, which is made up of a variety of equities, funds, and derivative securities. This graph can be scrolled to visualize trade for a period of an hour or more.
The following graph shows the common stock beating the rest of the maximum trade volume over a period of time, grouped by security type.
Data catalog: Data derived from different sensor stations placed on the city bridges and surface streets are a core information source. The road weather information station has a temperature sensor that measures the temperature of the street surface. It also has a sensor that measures the ambient air temperature at the station each second.
The following graph shows the present max air temperature in Seattle from different RWI station sensors.
The following graph shows the minimum temperature of the road surface at different times, which helps predicts road conditions at a particular time of the year.
Data catalog: Kaggle has come up with a platform where people can donate open datasets. Data engineers and other community members can have open access to these datasets and can contribute to the open data movement. They have more than 350 datasets in total, with more than 200 as featured datasets. It has a few interesting datasets on the platform that are not present at other places, and it’s a platform to connect with other data enthusiasts.
The following graph shows the trending YouTube videos and presents the max likes for the top 20 channels. This is one of the most popular datasets for data engineers.
The following graph shows the YouTube daily statistics for the max views of video titles published during a specific time period.
Data catalog: NYC Open data hosts some very popular open data sets for all New Yorkers. This platform allows you to get involved in dive deep into the data set to pull some useful visualizations. 2016 Green taxi trip dataset includes trip records from all trips completed in green taxis in NYC in 2016. Records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.
The following graph presents maximum fare amount grouped by the passenger count during a period of time during a day. This can be further expanded to follow through different day of the month based on the business need.
The following graph shows the NewYork taxi data from January 2016, showing the dip in the number of taxis ridden on January 23, 2016 across all types of taxis.
A quick search for that date and location shows you the following news report:
Summary
Using Amazon QuickSight, you can see patterns across a time-series data by building visualizations, performing ad hoc analysis, and quickly generating insights. We hope you’ll give it a try today!
Karthik Odapally is a Sr. Solutions Architect in AWS. His passion is to build cost effective and highly scalable solutions on the cloud. In his spare time, he bakes cookies and cupcakes for family and friends here in the PNW. He loves vintage racing cars.
Pranabesh Mandal is a Solutions Architect in AWS. He has over a decade of IT experience. He is passionate about cloud technology and focuses on Analytics. In his spare time, he likes to hike and explore the beautiful nature and wild life of most divine national parks around the United States alongside his wife.
This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team.
Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly important for healthcare providers.
Healthcare providers must have the ability to ingest, store and protect large volumes of data including clinical, genomic, device, financial, supply chain, and claims. AWS is well-suited to this data deluge with a wide variety of ingestion, storage and security services (e.g. AWS Direct Connect, Amazon Kinesis Streams, Amazon S3, Amazon Macie) for customers to handle their healthcare data. In a recent Healthcare IT News article, healthcare thought-leader, John Halamka, noted, “I predict that five years from now none of us will have datacenters. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”
I realize simply storing this data is challenging enough. Magnifying the problem is the fact that healthcare data is increasingly attractive to cyber attackers, making security a top priority. According to Mariya Yao in her Forbes column, it is estimated that individual medical records can be worth hundreds or even thousands of dollars on the black market.
In this first of a 2-part post, I will address the value that AWS can bring to customers for ingesting, storing and protecting provider’s healthcare data. I will describe key components of any cloud-based healthcare workload and the services AWS provides to meet these requirements. In part 2 of this post we will dive deep into the AWS services used for advanced analytics, artificial intelligence and machine learning.
The data tsunami is upon us
So where is this data coming from? In addition to the ubiquitous electronic health record (EHR), the sources of this data include:
genomic sequencers
devices such as MRIs, x-rays and ultrasounds
sensors and wearables for patients
medical equipment telemetry
mobile applications
Additional sources of data come from non-clinical, operational systems such as:
human resources
finance
supply chain
claims and billing
Data from these sources can be structured (e.g., claims data) as well as unstructured (e.g., clinician notes). Some data comes across in streams such as that taken from patient monitors, while some comes in batch form. Still other data comes in near-real time such as HL7 messages. All of this data has retention policies dictating how long it must be stored. Much of this data is stored in perpetuity as many systems in use today have no purge mechanism. AWS has services to manage all these data types as well as their retention, security and access policies.
