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1. How does the app address all required milestones mentioned in your chosen case study?

2. How does this simulation help to analyse and identify probable solution of the business problem?

The Challenge

AB Pty. Ltd is a delivery company that has 6 trucks to deliver their wide range of products for the third parties. They have their set delivery zone to cover the entire Austin downtown, Texas, USA. A truck on an average can delivers 5 times a day. However with expanding demand a truck can run only 3 times per day. Therefore the company is concerned about the delivery system to get updated soon so that maximum product delivery coverage per day can be possible with their existing number of trucks. The company is now planning to upgrade their existing delivery system with the following:

  • Development of Smart App to simulate the truck movement
  • Use of NODE-RED function node
  • Creating a visual dashboard using NODE-RED that will have a speed Gauge and a speed chart of specific car

The smart app is going to be developed in order to simulate the connected vehicle and this will be done through Bluemix. Bluemix is a cloud platform that is developed by IBM. It does support to provide services and integrated DevOps to design, build, run, implement and manage applications on the cloud. Bluemix is actually based on Cloud Foundry open technology.

  • Cloud Computing services with Bluemix

It is a type of internet based computing that perform several services including servers, storage and applications. These services are delivered on the organisation’s device and computers through the help of internet. Thereby the applications and the services provide to the company are being accessed by the service provider via web itself instead of any hard drive. The services delivered are used by t he cloud customers.

The cloud computing services are offered by companies like IBM’s Blue cloud technology. It is based on the open standards and open source software that could link the computers of the system all together.

The company is recently facing huge discrepancies in terms of delivery of order on time. And hence the customers are unsatisfied regarding the deliveries. The ever expanding demand of product delivery has led the company facing the issue of delayed delivery and long queue of pending delivery. As a result currently their trucks could deliver only 3 times per day instead of 5 times a day.

The solution can be provided with the help of software application. This application is going to track the delivery vehicle status. As a result it will update in the system on the spot and hence the delivery vehicle can be instructed as per the current status detected via the application. This will save huge amount of time and increase accessibility.

The purpose of the project to create a cloud based simulation environment using IBM BlueMix for smart application design in clod environment in order to run the delivery vehicles efficiently.  

The Solution

To analyse a business problem

To deploy a cloud based application to simulate the problem

To design a Node-RED based visual tool to debug telemetry data from simulation

The project focuses on the development of the software application. The focus of the project is based on the development of smart application for the company so that they can easily track their delivery vehicle status. The facility is provided by IBM BlueMix to design the application. Apart from that a strong database is also designed in order to keep record of the status as per the date and time. AB Truck scheduling system is going to be set for the handling of different products and connect them via the smart vehicle application.

The task is carried out by deploy and extend an Internet of Things (IoT) application on Bluemix. It enables to simulate and manage vehicle location and driving status. The task is proceeded with IoT starter kit for connected thing and followed by download the starter source code given with this assignment to configure, and deploy a cloud based smart/ IoT (Internet of Things) application to simulate the business case. In this project a smart application is designed with cloud based IoT and Geospatial analytics. The vehicle kit consists of a vehicle simulator, html5 applications to manage vehicles on a map and the geospatial analytics service on Bluemix along with Node-RED for analytics.

From the organization dashboard’s device page, Device type is created and a unique device ID is entered. There after the next page show the credentials of the device. Similarly other devices can be connected with their credentials. 

The source code for the IoT starter kit is stored in the IoT-vehicle-geospatial-starter-kit project on the DevOps services. Following the instructions will allow creating a ZIP file of the code and downloading it to the PC.   

  1. Click EDIT CODE from the IoT-connected-vehicle-starter-kit project.
  2. Highlight the root of the source tree and click File -> Export -> Zip from the menu and save the downloaded file locally. Extract the Zip file.  
  3. Open the manifest.yml.file and enter the name for the host and name
  4. Open config/settings.js. This file stores the device and the API key configuration data.
  • For iot_deviceType, enter vehicle.
  • For iot_deviceOrg, enter your six-character organization ID (for example, O4ze1w).
  • For iot_deviceSet, enter the ID and tokens for the three devices you registered.
  • For iot_apiKey, enter the API key you created.
  • For iot_apiToken, enter the API key token.

