Smart Component Organizer using Google Assistant

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Smart Component Organizer using Google Assistant

Swathi P

Dept. of Electronics and Communication

GSSS Institute of Engineering and Technology for Women Mysore, Karnataka, India

Sanjana G S

Dept. of Electronics and Communication

GSSS Institute of Engineering and Technology for Women Mysore, Karnataka, India

Swathi R

Dept. of Electronics and Communication

GSSS Institute of Engineering and Technology for Women Mysore, Karnataka, India

Dr. Rajendra R. Patil Professor and Head,

Dept. of Electronics and Communication

GSSS Institute of Engineering and Technology for Women Mysore, Karnataka, India

Priyanka P

Assistant Professor, Dept. of Electronics and Communication GSSS Institute of Engineering and Technology for Women Mysore, Karnataka, India

Abstract With the rapid development of electronics technology, there are many types of electronic components than ever before. This leads to many problems in the management of components. Some of the most commonly faced problems are unnecessary repeat purchases, time-consuming storage, poor management of existing parts and mistakes that could have been avoided. Management of such a variety of components has been a typical issue among various organizations. Huge time and money are invested in the management of these items. However, there are many instances where these resources are wasted due to the lack of proper management system. Consider the following scenarios: 1.When the item is available, but its location is unknown. 2.When an item is available, but it cannot be searched due to time constraint. 3.When an item is available, but still the operator places an order. In the above listed scenarios, there is no full utilization of the available items. The proposed framework aims to design a smart component organizer which helps the operator to organize and manage a cluster of items using voice commands, enable hands free-searching and maintain a database of all the components, which keeps track of name, location, available quantity and search tags of each component.

Keywords Google Assistant, Particle Photon, Microsoft Azure, IFTTT, SQL database, LED Strips, Components.

  1. INTRODUCTION

    The era of electronics began with the invention of the transistor in 1947 and silicon-based semiconductor technology. Seven decades later, we are surrounded by electronic devices and, much as we try to deny it, we rely on them in our everyday lives. Most of the electronic instruments and equipment would not have been possible without advances in electronics. In particular, the new, high volume applications has increased the pace of innovation in the development of a wide variety of components and devices.

    There are a variety of container options to store electronic components as per the user requirement. Moulded plastic boxes that can be side locked and stacked either vertically or horizontally are available. Each compartment with a label for easy identification. The electronic components need to be sorted and stored in these compartments manually. A big problem is that the user cannot locate the required components immediately. Other problems include repeat purchases of existing components, time-consuming storage and poor management of the components.

    In this paper, we propose a system to organize and digitally catalog each component to maintain a record of the available components and to mitigate the delay of searching the desired component. The objectives of the intended system are: to manage a cluster of components using voice commands, to enable hands-free searching, to eliminate duplication in ordering stocks and to maintain a record of each component.

    REQUIREMENTS

    The proposed project is an IoT – based component organizer which embraces the following elements: Google assistant, IFTTT (If This Then That), Particle Photon, Microsoft Azure, Azure SQL Database, 3.3 to 5V logic level converter and individually addressable LED strips. The block diagram of the intended project is as shown in figure 1.

    Figure 1: Block Diagram

    1. Google Assistant

      Google Assistant is an artificial intelligence-powered virtual assistant which is primarily available on mobile and smart home devices. Google Assistant can engage in two-way conversations. Google smart home lets users control the connected devices through the Google Home app[1]. In the proposed project, the operator gives voice commands to Google Assistant and those voice commands are converted to text. This text is used to trigger IFTTT.

    2. IFTTT

      If This Then That, also known as IFTTT, is a freeware web-based service that creates chains of simple conditional statements, called applets. In the proposed project, we have used IFTTT to integrate Google Home and Particle Photon[8]. We have created various applets which will enable the operator to search for the required components using suitable voice commands.

    3. Particle Photon

      Particle Photon is a Wi-Fi capable microcontroller, which is programmable Arduino style. Particle combines a powerful ARM Cortex M3 micro-controller with a Broadcom Wi-Fi chip in a minute module called the PØ (P-zero). Particle adds a 3.3VDC SMPS power supply, RF and user interface components to the PØ on a small single-sided PCB called the Photon[7]. The design is open source. In the proposed project Photon is used to publish an event to Azure Function App and to turn on the LED strip indicating the location of the desired component.