Imaging is a significant contributor to this data tsunami. Increasing demand for early-stage diagnoses along with aging populations drive increasing demand for images from CT, PET, MRI, ultrasound, digital pathology, X-ray and fluoroscopy. For example, a thin-slice CT image can be hundreds of megabytes. Increasing demand and strict retention policies make storage costly.
Due to the plummeting cost of gene sequencing, molecular diagnostics (including liquid biopsy) is a large contributor to this data deluge. Many predict that as the value of molecular testing becomes more identifiable, the reimbursement models will change and it will increasingly become the standard of care. According to the Washington Post article “Sequencing the Genome Creates so Much Data We Don’t Know What to do with It,”
“Some researchers predict that up to one billion people will have their genome sequenced by 2025 generating up to 40 exabytes of data per year.”
Although genomics is primarily used for oncology diagnostics today, it’s also used for other purposes, pharmacogenomics — used to understand how an individual will metabolize a medication.
Reference Architecture
It is increasingly challenging for the typical hospital, clinic or physician practice to securely store, process and manage this data without cloud adoption.
Amazon has a variety of ingestion techniques depending on the nature of the data including size, frequency and structure. AWS Snowball and AWS Snowmachine are appropriate for extremely-large, secure data transfers whether one time or episodic. AWS Glue is a fully-managed ETL service for securely moving data from on-premise to AWS and Amazon Kinesis can be used for ingesting streaming data.
Amazon S3, Amazon S3 IA, and Amazon Glacier are economical, data-storage services with a pay-as-you-go pricing model that expand (or shrink) with the customer’s requirements.
The above architecture has four distinct components – ingestion, storage, security, and analytics. In this post I will dive deeper into the first three components, namely ingestion, storage and security. In part 2, I will look at how to use AWS’ analytics services to draw value on, and optimize, your healthcare data.
Ingestion
A typical provider data center will consist of many systems with varied datasets. AWS provides multiple tools and services to effectively and securely connect to these data sources and ingest data in various formats. The customers can choose from a range of services and use them in accordance with the use case.
For use cases involving one-time (or periodic), very large data migrations into AWS, customers can take advantage of AWS Snowball devices. These devices come in two sizes, 50 TB and 80 TB and can be combined together to create a petabyte scale data transfer solution.
The devices are easy to connect and load and they are shipped to AWS avoiding the network bottlenecks associated with such large-scale data migrations. The devices are extremely secure supporting 256-bit encryption and come in a tamper-resistant enclosure. AWS Snowball imports data in Amazon S3 which can then interface with other AWS compute services to process that data in a scalable manner.
For use cases involving a need to store a portion of datasets on premises for active use and offload the rest on AWS, the Amazon storage gateway service can be used. The service allows you to seamlessly integrate on premises applications via standard storage protocols like iSCSI or NFS mounted on a gateway appliance. It supports a file interface, a volume interface and a tape interface which can be utilized for a range of use cases like disaster recovery, backup and archiving, cloud bursting, storage tiering and migration.
The AWS Storage Gateway appliance can use the AWS Direct Connect service to establish a dedicated network connection from the on premises data center to AWS.
Specific Industry Use Cases
By using the AWS proposed reference architecture for disaster recovery, healthcare providers can ensure their data assets are securely stored on the cloud and are easily accessible in the event of a disaster. The “AWS Disaster Recovery” whitepaper includes details on options available to customers based on their desired recovery time objective (RTO) and recovery point objective (RPO).
AWS is an ideal destination for offloading large volumes of less-frequently-accessed data. These datasets are rarely used in active compute operations but are exceedingly important to retain for reasons like compliance. By storing these datasets on AWS, customers can take advantage of the highly-durable platform to securely store their data and also retrieve them easily when they need to. For more details on how AWS enables customers to run back and archival use cases on AWS, please refer to the following set of whitepapers.
A healthcare provider may have a variety of databases spread throughout the hospital system supporting critical applications such as EHR, PACS, finance and many more. These datasets often need to be aggregated to derive information and calculate metrics to optimize business processes. AWS Glue is a fully-managed Extract, Transform and Load (ETL) service that can read data from a JDBC-enabled, on-premise database and transfer the datasets into AWS services like Amazon S3, Amazon Redshift and Amazon RDS. This allows customers to create transformation workflows that integrate smaller datasets from multiple sources and aggregates them on AWS.
Healthcare providers deal with a variety of streaming datasets which often have to be analyzed in near real time. These datasets come from a variety of sources such as sensors, messaging buses and social media, and often do not adhere to an industry standard. The Amazon Kinesis suite of services, that includes Amazon Kinesis Streams, Amazon Kinesis Firehose, and Amazon Kinesis Analytics, are the ideal set of services to accomplish the task of deriving value from streaming data.
