Magical Experience with Beacons

One month ago on 27th of March, my friend passed me a box of Estimote Proximity Beacons. That day marks the beginning of my journey towards a greater understanding of beacons and IoT.

Since the day I joined travel industry, I have always been thinking of providing a fun travel experience with beacon technology. When I joined Changi Airport team in 2015, I proposed to my manager the possibility of applying beacons in the airport. The idea was rejected. Now, I finally get the chance to build something with the small little Estimote Proximity Beacons.


We forcefully opened up the beacons and replaced the batteries.

Claiming Beacons

Every Estimote beacons are shipped with an unique ID which we can modify. By default, the beacon ID is in iBeacon format and consists of 3 values:

The three values are hierarchical. The purpose of UUID is to distinguish our beacons from all other beacons in the network. Major and Minor values allow us to label the beacons with higher accuracy.


An example of how a chain of retail shops will deploy and label their beacons. (Source: Estimote Developer Docs)

The iBeacon ID can be changed. One way is to use the Estimote app to do it. Since I wasn’t the owner of the beacons, my first step is to claim the beacon using the app. After I successfully claim the beacons, I can then proceed to retrieve detailed info of the beacons and modify their info.


Claiming beacon and modifying its info, such as its range (by default it’s ~3.5m).

Google Beacon Platform

After configuring our beacons, we can then proceed to claim the ownership of our beacons on the Google Beacon Registry. There is a mobile app called Beacon Tools available from Google to help us registering our beacons on Google Beacon Registry. There is a very interesting video interviewing Peter Lewis in the Coffee with a Googler season talking about the steps of beacon registration.


Peter shares about Google Beacon Registry and Google Beacon Platform. (Source: YouTube)

After that, we can associate a lot of information with our beacons. To do so, we first are recommended to use Google Beacon Dashboard. There is a very simple tutorial guiding us to use the Google Beacon Dashboard to associate the attachments with the beacons.


My beacon project, Icy Marshmallow, and the attachments of a beacon in the project.

Read Attachments

I’m using the Nearby Messages API to retrieve the attachments from the beacons. I did a small little Android app (which is properly configured following the recommended steps) with the codes as shown below to achieve this.

package gclprojects.icymarshmallow;


public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    private GoogleApiClient mGoogleApiClient;
    private MessageListener mMessageListener;

    protected void onCreate(Bundle savedInstanceState) {


        mGoogleApiClient = new GoogleApiClient.Builder(this)
                .enableAutoManage(this, this)

        mMessageListener = new MessageListener() {
            public void onFound(final Message message) {
                // Called when a new message is found.
                // Use message.getType().toString() to read the attachment Type
                // Use new String(message.getContent()) to read the attachment Value

    protected void onStart() {

    protected void onStop() {

    public void onConnected(@Nullable Bundle bundle) {
        if (mGoogleApiClient != null && mGoogleApiClient.isConnected()) {


    private void subscribe() {
        SubscribeOptions options = new SubscribeOptions.Builder()

        Nearby.Messages.subscribe(mGoogleApiClient, mMessageListener);

With the codes above, when beacon gets detected by the mobile app, the onFound method gets called for each of the attachment associated with the beacons. If we print the variable message into Log, we shall see something as follows.

Message{namespace='icy-marshmallow', type='string', content=[29 bytes]}

As shown above, the Value of the attachment is base64 encoded. So to read it, we just need to use new String(message.getContent()).

In the subscribe method, since we are only interested in messages attached to BLE (Bluetooth Low Energy) beacons, we use Strategy.BLE_ONLY.

Problem #1: Unsubscribe Method

When the app is running and another app comes into the foreground, we also need to stop subscribing to messages from the beacons. Otherwise, when we navigate back to the app, the messages can no longer be received even though we re-trigger the subscribe method.

So, I added the following codes.

protected void onPause() {
    if (mGoogleApiClient != null && mGoogleApiCLient.isConnected) {


protected void onResume() {
    if (mGoogleApiClient != null && mGoogleApiClient.isConnected) {


private void unsubscribe() {
    Nearby.Messages.unsubscribe(mGoogleApiClient, mMessageListener);

Problem #2: Stop Receiving Messages After Few Minutes

Another problem I notice is that the messages will stop be “found” after one to two minutes. However, if I re-trigger the mobile app, then I can start seeing the messages being detected for another one or two minutes.

