Tag Archives: IOT

IoT-ize Your Old Gadgets With a Mechanical Hijacking Device

Post Syndicated from Evan Ackerman original https://spectrum.ieee.org/iot-mechanical-hijack

Just about every device you own is probably available with Internet connectivity. Whether or not every device you own actually
needs Internet connectivity is up for debate. But if you want it, it’s there—as long as you’re able to afford it, of course. Connectivity usually comes at a premium, and it also usually involves buying a brand new whatever it is, because new hardware and software and services are required.

If connectivity is important to you, there aren’t many options for older devices. It’s not hard to turn them on and off with a connected socket adapter of some sort, but as you start to go back more than a few years, things become increasingly designed for direct human interaction rather than for the Internet, with buttons and switches and dials and whatnot.

IoTIZER is a prototype of a mechanical hijacking device (MHD), designed to replace human manipulation of existing products. As the name suggests, it can IoT-ize just about anything designed to be operated by a human, potentially giving a new connected life to your stuff.


The IoTIZER MHD is capable of handling just about anything with an interface that can be pushed, pulled, or twisted. It was inspired by a 2D plotter with the ability to rotate and extend, and also includes an extra degree of freedom that allows it to push buttons. A small stick-on adapter enables additional motions, like rotating knobs. Special attention was paid to the software, which was designed to be as easy to use as possible, making the MHD accessible for people without much in the way of technical background. Or, that was the idea, anyway.

As of right now, IoTIZER is a research-through-design project from KAIST in Korea. Part of the project involved putting some prototypes in the homes of potential users. According to the researchers, a number of the initial trial’s 14 participants liked the fact that the MHD was so customizable in terms of operating times, sequences, and conditions—adding functionality even to devices that already had some level of connectivity. And because MHD uses physical hardware to interact with physical interfaces, even non-technical users had success in setting it up. As one user commented, “I think it is easy, because I can do it while looking at the product. Well, I can just put it there and check.”

Collage of 8 images showing items participants installed the IoTIZER on including a recliner, intercom, electric stove, TV remote, coffee machine, AC remote, fan, and door lock.
Participants installed the IoTIZER on a range of products, including consumer appliances and devices as well as various items around the home, including a recliner and a door lock. KAIST/Korea Polytechnic University

The paper doesn’t directly address the cost of the MHD, and with three motors and some other electronics in it, it’s not likely to be super cheap. I’d expect it to cost at least USD $100, although perhaps it could get somewhat cheaper in volume. If there’s one specific device that you’d like to automate in one specific, many cheaper alternatives may be available (like connected power switches), or it could even be cheaper to buy an entirely new device. But doing so is wasteful, whereas the MHD potentially gives a smart new life to many old things.

It’s also possible to build your own MHD device for cheap, if you know what you’re doing. But for the vast majority of people, a DIY approach isn’t practical, and buying something that works out of the box and comes with a friendly app seems like it would be worth a cost premium. Although, we should reiterate that the IoTIZER is not commercially available, and may never be commercially available, so if you’re really into the idea, a DIY version might be the way to go.

The researchers also share this interesting take on the future of MHDs at the end of their paper:

One potential solution to these challenges is a mechanical hijacking robot rather than a device. Recent technological developments are introducing personal service robots into the home. If these robots are commercialized before the full deployment of the IoT, they could more intelligently provide the MHD experience.

I’m fairly certain that personal service robots with the ability to manipulate objects the way IoTIZER can won’t be in most homes for at least a decade. Although, it’s interesting to think about what you might be able to do with the addition of a small camera that could recognize things like sounds or flashing buttons or basic text. Perhaps I just need to expand my idea of what a personal service robot actually is—if it’s just a little box with a couple of servos and some sensors that can intelligently hijack whatever I want it to, that could be good enough for me.

Learn the Internet of Things with “IoT for Beginners” and Raspberry Pi

Post Syndicated from Ashley Whittaker original https://www.raspberrypi.org/blog/learn-the-internet-of-things-with-iot-for-beginners-and-raspberry-pi/

Want to dabble in the Internet of Things but don’t know where to start? Well, our friends at Microsoft have developed something fun and free just for you. Here’s Senior Cloud Advocate Jim Bennett to tell you all about their brand new online curriculum for IoT beginners.

IoT — the Internet of Things — is one of the biggest growth areas in technology, and one that, to me, is very exciting. You start with a device like a Raspberry Pi, sprinkle some sensors, dust with code, mix in some cloud services and poof! You have smart cities, self-driving cars, automated farming, robotic supermarkets, or devices that can clean your toilet after you shout at Alexa for the third time.

robot detecting a shelf restock is required
Why doesn’t my local supermarket have a restocking robot?

It feels like every week there is another survey out on what tech skills will be in demand in the next five years, and IoT always appears somewhere near the top. This is why loads of folks are interested in learning all about it.

In my day job at Microsoft, I work a lot with students and lecturers, and I’m often asked for help with content to get started with IoT. Not just how to use whatever cool-named IoT services come from your cloud provider of choice to enable digital whatnots to add customer value via thingamabobs, but real beginner content that goes back to the basics.

IoT for Beginners logo
‘IoT for Beginners’ is totally free for anyone wanting to learn about the Internet of Things

This is why a few of us have spent the last few months locked away building IoT for Beginners. It’s a free, open source, 24-lesson university-level IoT curriculum designed for teachers and students, and built by IoT experts, education experts and students.

What will you learn?

The lessons are grouped into projects that you can build with a Raspberry Pi so that you can deep-dive into use cases of IoT, following the journey of food from farm to table.

collection of cartoons of eye oh tee projects

You’ll build projects as you learn the concepts of IoT devices, sensors, actuators, and the cloud, including:

  • An automated watering system, controlling a relay via a soil moisture sensor. This starts off running just on your device, then moves to a free MQTT broker to add cloud control. It then moves on again to cloud-based IoT services to add features like security to stop Farmer Giles from hacking your watering system.
  • A GPS-based vehicle tracker plotting the route taken on a map. You get alerts when a vehicle full of food arrives at a location by using cloud-based mapping services and serverless code.
  • AI-based fruit quality checking using a camera on your device. You train AI models that can detect if fruit is ripe or not. These start off running in the cloud, then you move them to the edge running directly on your Raspberry Pi.
  • Smart stock checking so you can see when you need to restack the shelves, again powered by AI services.
  • A voice-controlled smart timer so you have more devices to shout at when cooking your food! This one uses AI services to understand what you say into your IoT device. It gives spoken feedback and even works in many different languages, translating on the fly.

Grab your Raspberry Pi and some sensors from our friends at Seeed Studio and get building. Without further ado, please meet IoT For Beginners: A Curriculum!

The post Learn the Internet of Things with “IoT for Beginners” and Raspberry Pi appeared first on Raspberry Pi.

How to import AWS IoT Device Defender audit findings into Security Hub

Post Syndicated from Joaquin Manuel Rinaudo original https://aws.amazon.com/blogs/security/how-to-import-aws-iot-device-defender-audit-findings-into-security-hub/

AWS Security Hub provides a comprehensive view of the security alerts and security posture in your accounts. In this blog post, we show how you can import AWS IoT Device Defender audit findings into Security Hub. You can then view and organize Internet of Things (IoT) security findings in Security Hub together with findings from other integrated AWS services, such as Amazon GuardDuty, Amazon Inspector, Amazon Macie, AWS Identity and Access Management (IAM) Access Analyzer, AWS Systems Manager, and more. You will gain a centralized security view across both enterprise and IoT types of workloads, and have an aggregated view of AWS IoT Device Defender audit findings. This solution can support AWS Accounts managed by AWS Organizations.

In this post, you’ll learn how the integration of IoT security findings into Security Hub works, and you can download AWS CloudFormation templates to implement the solution. After you deploy the solution, every failed audit check will be recorded as a Security Hub finding. The findings within Security Hub provides an AWS IoT Device Defender finding severity level and direct link to the AWS IoT Device Defender console so that you can take possible remediation actions. If you address the underlying findings or suppress the findings by using the AWS IoT Device Defender console, the solution function will automatically archive any related findings in Security Hub when a new audit occurs.

Solution scope

For this solution, we assume that you are familiar with how to set up an IoT environment and set up AWS IoT Device Defender. To learn more how to set up your environment, see the AWS tutorials, such as Getting started with AWS IoT Greengrass and Setting up AWS IoT Device Defender

The solution is intended for AWS accounts with fewer than 10,000 findings per scan. If AWS IoT Device Defender has more than 10,000 findings, the limit of 15 minutes for the duration of the serverless AWS Lambda function might be exceeded, depending on the network delay, and the function will fail.

The solution is designed for AWS Regions where AWS IoT Device Defender, serverless Lambda functionality and Security Hub are available; for more information, see AWS Regional Services. The China (Beijing) and China (Ningxia) Regions and the AWS GovCloud (US) Regions are excluded from the solution scope.

Solution overview

The templates that we provide here will provision an Amazon Simple Notification Service (Amazon SNS) topic notifying you when the AWS IoT Device Defender report is ready, and a Lambda function that imports the findings from the report into Security Hub. Figure 1 shows the solution architecture.
 

Figure 1: Solution architecture

Figure 1: Solution architecture

The solution workflow is as follows:

  1. AWS IoT Device Defender performs an audit of your environment. You should set up a regular audit as described in Audit guide: Enable audit checks.
  2. AWS IoT Device Defender sends an SNS notification with a summary of the audit report.
  3. A Lambda function named import-iot-defender-findings-to-security-hub is triggered by the SNS topic.
  4. The Lambda function gets the details of the findings from AWS IoT Device Defender.
  5. The Lambda function imports the new findings to Security Hub and archives the previous report findings. An example of findings in Security Hub is shown in Figure 2.
     
    Figure 2: Security Hub findings example

    Figure 2: Security Hub findings example

Prerequisites

  • You must have Security Hub turned on in the Region where you’re deploying the solution.
  • You must also have your IoT environment set, see step by step tutorial at Getting started with AWS IoT Greengrass
  • You must also have AWS IoT Device Defender audit checks turned on. Learn how to configure recurring audit checks across all your IoT devices by using this tutorial.

Deploy the solution

You will need to deploy the solution once in each AWS Region where you want to integrate IoT security findings into Security Hub.

To deploy the solution

  1. Choose Launch Stack to launch the AWS CloudFormation console with the prepopulated CloudFormation demo template.

    Select the Launch Stack button to launch the template

    Additionally, you can download the latest solution code from GitHub.