These are the configuration parameters that are used by the Geospatial analytics service.

From the root directory of the extracted application, type cf login and follow the prompts, enter https://api.ng. Bluemix.net is used for the API endpoint and collect Bluemix ID email address and password as login credentials. Type the cf push to Bluemix to deploy the application. After the deployment of the application, run the map app at https://app-name.mybluemix.net. The simulated vehicles can be seen across the map. 

Now by clicking any vehicle the telemetry data can be viewed. The 6 trucks of the company can be simulated in the similar manner and the count of the trucks can be controlled by Bluemix environment variable: VEHICLE_ COUNT. 

Building Analytics with Geospatial Analytics Service

Step 4.  Building analytics with Geospatial analytics service

Using the Bluemix Geospatial Analytics service, each of the vehicles can be tracked when vehicle enters or leaves a defined geofence region. The created Map app interacts with the service, and enables to simulate graphically.

Click ADD A SERVICE in the Bluemix dashboard. Choose Geospatial Analytics from the service catalog. Plan of Free is selected and clicks CREATE to bind the service to the application.

The starter-kit application includes APIs to start and stop Geospatial Analytics with the configured IoT Foundation organization. The service can be started through https://app-name.mybuemix.net/GeospatialService_start and wait for the page to show the success notification within 30-40 secs.

Now geofence is created using the Map app. Alert button is created on the toolbar to start the geofence creation process. After that by dragging the edges geofence is positioned. Geofence is created by click on the center black circle. 

Geospatial analytics service adds up this geofence to the listed watched regions and publish the MQTT message through the IoT Foundation whenever the assigned trucks of the company enters or leaves the area. Also a geofence can be deleted by selecting the region and click DELETE.

Step  5. Tester App

Instructions are sent through command in the Tester App. It was done to simulate the vehicle to change its properties like state and speed.

  1. Open the Map app and the Tester app side by side to view them simultaneously.
  2. On the bottom form of the Tester app, a vehicle is selected and its ID is entered. Then the speed of the vehicle is set to a value 100 and click Update property. The setProperty API can be used to dynamically change a custom property of the delivery vehicle.

To delete the property, update the property with an empty string so that the delivery vehicle will stop including the property in its telemetry message.

Step  5. Tester App 

Step 6. Creating Node-RED analytics application

Node-RED is a visual tool. It s used in this case yo wire together logical events in IoT. in Bluemix is used in order to extend the starter kit. It is extended with basic analytics.

  1. Implement the Node-RED application in Bluemix by clicking ADD APPLICATION using Bluemix dashboard
  2. After the application is in running mode, BIND A SERVICE is clicked and followed by choosing IoT service created in Step 1.
  3. After this, Node-RED canvas is opened at https://node-red-app.mybluemix.net/red/.
  4. An ibmiot input node is dragged from the canvas. Then a Debug node is dragged to the right side of ibmiot node. Wire the two nodes. Double-clicking the ibmiot node the configuration is build as follows:

Select Bluemix service -> from input type select device event -> for device type enter truck -> For device Id select check box -> for event,  enter telemetry -> for format,  enter json -> for name,  enter telemetry

Select the debug pane and click  Once the configuration is set up and the starter kit application is running, it can be seen for the connected vehicles in the debug pane:

The telemetry data for each of the individual vehicle for VEHICLE_COUNT vehicles are shown in the single array. In order to change the filter for the single vehicle, a function is added as a function node in the middle of the flow, followed by double clicking and fill with the given code:

Tester App

var data = msg. Payload;

for (var i in data) {

if (data[i].id = “ABC – 2”) {

msg.payload = data [i];

return msg;  

Hence the telemetry information is collected by the debug system of the simulation through Bluemix app service. The information collected in the form of geospatial data were helpful to decode the business problem and find suitable solution in the form of upgraded digitised delivery system.   

