Tag Archives: Qt

Raspberry Pi as car computer

Post Syndicated from Liz Upton original https://www.raspberrypi.org/blog/raspberry-pi-as-car-computer/

Carputers! Fabrice Aneche is documenting his ongoing build, which equips an older (2011) car with some of the features a 2018 model might have: thus far, a reversing camera (bought off the shelf, with a modified GUI to show the date and the camera’s output built with Qt and Golang), GPS and offline route guidance.

rearcam

We’re not sure how the car got through that little door there.

It was back in 2013, when the Raspberry Pi had been on the market for about a year, that we started to see carputer projects emerge. They tended to be focussed in two directions: in-car entertainment, and on-board diagnostics (OBD). We ended up hiring the wonderful Martin O’Hanlon, who wrote up the first OBD project we came across, just this year. Being featured on this blog can change your life, I tell you.

In the last five years, the Pi’s evolved: you’re now working with a lot more processing power, there’s onboard WiFi, and far more peripherals which can be useful in a…vehicular context are available. Consequently, the flavour of the car projects we’re seeing has changed somewhat, with navigation systems and cameras much more visible. Fabrice’s is one of the best examples we’ve found.

solarised map

Night-view navigation system

GPS is all very well, but you, the human person driver, will want directions at every turn. So Fabrice wrote a user interface to serve up live maps and directions, mostly in Qt5 and QML (he’s got some interesting discussion on his website about why he stopped using X11, which turned out to be too slow for his needs). All the non-QML work is done in Go. It’s all open-source, and on GitHub, if you’d like to contribute or roll your own project. He’s also worked over the Linux GPS daemons, found them lacking, and has produced his own:

…the Linux gps daemons are using obscure and over complicated protocols so I’ve decided to write my own gps daemon in Go using a gRPC stream interface. You can find it here.

I’m also not satisfied with the map matching of OSRM for real time display, I may rewrite one using mbmatch.

street map display

We’ll be keeping an eye on this project; given how much clever has gone into it already, we’re pretty sure that Fabrice will be adding new features. Thanks Fabrice!

The post Raspberry Pi as car computer appeared first on Raspberry Pi.

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/756020/rss

Security updates have been issued by Arch Linux (strongswan, wireshark-cli, wireshark-common, wireshark-gtk, and wireshark-qt), CentOS (libvirt, procps-ng, and thunderbird), Debian (apache2, git, and qemu), Gentoo (beep, git, and procps), Mageia (mariadb, microcode, python, virtualbox, and webkit2), openSUSE (ceph, pdns, and perl-DBD-mysql), Red Hat (kernel), SUSE (HA kernel modules, libmikmod, ntp, and tiff), and Ubuntu (nvidia-graphics-drivers-384).

[$] An introduction to MQTT

Post Syndicated from corbet original https://lwn.net/Articles/753705/rss

I was sure that somewhere there must be
physically-lightweight sensors with simple power, simple networking, and
a lightweight protocol that allowed them to squirt their data down the
network with a minimum of overhead. So my interest was piqued when Jan-Piet Mens spoke at FLOSS
UK’s Spring
Conference
on “Small Things for Monitoring”. Once he started passing
working demonstration systems around the room without interrupting the
demonstration, it was clear that MQTT was what I’d been looking for.

IoT Inspector Tool from Princeton

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/iot_inspector_t.html

Researchers at Princeton University have released IoT Inspector, a tool that analyzes the security and privacy of IoT devices by examining the data they send across the Internet. They’ve already used the tool to study a bunch of different IoT devices. From their blog post:

Finding #3: Many IoT Devices Contact a Large and Diverse Set of Third Parties

In many cases, consumers expect that their devices contact manufacturers’ servers, but communication with other third-party destinations may not be a behavior that consumers expect.

We have found that many IoT devices communicate with third-party services, of which consumers are typically unaware. We have found many instances of third-party communications in our analyses of IoT device network traffic. Some examples include:

  • Samsung Smart TV. During the first minute after power-on, the TV talks to Google Play, Double Click, Netflix, FandangoNOW, Spotify, CBS, MSNBC, NFL, Deezer, and Facebook­even though we did not sign in or create accounts with any of them.
  • Amcrest WiFi Security Camera. The camera actively communicates with cellphonepush.quickddns.com using HTTPS. QuickDDNS is a Dynamic DNS service provider operated by Dahua. Dahua is also a security camera manufacturer, although Amcrest’s website makes no references to Dahua. Amcrest customer service informed us that Dahua was the original equipment manufacturer.