  2. (Optional) In the CloudFormation console, you are presented with the template parameters before you deploy the stack. You can customize these parameters or keep the defaults:
    • S3 bucket with sources: This bucket contains all the solution sources, such as the Lambda function and templates. You can keep the default text if you’re not customizing the sources.
    • Prefix for S3 bucket with sources: The prefix for all the solution sources. You can keep the default if you’re not customizing the sources.
  3. Go to the AWS IoT Core console and set up an SNS alert notification parameter for the audit report. To do this, in the left navigation pane of the console, under Defend, choose Settings, and then choose Edit to edit the SNS alert. The SNS topic is created by the solution stack and named iot-defender-report-notification.
     
    Figure 3: SNS alert settings for AWS IoT Device Defender

    Figure 3: SNS alert settings for AWS IoT Device Defender

Test the solution

To test the solution, you can simulate an “AWS IoT policies are overly permissive” finding by creating an insecure policy.

To create an insecure policy

  1. Go to the AWS IoT Core console. In the left navigation pane, under Secure, choose Policies.
  2. Choose Create. For Name, enter InsecureIoTPolicy.
  3. For Action, select iot:*. For Resources, enter *. Choose Allow statement, and then choose Create.

Next, run a new IoT security audit by choosing IoT Core > Defend > Audit > Results > Create and selecting the option Run audit now (Once).

After the audit is finished, you’ll see audit reports in the AWS IoT Core console, similar to the ones shown in Figure 4. One of the reports shows that the IoT policies are overly permissive. The same findings are also imported into Security Hub as shown in Figure 2.
 

Figure 4: AWS IoT Device Defender report

Figure 4: AWS IoT Device Defender report

Troubleshooting

To troubleshoot the solution, use the Amazon CloudWatch Logs of the Lambda function import-iot-defender-findings-to-security-hub. The solution can fail if:

  • Security Hub isn’t turned on in your Region
  • Service control policies (SCPs) are preventing access to AWS IoT Device Defender audit reports
  • The wrong SNS topic is configured in the AWS IoT Device Defender settings
  • The Lambda function times out because there are more than 10,000 findings

To find these issues, go to the CloudWatch console, choose Log Group, and then choose /aws/lambda/import-iot-defender-findings-to-security-hub.

Conclusion

In this post, you’ve learned how to integrate AWS IoT Device Defender audit findings with Security Hub to gain a centralized view of security findings across both your enterprise and IoT workloads. If you have more questions about IoT, you can reach out to the AWS IoT forum, and if you have questions about Security Hub, visit the AWS Security Hub forum. If you need AWS experts to help you plan, build, or optimize your infrastructure, contact AWS Professional Services.

If you have feedback about this post, submit comments in the Comments section below.

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Author

Joaquin Manuel Rinaudo

Joaquin is a Senior Security Architect with AWS Professional Services. He is passionate about building solutions that help developers improve their software quality. Prior to AWS, he worked across multiple domains in the security industry, from mobile security to cloud and compliance related topics. In his free time, Joaquin enjoys spending time with family and reading science-fiction novels.

Author

Vesselin Tzvetkov

Vesselin is a Senior Security Architect at AWS Professional Services and is passionate about security architecture and engineering innovative solutions. Outside of technology, he likes classical music, philosophy, and sports. He holds a Ph.D. in security from TU-Darmstadt and a M.S. in electrical engineering from Bochum University in Germany.

HaXmas Hardware Hacking

Post Syndicated from Tod Beardsley original https://blog.rapid7.com/2021/01/02/haxmas-hardware-hacking/

HaXmas Hardware Hacking

Usually, when you read an IoT hacking report or blog post, it ends with something along the lines of, “and that’s how I got root,” or “and there was a secret backdoor credential,” or “and every device in the field uses the same S3 bucket with no authentication.” You know, something bad, and the whole reason for publishing the research in the first place. While such research is of course interesting, important, and worth publishing, we pretty much never hear about the other outcome: the IoT hacking projects that didn’t uncover something awful, but instead ended up with, “and everything looked pretty much okay.”

So, this HaXmas, I decided to dig around a little in Rapid7’s library of IoT investigations that never really went anywhere, just to see which tools were used. The rest of this blog post is basically a book report of the tooling used in a recent engagement performed by our own Jonathan Stines, and can be used as a starting point if you’re interested in getting into some casual IoT hacking yourself. Even though this particular engagement didn’t go anywhere, I had a really good time reading along with Stines’ investigation on a smart doorbell camera.

Burp Suite

While Burp Suite might be a familiar mainstay for web app hackers, it has a pretty critical role in IoT investigations as well. The “I” in IoT is what makes these Things interesting, so checking out what and how those gadgets are chatting on the internet is pretty critical in figuring out the security posture of those devices. Burp Suite lets investigators capture, inspect, and replay conversations in a proxied context, and the community edition is a great way to get started with this kind of manual, dynamic analysis.

Frida

While Burp is great, if the IoT mobile app you’re looking at (rightly) uses certificate pinning in order to secure communications, you won’t get very far with its proxy capabilities. In order to deal with this, you’ll need some mechanism to bypass the application’s pinned cert, and that mechanism is Frida. While Frida might be daunting for the casual IoT hacker, there’s a great HOWTO by Vedant that provides some verbose instructions for setting up Frida, adb, and Burp Suite in order to inject a custom SSL certificate and bypass that pesky pinning. Personally, I had never heard of Frida or how to use it for this sort of thing, so it looks like I’m one of today’s lucky 10,000.

HaXmas Hardware Hacking

Binwalk

When mucking about with firmware (the packaged operating system and applications that makes IoT devices go), Binwalk from Refirm Labs is the standard for exploring those embedded filesystems. In nearly all cases, a “check for updates” button on a newly opened device will trigger some kind of firmware download—IoT devices nearly always update themselves by downloading and installing an entirely new firmware—so if you can capture that traffic with something like Wireshark (now that you’ve set up your proxied environment), you can extract those firmware updates and explore them with Binwalk.

Allsocket eMMC153 chip reader

Now, with the software above, you will go far in figuring out how an IoT device does its thing, but the actual hands-on-hardware experience in IoT hacking is kinda the fun part that differentiates it from regular old web app testing. So for this, you will want to get your hands on a chip reader for your desoldered components. Pictured below is an Allsocket device that can be used to read both 153-pin and 169-pin configurations of eMMC storage, both of which are very common formats for solid-state flash memory in IoT-land. Depending on where you get it, they can run about $130, so not cheap, but also not bank-breaking.

HaXmas Hardware Hacking

Thanks!

Thanks again to Jonathan Stines, who did all the work that led to this post. If you need some validation of your IoT product, consider hiring him for your next IoT engagement. Rapid7’s IoT assessment experts are all charming humans who are pretty great at not just IoT hacking, but explaining what they did and how they did it. And, if you like this kind of thing, drop a comment below and let me know—I’m always happy to learn and share something new (to me) when it comes to hardware hacking.

More HaXmas blogs

UPnP With a Holiday Cheer

Post Syndicated from Deral Heiland original https://blog.rapid7.com/2020/12/22/upnp-with-a-holiday-cheer/

UPnP With a Holiday Cheer

T’was the night before HaXmas,
when all through the house,
Not a creature was stirring, not even a mouse.
The stockings were hung by the chimney with care,
in hopes that St. Nicholas soon would be there.

This may be the way you start your holiday cheer,
but before you get started, let me make you aware.
I spend my holidays quite differently, I fear.
As a white-hat hacker with a UPnP cheer.

And since you may not be aware,
let me share what I learned with you,
so that you can also care,
how to port forward with UPnP holiday cheer.

Universal Plug and Play (UPnP) is a service that has been with us for many years and is used to automate discovery and setup of network and communication services between devices on your network. For today’s discussion, this blog post will only cover the port forwarding services and will also share a Python script you can use to start examining this service.

UPnP port forwarding services are typically enabled by default on most consumer internet-facing Network Address Translation (NAT) routers supplied by internet service providers (ISP) for supporting IPv4 networks. This is done so that devices on the internal network can automate their setup of needed TCP and UDP port forwarding functions on the internet-facing router, so devices on the internet can connect to services on your internal network.

So, the first thing I would like to say about this is that if you are not running applications or systems such as internet gaming systems that require this feature, I would recommend disabling this on your internet-facing router. Why? Because it has been used by malicious actors to further compromise a network by opening up port access into internal networks via malware. So, if you don’t need it, you can remove the risk by disabling it. This is the best option to help reduce any unnecessary exposure.

To make all this work, UPnP uses a discovery protocol known as Simple Service Discovery Protocol (SSDP). This SSDP discovery service for UPnP is a UDP service that responds on port 1900 and can be enumerated by broadcasting an M-SEARCH message via the multicast address 239.255.255.250. This M-SEARCH message will return device information, including the URL and port number for the device description file ‘rootDesc.xml’. Here is an example of a returned M-SEARCH response from a NETGEAR Wi-Fi router device on my network:

UPnP With a Holiday Cheer

To send a M-SEARCH multicast message, here is a simple Python script:

# simple script to enumerate UPNP devices
 
import socket
 
# M-Search message body
MS = \
    'M-SEARCH * HTTP/1.1\r\n' \
    'HOST:239.255.255.250:1900\r\n' \
    'ST:upnp:rootdevice\r\n' \
    'MX:2\r\n' \
    'MAN:"ssdp:discover"\r\n' \
    '\r\n'
 
# Set up a UDP socket for multicast
SOC = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)
SOC.settimeout(2)
 
# Send M-Search message to multicast address for UPNP
SOC.sendto(MS.encode('utf-8'), ('239.255.255.250', 1900) )
 
#listen and capture returned responses
try:
    while True:
        data, addr = SOC.recvfrom(8192)
        print (addr, data)
except socket.timeout:
        pass

The next step is to access the rootDesc.xml file. In this case, this is accessible on my device via http://192.168.2.74:5555/rootDesc.xml. Looking at the M-SEARCH response above, we can see that the IP address for rootDesc.xml at 169.254.39.187.  169.254.*.* is known as an Automatic Private IP address. It is not uncommon to see an address in that range returned by an M-SEARCH request. Trying to access it will fail because it is incorrect. To actually access the rootDesc.xml file, you will need to use the device’s true IP address, which in my case was 192.168.2.74 and was shown in the header of the M-SEARCH message response.