IBM Bluemix cloud platform has helped tremendously to solve the company’s delivery issue. With Bluemix cloud platform, it was possible to explore the existing through simulation technique and drive the business scenario with application and its service. It is data rich software and through the help of this app, it could possible for the company to manage all their entire delivery services through their digital channel. The entire system that was created was cloud based and thus enabled to provide the geospatial services through the Map app provided by Bluemix. Through the simulation technique, the delivery vehicle scenario was able to identify and steps were taken to upgrade the entire system of the delivery company as per their current requirement.Risk

To enable the service the entire delivery system of the company was analysed and thus the existing scenario is simulated using Bluemix platform. This required performing the basic service enable process through Bluemix which was not a simple task to perform. Since the connection building process was extremely time taking and thus the applications were often not responding.

However with the gradual trial method, link could be able to build among the service provider and the actual business scenario to control the delivery vehicle properties. The procedure that was explained in the step by step process could give a clear indication that how the function was performed using command and addressing table at a core level. The logical sequence of the app procedure was maintained while performing the task of connectivity one by one.

The Map app has to be enabled before that in order to access and link the geospatial service along with Bluemix smart app development procedure.   

The geospatial service was not that easy to link with the app service. In order to access the service the entry and the exit of the delivery vehicle has to be tracked through map app in Bluemix. Thereby the characterization of geofence technique was also mentioned in the step by step process.

Creating Node-RED Analytics Application

Conclusion

Cloud computing service through Bluemix was a enormous guidance by the system service for the delivery company. It provided huge opportunities to optimise the existing application and making the entire system of delivery vehicle more efficient. In terms of business value the platform was extremely scalable and cost-effective. The geospatial service and the telemetry option in the Bluemix made the delivery system traceable at any point of time. Also the system of delivery vehicle has become more productive. The properties of the delivery vehicles such as speed and state could also be changed as per the change in system directly with the service provider. 

Before connecting devices and utilizing data, register for a Bluemix account and create an instance of the Watson IoT Platform service in your Bluemix organization. You can create an Watson IoT Platform instance directly from the Watson IoT Platform page in the Bluemix Services Catalog .

For detailed information about how to sign up for an account on Bluemix, configure regions, and other account management settings, see Managing your Bluemix account.

You can set up and configure your Watson IoT Platform instance from the dashboard. To open the dashboard, go to your Watson IoT Platform service instance in Bluemix, and then click Launch.

The following steps describe how you can quickly get started with your Watson IoT Platform service.

A more detailed set of getting started guides and sample applications that step through the basics of developing a ready-for-production, end-to-end IoT prototype system with Watson IoT Platform are also available. If you are a developer who is new to working with Watson IoT Platform, use the step-by-step processes in the Getting started guides section.

To get up and running with the service, explore the following options depending on your situation:

The service is deployed

I have a device to connect

Connect your device to Watson IoT Platform.

I do not have a device to connect

Create and connect a Node-RED device simulator.

For more information on how to connect specific device types to Watson IoT Platform, see developerWorks recipes .

For device connection developer documentation, see:

  • MQTT connectivity for devices.
  • MQTT connectivity for gateways.

Start exploring the real-time data that the devices are sending to Watson IoT Platform.

Watson IoT Platform includes the following analytics tools:

  • Boards and cardsto visualize your real-time device data.
  • Rules and actionsthat are triggered by real-time device data.

For a quick getting started example, see the Using Rules and Actions with IBM Watson IoT Platform Cloud Analytics developerWorks recipe.

Extend the data analytics features of Watson IoT Platform by creating and connecting your own applications to consume real-time and historical device data.

For more information, see the following topics:

  • Explore the application developer documentationand the Watson IoT Platform API Documentation.
  • Explore the Watson IoT Platform client librariesthat provide tools and files to build and develop code for integrating and connecting your devices and applications.
  • Connect a IBM® Cloudant® NoSQL DB for Bluemix® serviceto your Watson IoT Platform to store historical device data. 