  • Halo Smoke Detector. The smart smoke detector communicates with broker.xively.com. Xively offers an MQTT service, which allows manufacturers to communicate with their devices.

  • Geeni Light Bulb. The Geeni smart bulb communicates with gw.tuyaus.com, which is operated by TuYa, a China-based company that also offers an MQTT service.

We also looked at a number of other devices, such as Samsung Smart Camera and TP-Link Smart Plug, and found communications with third parties ranging from NTP pools (time servers) to video storage services.

Their first two findings are that “Many IoT devices lack basic encryption and authentication” and that “User behavior can be inferred from encrypted IoT device traffic.” No surprises there.

Boingboing post.

Related: IoT Hall of Shame.

AWS AppSync – Production-Ready with Six New Features

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-appsync-production-ready-with-six-new-features/

If you build (or want to build) data-driven web and mobile apps and need real-time updates and the ability to work offline, you should take a look at AWS AppSync. Announced in preview form at AWS re:Invent 2017 and described in depth here, AWS AppSync is designed for use in iOS, Android, JavaScript, and React Native apps. AWS AppSync is built around GraphQL, an open, standardized query language that makes it easy for your applications to request the precise data that they need from the cloud.

I’m happy to announce that the preview period is over and that AWS AppSync is now generally available and production-ready, with six new features that will simplify and streamline your application development process:

Console Log Access – You can now see the CloudWatch Logs entries that are created when you test your GraphQL queries, mutations, and subscriptions from within the AWS AppSync Console.

Console Testing with Mock Data – You can now create and use mock context objects in the console for testing purposes.

Subscription Resolvers – You can now create resolvers for AWS AppSync subscription requests, just as you can already do for query and mutate requests.

Batch GraphQL Operations for DynamoDB – You can now make use of DynamoDB’s batch operations (BatchGetItem and BatchWriteItem) across one or more tables. in your resolver functions.

CloudWatch Support – You can now use Amazon CloudWatch Metrics and CloudWatch Logs to monitor calls to the AWS AppSync APIs.

CloudFormation Support – You can now define your schemas, data sources, and resolvers using AWS CloudFormation templates.

A Brief AppSync Review
Before diving in to the new features, let’s review the process of creating an AWS AppSync API, starting from the console. I click Create API to begin:

I enter a name for my API and (for demo purposes) choose to use the Sample schema:

The schema defines a collection of GraphQL object types. Each object type has a set of fields, with optional arguments:

If I was creating an API of my own I would enter my schema at this point. Since I am using the sample, I don’t need to do this. Either way, I click on Create to proceed:

The GraphQL schema type defines the entry points for the operations on the data. All of the data stored on behalf of a particular schema must be accessible using a path that begins at one of these entry points. The console provides me with an endpoint and key for my API:

It also provides me with guidance and a set of fully functional sample apps that I can clone:

When I clicked Create, AWS AppSync created a pair of Amazon DynamoDB tables for me. I can click Data Sources to see them:

I can also see and modify my schema, issue queries, and modify an assortment of settings for my API.

Let’s take a quick look at each new feature…

Console Log Access
The AWS AppSync Console already allows me to issue queries and to see the results, and now provides access to relevant log entries.In order to see the entries, I must enable logs (as detailed below), open up the LOGS, and check the checkbox. Here’s a simple mutation query that adds a new event. I enter the query and click the arrow to test it:

I can click VIEW IN CLOUDWATCH for a more detailed view:

To learn more, read Test and Debug Resolvers.

Console Testing with Mock Data
You can now create a context object in the console where it will be passed to one of your resolvers for testing purposes. I’ll add a testResolver item to my schema:

Then I locate it on the right-hand side of the Schema page and click Attach:

I choose a data source (this is for testing and the actual source will not be accessed), and use the Put item mapping template:

Then I click Select test context, choose Create New Context, assign a name to my test content, and click Save (as you can see, the test context contains the arguments from the query along with values to be returned for each field of the result):

After I save the new Resolver, I click Test to see the request and the response:

Subscription Resolvers
Your AWS AppSync application can monitor changes to any data source using the @aws_subscribe GraphQL schema directive and defining a Subscription type. The AWS AppSync client SDK connects to AWS AppSync using MQTT over Websockets and the application is notified after each mutation. You can now attach resolvers (which convert GraphQL payloads into the protocol needed by the underlying storage system) to your subscription fields and perform authorization checks when clients attempt to connect. This allows you to perform the same fine grained authorization routines across queries, mutations, and subscriptions.