Example: Using AWS Glue to de-identify and ingest healthcare data into S3 Let’s consider a scenario in which a provider maintains patient records in a database they want to ingest into S3. The provider also wants to de-identify the data by stripping personally- identifiable attributes and store the non-identifiable information in an S3 bucket. This bucket is different from the one that contains identifiable information. Doing this allows the healthcare provider to separate sensitive information with more restrictions set up via S3 bucket policies.
To ingest records into S3, we create a Glue job that reads from the source database using a Glue connection. The connection is also used by a Glue crawler to populate the Glue data catalog with the schema of the source database. We will use the Glue development endpoint and a zeppelin notebook server on EC2 to develop and execute the job.
Step 1: Import the necessary libraries and also set a glue context which is a wrapper on the spark context:
Step 2: Create a dataframe from the source data. I call the dataframe “readmissionsdata”. Here is what the schema would look like:
Step 3: Now select the columns that contains indentifiable information and store it in a new dataframe. Call the new dataframe “phi”.
Step 4: Non-PHI columns are stored in a separate dataframe. Call this dataframe “nonphi”.
Step 5: Write the two dataframes into two separate S3 buckets
Once successfully executed, the PHI and non-PHI attributes are stored in two separate files in two separate buckets that can be individually maintained.
Storage
In 2016, 327 healthcare providers reported a protected health information (PHI) breach, affecting 16.4m patient records[1]. There have been 342 data breaches reported in 2017 — involving 3.2 million patient records.[2]
To date, AWS has released 51 HIPAA-eligible services to help customers address security challenges and is in the process of making many more services HIPAA-eligible. These HIPAA-eligible services (along with all other AWS services) help customers build solutions that comply with HIPAA security and auditing requirements. A catalogue of HIPAA-enabled services can be found at AWS HIPAA-eligible services. It is important to note that AWS manages physical and logical access controls for the AWS boundary. However, the overall security of your workloads is a shared responsibility, where you are responsible for controlling user access to content on your AWS accounts.
AWS storage services allow you to store data efficiently while maintaining high durability and scalability. By using Amazon S3 as the central storage layer, you can take advantage of the Amazon S3 storage management features to get operational metrics on your data sets and transition them between various storage classes to save costs. By tagging objects on Amazon S3, you can build a governance layer on Amazon S3 to grant role based access to objects using Amazon IAM and Amazon S3 bucket policies.
To learn more about the Amazon S3 storage management features, see the following link.
Security
In the example above, we are storing the PHI information in a bucket named “phi.” Now, we want to protect this information to make sure its encrypted, does not have unauthorized access, and all access requests to the data are logged.
Encryption: S3 provides settings to enable default encryption on a bucket. This ensures any object in the bucket is encrypted by default.
Logging: S3 provides object level logging that can be used to capture all API calls to the object. The API calls are logged in cloudtrail for easy access and consolidation. Moreover, it also supports events to proactively alert customers of read and write operations.
Access control: Customers can use S3 bucket policies and IAM policies to restrict access to the phi bucket. It can also put a restriction to enforce multi-factor authentication on the bucket. For example, the following policy enforces multi-factor authentication on the phi bucket:
In Part 1 of this blog, we detailed the ingestion, storage, security and management of healthcare data on AWS. Stay tuned for part two where we are going to dive deep into optimizing the data for analytics and machine learning.
Security updates have been issued by Debian (lucene-solr and psensor), Oracle (librelp and PackageKit), Red Hat (kernel, librelp, and PackageKit), Scientific Linux (librelp), and Ubuntu (mysql-5.5 and packagekit).
A simple Raspberry Pi based project using TCS3200 Color Sensor. The project demonstrates how to interface a Color Sensor (like TCS3200) with Raspberry Pi and implement a simple Color Detector using Raspberry Pi.
What is a TCS3200 colour sensor?
Colour sensors sense reflected light from nearby objects. The bright light of the TCS3200’s on-board white LEDs hits an object’s surface and is reflected back. The sensor has an 8×8 array of photodiodes, which are covered by either a red, blue, green, or clear filter. The type of filter determines what colour a diode can detect. Then the overall colour of an object is determined by how much light of each colour it reflects. (For example, a red object reflects mostly red light.)