To solve this issue, I use a simple timer which helps to check whether it has been quite some time the app doesn’t detect the beacons. If it’s more than 1 minute, then the timer will do a unsubscribe-then-subscribe-again action. This will help the mobile app to keep receiving the messages from the beacons. It also solve the problem of the mobile app re-visiting the beacons.

Problem #3: Geo-Location

This is not a real problem if we don’t need the geo-location information of the beacons. However, if we need to know the geo-location of the beacon, one simple way is to just use the LocationManager which provides periodic updates of the mobile geographical location.

package gclprojects.icymarshmallow;

import android.location.Location;
import android.location.LocationListener;
import android.location.LocationManager;

public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    LocationManager locationManager;

    protected void onCreate(Bundle savedInstanceState) {


        LocationListener locationListener = new LocationListener() {
            public void onLocationChanged(Location location) {
                // Record down the latitude and longitude of the mobile


        locationManager = (LocationManager) this.getSystemService(Context.LOCATION_SERVICE);
        int permissionCheck = ContextCompat.checkSelfPermission(this, Manifest.permission.ACCESS_FINE_LOCATION);
        if (permissionCheck == PackageManager.PERMISSION_GRANTED) {
            locationManager.requestLocationUpdates(LocationManager.NETWORK.PROVIDER, 0, 0, locationListener);


Writing Data to Firebase

This step is optional unless the data collected needs to be stored for future use.

I use the following codes to write the beacon data to Firebase database.

package gclprojects.icymarshmallow;


public class MainActivity extends AppCompatActivity 
        implements GoogleApiClient.ConnectionCallbacks,
        GoogleApiClient.OnConnectionFailedListener {
    private DatabaseReference mDatabase;

    protected void onCreate(Bundle savedInstanceState) {

        mDatabase = FirebaseDatabase.getInstance().getReference();

        mMessageListener = new MessageListener() {
            public void onFound(final Message message) {

                Beacon beaconInfo = new Beacon(...);

                Format formatter = new SimpleDateFormat("yyyy-MM-dd-HH:mm:ss");
                mDatabase.child("Person A")
                        .child(formatter.format(new Date())


public class Beacon { ... }

Successfully recorded the data from beacons in my Firebase database!

To integrate our Android app with Firebase, our friendly Android Studio comes with a tool called the Firebase Assistance which will help us connect to the Firebase. The assistance also comes with short getting-started tutorial to show us how to cinfigure and add realtime database to our mobile app.


Spot the beacon. =)

Installing Beacons in Changi Airport

Installing beacons in our Changi Airport is always one of my dreams to enhance the experience of millions of travelers flying in and out of the airport. In fact, currently the Armsterdam city is already making use of beacon technology to build a powerful beacon networks to give the people a better experience when they are walking around in the city. So why can’t we do the same in our friendly Changi Airport? =)


IoT Hub First Peek

The Internet of Things (IoT) is here today, and it begins with the data, devices, and services already at work in your organization. When your “things” are connected to each other and to the cloud, you create new ways to improve efficiency, enable innovation, and transform your business.

This line is printed on the front page of a Microsoft booklet distributed during the lunchtime workshop “Connecting and Building the Internet of Things (IoT)” conducted by Gerald Goh, Microsoft Technical Evangelist. Gerald shared with us technologies such as AMQP, MQTT, Message Broker in Azure, Device Explorer, and so on.


Gerald is sharing Azure IoT Hub during the lunchtime workshop.

IoT hasn’t gone totally mainstream, however, and we have yet to feel its impact. In many ways it is roughly where the big data movement was few years ago — consisting mainly of a buzzword that’s not yet widely understood.

Nevertheless, Gerald’s workshop does give me, a web developer who doesn’t know much about this field, a helpful quick start about IoT. After reading and experimenting, I learn more about the capability of Microsoft Azure in IoT and thus I’d like to share with you about what I’ve learnt so far about Azure IoT Hub.