To learn more about this feature, read Real-Time Data.

Batch GraphQL Operations
Your resolvers can now make use of DynamoDB batch operations that span one or more tables in a region. This allows you to use a list of keys in a single query, read records multiple tables, write records in bulk to multiple tables, and conditionally write or delete related records across multiple tables.

In order to use this feature the IAM role that you use to access your tables must grant access to DynamoDB’s BatchGetItem and BatchPutItem functions.

To learn more, read the DynamoDB Batch Resolvers tutorial.

CloudWatch Logs Support
You can now tell AWS AppSync to log API requests to CloudWatch Logs. Click on Settings and Enable logs, then choose the IAM role and the log level:

CloudFormation Support
You can use the following CloudFormation resource types in your templates to define AWS AppSync resources:

AWS::AppSync::GraphQLApi – Defines an AppSync API in terms of a data source (an Amazon Elasticsearch Service domain or a DynamoDB table).

AWS::AppSync::ApiKey – Defines the access key needed to access the data source.

AWS::AppSync::GraphQLSchema – Defines a GraphQL schema.

AWS::AppSync::DataSource – Defines a data source.

AWS::AppSync::Resolver – Defines a resolver by referencing a schema and a data source, and includes a mapping template for requests.

Here’s a simple schema definition in YAML form:

  AppSyncSchema:
    Type: "AWS::AppSync::GraphQLSchema"
    DependsOn:
      - AppSyncGraphQLApi
    Properties:
      ApiId: !GetAtt AppSyncGraphQLApi.ApiId
      Definition: |
        schema {
          query: Query
          mutation: Mutation
        }
        type Query {
          singlePost(id: ID!): Post
          allPosts: [Post]
        }
        type Mutation {
          putPost(id: ID!, title: String!): Post
        }
        type Post {
          id: ID!
          title: String!
        }

Available Now
These new features are available now and you can start using them today! Here are a couple of blog posts and other resources that you might find to be of interest:

Jeff;

 

 

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/750150/rss

Security updates have been issued by Arch Linux (bchunk, thunderbird, and xerces-c), Debian (freeplane, icu, libvirt, and net-snmp), Fedora (monitorix, php-simplesamlphp-saml2, php-simplesamlphp-saml2_1, php-simplesamlphp-saml2_3, puppet, and qt5-qtwebengine), openSUSE (curl, libmodplug, libvorbis, mailman, nginx, opera, python-paramiko, and samba, talloc, tevent), Red Hat (python-paramiko, rh-maven35-slf4j, rh-mysql56-mysql, rh-mysql57-mysql, rh-ruby22-ruby, rh-ruby23-ruby, and rh-ruby24-ruby), Slackware (thunderbird), SUSE (clamav, kernel, memcached, and php53), and Ubuntu (samba and tiff).

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/748276/rss

Security updates have been issued by Arch Linux (mbedtls), CentOS (gcab and java-1.7.0-openjdk), Debian (drupal7, lucene-solr, wavpack, and xmltooling), Fedora (dnsmasq, gcab, gimp, golang, knot-resolver, ldns, libsamplerate, mingw-OpenEXR, mingw-poppler, python-crypto, qt5-qtwebengine, sblim-sfcb, systemd, unbound, and wavpack), Mageia (ioquake3, TiMidity++, tomcat, tomcat-native, and wireshark), openSUSE (systemd and zziplib), Red Hat (erlang and openstack-nova and python-novaclient), and SUSE (kernel).

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/748073/rss

Security updates have been issued by Arch Linux (lib32-wavpack, phpmyadmin, unixodbc, and wavpack), Debian (drupal7, golang, imagemagick, libdatetime-timezone-perl, libvpx, and tzdata), Fedora (exim, irssi, kernel, milkytracker, qt5-qtwebengine, seamonkey, and suricata), Mageia (advancecomp, apache-commons-email, freetype2, ghostscript, glpi, jackson-databind, kernel, mariadb, and postgresql), openSUSE (dhcp, GraphicsMagick, lame, php5, phpMyAdmin, timidity, and wireshark), and Oracle (kernel).

Running ActiveMQ in a Hybrid Cloud Environment with Amazon MQ

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/running-activemq-in-a-hybrid-cloud-environment-with-amazon-mq/

This post courtesy of Greg Share, AWS Solutions Architect

Many organizations, particularly enterprises, rely on message brokers to connect and coordinate different systems. Message brokers enable distributed applications to communicate with one another, serving as the technological backbone for their IT environment, and ultimately their business services. Applications depend on messaging to work.