Once the rootDesc.xml is returned, you will see some very interesting things listed, but in this case, we are only interested in port forwarding. If port forwarding service is available, it will be listed in the rootDesc.xml file as service type WANIPConnection, as shown below:

UPnP With a Holiday Cheer

You can open WANIPCn.xml on the same http service and TCP port location that you retrieved the rootDesc.xml file. The WANIPCn.xml file identifies various actions that are available, and this will often include the following example actions:

  • AddPortMapping
  • GetExternalIPAddress
  • DeletePortMapping
  • GetStatusInfo
  • GetGenericPortMappingEntry
  • GetSpecificPortMappingEntry

Under each of these actions will be an argument list. This argument list specifies the argument values that can be sent via Simple Object Access Protocol (SOAP) messages to the control URL at http://192.168.2.74:5555/ctl/IPConn, which is used to configure settings or retrieve status on the router device. SOAP is a messaging specification that uses a Extensible Markup Language (XML) format to exchange information.

Below are a couple captured SOAP messages, with the first one showing AddPortMapping. This will set up port mapping on the router at the IP address 192.168.1.1. The port being added in this case is TCP 1234 and it is set up to map the internet side of the router to the internal IP address of 192.168.1.241, so anyone connecting to TCP port 1234 on the external IP address of the router will be connected to port 1234 on internal host at 192.168.1.241.

UPnP With a Holiday Cheer

The following captured SOAP message shows the action DeletePortMapping being used to delete the port mapping that was created in the above SOAP message:

UPnP With a Holiday Cheer

To conclude this simple introduction to UPnP, SSDP, and port forwarding services, I highly recommend that you do not experiment on your personal internet-facing router or DSL modem where you could impact your home network’s security posture. But I do recommend that you set up a test environment. This can easily be done with any typical home router or Wi-Fi access point with router services. These can often be purchased used, or you may even have one laying around that you have upgraded from. It is amazing how simple it is to modify a router using these UPnP services by sending SOAP messages, and I hope you will take this introduction and play with these services to further expand your knowledge in this area. If you are looking for further tools for experimenting with port forwarding services, you can use the UPnP IGD SOAP Port Mapping Utility in  Metasploit to create and delete these port mappings.

But I heard him exclaim, ere he drove out of sight-
Happy HaXmas to all, and to all a good UPnP night

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More HaXmas blogs

The Satellite Ear Tag that is Changing Cattle Management

Post Syndicated from Karen Hildebrand original https://aws.amazon.com/blogs/architecture/the-satellite-ear-tag-that-is-changing-cattle-management/

Most cattle are not raised in cities—they live on cattle stations, large open plains, and tracts of land largely unpopulated by humans. It’s hard to keep connected with the herd. Cattle don’t often carry their own mobile phones, and they don’t pay a mobile phone bill. Naturally, the areas in which cattle live, often do not have cellular connectivity or reception. But they now have one way to stay connected: a world-first satellite ear tag.

Ceres Tag co-founders Melita Smith and David Smith recognized the problem given their own farming background. David explained that they needed to know simple things to begin with, such as:

  • Where are they?
  • How many are out there?
  • What are they doing?
  • What condition are they in?
  • Are they OK?

Later, the questions advanced to:

  • Which are the higher performing animals that I want to keep?
  • Where do I start when rounding them up?
  • As assets, can I get better financing and insurance if I can prove their location, existence, and condition?

To answer these questions, Ceres Tag first had to solve the biggest challenge, and it was not to get cattle to carry their mobile phones and pay mobile phone bills to generate the revenue needed to get greater coverage. David and Melita knew they needed help developing a new method of tracking, but in a way that aligned with current livestock practices. Their idea of a satellite connected ear tag came to life through close partnership and collaboration with CSIRO, Australia’s national science agency. They brought expertise to the problem, and rallied together teams of experts across public and private partnerships, never accepting “that’s not been done before” as a reason to curtail their innovation.

 

Figure 1: How Ceres Tag works in practice

Thinking Big: Ceres Tag Protocol

Melita and David constructed their idea and brought the physical hardware to reality. This meant finding strategic partners to build hardware, connectivity partners that provided global coverage at a cost that was tenable to cattle operators, integrations with existing herd management platforms and a global infrastructure backbone that allowed their solution to scale. They showed resilience, tenacity and persistence that are often traits attributed to startup founders and lifelong agricultural advocates. Explaining the purpose of the product often requires some unique approaches to defining the value proposition while fundamentally breaking down existing ways of thinking about things. As David explained, “We have an internal saying, ‘As per Ceres Tag protocol …..’ to help people to see the problem through a new lens.” This persistence led to the creation of an easy to use ear tagging applicator and a two-prong smart ear tag. The ear tag connects via satellite for data transmission, providing connectivity to more than 120 countries in the world and 80% of the earth’s surface.

The Ceres Tag applicator, smart tag, and global satellite connectivity

Figure 2: The Ceres Tag applicator, smart tag, and global satellite connectivity

Unlocking the blocker: data-driven insights

With the hardware and connectivity challenges solved, Ceres Tag turned to how the data driven insights would be delivered. The company needed to select a technology partner that understood their global customer base, and what it means to deliver a low latency solution for web, mobile and API-driven solutions. David, once again knew the power in leveraging the team around him to find the best solution. The evaluation of cloud providers was led by Lewis Frost, COO, and Heidi Perrett, Data Platform Manager. Ceres Tag ultimately chose to partner with AWS and use the AWS Cloud as the backbone for the Ceres Tag Management System.

Ceres Tag conceptual diagram

Figure 3: Ceres Tag conceptual diagram

The Ceres Tag Management System houses the data and metadata about each tag, enabling the traceability of that tag throughout each animal’s life cycle. This includes verification as to whom should have access to their health records and history. Based on the nature of the data being stored and transmitted, security of the application is critical. As a startup, it was important for Ceres Tag to keep costs low, but to also to be able to scale based on growth and usage as it expands globally.

Ceres Tag is able to quickly respond to customers regardless of geography, routing traffic to the appropriate end point. They accomplish this by leveraging Amazon CloudFront as the Content Delivery Network (CDN) for traffic distribution of front-end requests and Amazon Route 53 for DNS routing. A multi-Availability Zone deployment and AWS Application Load Balancer distribute incoming traffic across multiple targets, increasing the availability of your application.

Ceres Tag is using AWS Fargate to provide a serverless compute environment that matches the pay-as-you-go usage-based model. AWS also provides many advanced security features and architecture guidance that has helped to implement and evaluate best practice security posture across all of the environments. Authentication is handled by Amazon Cognito, which allows Ceres Tag to scale easily by supporting millions of users. It leverages easy-to-use features like sign-in with social identity providers, such as Facebook, Google, and Amazon, and enterprise identity providers via SAML 2.0.

The data captured from the ear tag on the cattle is will be ingested via AWS PrivateLink. By providing a private endpoint to access your services, AWS PrivateLink ensures your traffic is not exposed to the public internet. It also makes it easy to connect services across different accounts and VPCs to significantly simplify your network architecture. In leveraging a satellite connectivity provider running on AWS, Ceres Tag will benefit from the AWS Ground Station infrastructure leveraged by the provider in addition to the streaming IoT database.

 

Raspberry Pi smart IoT glove

Post Syndicated from Ashley Whittaker original https://www.raspberrypi.org/blog/raspberry-pi-smart-iot-glove/

Animator/engineer Ashok Fair has put witch-level finger pointing powers in your hands by sticking a SmartEdge Agile, wirelessly controlled by Raspberry Pi Zero, to a golf glove. You could have really freaked the bejeezus out of Halloween party guests with this (if we were allowed to have Halloween parties that is).

The build uses a Smart Edge Agile IoT device with Brainium, a cloud-based tool for performing machine learning tasks.

The Rapid IoT kit is interfaced with Raspberry Pi Zero and creates a thread network connecting to light, car, and fan controller nodes.

The Brainium app is installed on Raspberry Pi and bridges between the cloud and Smart Edge device. MQTT is running on Python and processes the Rapid IoT Kit’s data.

The device is mounted onto a golf glove, giving the wearer seemingly magical powers with the wave of a hand.

Kit list

  • Raspberry Pi Zero
  • Avnet SmartEdge Agile (the white box attached to the glove)
  • NXP Rapid IoT Prototyping Kit (the square blue screen stuck on the adaptor board with the Raspberry Pi Zero)
  • Brainium AI Studio app
  • Golf glove
Waking up the Rapid IoT screen

To get started, the glove wearer draws a pattern above the screen attached to the Raspberry Pi to unlock it and wake up all the controller nodes.

The light controller node is turned on by drawing a clockwise circle, and turned off with an counter-clockwise circle.

The full kit and caboodle

The fan is turned on and off in the same way, and you can increase the fan’s speed by moving your hand upwards and reduce the speed by moving your hand down. You know it’s working by the look of the fan’s LEDs: they blinker faster as the fan speeds up.

Make a pushing motion in the air above the car to make it move forward, and you can also make it turn and reverse.

“Driving glove”

If you wear the glove while driving, it collects data in real time and logs it on the Brainium cloud so you can review your driving style.

Keep up with Ashok’s projects on Twitter or Facebook.

The post Raspberry Pi smart IoT glove appeared first on Raspberry Pi.

Democratizing LoRaWAN and IoT with The Things Network

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/democratizing-lorawan-iot-with-the-things-network/

With the Internet of Things (IoT), what happens to your thing when there’s no internet? Johan Stokking, co-founder of The Things Network and The Things Industries, along with Matt Yanchyshyn from AWS, dig into this.

About The Things Network and The Things Industries

The Things Network is a community project building a global IoT data network in more than 84 countries. Its devices connect to community-maintained gateways, which can communicate over very long distances and last on a single alkaline battery for up to 5-10 years, thanks to the LoRaWAN protocol. The Things Network’s commercial wing, The Things Industries, built a platform using AWS IoT that allows device data to be collected and processed in the cloud using multiple AWS services.

In this special long-format episode of This Is My Architecture, learn how The Things Network and The Things Industries are helping both hobbyists and businesses connect low-power, long-range devices to the cloud. You’ll learn about:

  • The Things Industries’ architecture
  • How Netcetera is leveraging The Things Network for air quality monitoring in Skopje, North Macedonia
  • How Decentlab builds high-quality, long-lasting LoRaWAN devices that work with The Things Network to track environmental conditions

You’ll also get a feel for the community at a local meetup.

Check out more This Is My Architecture video series.

Build an IoT device with Ubuntu Appliance and Raspberry Pi

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/build-an-iot-device-with-ubuntu-appliance-and-raspberry-pi/

The new Ubuntu Appliance portfolio provides free images to help you turn your Raspberry Pi into an IoT device: just install them to your SD card and you have all the software you need to make a media server, get started with home automation, and more. Canonical’s Rhys Davies is here to tell us all about it.

We are delighted to announce the new Ubuntu Appliance portfolio. Together with NextCloud, AdGuard, Plex, Mosquitto and openHAB, we have created the first in a new class of Ubuntu derivatives. Ubuntu Appliances are software-defined projects that enable users to download everything they need to turn a Raspberry Pi into a device that does one thing – beautifully.