    4. Microsoft Azure Function

      Azure Function is a serverless compute service that lets the user run event-triggered code without having to explicitly provision or manage infrastructure[3]. Azure Functions allows the user to run small pieces of code (called "functions") without worrying about application infrastructure. With Azure Functions, the cloud infrastructure provides all the up-to-date servers the user needs to keep the application running at scale. A function is "triggered" by a specific type of event. Supported triggers include responding to changes in data, responding to messages, or as the result of an HTTP request.

    5. Azure SQL Database

      SQL Database is a cloud-computing database service (Database as a Service), that is offered by Microsoft Azure

      Platform which helps to host and use a relational SQL database in the cloud without requiring any hardware or software installation. Also, it provides various advanced features to its users and some of them are: long-term backup retention, geo-replication, automatic tuning, high-availability, scaling database resources and automated backups[3][4].

    6. Logic Level Converter

    A level shifter in digital electronics, also called logic-level shifter or voltage level translation, is a circuit used to translate signals from one logic level or voltage domain to another, allowing compatibility between ICs with different voltage requirements, such as CMOS(Complementary Metal Oxide Semiconductor) and TTL(Transistor-Transistor Logic). In the proposed project, we use the level shifter to convert 3.3V signal received from the microcontroller to 5V for driving the LED strip.

  2. IMPLEMENTATION

    1. Circuit Diagram

      The circuit consists of Particle Photon, 3.3V to 5V logic level converter, 5V 4A power supply, terminal blocks, LEDs, relay, capacitors, resistors and diodes. A 5V 4A power supply is fed to the circuit with the help of a terminal block. The LED strips are connected to the four terminal blocks on the right side of the schematic. The pin 2 of the termina blocks is connected to the data wire of the LED strip. The VIN pin of Particle Photon is connected to 5V, 4A supply and the GND pin is connected to ground. The D3 pin of Particle Photon is connected to LV(3.3V) of the logic level converter. The logic level converter considered is of a single channel. It is composed of 2N7000 N-channel E-MOSFET and 10k resistors. The 5V signal from the logic level converter drives the LED strip. The digital input/output pin D3 is used to publish the location of the component by turning on the LEDs of the corresponding tray. The relay is used to turn on/off the LED strip display. Figure 2 shows the schematic diagram of the project. Figure 3 shows the designed printed circuit board.

      Figure 2: Schematic Diagram

      The voice commands used to search for components are listed in the below table.

    2. Flow Diagram

      Figure 3: Designed PCB.

      In this project, Google Assistant is used to receive the voice command regarding the required component from the operator. Google Assistant converts this voice command into text. This text then triggers IFTTT. IFTTT is an online platform which enables different applications to communicate with each other. IFTTT publishes an event via webhook to Particle Photon. Webhook is used to send or receive data from Particle devices and applications. Particle Photon sends the JSON payload via webhook to Microsoft Azure Function App. The Function App deserializes the JSON payload, generates a query and then queries the database. The database then sends the results of querying back to the Function App. The Function App sends results to Particle Photon via webhook. Finally, Photon publishes the location of the required component by turning on the LEDs of the storage unit containing it. Figure 4 shows the sequence of operation of the proposed project.

      Figure 4: Sequence of Operation

      IFTTT Commands

      Description

      FindItem

      Find the component

      FindTags

      Finds all the components matching the provided tags

      InsertItem

      Inserts a new component into the organizer

      RemoveItem

      Removes a component from the organizer

      AddTags

      Add tags to an existing component

      SetQuantity

      Set the quantity of the component to the said value

      UpdateQuantity

      Updated the quantity of an existing component

      IFTTT Commands

      Description

      FindItem

      Find the component

      FindTags

      Finds all the components matching the provided tags

      InsertItem

      Inserts a new component into the organizer

      RemoveItem

      Removes a component from the organizer

      AddTags

      Add tags to an existing component

      SetQuantity

      Set the quantity of the component to the said value

      UpdateQuantity

      Updated the quantity of an existing component

    3. SQL Database

      The SQL Database keeps track of each component in terms of names, quantities, locations, search tags. The database is queried to insert, remove or find a component, or update the component details. The SQL database consists of four tables. They are: dbo.Commands, dbo.HttpRequests, dbo.Items and dbo.Tags. The Items table stores the NameKey, Name, Quantity, Row, Column, Date of Creation information and last updated date and time information. NameKey is a unique identifier. The Tags table stores the Component Id, NameKey and the associated Tags for the component. The Commands table stores the Component Id, Date of Creation Information, associated Commands, DataIn and DataOut information. The HttpRequests table stores the component id, name of the published event and the date of creation information. The Commands and HttpRequests tables will show raw data logged from the Azure Function App, which may help debug malformed JSON input or bad speech-to-text translations from Google Assistant.