In many cases, those organizations have started to build new or “lift and shift” applications to AWS. In some cases, there are applications, such as mainframe systems, too costly to migrate. In these scenarios, those on-premises applications still need to interact with cloud-based components.

Amazon MQ is a managed message broker service for ActiveMQ that enables organizations to send messages between applications in the cloud and on-premises to enable hybrid environments and application modernization. For example, you can invoke AWS Lambda from queues and topics managed by Amazon MQ brokers to integrate legacy systems with serverless architectures. ActiveMQ is an open-source message broker written in Java that is packaged with clients in multiple languages, Java Message Server (JMS) client being one example.

This post shows you can use Amazon MQ to integrate on-premises and cloud environments using the network of brokers feature of ActiveMQ. It provides configuration parameters for a one-way duplex connection for the flow of messages from an on-premises ActiveMQ message broker to Amazon MQ.

ActiveMQ and the network of brokers

First, look at queues within ActiveMQ and then at the network of brokers as a mechanism to distribute messages.

The network of brokers behaves differently from models such as physical networks. The key consideration is that the production (sending) of a message is disconnected from the consumption of that message. Think of the delivery of a parcel: The parcel is sent by the supplier (producer) to the end customer (consumer). The path it took to get there is of little concern to the customer, as long as it receives the package.

The same logic can be applied to the network of brokers. Here’s how you build the flow from a simple message to a queue and build toward a network of brokers. Before you look at setting up a hybrid connection, I discuss how a broker processes messages in a simple scenario.

When a message is sent from a producer to a queue on a broker, the following steps occur:

  1. A message is sent to a queue from the producer.
  2. The broker persists this in its store or journal.
  3. At this point, an acknowledgement (ACK) is sent to the producer from the broker.

When a consumer looks to consume the message from that same queue, the following steps occur:

  1. The message listener (consumer) calls the broker, which creates a subscription to the queue.
  2. Messages are fetched from the message store and sent to the consumer.
  3. The consumer acknowledges that the message has been received before processing it.
  4. Upon receiving the ACK, the broker sets the message as having been consumed. By default, this deletes it from the queue.
    • You can set the consumer to ACK after processing by setting up transaction management or handle it manually using Session.CLIENT_ACKNOWLEDGE.

Static propagation

I now introduce the concept of static propagation with the network of brokers as the mechanism for message transfer from on-premises brokers to Amazon MQ.  Static propagation refers to message propagation that occurs in the absence of subscription information. In this case, the objective is to transfer messages arriving at your selected on-premises broker to the Amazon MQ broker for consumption within the cloud environment.

After you configure static propagation with a network of brokers, the following occurs:

  1. The on-premises broker receives a message from a producer for a specific queue.
  2. The on-premises broker sends (statically propagates) the message to the Amazon MQ broker.
  3. The Amazon MQ broker sends an acknowledgement to the on-premises broker, which marks the message as having been consumed.
  4. Amazon MQ holds the message in its queue ready for consumption.
  5. A consumer connects to Amazon MQ broker, subscribes to the queue in which the message resides, and receives the message.
  6. Amazon MQ broker marks the message as having been consumed.

Getting started

The first step is creating an Amazon MQ broker.

  1. Sign in to the Amazon MQ console and launch a new Amazon MQ broker.
  2. Name your broker and choose Next step.
  3. For Broker instance type, choose your instance size:
    mq.t2.micro
    mq.m4.large
  4. For Deployment mode, enter one of the following:
    Single-instance broker for development and test implementations (recommended)
    Active/standby broker for high availability in production environments
  5. Scroll down and enter your user name and password.
  6. Expand Advanced Settings.
  7. For VPC, Subnet, and Security Group, pick the values for the resources in which your broker will reside.
  8. For Public Accessibility, choose Yes, as connectivity is internet-based. Another option would be to use private connectivity between your on-premises network and the VPC, an example being an AWS Direct Connect or VPN connection. In that case, you could set Public Accessibility to No.
  9. For Maintenance, leave the default value, No preference.
  10. Choose Create Broker. Wait several minutes for the broker to be created.

After creation is complete, you see your broker listed.

For connectivity to work, you must configure the security group where Amazon MQ resides. For this post, I focus on the OpenWire protocol.