As Electronics Hub explains:
TCS3200 is one of the easily available colour sensors that students and hobbyists can work on. It is basically a light-to-frequency converter, i.e. based on colour and intensity of the light falling on it, the frequency of its output signal varies.
I’ll save you a physics lesson here, but you can find a detailed explanation of colour sensing and the TCS3200 on the Electronics Hub blog.
Raspberry Pi colour sensor
The TCS3200 colour sensor is connected to several of the onboard General Purpose Input Output (GPIO) pins on the Raspberry Pi.
These connections allow the Raspberry Pi 3 to run one of two Python scripts that Electronics Hub has written for the project. The first displays the RAW RGB values read by the sensor. The second detects the primary colours red, green, and blue, and it can be expanded for more colours with the help of the first script.
Electronic Hub’s complete build uses a breadboard for simply prototyping
Use it in your projects
This colour sensing setup is a simple means of adding a new dimension to your builds. Why not build a candy-sorting robot that organises your favourite sweets by colour? Or add colour sensing to your line-following buggy to allow for multiple path options!
If your Raspberry Pi project uses colour sensing, we’d love to see it, so be sure to share it in the comments!
What happens when you combine the Internet of Things, Machine Learning, and Edge Computing? Before I tell you, let’s review each one and discuss what AWS has to offer.
Internet of Things (IoT) – Devices that connect the physical world and the digital one. The devices, often equipped with one or more types of sensors, can be found in factories, vehicles, mines, fields, homes, and so forth. Important AWS services include AWS IoT Core, AWS IoT Analytics, AWS IoT Device Management, and Amazon FreeRTOS, along with others that you can find on the AWS IoT page.
Machine Learning (ML) – Systems that can be trained using an at-scale dataset and statistical algorithms, and used to make inferences from fresh data. At Amazon we use machine learning to drive the recommendations that you see when you shop, to optimize the paths in our fulfillment centers, fly drones, and much more. We support leading open source machine learning frameworks such as TensorFlow and MXNet, and make ML accessible and easy to use through Amazon SageMaker. We also provide Amazon Rekognition for images and for video, Amazon Lex for chatbots, and a wide array of language services for text analysis, translation, speech recognition, and text to speech.
Edge Computing – The power to have compute resources and decision-making capabilities in disparate locations, often with intermittent or no connectivity to the cloud. AWS Greengrass builds on AWS IoT, giving you the ability to run Lambda functions and keep device state in sync even when not connected to the Internet.
ML Inference at the Edge Today I would like to toss all three of these important new technologies into a blender! You can now perform Machine Learning inference at the edge using AWS Greengrass. This allows you to use the power of the AWS cloud (including fast, powerful instances equipped with GPUs) to build, train, and test your ML models before deploying them to small, low-powered, intermittently-connected IoT devices running in those factories, vehicles, mines, fields, and homes that I mentioned.
Here are a few of the many ways that you can put Greengrass ML Inference to use:
Precision Farming – With an ever-growing world population and unpredictable weather that can affect crop yields, the opportunity to use technology to increase yields is immense. Intelligent devices that are literally in the field can process images of soil, plants, pests, and crops, taking local corrective action and sending status reports to the cloud.
Physical Security – Smart devices (including the AWS DeepLens) can process images and scenes locally, looking for objects, watching for changes, and even detecting faces. When something of interest or concern arises, the device can pass the image or the video to the cloud and use Amazon Rekognition to take a closer look.
Industrial Maintenance – Smart, local monitoring can increase operational efficiency and reduce unplanned downtime. The monitors can run inference operations on power consumption, noise levels, and vibration to flag anomalies, predict failures, detect faulty equipment.
Greengrass ML Inference Overview There are several different aspects to this new AWS feature. Let’s take a look at each one:
Machine Learning Models – Precompiled TensorFlow and MXNet libraries, optimized for production use on the NVIDIA Jetson TX2 and Intel Atom devices, and development use on 32-bit Raspberry Pi devices. The optimized libraries can take advantage of GPU and FPGA hardware accelerators at the edge in order to provide fast, local inferences.
Model Building and Training – The ability to use Amazon SageMaker and other cloud-based ML tools to build, train, and test your models before deploying them to your IoT devices. To learn more about SageMaker, read Amazon SageMaker – Accelerated Machine Learning.
Model Deployment – SageMaker models can (if you give them the proper IAM permissions) be referenced directly from your Greengrass groups. You can also make use of models stored in S3 buckets. You can add a new machine learning resource to a group with a couple of clicks:
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