Message Broker

I’m working in Changi Airport. In the airport, we have several shops serving the travelers and visitors. Most of the shops have a back-end system that integrates several systems such as the retail system, e-commerce website, payment system, Changi Rewards system, inventory management system, the finance system.

So there will be cases where, when a customer buys something at the shop, the retail system needs to send as request to the payment system. Then when the purchase is successful, another purchase request will be sent to the inventory management system and the finance system.

I’m not too sure how the shops link different systems, especially this kind of point-to-point integration will cause a large number of connections among the systems. Hence, the developers of their system may find Message Broker useful.

Message Broker is a physical component that handles the communication between systems. A system sends a message to the message broker, providing the logical name of the receiving systems. The message broker will then search for the receiving systems and then passes the message to them.


A message broker mediating the communication between systems. (Image Credit: Message Broker – MSDN)

Messaging Protocols: AMQP and MQTT

Sending a message between systems seems to be an easy task, however, doing it in a reliable and secure manner can be a challenging work.

As shown in the article “Scalable Eventing over Mesos!”, Autodesk is using AMQP (Advanced Message Queuing Protocol) as messaging protocols between two parties with the following main characteristics as goals.

  • Security
  • Reliability
  • Interoperability
  • Standard
  • Open

AMQP communication between two parties (Image Credit: Autodesk)

AMQP 1.0 is the current specification version. It is also the primary protocol of Azure Event Hubs and Azure Service Bus Messaging after the SBMP (Service Bus Messaging Protocol), the TCP-based protocol which is used inside of .NET client library, is phased out.

Besides AMQP, MQTT (Message Queue Telemetry Transport) is another open protocol based on TCP/IP for asynchronous message queuing which has been developed and matured over past few years.


Dr Andy Stanform-Clark from IBM invented the MQTT protocol. (Image Source: IBM – Wikipedia)

While AMQP is designed to provide the full vibrancy of messaging scenarios, MQTT is designed as an extremely lightweight publish/subscribe message transport for small and simple devices sending small messages on low-bandwidth networks. Hence, MQTT is said to be ideal for mobile applications because of its low power usage and minimized data packets.

MQTT is also simple because it just has five API methods:

  • Connect to an MQTT broker;
  • Disconnect from an MQTT broker;
  • Subscribe to an MQTT topic filter;
  • Unsubscribe from an MQTT topic filter;
  • Publish MQTT messages.

If you are interested to know more about the comparison of AMQP and MQTT, there is a detailed white paper from StormMQ discussing the difference between AMQP and MQTT.

Brokered Messaging – Service Bus Messaging

When two or more systems want to exchange information, they need a communication facilitator. This is where Microsoft Azure Service Bus comes into picture.

Azure Service Bus is a reliable information delivery service, which is similar to a postal service in the physical world.

One of the messaging patterns offered in Azure Service Bus is called Service Bus Messaging, or Brokered Messaging. By using it, both senders and receivers do not have to be available at the exact same time.

AMQP 1.0 support is available in the Service Bus SDK since its version 2.1. Since the Service Bus .NET client library by default using a dedicated SOAP-based protocol, to use AMQP 1.0, we need to specify in the Service Bus Connection String as highlighted below in bold.

<?xml version="1.0" encoding="utf-8" ?>
            value="Endpoint=sb://[namespace];SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=[SAS key];TransportType=Amqp" /> 

In AMQP transport mode, the client library of sender will serialize the brokered message into an AMQP message so that the message can be received and interpreted by a receiver running on a different platform.

Azure Event Hub

When our event-based messaging needs to be handled at a very huge scale, we can either continue to pay even more to use Azure Service Bus or we can switch to use Event Hub. Event Hub is a cheaper way for us to be able to deal with huge bursts of messages and retain messages for a longer period of time.


Event Hub is cheaper, reliable and also fully managed. (Full video: Azure Service Bus Event Hubs 101 with Dan Rosanova)

Although Event Hub does not support MQTT, it does support AMQP (and HTTP) where there could be at most 5,000 concurrent AMQP connections.