For Openwire connectivity, allow port 61617 access for Amazon MQ from your on-premises ActiveMQ broker source IP address. For alternate protocols, see the Amazon MQ broker configuration information for the ports required:

OpenWire – ssl://xxxxxxx.xxx.com:61617
AMQP – amqp+ssl:// xxxxxxx.xxx.com:5671
STOMP – stomp+ssl:// xxxxxxx.xxx.com:61614
MQTT – mqtt+ssl:// xxxxxxx.xxx.com:8883
WSS – wss:// xxxxxxx.xxx.com:61619

Configuring the network of brokers

Configuring the network of brokers with static propagation occurs on the on-premises broker by applying changes to the following file:
<activemq install directory>/conf activemq.xml

Network connector

This is the first configuration item required to enable a network of brokers. It is only required on the on-premises broker, which initiates and creates the connection with Amazon MQ. This connection, after it’s established, enables the flow of messages in either direction between the on-premises broker and Amazon MQ. The focus of this post is the uni-directional flow of messages from the on-premises broker to Amazon MQ.

The default activemq.xml file does not include the network connector configuration. Add this with the networkConnector element. In this scenario, edit the on-premises broker activemq.xml file to include the following information between <systemUsage> and <transportConnectors>:

<networkConnectors>
             <networkConnector 
                name="Q:source broker name->target broker name"
                duplex="false" 
                uri="static:(ssl:// aws mq endpoint:61617)" 
                userName="username"
                password="password" 
                networkTTL="2" 
                dynamicOnly="false">
                <staticallyIncludedDestinations>
                    <queue physicalName="queuename"/>
                </staticallyIncludedDestinations> 
                <excludedDestinations>
                      <queue physicalName=">" />
                </excludedDestinations>
             </networkConnector> 
     <networkConnectors>

The highlighted components are the most important elements when configuring your on-premises broker.

  • name – Name of the network bridge. In this case, it specifies two things:
    • That this connection relates to an ActiveMQ queue (Q) as opposed to a topic (T), for reference purposes.
    • The source broker and target broker.
  • duplex –Setting this to false ensures that messages traverse uni-directionally from the on-premises broker to Amazon MQ.
  • uri –Specifies the remote endpoint to which to connect for message transfer. In this case, it is an Openwire endpoint on your Amazon MQ broker. This information could be obtained from the Amazon MQ console or via the API.
  • username and password – The same username and password configured when creating the Amazon MQ broker, and used to access the Amazon MQ ActiveMQ console.
  • networkTTL – Number of brokers in the network through which messages and subscriptions can pass. Leave this setting at the current value, if it is already included in your broker connection.
  • staticallyIncludedDestinations > queue physicalName – The destination ActiveMQ queue for which messages are destined. This is the queue that is propagated from the on-premises broker to the Amazon MQ broker for message consumption.

After the network connector is configured, you must restart the ActiveMQ service on the on-premises broker for the changes to be applied.

Verify the configuration

There are a number of places within the ActiveMQ console of your on-premises and Amazon MQ brokers to browse to verify that the configuration is correct and the connection has been established.

On-premises broker

Launch the ActiveMQ console of your on-premises broker and navigate to Network. You should see an active network bridge similar to the following:

This identifies that the connection between your on-premises broker and your Amazon MQ broker is up and running.

Now navigate to Connections and scroll to the bottom of the page. Under the Network Connectors subsection, you should see a connector labeled with the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

Amazon MQ broker

Launch the ActiveMQ console of your Amazon MQ broker and navigate to Connections. Scroll to the Connections openwire subsection and you should see a connection specified that references the name: value that you provided within the ActiveMQ.xml configuration file. You should see an entry similar to:

If you configured the uri: for AMQP, STOMP, MQTT, or WSS as opposed to Openwire, you would see this connection under the corresponding section of the Connections page.

Testing your message flow

The setup described outlines a way for messages produced on premises to be propagated to the cloud for consumption in the cloud. This section provides steps on verifying the message flow.

Verify that the queue has been created

After you specify this queue name as staticallyIncludedDestinations > queue physicalName: and your ActiveMQ service starts, you see the following on your on-premises ActiveMQ console Queues page.

As you can see, no messages have been sent but you have one consumer listed. If you then choose Active Consumers under the Views column, you see Active Consumers for TestingQ.

This is telling you that your Amazon MQ broker is a consumer of your on-premises broker for the testing queue.