The Ubuntu Appliance mission is to enable you to build your own secure, self-updating, single-purpose devices. Tell us what you want to see next, or let’s talk about turning your project into the next Ubuntu Appliance in Discourse. For now, we are excited to bring these initial appliances to your attention.

The initial portfolio of five

  • Plex Media Server allows its users to organise and stream their own collection of movies, TV, music, podcasts and more from one place.
  • Mosquitto is a lightweight open source MQTT message broker, for use on all devices from low power single board computers to full-scale industrial grade servers.
  • OpenHAB is a pluggable architecture that allows users to design rules for automating their home, with time- and event-based triggers, scripts, actions, notifications and voice control.
  • AdGuard Home blocks annoying banners, pop-ups and video ads to make web surfing faster, safer and more comfortable.
  • NextCloud is an on-premise content collaboration platform that allows users to host their own private cloud at home or in the office.

How it all works

Head over to the Ubuntu Appliances website, click the appliance you would like, select download, follow the instructions, and away you go. Once you get to this stage, there are links to tutorials and documentation written by the upstream project themselves, so you can get next steps from the horse’s mouth. If you run into any bother let us know with a new topic and we’ll get on it.

But why bother?

The problem we are trying to solve is to do with the fragmentation in IoT. We want to give publishers and developers a platform to get their software in the hands of their users and into their devices. We work with them to securely bundle the OS, their applications and configurations into a single download that is available for anyone to turn a Raspberry Pi into a dedicated device. You can go to the portfolio and download as many of the appliances as you like and start using them today.

How to add your project to the Ubuntu Appliance portfolio

All of this gives a stage and a secure, production-grade base to projects. There are no restrictions on who can make an Ubuntu Appliance; all you need is an application that runs on a Raspberry Pi or another certified board, and to let us know what you’ve got so we can help you over the line. If you need more information, head to our community page where you’ll find the rules and the exact steps to become featured as an Ubuntu Appliance.

Try them out!

All that’s left to say is to try them out. All five of the initial appliances work on Raspberry Pi, so if you have one, you can get started. And if you don’t have one – maybe your Raspberry Pi is still in the post – then you can also ‘try before you Pi’: install the appliance in a virtual machine and see what you think.

The list of appliances is already growing. This launch marks the first five appliances, but we are already working with developers on the next wave and are looking for more. Start with these ones and go to our discourse to tell us what you think.

Thanks for having me, Raspberry Pi <3

The post Build an IoT device with Ubuntu Appliance and Raspberry Pi appeared first on Raspberry Pi.

Design your own Internet of Things with HackSpace magazine

Post Syndicated from Andrew Gregory original https://www.raspberrypi.org/blog/design-your-own-internet-of-things-with-hackspace-magazine/

In issue 31 of HackSpace magazine, out today, PJ Evans looks at DIY smart homes and homemade Internet of Things devices.

In the last decade, various companies have come up with ‘smart’ versions of almost everything. Microcontrollers have been unceremoniously crowbarred into devices that had absolutely no need for microcontrollers, and often tied to phone apps or web services that are hard to use and don’t work well with other products.

Put bluntly, the commercial world has struggled to deliver an ecosystem of useful smart products. However, the basic principle behind the connected world is good – by connecting together sensors, we can understand our local environment and control it to make our lives better. That could be as simple as making sure the plants are correctly watered, or something far more complex.

The simple fact is that we each lead different lives, and we each want different things out of our smart homes. This is why companies have struggled to create a useful smart home system, but it’s also why we, as makers, are perfectly placed to build our own. Let’s dive in and take a look at one way of doing this – using the TICK Stack – but there are many more, and we’ll explore a few alternatives later on.

Many of our projects create data, sometimes a lot of it. This could be temperature, humidity, light, position, speed, or anything else that we can measure electronically. To be useful, that data needs to be turned into information. A list of numbers doesn’t tell you a lot without careful study, but a line graph based on those numbers can show important information in an instant. Often makers will happily write scripts to produce charts and other types of infographics, but now open-source software allows anyone to log data to a database, generate dashboards of graphs, and even trigger alerts and scripts based on the incoming data. There are several solutions out there, so we’re going to focus on just one: a suite of products from InfluxData collectively known as the TICK Stack.

InfluxDB

The ‘I’ in TICK is the database that stores your precious data. InfluxDB is a time series database. It differs from regular SQL databases as it always indexes based on the time stamp of the incoming data. You can use a regular SQL database if you wish (and we’ll show you how later), but what makes InfluxDB compelling for logging data is not only its simplicity, but also its data-management features and built-in web-based API interface. Getting data into InfluxDB can be as easy as a web post, which places it within the reach of most internet-capable microcontrollers.

Kapacitor

Next up is our ‘K’. Kapacitor is a complex data processing engine that acts on data coming into your InfluxDB. It has several purposes, but the common use is to generate alerts based on data readings. Kapacitor supports a wide range of alert ‘endpoints’, from sending a simple email to alerting notification services like Pushover, or posting a message to the ubiquitous Slack. Multiple alerts to multiple destinations can be configured, and what constitutes an alert status is up to you. More advanced uses of Kapacitor include machine learning and anomaly detection.

Chronograf

The problem with Kapacitor is the configuration. It’s a lot of work with config files and the command line. Thoughtfully, InfluxData has created Chronograf, a graphical user interface to both Kapacitor and InfluxDB. If you prefer to keep away from the command line, you can query and manage your databases here as well as set up alerts, metrics that trigger an alert, and the configurations for the various handlers. This is all presented through a web app that you can access from anywhere on your network. You can also build ‘Dashboards’ – collections of charts displayed on a single page based on your InfluxDB data.

Telegraf

Finally, our ’T’ in TICK. One of the most common uses for time series databases is measuring computer performance. Telegraf provides the link between the machine it is installed on and InfluxDB. After a simple install, Telegraf will start logging all kinds of data about its host machine to your InfluxDB installation. Memory usage, CPU temperatures and load, disk space, and network performance can all be logged to your database and charted using Chronograf. This is more due to the Stack’s more common use for monitoring servers, but it’s still useful for making sure the brains of our network-of-things is working properly. If you get a problem, Kapacitor can not only trigger alerts but also user-defined scripts that may be able to remedy the situation.

Get HackSpace magazine issue 31 — out today

HackSpace magazine issue 31: on sale now!

You can read the rest of HackSpace magazine’s DIY IoT feature in issue 31, out today and available online from the Raspberry Pi Press online store. You can also download issue 31 for free.

The post Design your own Internet of Things with HackSpace magazine appeared first on Raspberry Pi.

TMA Special: Connecting Taza Chocolate’s Legacy Equipment to the Cloud

Post Syndicated from Todd Escalona original https://aws.amazon.com/blogs/architecture/tma-special-connecting-taza-chocolates-legacy-equipment-to-the-cloud/

As a “bean to bar” chocolate manufacturer, Taza Chocolate uses traditional stone ground mills for the production of its famous chocolate discs. The analog, mid-century machines that the company imported from Central America were never built to connect to the cloud.

Along comes Tulip Interfaces, an AWS Industrial Software Competency Partner that makes the human and machine interaction easier by replacing paper processes with digital automation. Tulip retrofitted Taza’s legacy equipment with Internet of Things (IoT) sensors and connected it back to the AWS cloud.

Taza’s AWS cloud integration begins with Tulip’s own physical gateway that connects systems and machinery on the plant floor. Tulip then deploys IoT sensors to the machinery and passes outputs to the AWS cloud using an encrypted web socket where Tulip’s Kubernetes workers, managed by Kops, automatically schedule services across highly available instances and processes requests.

All job completion data is then fed to an Amazon RDS Multi-AZ PostgreSQL database that allows Taza to run visualizations and analytics for more insight using Prometheus and Garfana. In addition, all of the application definition metadata is contained in a MongoDB database service running on Amazon Elastic Cloud Compute (EC2) instances, which in return is VPC-peered with Kubernetes clusters. On top of this backend, Tulip uses a player application to stream metrics in near real-time that are displayed on the dashboard down on the shop floor and can be easily examined in order to help guide their operations and foster continuous improvements efforts to manufacturing operations.

Taza has realized many benefits from monitoring machine availability, performance, ambient conditions as well as overall process enhancements.

In this special, on-site This is My Architecture video, AWS Solutions Architect Evangelist Todd Escalona takes us on his journey through the Taza Chocolate factory where he meets with Taza’s Director of Manufacturing, Rich Moran, and Tulip’s DevOps lead, John Defreitas, to further explore how Tulip enables Taza Chocolate’s legacy equipment for cloud-based plant automation.

*Check out more This Is My Architecture video series.

Building an AWS IoT Core device using AWS Serverless and an ESP32

Post Syndicated from Moheeb Zara original https://aws.amazon.com/blogs/compute/building-an-aws-iot-core-device-using-aws-serverless-and-an-esp32/

Using a simple Arduino sketch, an AWS Serverless Application Repository application, and a microcontroller, you can build a basic serverless workflow for communicating with an AWS IoT Core device.

A microcontroller is a programmable chip and acts as the brain of an electronic device. It has input and output pins for reading and writing on digital or analog components. Those components could be sensors, relays, actuators, or various other devices. It can be used to build remote sensors, home automation products, robots, and much more. The ESP32 is a powerful low-cost microcontroller with Wi-Fi and Bluetooth built in and is used this walkthrough.

The Arduino IDE, a lightweight development environment for hardware, now includes support for the ESP32. There is a large collection of community and officially supported libraries, from addressable LED strips to spectral light analysis.

The following walkthrough demonstrates connecting an ESP32 to AWS IoT Core to allow it to publish and subscribe to topics. This means that the device can send any arbitrary information, such as sensor values, into AWS IoT Core while also being able to receive commands.

Solution overview

This post walks through deploying an application from the AWS Serverless Application Repository. This allows an AWS IoT device to be messaged using a REST endpoint powered by Amazon API Gateway and AWS Lambda. The AWS SAR application also configures an AWS IoT rule that forwards any messages published by the device to a Lambda function that updates an Amazon DynamoDB table, demonstrating basic bidirectional communication.

The last section explores how to build an IoT project with real-world application. By connecting a thermal printer module and modifying a few lines of code in the example firmware, the ESP32 device becomes an AWS IoT–connected printer.

All of this can be accomplished within the AWS Free Tier, which is necessary for the following instructions.

An example of an AWS IoT project using an ESP32, AWS IoT Core, and an Arduino thermal printer

An example of an AWS IoT project using an ESP32, AWS IoT Core, and an Arduino thermal printer.