Event Hubs event and telemetry handling capabilities, such as ingesting millions of events per second, make it especially usefu for IoT scenarios. However, since it is ingestion only thus Event Hub has no facility for sending traffic, for example, from the cloud back to the devices (C2D).

Azure IoT Hub

Since Event Hubs only enable event ingress, i.e. C2D, Azure offers another service, IoT Hub, for both C2D and D2C (Device-to-Cloud) communications which are reliable and secure. Not only allowing bi-directional communication, IoT Hub also supports AMQP, HTTP, and MQTT.

IoT Hub has an identity registry storing information about devices which are given the permission to connect to the IoT Hub. Before a device can connect to an IoT Hub, there must be an entry for that device in the identity registry of the IoT Hub.

In a Hello World tutorial of connecting stimulated device to IoT Hub using C#, there is a way to add device and retrieve device identity programmatically as shown below.

private static async Task AddDeviceAsync()
    string deviceId = "gclRasPi2";
    Device device;

        device = await registryManager.AddDeviceAsync(new Device(deviceId));
    catch (DeviceAlreadyExistsException)
        device = await registryManager.GetDeviceAsync(deviceId);

    Console.WriteLine("Generated device key: {0}", device.Authentication.SymmetricKey.PrimaryKey);

The Registry Manager, which is connecting to the IoT Hub using a Connection String with proper Policy, will add an device identity with the Device ID “gclRasPi2” to the Device Explorer in Azure.


The device “gclRasPi2” is now in the Device Explorer.

After doing so, a message then can be sent from (stimulated) device to the IoT Hub. For example, the device wants to send data about the temperature and humidity at that moment using MQTT, we can use the following code.

var deviceClient = DeviceClient.Create(
    new DeviceAuthenticationWithRegistrySymmetricKey("gclRasPi2", deviceKey), 

var telemetryDataPoint = new
    deviceId = "gclRasPi2",
    temperature = currentTemperature,
    humidity = currentHumidity

var messageString = JsonConvert.SerializeObject(telemetryDataPoint);

var message = new Message(Encoding.ASCII.GetBytes(messageString));
message.Properties.Add("temperatureAlert", (currentTemperature > 30) ? "true" : "false");

await deviceClient.SendEventAsync(message);

To read the message, please follow the steps shared by the tutorial on setting up to read data-point messages.

Message Routing

Besides reading normal data-point messages, what really interests me is another tutorial about message processing with Message Routing.


Message Routing (Image Source: Microsoft Azure Blog)

According to the tutorial, we first need to setup a Service Bus queue in the same Azure subscription and region as our IoT Hub.


Created a Queue in the Service Bus.

We can then add an Endpoint in the IoT Hub for the queue we just created. As shown in the following screenshot, there is a message saying that “You may have up to 1 endpoint on the IoT hub.” This is because I am using the free IoT Hub. For its paid versions, only at most 10 custom endpoints are allowed.

Interestingly, each Azure subscription can only have at most 10 IoT Hubs, and only 1 free IoT Hub.


Adding a new endpoint to the IoT Hub.

After adding endpoint, we need to setup the Message Routing. For free version, we can only have 5 routing rules.


Creating new route with query string following special syntax.

In the query string, I used temperatureAlert = “true” as the condition. Also, as shown on the screenshot above, there is a line saying “Messages which do not match any rules will be written to the ‘Events (messages/events)’ endpoint.” Hence, the following two console applications will show different results: The left one is connecting to the messages/events endpoint while the right one is showing messages that match the CustomizedMessageRoutingRule created above.


Only data with temperatureAlert = “true” will be sent to the “CustomizedMessageRoute”.

Now if we visit the Service Bus Queue page and IoT Hub page again, we will see some updates on the numbers.


Usage statistics in Service Bus Queue.


2% of 8k messages sent from the stimulated device console application.


That’s all about my first try of Azure IoT Hub after attending the workshop delivered by Gerald. It’s a great lunchtime workshop.