Produce and send a message to the on-premises broker

Now, produce a message on an on-premises producer and send it to your on-premises broker to a queue named TestingQ. If you navigate back to the queues page of your on-premises ActiveMQ console, you see that the messages enqueued and messages dequeued column count for your TestingQ queue have changed:

What this means is that the message originating from the on-premises producer has traversed the on-premises broker and propagated immediately to the Amazon MQ broker. At this point, the message is no longer available for consumption from the on-premises broker.

If you access the ActiveMQ console of your Amazon MQ broker and navigate to the Queues page, you see the following for the TestingQ queue:

This means that the message originally sent to your on-premises broker has traversed the network of brokers unidirectional network bridge, and is ready to be consumed from your Amazon MQ broker. The indicator is the Number of Pending Messages column.

Consume the message from an Amazon MQ broker

Connect to the Amazon MQ TestingQ queue from a consumer within the AWS Cloud environment for message consumption. Log on to the ActiveMQ console of your Amazon MQ broker and navigate to the Queue page:

As you can see, the Number of Pending Messages column figure has changed to 0 as that message has been consumed.

This diagram outlines the message lifecycle from the on-premises producer to the on-premises broker, traversing the hybrid connection between the on-premises broker and Amazon MQ, and finally consumption within the AWS Cloud.

Conclusion

This post focused on an ActiveMQ-specific scenario for transferring messages within an ActiveMQ queue from an on-premises broker to Amazon MQ.

For other on-premises brokers, such as IBM MQ, another approach would be to run ActiveMQ on-premises broker and use JMS bridging to IBM MQ, while using the approach in this post to forward to Amazon MQ. Yet another approach would be to use Apache Camel for more sophisticated routing.

I hope that you have found this example of hybrid messaging between an on-premises environment in the AWS Cloud to be useful. Many customers are already using on-premises ActiveMQ brokers, and this is a great use case to enable hybrid cloud scenarios.

To learn more, see the Amazon MQ website and Developer Guide. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

 

This IoT Pet Monitor barks back

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/iot-pet-monitor/

Jennifer Fox, founder of FoxBot Industries, uses a Raspberry Pi pet monitor to check the sound levels of her home while she is out, allowing her to keep track of when her dog Marley gets noisy or agitated, and to interact with the gorgeous furball accordingly.

Bark Back Project Demo

A quick overview and demo of the Bark Back, a project to monitor and interact with Check out the full tutorial here: https://learn.sparkfun.com/tutorials/bark-back-interactive-pet-monitor For any licensing requests please contact [email protected]

Marley, bark!

Using a Raspberry Pi 3, speakers, SparkFun’s MEMS microphone breakout board, and an analogue-to-digital converter (ADC), the IoT Pet Monitor is fairly easy to recreate, all thanks to Jennifer’s full tutorial on the FoxBot website.

Building the pet monitor

In a nutshell, once the Raspberry Pi and the appropriate bits and pieces are set up, you’ll need to sign up at CloudMQTT — it’s free if you select the Cute Cat account. CloudMQTT will create an invisible bridge between your home and wherever you are that isn’t home, so that you can check in on your pet monitor.

Screenshot CloudMQTT account set-up — IoT Pet Monitor Bark Back Raspberry Pi

Image c/o FoxBot Industries

Within the project code, you’ll be able to calculate the peak-to-peak amplitude of sound the microphone picks up. Then you can decide how noisy is too noisy when it comes to the occasional whine and bark of your beloved pup.

MEMS microphone breakout board — IoT Pet Monitor Bark Back Raspberry Pi

The MEMS microphone breakout board collects sound data and relays it back to the Raspberry Pi via the ADC.
Image c/o FoxBot Industries

Next you can import sounds to a preset song list that will be played back when the volume rises above your predefined threshold. As Jennifer states in the tutorial, the sounds can easily be recorded via apps such as Garageband, or even on your mobile phone.

Using the pet monitor

Whenever the Bark Back IoT Pet Monitor is triggered to play back audio, this information is fed to the CloudMQTT service, allowing you to see if anything is going on back home.

A sitting dog with a doll in its mouth — IoT Pet Monitor Bark Back Raspberry Pi

*incoherent coos of affection from Alex*
Image c/o FoxBot Industries

And as Jennifer recommends, a update of the project could include a camera or sensors to feed back more information about your home environment.

If you’ve created something similar, be sure to let us know in the comments. And if you haven’t, but you’re now planning to build your own IoT pet monitor, be sure to let us know in the comments. And if you don’t have a pet but just want to say hi…that’s right, be sure to let us know in the comments.

The post This IoT Pet Monitor barks back appeared first on Raspberry Pi.