Required steps

To complete the walkthrough, follow these steps:

  • Create an AWS IoT device.
  • Install and configure the Arduino IDE.
  • Configure and flash an ESP32 IoT device.
  • Deploying the lambda-iot-rule AWS SAR application.
  • Monitor and test.
  • Create an IoT thermal printer.

Creating an AWS IoT device

To communicate with the ESP32 device, it must connect to AWS IoT Core with device credentials. You must also specify the topics it has permissions to publish and subscribe on.

  1. In the AWS IoT console, choose Register a new thing, Create a single thing.
  2. Name the new thing. Use this exact name later when configuring the ESP32 IoT device. Leave the remaining fields set to their defaults. Choose Next.
  3.  Choose Create certificate. Only the thing cert, private key, and Amazon Root CA 1 downloads are necessary for the ESP32 to connect. Download and save them somewhere secure, as they are used when programming the ESP32 device.
  4. Choose Activate, Attach a policy.
  5. Skip adding a policy, and choose Register Thing.
  6. In the AWS IoT console side menu, choose Secure, Policies, Create a policy.
  7. Name the policy Esp32Policy. Choose the Advanced tab.
  8. Paste in the following policy template.
    {
      "Version": "2012-10-17",
      "Statement": [
        {
          "Effect": "Allow",
          "Action": "iot:Connect",
          "Resource": "arn:aws:iot:REGION:ACCOUNT_ID:client/THINGNAME"
        },
        {
          "Effect": "Allow",
          "Action": "iot:Subscribe",
          "Resource": "arn:aws:iot:REGION:ACCOUNT_ID:topicfilter/esp32/sub"
        },
    	{
          "Effect": "Allow",
          "Action": "iot:Receive",
          "Resource": "arn:aws:iot:REGION:ACCOUNT_ID:topic/esp32/sub"
        },
        {
          "Effect": "Allow",
          "Action": "iot:Publish",
          "Resource": "arn:aws:iot:REGION:ACCOUNT_ID:topic/esp32/pub"
        }
      ]
    }
  9. Replace REGION with the matching AWS Region you’re currently operating in. This can be found on the top right corner of the AWS console window.
  10.  Replace ACCOUNT_ID with your own, which can be found in Account Settings.
  11. Replace THINGNAME with the name of your device.
  12. Choose Create.
  13. In the AWS IoT console, choose Secure, Certification. Select the one created for your device and choose Actions, Attach policy.
  14. Choose Esp32Policy, Attach.

Your AWS IoT device is now configured to have permission to connect to AWS IoT Core. It can also publish to the topic esp32/pub and subscribe to the topic esp32/sub. For more information on securing devices, see AWS IoT Policies.

Installing and configuring the Arduino IDE

The Arduino IDE is an open-source development environment for programming microcontrollers. It supports a continuously growing number of platforms including most ESP32-based modules. It must be installed along with the ESP32 board definitions, MQTT library, and ArduinoJson library.

  1. Download the Arduino installer for the desired operating system.
  2. Start Arduino and open the Preferences window.
  3. For Additional Board Manager URLs, add
    https://dl.espressif.com/dl/package_esp32_index.json.
  4. Choose Tools, Board, Boards Manager.
  5. Search esp32 and install the latest version.
  6. Choose Sketch, Include Library, Manage Libraries.
  7. Search MQTT, and install the latest version by Joel Gaehwiler.
  8. Repeat the library installation process for ArduinoJson.

The Arduino IDE is now installed and configured with all the board definitions and libraries needed for this walkthrough.

Configuring and flashing an ESP32 IoT device

A collection of various ESP32 development boards.

A collection of various ESP32 development boards.

For this section, you need an ESP32 device. To check if your board is compatible with the Arduino IDE, see the boards.txt file. The following code connects to AWS IoT Core securely using MQTT, a publish and subscribe messaging protocol.

This project has been tested on the following devices:

  1. Install the required serial drivers for your device. Some boards use different USB/FTDI chips for interfacing. Here are the most commonly used with links to drivers.
  2. Open the Arduino IDE and choose File, New to create a new sketch.
  3. Add a new tab and name it secrets.h.
  4. Paste the following into the secrets file.
    #include <pgmspace.h>
    
    #define SECRET
    #define THINGNAME ""
    
    const char WIFI_SSID[] = "";
    const char WIFI_PASSWORD[] = "";
    const char AWS_IOT_ENDPOINT[] = "xxxxx.amazonaws.com";
    
    // Amazon Root CA 1
    static const char AWS_CERT_CA[] PROGMEM = R"EOF(
    -----BEGIN CERTIFICATE-----
    -----END CERTIFICATE-----
    )EOF";
    
    // Device Certificate
    static const char AWS_CERT_CRT[] PROGMEM = R"KEY(
    -----BEGIN CERTIFICATE-----
    -----END CERTIFICATE-----
    )KEY";
    
    // Device Private Key
    static const char AWS_CERT_PRIVATE[] PROGMEM = R"KEY(
    -----BEGIN RSA PRIVATE KEY-----
    -----END RSA PRIVATE KEY-----
    )KEY";
  5. Enter the name of your AWS IoT thing, as it is in the console, in the field THINGNAME.
  6. To connect to Wi-Fi, add the SSID and PASSWORD of the desired network. Note: The network name should not include spaces or special characters.
  7. The AWS_IOT_ENDPOINT can be found from the Settings page in the AWS IoT console.
  8. Copy the Amazon Root CA 1, Device Certificate, and Device Private Key to their respective locations in the secrets.h file.
  9. Choose the tab for the main sketch file, and paste the following.
    #include "secrets.h"
    #include <WiFiClientSecure.h>
    #include <MQTTClient.h>
    #include <ArduinoJson.h>
    #include "WiFi.h"
    
    // The MQTT topics that this device should publish/subscribe
    #define AWS_IOT_PUBLISH_TOPIC   "esp32/pub"
    #define AWS_IOT_SUBSCRIBE_TOPIC "esp32/sub"
    
    WiFiClientSecure net = WiFiClientSecure();
    MQTTClient client = MQTTClient(256);
    
    void connectAWS()
    {
      WiFi.mode(WIFI_STA);
      WiFi.begin(WIFI_SSID, WIFI_PASSWORD);
    
      Serial.println("Connecting to Wi-Fi");
    
      while (WiFi.status() != WL_CONNECTED){
        delay(500);
        Serial.print(".");
      }
    
      // Configure WiFiClientSecure to use the AWS IoT device credentials
      net.setCACert(AWS_CERT_CA);
      net.setCertificate(AWS_CERT_CRT);
      net.setPrivateKey(AWS_CERT_PRIVATE);
    
      // Connect to the MQTT broker on the AWS endpoint we defined earlier
      client.begin(AWS_IOT_ENDPOINT, 8883, net);
    
      // Create a message handler
      client.onMessage(messageHandler);
    
      Serial.print("Connecting to AWS IOT");
    
      while (!client.connect(THINGNAME)) {
        Serial.print(".");
        delay(100);
      }
    
      if(!client.connected()){
        Serial.println("AWS IoT Timeout!");
        return;
      }
    
      // Subscribe to a topic
      client.subscribe(AWS_IOT_SUBSCRIBE_TOPIC);
    
      Serial.println("AWS IoT Connected!");
    }
    
    void publishMessage()
    {
      StaticJsonDocument<200> doc;
      doc["time"] = millis();
      doc["sensor_a0"] = analogRead(0);
      char jsonBuffer[512];
      serializeJson(doc, jsonBuffer); // print to client
    
      client.publish(AWS_IOT_PUBLISH_TOPIC, jsonBuffer);
    }
    
    void messageHandler(String &topic, String &payload) {
      Serial.println("incoming: " + topic + " - " + payload);
    
    //  StaticJsonDocument<200> doc;
    //  deserializeJson(doc, payload);
    //  const char* message = doc["message"];
    }
    
    void setup() {
      Serial.begin(9600);
      connectAWS();
    }
    
    void loop() {
      publishMessage();
      client.loop();
      delay(1000);
    }
  10. Choose File, Save, and give your project a name.

Flashing the ESP32

  1. Plug the ESP32 board into a USB port on the computer running the Arduino IDE.
  2. Choose Tools, Board, and then select the matching type of ESP32 module. In this case, a Sparkfun ESP32 Thing was used.
  3. Choose Tools, Port, and then select the matching port for your device.
  4. Choose Upload. Arduino reads Done uploading when the upload is successful.
  5. Choose the magnifying lens icon to open the Serial Monitor. Set the baud rate to 9600.

Keep the Serial Monitor open. When connected to Wi-Fi and then AWS IoT Core, any messages received on the topic esp32/sub are logged to this console. The device is also now publishing to the topic esp32/pub.

The topics are set at the top of the sketch. When changing or adding topics, remember to add permissions in the device policy.

// The MQTT topics that this device should publish/subscribe
#define AWS_IOT_PUBLISH_TOPIC   "esp32/pub"
#define AWS_IOT_SUBSCRIBE_TOPIC "esp32/sub"

Within this sketch, the relevant functions are publishMessage() and messageHandler().

The publishMessage() function creates a JSON object with the current time in milliseconds and the analog value of pin A0 on the device. It then publishes this JSON object to the topic esp32/pub.

void publishMessage()
{
  StaticJsonDocument<200> doc;
  doc["time"] = millis();
  doc["sensor_a0"] = analogRead(0);
  char jsonBuffer[512];
  serializeJson(doc, jsonBuffer); // print to client

  client.publish(AWS_IOT_PUBLISH_TOPIC, jsonBuffer);
}

The messageHandler() function prints out the topic and payload of any message from a subscribed topic. To see all the ways to parse JSON messages in Arduino, see the deserializeJson() example.

void messageHandler(String &topic, String &payload) {
  Serial.println("incoming: " + topic + " - " + payload);

//  StaticJsonDocument<200> doc;
//  deserializeJson(doc, payload);
//  const char* message = doc["message"];
}

Additional topic subscriptions can be added within the connectAWS() function by adding another line similar to the following.

// Subscribe to a topic
  client.subscribe(AWS_IOT_SUBSCRIBE_TOPIC);

  Serial.println("AWS IoT Connected!");

Deploying the lambda-iot-rule AWS SAR application

Now that an ESP32 device has been connected to AWS IoT, the following steps walk through deploying an AWS Serverless Application Repository application. This is a base for building serverless integration with a physical device.

  1. On the lambda-iot-rule AWS Serverless Application Repository application page, make sure that the Region is the same as the AWS IoT device.
  2. Choose Deploy.
  3. Under Application settings, for PublishTopic, enter esp32/sub. This is the topic to which the ESP32 device is subscribed. It receives messages published to this topic. Likewise, set SubscribeTopic to esp32/pub, the topic on which the device publishes.
  4. Choose Deploy.
  5. When creation of the application is complete, choose Test app to navigate to the application page. Keep this page open for the next section.