For those who are interested, there is an article on Microsoft sharing the benefits of using Azure IoT Hub service, you can read it to understand more.

This is just the beginning of my IoT learning journey. There are still more things for me to learn, such as Azure Stream Analysis and Microsoft Azure IoT Suite which is briefly brought up in the booklet mentioned above.

If you spot any mistake in this article or you have more to talk about IoT and in particular IoT in Azure ecosystem, please share with me. =)

Machine Learning in Microsoft Azure

Let me begin with a video showing how Machine Learning helps to improve our life.

The lift is called ThyssenKrupp Elevator, an example of Predictive Maintenance. For more information about it, please read an article about how the system works and the challenges of implementing it on different types of lift.

I first learnt about the term “Machine Learning” when I was taking the online Stanford AI course in 2011. The course basically taught us about the basics of Artificial Intelligence. So, I got the opportunity to learn about Game Theory, object recognition, robotic car, path planning, machine learning, etc.

We learnt stuff like Machine Leaning, Path Planning, AI in the online Stanford AI course.

We learnt stuff like Machine Leaning, Path Planning, AI in the online Stanford AI course.

Meetup in Microsoft

I was very excited to see the announcement from Azure Community Singapore saying that there would be a Big Data expert to talk about Azure Machine Learning in the community monthly meetup.

Doli was telling us story about Azure Machine Learning.

Doli was telling us story about Azure Machine Learning. (Photo Credit: Azure Community Singapore)

The speaker is Doli, Big Data engineer working in Malaysia iProperty Group. He gave us a good introduction to Azure Machine Learning, and then followed by Market Basket Analysis, Regression, and a recommendation system works on Azure Machine Learning.

I found the talk to be interesting, especially for those who want to know more about Big Data and Machine Learning but still new to them. I will try my best to share with you here what I have learned from Doli’s 2-hour presentation.

Ano… What is Machine Learning?

Could we make the computer to learn and behave more intelligently based on the data? For example, is it possible that from both the flight and weather data, we can know which scheduled flights are going to be delayed? Machine Learning makes it possible. Machine Learning takes historical data and make prediction about future trend.

This Sounds Similar to Data Mining

During the meetup, there was a question raised. What is the difference between Data Mining and Machine Learning?

Data Mining is normally carried out by a person to discover the pattern from a massive, complicated dataset. However, Machine Learning can be done without human guidance to predict based on previous patterns and data.

There is a very insightful discussion on Cross Validated that I recommend for those who want to understand more about Data Mining and Machine Learning.

Supervised vs. Unsupervised Learning

Two types of Machine Learning tasks are highlighted in Doli’s talk. Supervised and unsupervised learning.

Machine Learning - Supervised vs Unsupervised Learning

Machine Learning – Supervised vs Unsupervised Learning

In supervised learning, new data is classified based on the training data which are accompanied with labels to help the system to learn by example. The web app which went viral recently is using supervised learning. There is an interesting discussion on Quora about how works. In the discussion, the Microsoft Bing Senior Program Manager, Eason Wang, also shared his blog post about this project that he works on.

Gmail is also using supervised learning to find out which emails are spam or need to be prioritized. In the slides of Introduction to Apache Mahout, it uses YouTube Recommendation an example of supervised learning. This is because the recommendation given by YouTube has taken videos explicitly liked, added to favourites, rated by the user.

I love watching anime so YouTube recommended me some great anime videos. =P

I love watching anime so YouTube recommended me some great anime videos. =P

Unlike supervised learning, unsupervised learning is trying to find structure in unlabeled data. Clustering, as one of the unsupervised learning techniques, is grouping data into small groups based on similarity such that data in the same group are as similar as possible and data in different groups are as different as possible. An example for unsupervised learning is called the k-means Clustering.

Clearly, the prediction of Machine Learning is not about perfect accuracy.

Azure Machine Learning: Experiment!

With Azure Machine Learning, we are now able to perform cloud-based predictive analysis.

Azure Machine Learning is a service that developer can use to build predictive analytic models with training datasets. Those models then can be deployed for consumption as web service in C#, Python, and R. Hence, the process can be summarized as follows.