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/744398/rss

Security updates have been issued by Arch Linux (qtpass), Debian (libkohana2-php, libxml2, transmission, and xmltooling), Fedora (kernel and qpid-cpp), Gentoo (PolarSSL and xen), Mageia (flash-player-plugin, irssi, kernel, kernel-linus, kernel-tmb, libvorbis, microcode, nvidia-current, php & libgd, poppler, webkit2, and wireshark), openSUSE (gifsicle, glibc, GraphicsMagick, gwenhywfar, ImageMagick, libetpan, mariadb, pngcrush, postgresql94, rsync, tiff, and wireshark), and Oracle (kernel).

MusE 3.0.0 released

Post Syndicated from ris original https://lwn.net/Articles/743598/rss

Three years after the last stable release, version 3.0 of the MusE
MIDI/Audio sequencer is now available. As you might expect there many
changes since the last release including a switch to Qt5, a new Plugin Path
editor in Global Settings, a mixer makeover with lots of fixes, a
system-wide move to double precision of all audio paths, and much more.

timeShift(GrafanaBuzz, 1w) Issue 28

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/05/timeshiftgrafanabuzz-1w-issue-28/

Happy new year! Grafana Labs is getting back in the swing of things after taking some time off to celebrate 2017, and spending time with family and friends. We’re diligently working on the new Grafana v5.0 release (planning v5.0 beta release by end of January), which includes a ton of new features, a new layout engine, and a polished UI. We’d love to hear your feedback!


Latest Stable Release

Grafana 4.6.3 is now available. Latest bugfixes include:

  • Gzip: Fixes bug Gravatar images when gzip was enabled #5952
  • Alert list: Now shows alert state changes even after adding manual annotations on dashboard #99513
  • Alerting: Fixes bug where rules evaluated as firing when all conditions was false and using OR operator. #93183
  • Cloudwatch: CloudWatch no longer display metrics’ default alias #101514, thx @mtanda

Download Grafana 4.6.3 Now


From the Blogosphere

Why Observability Matters – Now and in the Future: Our own Carl Bergquist teamed up with Neil Gehani, Director of Product at Weaveworks to discuss best practices on how to get started with monitoring your application and infrastructure. This video focuses on modern containerized applications instrumented to use Prometheus to generate metrics and Grafana to visualize them.

How to Install and Secure Grafana on Ubuntu 16.04: In this tutorial, you’ll learn how to install and secure Grafana with a SSL certificate and a Nginx reverse proxy, then you’ll modify Grafana’s default settings for even tighter security.

Monitoring Informix with Grafana: Ben walks us through how to use Grafana to visualize data from IBM Informix and offers a practical demonstration using Docker containers. He also talks about his philosophy of sharing dashboards across teams, important metrics to collect, and how he would like to improve his monitoring stack.

Monitor your hosts with Glances + InfluxDB + Grafana: Glances is a cross-platform system monitoring tool written in Python. This article takes you step by step through the pieces of the stack, installation, confirguration and provides a sample dashboard to get you up and running.


GrafanaCon Tickets are Going Fast!

Lock in your seat for GrafanaCon EU while there are still tickets avaialable! Join us March 1-2, 2018 in Amsterdam for 2 days of talks centered around Grafana and the surrounding monitoring ecosystem including Graphite, Prometheus, InfluxData, Elasticsearch, Kubernetes, and more.

We have some exciting talks lined up from Google, CERN, Bloomberg, eBay, Red Hat, Tinder, Fastly, Automattic, Prometheus, InfluxData, Percona and more! You can see the full list of speakers below, but be sure to get your ticket now.

Get Your Ticket Now

GrafanaCon EU will feature talks from:

“Google Bigtable”
Misha Brukman
PROJECT MANAGER,
GOOGLE CLOUD
GOOGLE

“Monitoring at Bloomberg”
Stig Sorensen
HEAD OF TELEMETRY
BLOOMBERG

“Monitoring at Bloomberg”
Sean Hanson
SOFTWARE DEVELOPER
BLOOMBERG

“Monitoring Tinder’s Billions of Swipes with Grafana”
Utkarsh Bhatnagar
SR. SOFTWARE ENGINEER
TINDER

“Grafana at CERN”
Borja Garrido
PROJECT ASSOCIATE
CERN

“Monitoring the Huge Scale at Automattic”
Abhishek Gahlot
SOFTWARE ENGINEER
Automattic