After Launching, IoT foundation dashboard is opened where registration is done to generate the API keys for application. API keys are generated which are used by the Map and Tester apps to connect with IoT Foundation. 

References

[1] T. Dillon, C. Wu, and E. Chang, “Cloud Computing: Issues and Challenges,” 2010 24th IEEE International Conference on Advanced Information Networking and Applications(AINA), pp. 27-33, DOI= 20-23 April 2010

[2] M. Q. Zhou, R. Zhang, W. Xie, W. N. Qian, and A. Zhou, “Security and Privacy in Cloud Computing: A Survey,” 2010 Sixth International Conference on Semantics, Knowledge and Grids(SKG), pp.105-112, DOI= 1-3 Nov. 2010

[3] J. F. Yang and Z. B. Chen, “Cloud Computing Research and Security Issues,” 2010 IEEE International Conference on Computational Intelligence and Software Engineering (CiSE), Wuhan pp. 1-3, DOI= 10-12 Dec. 2010.

[4] S. Zhang, S. F. Zhang, X. B. Chen, and X. Z. Huo, “Cloud Computing Research and Development Trend,” In Proceedings of the 2010 Second International Conference on Future Networks (ICFN '10). IEEE Computer Society, Washington, DC, USA, pp. 93-97. DOI=10.1109/ICFN.2010. 58.

[5] J. J. Peng, X. J. Zhang, Z. Lei, B. F. Zhang, W. Zhang, and Q. Li, “Comparison of Several Cloud Computing Platforms,” 2009 Second International Symposium on Information Science and Engineering (ISISE '09). IEEE Computer Society, Washington, DC, USA, pp. 23-27, DOI=10.1109/ISISE.2009.94.

[6] S. Zhang, S. F. Zhang, X. B. Chen, and X. Z. Huo, “The Comparison between Cloud Computing and Grid Computing,” 2010 International Conference on Computer Application and System Modeling (ICCASM), pp. V11-72 - V11-75, DOI= 22-24 Oct. 2010.

[7] M. M. Alabbadi, “Cloud Computing for Education and Learning: Education and Learning as a Service (ELaaS),” 2011 14th International Conference on Interactive Collaborative Learning (ICL), pp. 589 – 594, DOI=21-23 Sept. 2011.

[8] P. Kalagiakos “Cloud Computing Learning,” 2011 5th International Conference on Application of Information and Communication Technologies (AICT), Baku pp. 1 - 4, DOI=12-14 Oct. 2011.

[9] P. Mell and T. Grance, “Draft nist working definition of cloud computing - vol. 21, Aug 2009, 2009.

[10] W. Dawoud, I. Takouna, and C. Meinel, “Infrastructure as a Service Security: Challenges and Solutions,” 2010 7th International Conference on Informatics and System, pp. 1-8, March 2010.

[11] B. Grobauer, T. Walloschek, and E. Stöcker, “Understanding Cloud Computing Vulnerabilities,” 2011 IEEE Security and Privacy, pp. 50-57, DOI= March/April 2011.

[12] W. A. Jansen, “Cloud Hooks: Security and Privacy Issues in Cloud Computing,” Proceedings of the 44th Hawaii International Conference on System Sciences, 2011.

[13] SonalAnand, Sarvesh Gupta, ShwetaFatnani, Varsha Sharma and Deepti Jain. Article: Semantic Cloud for Mobile Technology. International Journal of Computer Applications 8(12):1–4, October 2010.

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My Assignment Help (2021) AB Pty. Ltd Delivery Company's Cloud-based Simulation Essay System. [Online]. Available from: https://myassignmenthelp.com/free-samples/coit20260-cloud-computing-and-internet-of-things/device-data.html
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My Assignment Help. 'AB Pty. Ltd Delivery Company's Cloud-based Simulation Essay System.' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/coit20260-cloud-computing-and-internet-of-things/device-data.html> accessed 13 July 2024.

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