      Figure 5 shows the Items table and figure 6 shows the Tags table. Visual Studio 2019 is used to view the contents of the SQL database.

      Figure 5: SQL Database: Items table.

      Figure 6: SQL database: Tags table.

  3. OPERATION

    The sequence of operation is discussed in this section.

      • First, the user gives the voice command. In this case, the voice command is Find NodeMCU.

      • This command triggers IFTTT. IFTTT then publishes 2 events: Google_FindItem and callAzureFunctionEvent. Since we are finding an item, Google_FindItem event is generated. The callAzureFunctionEvent is published to Particle Photon.

      • Particle Photon then sends the JSON payload to the Azure function app via webhook.

      • The function app deserialises the payload, and queries the database.

      • The database then sends the results of querying back to function app.

      • The function app sends the webhook response to Particle Photon.

      • Finally, Particle Photon gives a visual indication of the location of NodeMCU. Figure 7 shows the sequence of operation.

    Figure 7: Sequence of operation

  4. APPLICATIONS

    The proposed system find applications in the following fields:

    1. In Industries

    2. In Pharmacies

    3. In laboratories

  5. ADVANTAGES

    • Efficient management and Storage: The organizer provides an effective method to store and manage the components, and ensure that the user acquires the desired components by ejecting the right storage units.

    • Easy to incorporate: The organizer caters to many fields such as industries, laboratories, pharmacies etc.

    • Precision mechanisms and user-friendly: All that the user requires is a voice assistant to organize the components.

    • Zero-delay: There is zero delay in seeking the desired components.

    • Easy to handle and operate: As the system is voice- controlled, the user does not have to put too much work in organizing and managing the components.

    • Collective Documentation: The component name, quantity and location is maintained in a database and it updates itself when the new components are inserted or when existing components are removed.

  6. DISADVANTAGES

    • The proposed system requires it to be connected to the internet when in use.

    • The user should have knowledge of the correct tags before using the organizer.

  7. CONCLUSION AND FUTURE WORK

In this paper, it is shown how Smart Component Organizer organizes and manages the components significantly with the use of voice assistant. We believe that the derived smart organizer model is important not only because it helps to improve the efficiency of the operations in the specific domain but it also has good implications to a more general domain. Figure 8 shows the Smart Component Organizer. In future research, we plan to extend our analysis to a more challenging case when the storage space is a decision variable. We also plan to use actuators to push the storage unit of the most promising location identified for the searched component.

  1. Pratiksha P. Nikam , Ranjeetsingh S. Suryawanshi, " Microsoft Windows Azure: Developing Applications for Highly Available Storage of Cloud Service ", International Journal of Science and Research (2014).

  2. Shweta Dinesh Bijwe, P. L. Ramteke, " Database in Cloud Computing – Database-as-a Service with its challenges ", International Journal of Computer Science and Mobile Computing (2015).

  3. S.Nagaprasad, & A.VinayaBabu, & K.Madhukar, & Verghese, D.Marlene & V.Mallaiah, & A.Sreelatha, "Reviewing some platforms in cloud computing", International Journal of Engineering and Technology (2010).

  4. Arabolu Chandra Sekhar, Dr. R. Praveen Sam, " Architecture of SQL Azure", International Research Journal of Engineering and Technology (2015)

  5. Particle Photon: https://docs.particle.io/photon/

  6. IFTTT applets: https://ifttt.com/

Figure 8: Setup of Smart Component Organizer

ACKNOWLEDGMENT

We consider it is a privilege and honour to express our sincere thanks to Dr. Rajendra R Patil, Professor and Head, Department of Electronics Communication Engineering for his support and invaluable guidance throughout the tenure of this project.

We would like to thank our internal guide Dr. Rajendra R. Patil, Professor and Head, Department of Electronics Communication Engineering and Priyanka P, Assistant Professor, Department of Electronics Communication Engineering for their support, guidance and motivation for the successful completion of this project.

REFERENCES

  1. "Preliminary Study of a Google Home Mini", Min Jin Park, Joshua I James, Journal of Digital Forensics (2018)

  2. Giuseppe Ghiani, Marco Manca, Fabio Paterno, And Carmen Santoro, " Personalization of Context-Dependent Applications Through Trigger- Action Rules ", ACM Transactions on Computer human Interaction (2017).

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