  1. Data Collection: Understanding the problem and collecting data
  2. Train: Training the model
  3. Analyze: Validating and tuning the data
  4. Deploy: Exposing the model to be consumed

Data Collection

Collecting data is part of the Experiment stage in Machine Learning. In case some of you wonder where to get large datasets, Doli shared with us a link to a discussion on Quora about where to find those public accessible large datasets.

In fact, there are quite a number of sample datasets available in Azure Machine Learning Studio too. During the presentation, Doli also showed us how to use Reader to connect to a MS SQL server to get data.

Get data either from sample dataset or from reader (database, Azure Blob Storage, data feed reader, etc.)

Get data either from sample dataset or from reader (database, Azure Blob Storage, data feed reader, etc.).

To see the data of the dataset, we can click on the output port at the bottom of the box and then select “Visualize”.

Visualize the dataset.

Visualize the dataset.

After getting the data, we need to do pre-processing, i.e. cleaning up the data. For example, we need to remove rows which have missing data.

In addition, we will choose relevant columns from the dataset (aka features in machine learning) which will help in the prediction. Choosing columns requires a few rounds of experiments before finding a good set of features to use for a predictive model.

Let's clean up the data and select only what we need.

Let’s clean up the data and select only what we need.

Train and Analyze

As mentioned earlier, Machine Learning learns from a dataset and apply it to new data. Hence, in order to evaluate an algorithm in Machine Learning, the data collected will be split into two sets, the Training Set for Machine Learning to train the algorithm and Testing Set for prediction.

Doli said that the more data we use to train the model the better. However, they are many people having different opinions. For example, there is one online discussion about the optimal ratio between the Training Set and Testing Set. Some said 3:2, some said 1:1, and some said 3:1. I don’t know much about Statistical Analysis, so I will just make it 1:1, as shown in the tutorial in Machine Learning Studio.

Randomly split the dataset into two halves: a training set and a testing set.

Randomly split the dataset into two halves: a training set and a testing set.

So, what “algorithm” are we talking about here? In Machine Learning Studio, there are many learning algorithms to choose from. I won’t go into details about which algo to choose here. =)

Choose learning algorithm and specify the prediction target.

Choose learning algorithm and specify the prediction target.

Finally, we just hit the “Run” button located at the command bar to train the model and make a prediction on the test dataset.

After the run is successfully completed, we can view the prediction results.

Visualize results.

Visualize results.


From here, we can improve the model by changing the features, properties of algorithm, or even algorithm itself.

When we are satisfied with the model, we can publish it as a web service so that we can directly use it for new data in the future. Alternatively, you can also download an Excel workbook from the Machine Learning Studio which has macro added to compute the predicted values.

Read More and Join Our Meetup!

If you would like to find out more about Azure Machine Learning, there is a detailed step-by-step guide available on Microsoft Azure documentation about how to create an experiment in Machine Learning Studio. There is also a free e-book from Microsoft about Azure Machine Learning. Please take a look!

Oh ya, in case you would like to know more about which is using Machine Learning, please visit the homepage of Microsoft Project Oxford to find out more about the Face APIs, Speech APIs, Computer Vision APIs, and other cools APIs that you can use.

Please correct me if you spot any mistake in my post because I am still very, very new to Machine Learning. Please join our meetup too, if you would like to know more about Azure.

Entertainment Hub Version 1

I received my first Raspberry Pi back in October, ten days after I ordered it online. After that I brought it back to my home in Kluang, Malaysia. The reason is that I would like to setup a home theatre with the help of Raspberry Pi. Hopefully in near future, I can have a complete entertainment hub setup for my family. Thus, I name this project, the Entertainment Hub.

Gunung Lambak, the highest point in Kluang.

Gunung Lambak, the highest point in Kluang.

Getting Raspberry Pi

Raspberry Pi is a credit-card-sized computer. According to the official website, it is designed to help the students to learn programming with lower cost. To understand more about Raspberry Pi, you can read its detailed FAQ. By saving S$1 per day, I easily got myself a new Raspberry Pi Model B (with 8GB SD card) after 2 months.