Monitoring and testing

At this stage, two Lambda functions, a DynamoDB table, and an AWS IoT rule have been deployed. The IoT rule forwards messages on topic esp32/pub to TopicSubscriber, a Lambda function, which inserts the messages on to the DynamoDB table.

  1. On the application page, under Resources, choose MyTable. This is the DynamoDB table that the TopicSubscriber Lambda function updates.
  2. Choose Items. If the ESP32 device is still active and connected, messages that it has published appear here.

The TopicPublisher Lambda function is invoked by the API Gateway endpoint and publishes to the AWS IoT topic esp32/sub.

1.     On the application page, find the Application endpoint.

2.     To test that the TopicPublisher function is working, enter the following into a terminal or command-line utility, replacing ENDPOINT with the URL from above.

curl -d '{"text":"Hello world!"}' -H "Content-Type: application/json" -X POST https://ENDPOINT/publish

Upon success, the request returns a copy of the message.

Back in the Serial Monitor, the message published to the topic esp32/sub prints out.

Creating an IoT thermal printer

With the completion of the previous steps, the ESP32 device currently logs incoming messages to the serial console.

The following steps demonstrate how the code can be modified to use incoming messages to interact with a peripheral component. This is done by wiring a thermal printer to the ESP32 in order to physically print messages. The REST endpoint from the previous section can be used as a webhook in third-party applications to interact with this device.

A wiring diagram depicting an ESP32 connected to a thermal printer.

A wiring diagram depicting an ESP32 connected to a thermal printer.

  1. Follow the product instructions for powering, wiring, and installing the correct Arduino library.
  2. Ensure that the thermal printer is working by holding the power button on the printer while connecting the power. A sample receipt prints. On that receipt, the default baud rate is specified as either 9600 or 19200.
  3. In the Arduino code from earlier, include the following lines at the top of the main sketch file. The second line defines what interface the thermal printer is connected to. &Serial2 is used to set the third hardware serial interface on the ESP32. For this example, the pins on the Sparkfun ESP32 Thing, GPIO16/GPIO17, are used for RX/TX respectively.
    #include "Adafruit_Thermal.h"
    
    Adafruit_Thermal printer(&Serial2);
  4. Replace the setup() function with the following to initialize the printer on device bootup. Change the baud rate of Serial2.begin() to match what is specified in the test print. The default is 19200.
    void setup() {
      Serial.begin(9600);
    
      // Start the thermal printer
      Serial2.begin(19200);
      printer.begin();
      printer.setSize('S');
    
      connectAWS();
    }
    
  5. Replace the messageHandler() function with the following. On any incoming message, it parses the JSON and prints the message on the thermal printer.
    void messageHandler(String &topic, String &payload) {
      Serial.println("incoming: " + topic + " - " + payload);
    
      // deserialize json
      StaticJsonDocument<200> doc;
      deserializeJson(doc, payload);
      String message = doc["message"];
    
      // Print the message on the thermal printer
      printer.println(message);
      printer.feed(2);
    }
  6. Choose Upload.
  7. After the firmware has successfully uploaded, open the Serial Monitor to confirm that the board has connected to AWS IoT.
  8. Enter the following into a command-line utility, replacing ENDPOINT, as in the previous section.
    curl -d '{"message": "Hello World!"}' -H "Content-Type: application/json" -X POST https://ENDPOINT/publish

If successful, the device prints out the message “Hello World” from the attached thermal printer. This is a fully serverless IoT printer that can be triggered remotely from a webhook. As an example, this can be used with GitHub Webhooks to print a physical readout of events.

Conclusion

Using a simple Arduino sketch, an AWS Serverless Application Repository application, and a microcontroller, this post demonstrated how to build a basic serverless workflow for communicating with a physical device. It also showed how to expand that into an IoT thermal printer with real-world applications.

With the use of AWS serverless, advanced compute and extensibility can be added to an IoT device, from machine learning to translation services and beyond. By using the Arduino programming environment, the vast collection of open-source libraries, projects, and code examples open up a world of possibilities. The next step is to explore what can be done with an Arduino and the capabilities of AWS serverless. The sample Arduino code for this project and more can be found at this GitHub repository.

IoT ugly Christmas sweaters

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/iot-ugly-christmas-sweaters/

If there’s one thing we Brits love, it’s an ugly Christmas sweater. Jim Bennett, a Senior Cloud Advocate at Microsoft, has taken his ugly sweater game to the next level by adding IoT-controlled, Twitter-connected LEDs thanks to a Raspberry Pi Zero.

IoT is Fun for Everyone! (Ugly Sweater Edition)

An Ugly Sweater is great-but what’s even better (https://aka.ms/IoTShow/UglySweater) is an IoT-enabled Ugly Sweater. In this episode of the IoT Show, Olivier Bloch is joined by Jim Bennett, a Senior Cloud Advocate at Microsoft. Jim has built an Ugly Sweater using Azure IoT Central, Microsoft’s IoT app platform, and a Raspberry Pi Zero.

Jim upgraded his ugly sweater to become IoT-compatible using Microsoft’s IoT app platform Azure IoT Central, Adafruit’s programmable NeoPixel LED Dots Strand and, of course, our sweet baby, the Raspberry Pi Zero W.

After sewing the LED strand into the ugly sweater and connecting it to Raspberry Pi Zero, Jim was able to control the colour of the LEDs. Taking it one step further, he then built a list of commands within Azure IoT Central and linked the Raspberry Pi Zero to a Twitter account to create the IoT element of the project.

Watch the video above for full details on the project, and find all the code on Github.

The post IoT ugly Christmas sweaters appeared first on Raspberry Pi.

AWS Architecture Monthly Magazine: Manufacturing

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/aws-architecture-monthly-magazine-manufacturing/

Architecture Monthly Magazine - Nov-Dec 2019

For more than 25 years, Amazon has designed and manufactured smart products and distributed billions of products through its globally connected distribution network using cutting edge automation, machine learning and AI, and robotics, with AWS at its core. From product design to smart factory and smart products, AWS helps leading manufacturers transform their manufacturing operations with the most comprehensive and advanced set of cloud solutions available today, while taking advantage of the highest level of security.

In this Manufacturing-themed end-of-year issue of the AWS Architecture Monthly magazine, Steve Blackwell, AWS Manufacturing Tech Leader, talks about how manufacturers can experiment with and take advantage of emerging technologies using three main architectural patterns: demand forecasting, smart factories, and extending the manufacturing value chain with smart products.

In This Issue

We’ve assembled architectural best practices about Manufacturing from all over AWS, and we’ve made sure that a broad audience can appreciate it. Note that this will be our last issue of the year. We’ll be back in January with highlights and insights about AWS re:Invent 2019 (December 2-6 in Las Vegas).

  • Case Study: iRobot Ready to Unlock the Next Generation of Smart Homes Using the AWS Cloud
  • Ask an Expert: Steve Blackwell, Manufacturing Tech Leader
  • Blog Post: Reinventing the IoT Platform for Discrete Manufacturers
  • Solution: Smart Product Solution
  • AWS Coffee Break: IoT Helps Manufacturing Hit the Right Note
  • Whitepaper: Practical Ways To Achieve Smarter, Faster, and More Responsive Operations
  • Reference Architecture: EDA on AWS with IBM Spectrum LSF

How to Access the Magazine

We hope you’re enjoying Architecture Monthly, and we’d like to hear from you—leave us star rating and comment on the Amazon Kindle Newsstand page or contact us anytime at [email protected].

FogHorn: Edge-to-Edge Communication and Deep Learning

Post Syndicated from Annik Stahl original https://aws.amazon.com/blogs/architecture/foghorn-edge-to-edge-communication-and-deep-learning/

FogHorn is an intelligent Internet of Things ( IoT) edge solution that delivers data processing and real-time inference where data is created. Referring to itself as “the only ‘real’ edge intelligence solution in the market today,”  FogHorn is powered by a hyper-efficient Complex Event Processor (CEP) and delivers comprehensive data enrichment and real-time analytics on high volumes, varieties, and velocities of streaming sensor data, and is optimized for constrained compute footprints and limited connectivity.

Andrea Sabet, AWS Solutions Architect speaks with Ramya Ravichandar, Vice President of Products at Foghorn to talk about how FogHorn integrates with IoT MQTT for edge-to-edge communication as well as Amazon SageMaker for deep learning model deployment. The edgefication process involves running inference with real-time streaming data against a trained deep learning model. Drifts in the model accuracy trigger a callback to SageMaker for retraining.

*Check out more This Is My Architecture video series.

 

Visualizing Sensor Data in Amazon QuickSight

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/visualizing-sensor-data-in-amazon-quicksight/

This post is courtesy of Moheeb Zara, Developer Advocate, AWS Serverless

The Internet of Things (IoT) is a term used wherever physical devices are networked in some meaningful connected way. Often, this takes the form of sensor data collection and analysis. As the number of devices and size of data scales, it can become costly and difficult to keep up with demand.

Using AWS Serverless Application Model (AWS SAM), you can reduce the cost and time to market of an IoT solution. This guide demonstrates how to collect and visualize data from a low-cost, Wi-Fi connected IoT device using a variety of AWS services. Much of this can be accomplished within the AWS Free Usage Tier, which is necessary for the following instructions.

Services used

The following services are used in this example:

What’s covered in this post?

This post covers:

  • Connecting an Arduino MKR 1010 Wi-Fi device to AWS IoT Core.
  • Forwarding messages from an AWS IoT Core topic stream to a Lambda function.
  • Using a Kinesis Data Firehose delivery stream to store data in S3.
  • Analyzing and visualizing data stored in S3 using Amazon QuickSight.

Connect the device to AWS IoT Core using MQTT

The Arduino MKR 1010 is a low-cost, Wi-Fi enabled, IoT device, shown in the following image.

An Arduino MKR 1010 Wi-Fi microcontroller

Its analog and digital input and output pins can be used to read sensors or to write to actuators. Arduino provides a detailed guide on how to securely connect this device to AWS IoT Core. The following steps build upon it to push arbitrary sensor data to a topic stream and ultimately visualize that data using Amazon QuickSight.

  1. Start by following this comprehensive guide to using an Arduino MKR 1010 with AWS IoT Core. Upon completion, your device is connected to AWS IoT Core using MQTT (Message Queuing Telemetry Transport), a protocol for publishing and subscribing to messages using topics.
  2. In the Arduino IDE, choose File, Sketch, Include Library, and Manage Libraries.
  3. In the window that opens, search for ArduinoJson and select the library by Benoit Blanchon. Choose install.