“Real-time Engagement During the 2016 US Presidential Election”
Anna MacLachlan
CONTENT MARKETING MANAGER
Fastly

“Real-time Engagement During the 2016 US Presidential Election”
Gerlando Piro
FRONT END DEVELOPER
Fastly

“Grafana v5 and the Future”
Torkel Odegaard
CREATOR | PROJECT LEAD
GRAFANA

“Prometheus for Monitoring Metrics”
Brian Brazil
FOUNDER
ROBUST PERCEPTION

“What We Learned Integrating Grafana with Prometheus”
Peter Zaitsev
CO-FOUNDER | CEO
PERCONA

“The Biz of Grafana”
Raj Dutt
CO-FOUNDER | CEO
GRAFANA LABS

“What’s New In Graphite”
Dan Cech
DIR, PLATFORM SERVICES
GRAFANA LABS

“The Design of IFQL, the New Influx Functional Query Language”
Paul Dix
CO-FOUNTER | CTO
INFLUXDATA

“Writing Grafana Dashboards with Jsonnet”
Julien Pivotto
OPEN SOURCE CONSULTANT
INUITS

“Monitoring AI Platform at eBay”
Deepak Vasthimal
MTS-2 SOFTWARE ENGINEER
EBAY

“Running a Power Plant with Grafana”
Ryan McKinley
DEVELOPER
NATEL ENERGY

“Performance Metrics and User Experience: A “Tinder” Experience”
Susanne Greiner
DATA SCIENTIST
WÜRTH PHOENIX S.R.L.

“Analyzing Performance of OpenStack with Grafana Dashboards”
Alex Krzos
SENIOR SOFTWARE ENGINEER
RED HAT INC.

“Storage Monitoring at Shell Upstream”
Arie Jan Kraai
STORAGE ENGINEER
SHELL TECHNICAL LANDSCAPE SERVICE

“The RED Method: How To Instrument Your Services”
Tom Wilkie
FOUNDER
KAUSAL

“Grafana Usage in the Quality Assurance Process”
Andrejs Kalnacs
LEAD SOFTWARE DEVELOPER IN TEST
EVOLUTION GAMING

“Using Prometheus and Grafana for Monitoring my Power Usage”
Erwin de Keijzer
LINUX ENGINEER
SNOW BV

“Weather, Power & Market Forecasts with Grafana”
Max von Roden
DATA SCIENTIST
ENERGY WEATHER

“Weather, Power & Market Forecasts with Grafana”
Steffen Knott
HEAD OF IT
ENERGY WEATHER

“Inherited Technical Debt – A Tale of Overcoming Enterprise Inertia”
Jordan J. Hamel
HEAD OF MONITORING PLATFORMS
AMGEN

“Grafanalib: Dashboards as Code”
Jonathan Lange
VP OF ENGINEERING
WEAVEWORKS

“The Journey of Shifting the MQTT Broker HiveMQ to Kubernetes”
Arnold Bechtoldt
SENIOR SYSTEMS ENGINEER
INOVEX

“Graphs Tell Stories”
Blerim Sheqa
SENIOR DEVELOPER
NETWAYS

[email protected] or How to Store Millions of Metrics per Second”
Vladimir Smirnov
SYSTEM ADMINISTRATOR
Booking.com


Upcoming Events:

In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. There is no need to register; all are welcome.

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Carl Bergquist – Quickie: Monitoring? Not OPS Problem

Why should we monitor our system? Why can’t we just rely on the operations team anymore? They use to be able to do that. What’s currently changing? Presentation content: – Why do we monitor our system – How did it use to work? – Whats changing – Why do we need to shift focus – Everyone should be on call. – Resilience is the goal (Best way of having someone care about quality is to make them responsible).

Register Now

Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
Leonard Gram – Presentation: DevOps Deconstructed

What’s a Site Reliability Engineer and how’s that role different from the DevOps engineer my boss wants to hire? I really don’t want to be on call, should I? Is Docker the right place for my code or am I better of just going straight to Serverless? And why should I care about any of it? I’ll try to answer some of these questions while looking at what DevOps really is about and how commodisation of servers through “the cloud” ties into it all. This session will be an opinionated piece from a developer who’s been on-call for the past 6 years and would like to convince you to do the same, at least once.

Register Now

Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Awesome! Let us know if you have any questions – we’re happy to help out. We also have a bunch of screencasts to help you get going.


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


How are we doing?

That’s a wrap! Let us know what you think about timeShift. Submit a comment on this article below, or post something at our community forum. See you next year!

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