Entertainment Hub Project

Before the use of Raspberry Pi, I was using a Wireless 1080p Computer to HD Display Kit from IOGEAR to stream video from my laptop to the home TV. It requires a one-time installation of both the software and drivers on the laptop before I can use its wireless USB transmitter to connect between the PC and the wireless receiver which is connected to the TV with HDMI. Afer the installation, whenever I want to show the videos stored in external hard disk on the big screen, I always first need to switch on the receiver at TV side and then plug in the wireless USB transmitter on laptop. Now with the use of Raspberry Pi, I can easily browse the videos directly on the TV.

I only worked on the Entertainment Hub when I was at home. Also due to the fact that I only went back to home on Saturday and I would need to go back to Singapore on the following day, I didn’t really got much time to work on the project. Hence, I finally got video to show on the TV only after four times of travelling back to home.

Connecting External Hard Disk to Raspberry Pi

Before I started this project, I thought connecting an external hard disk directly to the Raspberry Pi would be enough. However, it turned out that it’s not the case. When I connected the external hard disk to the Raspberry Pi directly, the USB optical mouse, which was connected to another USB port of the Raspebrry Pi, lost its power. After doing some searches online, I found that it was most probably due to the fact that the Raspberry Pi didn’t have enough power to power up both the hard disk and the optical mouse at the same time.

The USB hard disk I have is 2.5” Portable HDD (Model: IM100-0500K) from Imation. After finding out that Raspebrry Pi had insufficient power for the portal hard disk, I chose to get a powered USB hub. Fortunately, there are nice people done a lot of tests on many, many USB hubs to find out which powered USB hubs are best to use together with Raspberry Pi. They posted a useful list of working powered USB hubs online for us to use as a guideline when choosing USB hub for Raspberry Pi. I bought the Hi-Speed USB 2.0 7-Port Hub by Belkin at Funan. Even though the model isn’t same as the one in the list, the USB hub works fine in my case.

To find out if Raspberry Pi can detect the portable hard disk or not, simply use the following command.

sudo blkid

If the external hard disk can be detected, then a similar results as follows should be printed.

/dev/sda1: LABEL=”HDD Name” UUID=”xxxxxxxxxxxxxxxxxx” TYPE=”ntfs”

Luckily my Raspberry Pi can auto detect the external hard disk and then can mount it automatically.

Entertainment Hub Version 1 Structure

Entertainment Hub Version 1 Structure

Enjoy Movies on Raspberry Pi

After successfully mounting the external hard disk on Raspberry Pi, I just need to browse to the folders on the hard disk to pick the video files and then play them using OMXPlayer, a video player pre-installed on Raspberry Pi. As I used HDMI cable to connect Raspberry Pi and TV, so by using the following command, both audio and video can be successfully transferred to the TV.

omxplayer -o hdmi -r video.flv

The reason of having -r here is to adjust video framerate and resolution. Without it, not only the video won’t be displayed in full screen on TV, but there also won’t be any audio from TV.

When I first used omxplayer, it showed a black screen after I closed the program. There are online documentation and solution about this issue as well. For me, after I rebooted the Raspberry Pi, the issue disappeared.

Watching Movie with Help of Raspberry Pi

Watching movie with the help of Raspberry Pi.

Dad’s Help in the Project

The case of my Raspberry Pi is designed and made by my Dad. I am very happy and thankful that my Dad helped making a case for my Raspberry Pi. Usually the case of Raspberry Pi is box-shaped. However, the case I have here is a cylinder. So my Raspberry Pi looks special. =)

A closer look of my Raspberry Pi.

A closer look of my Raspberry Pi.

Future Work

With this little success of having movies played on Raspberry Pi, the first part of the Entertainment Hub is done. Now, there are more things needed to be done in order to make it more user friendly and robust. First of all, there needs a playlist support. Secondly, the ability of replaying the videos. Thirdly, a better GUI to select videos, instead of just a command-line UI. All of these depend on how fast I learn to program an app in Raspberry Pi.

Let’s look forward to completion of this project.