4. Add #include <ArduinoJson.h> to the top of your sketch from the Arduino guide.

5. Modify the publishMessage() function with this code. It publishes a JSON message with two keys: time (ms) and the current value read from the first analog pin.

void publishMessage() {  
  Serial.println("Publishing message");

  // send message, the Print interface can be used to set the message contents
  mqttClient.beginMessage("arduino/outgoing");
  
  // create json message to send
  StaticJsonDocument<200> doc;
  doc["time"] = millis();
  doc["sensor_a0"] = analogRead(0);
  serializeJson(doc, mqttClient); // print to client
  
  mqttClient.endMessage();
}

6. Save and upload the sketch to your board.

Create a Kinesis Firehose delivery stream

Amazon Kinesis Data Firehose is a service that reliably loads streaming data into data stores, data lakes, and analytics tools. Amazon QuickSight requires a data store to create visualizations of the sensor data. This simple Kinesis Data Firehose delivery stream continuously uploads data to an S3 storage bucket. The next sections cover how to add records to this stream using a Lambda function.

  1. In the Kinesis Data Firehose console, create a new delivery stream, called SensorDataStream.
  2. Leave the default source as a Direct PUT or other sources and choose Next.
  3. On the next screen, leave all the default values and choose Next.
  4. Select Amazon S3 as the destination and create a new bucket with a unique name. This is where records are continuously uploaded so that they can be used by Amazon QuickSight.
  5. On the next screen, choose Create New IAM Role, Allow. This gives the Firehose delivery stream permission to upload to S3.
  6. Review and then choose Create Delivery Stream.

It can take some time to fully create the stream. In the meantime, continue on to the next section.

Invoking Lambda using AWS IoT Core rules

Using AWS IoT Core rules, you can forward messages from devices to a Lambda function, which can perform actions such as uploading to an Amazon DynamoDB table or an S3 bucket, or running data against various Amazon Machine Learning services. In this case, the function transforms and adds a message to the Kinesis Data Firehose delivery stream, which then adds that data to S3.

AWS IoT Core rules use the MQTT topic stream to trigger interactions with other AWS services. An AWS IoT Core rule is created by using an SQL statement, a topic filter, and a rule action. The Arduino example publishes messages every five seconds on the topic arduino/outgoing. The following instructions show how to consume those messages with a Lambda function.

Create a Lambda function

Before creating an AWS IoT Core rule, you need a Lambda function to consume forwarded messages.

  1. In the AWS Lambda console, choose Create function.
  2. Name the function ArduinoConsumeMessage.
  3. For Runtime, choose Author From Scratch, Node.js10.x. For Execution role, choose Create a new role with basic Lambda permissions. Choose Create.
  4. On the Execution role card, choose View the ArduinoConsumeMessage-role-xxxx on the IAM console.
  5. Choose Attach Policies. Then, search for and select AmazonKinesisFirehoseFullAccess.
  6. Choose Attach Policy. This applies the necessary permissions to add records to the Firehose delivery stream.
  7. In the Lambda console, in the Designer card, select the function name.
  8. Paste the following in the code editor, replacing SensorDataStream with the name of your own Firehose delivery stream. Choose Save.
const AWS = require('aws-sdk')

const firehose = new AWS.Firehose()
const StreamName = "SensorDataStream"

exports.handler = async (event) => {
    
    console.log('Received IoT event:', JSON.stringify(event, null, 2))
    
    let payload = {
        time: new Date(event.time),
        sensor_value: event.sensor_a0
    }
    
    let params = {
            DeliveryStreamName: StreamName,
            Record: { 
                Data: JSON.stringify(payload)
            }
        }
        
    return await firehose.putRecord(params).promise()

}

Create an AWS IoT Core rule

To create an AWS IoT Core rule, follow these steps.

  1. In the AWS IoT console, choose Act.
  2. Choose Create.
  3. For Rule query statement, copy and paste SELECT * FROM 'arduino/outgoing’. This subscribes to the outgoing message topic used in the Arduino example.
  4. Choose Add action, Send a message to a Lambda function, Configure action.
  5. Select the function created in the last set of instructions.
  6. Choose Create rule.

At this stage, any message published to the arduino/outgoing topic forwards to the ArduinoConsumeMessage Lambda function, which transforms and puts the payload on the Kinesis Data Firehose stream and also logs the message to Amazon CloudWatch. If you’ve connected an Arduino device to AWS IoT Core, it publishes to that topic every five seconds.

The following steps show how to test functionality using the AWS IoT console.

  1. In the AWS IoT console, choose Test.
  2. For Publish, enter the topic arduino/outgoing .
  3. Enter the following test payload:
    {
      “time”: 1567023375013,  
      “sensor_a0”: 456
    }
  4. Choose Publish to topic.
  5. Navigate back to your Lambda function.
  6. Choose Monitoring, View logs in CloudWatch.
  7. Select a log item to view the message contents, as shown in the following screenshot.

Visualizing data with Amazon QuickSight

To visualize data with Amazon QuickSight, follow these steps.

  1. In the Amazon QuickSight console, sign up.
  2. Choose Manage Data, New Data Set. Select S3 as the data source.
  3. A manifest file is necessary for Amazon QuickSight to be able to fetch data from your S3 bucket. Copy the following into a file named manifest.json. Replace YOUR-BUCKET-NAME with the name of the bucket created for the Firehose delivery stream.
    {
       "fileLocations":[
          {
             "URIPrefixes":[
                "s3://YOUR-BUCKET-NAME/"
             ]
          }
       ],
       "globalUploadSettings":{
          "format":"JSON"
       }
    }
  4. Upload the manifest.json file.
  5. Choose Connect, then Visualize. You may have to give Amazon QuickSight explicit permissions to your S3 bucket.
  6. Finally, design the Amazon QuickSight visualizations in the drag and drop editor. Drag the two available fields into the center card to generate a Sum of Sensor_value by Time visual.

Conclusion

This post demonstrated visualizing data from a securely connected remote IoT device. This was achieved by connecting an Arduino to AWS IoT Core using MQTT, forwarding messages from the topic stream to Lambda using IoT Core rules, putting records on an Amazon Kinesis Data Firehose delivery stream, and using Amazon QuickSight to visualize the data stored within an S3 bucket.

With these building blocks, it is possible to implement highly scalable and customizable IoT data collection, analysis, and visualization. With the use of other AWS services, you can build a full end-to-end platform for an IoT product that can reliably handle volume. To further explore how hardware and AWS Serverless can work together, visit the Amazon Web Services page on Hackster.

Learn about AWS Services & Solutions – September AWS Online Tech Talks

Post Syndicated from Jenny Hang original https://aws.amazon.com/blogs/aws/learn-about-aws-services-solutions-september-aws-online-tech-talks/

Learn about AWS Services & Solutions – September AWS Online Tech Talks

AWS Tech Talks

Join us this September to learn about AWS services and solutions. The AWS Online Tech Talks are live, online presentations that cover a broad range of topics at varying technical levels. These tech talks, led by AWS solutions architects and engineers, feature technical deep dives, live demonstrations, customer examples, and Q&A with AWS experts. Register Now!

Note – All sessions are free and in Pacific Time.

Tech talks this month:

 

Compute:

September 23, 2019 | 11:00 AM – 12:00 PM PTBuild Your Hybrid Cloud Architecture with AWS – Learn about the extensive range of services AWS offers to help you build a hybrid cloud architecture best suited for your use case.

September 26, 2019 | 1:00 PM – 2:00 PM PTSelf-Hosted WordPress: It’s Easier Than You Think – Learn how you can easily build a fault-tolerant WordPress site using Amazon Lightsail.

October 3, 2019 | 11:00 AM – 12:00 PM PTLower Costs by Right Sizing Your Instance with Amazon EC2 T3 General Purpose Burstable Instances – Get an overview of T3 instances, understand what workloads are ideal for them, and understand how the T3 credit system works so that you can lower your EC2 instance costs today.

 

Containers:

September 26, 2019 | 11:00 AM – 12:00 PM PTDevelop a Web App Using Amazon ECS and AWS Cloud Development Kit (CDK) – Learn how to build your first app using CDK and AWS container services.

 

Data Lakes & Analytics:

September 26, 2019 | 9:00 AM – 10:00 AM PTBest Practices for Provisioning Amazon MSK Clusters and Using Popular Apache Kafka-Compatible Tooling – Learn best practices on running Apache Kafka production workloads at a lower cost on Amazon MSK.

 

Databases:

September 25, 2019 | 1:00 PM – 2:00 PM PTWhat’s New in Amazon DocumentDB (with MongoDB compatibility) – Learn what’s new in Amazon DocumentDB, a fully managed MongoDB compatible database service designed from the ground up to be fast, scalable, and highly available.

October 3, 2019 | 9:00 AM – 10:00 AM PTBest Practices for Enterprise-Class Security, High-Availability, and Scalability with Amazon ElastiCache – Learn about new enterprise-friendly Amazon ElastiCache enhancements like customer managed key and online scaling up or down to make your critical workloads more secure, scalable and available.

 

DevOps:

October 1, 2019 | 9:00 AM – 10:00 AM PT – CI/CD for Containers: A Way Forward for Your DevOps Pipeline – Learn how to build CI/CD pipelines using AWS services to get the most out of the agility afforded by containers.

 

Enterprise & Hybrid:

September 24, 2019 | 1:00 PM – 2:30 PM PT Virtual Workshop: How to Monitor and Manage Your AWS Costs – Learn how to visualize and manage your AWS cost and usage in this virtual hands-on workshop.

October 2, 2019 | 1:00 PM – 2:00 PM PT – Accelerate Cloud Adoption and Reduce Operational Risk with AWS Managed Services – Learn how AMS accelerates your migration to AWS, reduces your operating costs, improves security and compliance, and enables you to focus on your differentiating business priorities.

 

IoT:

September 25, 2019 | 9:00 AM – 10:00 AM PTComplex Monitoring for Industrial with AWS IoT Data Services – Learn how to solve your complex event monitoring challenges with AWS IoT Data Services.

 

Machine Learning:

September 23, 2019 | 9:00 AM – 10:00 AM PTTraining Machine Learning Models Faster – Learn how to train machine learning models quickly and with a single click using Amazon SageMaker.

September 30, 2019 | 11:00 AM – 12:00 PM PTUsing Containers for Deep Learning Workflows – Learn how containers can help address challenges in deploying deep learning environments.

October 3, 2019 | 1:00 PM – 2:30 PM PTVirtual Workshop: Getting Hands-On with Machine Learning and Ready to Race in the AWS DeepRacer League – Join DeClercq Wentzel, Senior Product Manager for AWS DeepRacer, for a presentation on the basics of machine learning and how to build a reinforcement learning model that you can use to join the AWS DeepRacer League.

 

AWS Marketplace:

September 30, 2019 | 9:00 AM – 10:00 AM PTAdvancing Software Procurement in a Containerized World – Learn how to deploy applications faster with third-party container products.

 

Migration:

September 24, 2019 | 11:00 AM – 12:00 PM PTApplication Migrations Using AWS Server Migration Service (SMS) – Learn how to use AWS Server Migration Service (SMS) for automating application migration and scheduling continuous replication, from your on-premises data centers or Microsoft Azure to AWS.

 

Networking & Content Delivery:

September 25, 2019 | 11:00 AM – 12:00 PM PTBuilding Highly Available and Performant Applications using AWS Global Accelerator – Learn how to build highly available and performant architectures for your applications with AWS Global Accelerator, now with source IP preservation.

September 30, 2019 | 1:00 PM – 2:00 PM PTAWS Office Hours: Amazon CloudFront – Just getting started with Amazon CloudFront and [email protected]? Get answers directly from our experts during AWS Office Hours.

 

Robotics:

October 1, 2019 | 11:00 AM – 12:00 PM PTRobots and STEM: AWS RoboMaker and AWS Educate Unite! – Come join members of the AWS RoboMaker and AWS Educate teams as we provide an overview of our education initiatives and walk you through the newly launched RoboMaker Badge.

 

Security, Identity & Compliance:

October 1, 2019 | 1:00 PM – 2:00 PM PTDeep Dive on Running Active Directory on AWS – Learn how to deploy Active Directory on AWS and start migrating your windows workloads.

 

Serverless:

October 2, 2019 | 9:00 AM – 10:00 AM PTDeep Dive on Amazon EventBridge – Learn how to optimize event-driven applications, and use rules and policies to route, transform, and control access to these events that react to data from SaaS apps.

 

Storage:

September 24, 2019 | 9:00 AM – 10:00 AM PTOptimize Your Amazon S3 Data Lake with S3 Storage Classes and Management Tools – Learn how to use the Amazon S3 Storage Classes and management tools to better manage your data lake at scale and to optimize storage costs and resources.

October 2, 2019 | 11:00 AM – 12:00 PM PTThe Great Migration to Cloud Storage: Choosing the Right Storage Solution for Your Workload – Learn more about AWS storage services and identify which service is the right fit for your business.

 

 

The world’s first Raspberry Pi-powered Twitter-activated jelly bean-pooping unicorn

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/raspberry-pi-powered-twitter-activated-jelly-bean-pooping-unicorn/

When eight-year-old Tru challenged the Kids Invent Stuff team to build a sparkly, pooping, rainbow unicorn electric vehicle, they did exactly that. And when Kids Invent Stuff, also known as Ruth and Shawn, got in contact with Estefannie Explains it All, their unicorn ended up getting an IoT upgrade…because obviously.

You tweet and the Unicorn poops candy! | Kids Invent Stuff

We bring kids’ inventions to life and this month we teamed up with fellow youtube Estefannie (from Estefannie Explains It All https://www.youtube.com/user/estefanniegg SHE IS EPIC!) to modify Tru’s incredible sweet pooping unicorn to be activated by the internet! Featuring the AMAZING Allen Pan https://www.youtube.com/channel/UCVS89U86PwqzNkK2qYNbk5A (Thanks Allen for your filming and tweeting!)

Kids Invent Stuff

If you’re looking for an exciting, wholesome, wonderful YouTube channel suitable for the whole family, look no further than Kids Invent Stuff. Challenging kids to imagine wonderful inventions based on monthly themes, channel owners Ruth and Shawn then make these kids’ ideas a reality. Much like the Astro Pi Challenge, Kids Invent Stuff is one of those things we adults wish existed when we were kids. We’re not jealous, we’re just…OK, we’re definitely jealous.

ANYWAY, when eight-year-old Tru’s sparkly, pooping, rainbow unicorn won the channel’s ‘crazy new vehicle’ challenge, the team got to work, and the result is magical.

Riding an ELECTRIC POOPING UNICORN! | Kids Invent Stuff

We built 8-year-old Tru’s sparkly, pooping, rainbow unicorn electric vehicle and here’s what happened when we drove it for the first time and pooped out some jelly beans! A massive THANK YOU to our challenge sponsor The Big Bang Fair: https://www.thebigbangfair.co.uk The Big Bang Fair is the UK’s biggest celebration of STEM for young people!

But could a sparkly, pooping, rainbow unicorn electric vehicle ever be enough? Is anything ever enough if it’s not connected to the internet? Of course not. And that’s where Estefannie came in.

At Maker Central in Birmingham earlier this year, the two YouTube teams got together to realise their shared IoT dream.

They ran out of chairs for their panel, so Allen had to improvise

With the help of a Raspberry Pi Zero W connected to the relay built into the unicorn, the team were able to write code that combs through Twitter, looking for mentions of @mythicalpoops. A positive result triggers the Raspberry Pi to activate the relay, and the unicorn lifts its tail to shoot jelly beans at passers-by.

You can definitely tell this project was invented by an eight-year-old, and we love it!

IoT unicorn

As you can see in the video above, the IoT upgrades to the unicorn allow Twitter users to control when the mythical beast poops its jelly beans. There are rumours that the unicorn may be coming to live with us at Pi Towers, but if these turn out to be true, we’ll ensure that this function is turned off. So no tweeting the unicorn!

You know what to do

Be sure to subscribe to both Kids Invent Stuff and Estefannie Explains It All on YouTube. They’re excellent makers producing wonderful content, and we know you’ll love them.

How to milk a unicorn

The post The world’s first Raspberry Pi-powered Twitter-activated jelly bean-pooping unicorn appeared first on Raspberry Pi.

IoT community sprinkler system using Raspberry Pi | The MagPi issue 83

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/iot-community-sprinkler-system-using-raspberry-pi-the-magpi-issue-83/

Saving water, several thousand lawns at a time: The MagPi magazine takes a look at the award-winning IoT sprinkler system of Coolest Projects USA participant Adarsh Ambati.

At any Coolest Projects event, you’re bound to see incredible things built by young makers. At Coolest Projects USA, we had the chance to talk to Adarsh Ambati about his community sprinkler and we were, frankly, amazed.

“The extreme, record-breaking drought in California inspired me to think of innovative ways to save water,” Adarsh tells us. “While going to school in the rain one day, I saw one of my neighbours with their sprinklers on, creating run-offs. Through research, I found that 25% of the water used in an average American household is wasted each day due to overwatering and inefficient watering methods. Thus, I developed a sprinkler system that is compliant with water regulations, to cost-effectively save water for entire neighbourhoods using a Raspberry Pi, moisture sensors, PyOWM (weather database), and by utilising free social media networks like Twitter.”

Efficient watering

In California, it’s very hot year round, so if you want a lush, green lawn you need to keep the grass watered. The record-breaking drought Adarsh was referring to resulted in extreme limitations on how much you could water your grass. The problem is, unless you have a very expensive sprinkler system, it’s easy to water the grass when it doesn’t need to be.

“The goal of my project is to save water wasted during general-purpose landscape irrigation of an entire neighbourhood by building a moisture sensor-based smart sprinkler system that integrates real-time weather forecast data to provide only optimum levels of water required,” Adarsh explains. “It will also have Twitter capabilities that will be able to publish information about when and how long to turn on the sprinklers, through the social networks. The residents in the community will subscribe to this information by following an account on Twitter, and utilise it to prevent water wasted during general-purpose landscaping and stay compliant with water regulations imposed in each area.”

Using the Raspberry Pi, Adarsh was able to build a prototype for about $50 — a lot cheaper than smart sprinklers you can currently buy on the market.

“I piloted it with ten homes, so the cost per home is around $5,” he reveals. “But since it has the potential to serve an entire community, the cost per home can be a few cents. For example, there are about 37000 residents in Almaden Valley, San Jose (where I live). If there is an average of two to four residents per home, there should be 9250 to 18500 homes. If I strategically place ten such prototypes, the cost per house would be five cents or less.”

Massive saving

Adarsh continues, “Based on two months of data, 83% of the water used for outdoor landscape watering can be saved. The average household in northern California uses 100 gallons of water for outdoor landscaping on a daily basis. The ten homes in my pilot had the potential to save roughly 50000 gallons over a two-month period, or 2500 gallons per month per home. At $0.007 per gallon, the savings equate to $209 per year, per home. For Almaden Valley alone, we have the potential to save around $2m to $4m per year!”

The results from Adarsh’s test were presented to the San Jose City Council, and they were so impressed they’re now considering putting similar systems in their public grass areas. Oh, and he also won the Hardware project category at Coolest Projects USA.

The MagPi magazine #83

This article is from today’s brand-new issue of The MagPi, the official Raspberry Pi magazine. Buy it from all good newsagents, subscribe to pay less per issue and support our work, or download the free PDF to give it a try first.

The post IoT community sprinkler system using Raspberry Pi | The MagPi issue 83 appeared first on Raspberry Pi.

AWS Security releases IoT security whitepaper

Post Syndicated from Momena Cheema original https://aws.amazon.com/blogs/security/aws-security-releases-iot-security-whitepaper/

We’ve published a whitepaper, Securing Internet of Things (IoT) with AWS, to help you understand and address data security as it relates to your IoT devices and the data generated by them. The whitepaper is intended for a broad audience who is interested in learning about AWS IoT security capabilities at a service-specific level and for compliance, security, and public policy professionals.

IoT technologies connect devices and people in a multitude of ways and are used across industries. For example, IoT can help manage thermostats remotely across buildings in a city, efficiently control hundreds of wind turbines, or operate autonomous vehicles more safely. With all of the different types of devices and the data they transmit, security is a top concern.

The specific challenges using IoT technologies present has piqued the interest of governments worldwide who are currently assessing what, if any, new regulatory requirements should take shape to keep pace with IoT innovation and the general problem of securing data. This whitepaper uses a specific example to cover recent developments published by the National Institute of Standards and Technology (NIST) and the United Kingdom’s Code of Practice that are specific to IoT.

If you have questions or want to learn more, contact your account executive, or leave a comment below.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author photo

Momena Cheema

Momena is passionate about evangelizing the security and privacy capabilities of AWS services through the lens of global emerging technology and trends, such as Internet of Things, artificial intelligence, and machine learning through written content, workshops, talks, and educational campaigns. Her goal is to bring the security and privacy benefits of the cloud to customers across industries in both public and private sectors.