Category: Fog computing

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APIs Integration with IOT and CRM improves Customer Service

APIs integration helping IOT and CRM to enable better customer experience

IOT (Internet of things) is revolutionizing our lives. As per Gartner report by 2025 IOT market will expand a 58-billion-dollar opportunity. It is affecting all parts of our life. In our pandemic era we found more use of IOT device to maintain social distancing.

IOT is also one of the main disruptive technologies in our businesses. It is affecting all business domain including healthcare, retail, automotive, security.

There are wide range of IOT benefits in business.

  • Enhanced productivity
  • Better customer experience
  • Cost-effectiveness

CRM system is keeping all your customer relationship like data, notes, metrics and more – in one place. CRM is helping small business to take off all burden from the IT management team by automating the business process. It is also helping employee to keep the focus on the critical business areas.

API is helping to integrate these two unrelated systems. APIs are enabling this system to optimize process and streamline whole business process. API is the main communication channel to build robust process and keeping real time update to these systems. APIs are allowing to build context-based application with IOT and CRM to interact with the physical world.

Now here are few areas where IOT is helping CRM system with help of APIs to optimize business process.

  1. Optimize customer service – Before your customer finds any error in your service/product you proactively acting on error and fixing those error. This will help to build relationship with customer.
  2. Increase sales – With help of IOT and CRM system you are finding untouched opportunity and using those opportunity to increase your sale.
  3. Personalize customer experience – You are analyzing data provided by IOT and CRM system and building user based predictive model to enable personalize experience to user.
  4. Customer retention – CRM provide customer data and relationship. IOT data providing customer behavior. This will help any business to personalize and target marketing for their customer.
  5. Omnichannel instore experience – IOT and CRM is helping business to enable 360 omnichannel customer experience. This process will help and suggest the products which the customer might purchase.

APIs  integration with IOT and CRM helping business to enable higher degree of personalization, target marketing, optimize price model, higher revenue and enhance customer satisfaction.

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MuleSoft 4: Using Java in Mule Flow

MuleSoft is a lightweight integration and API platform that allows you to connect anything anywhere and enable your data through API. Mule evolved from java and spring framework. MuleSoft supports multiple language although all Mule module are developed in java.

 Since Mule evolved from java it has capability to use direct java class and method in Mule flow. This capability gives flexibility to Mule developer to use java for complex business logic.

There are several ways you can use java within Mule. Here are some of Java modules available to use within MuleSoft application

There are 4 java modules are available in MuleSoft flow

  1. New
  2. Invoke
  3. Invoke static
  4. Validate type

To explain all these components and uses in Mule flow I created Utils.java and AppUtils.java classes

1. New – AppUtils.java class instantiation can be achieved by calling constructor of this class through MuleSoft New component within Mule flow.

AppUtils java class defined 2 contractors, So Mule constructor properties for NEW component is showing 2 options.

New module without parameter

<java:new doc:name="Instantiate appUtils" doc:id="22ddcb7e-82ed-40f8-bc11-b779ceedd1a1"
    constructor="AppUtils()"  class="com.vanrish.AppUtils" target="appInst">
</java:new>

New module with parameter

<java:new doc:name="Instantiate appUtils" doc:id="22ddcb7e-82ed-40f8-bc11-b779ceedd1a1"
    constructor="AppUtils(String)"  class="com.vanrish.AppUtils" target="appInst">
    <java:args ><![CDATA[#[{paramVal:"Hello world}]]]>
</java:new>

In above code, Instance of AppUtils class is created and placed into the “appInst”  target variables to reuse same instance in Mule flow.

New module
2. Invoke – In new java module we instantiate AppUtils.java class and placed into “appInst” variable. Now to use this variable set Invoke module and call one of method define in AppUtils.java class. In AppUtils.java class, there is one non static method “generateRandomNumber” defined with String parameter. In example we call this method through Invoke module. 
<java:invoke doc:name="Invoke" doc:id="9348e2cf-87fe-4ff7-958c-f430d0421702"
      instance="#[vars.appInst]" class="com.vanrish.AppUtils" method="generateRandomNumber(String)">
     <java:args ><![CDATA[#[{numVal:”100”}]]]></java:args>
</java:invoke>
Invoke module
3. Invoke static—Invoke static java module enable mule flow to call java static method. This is one of the easy ways to call any java method in Mule flow.

Mule code is calling to java static method

<java:invoke-static doc:name="Invoke static" doc:id="bc3e110c-d970-47ef-891e-93fb3ffb61bd" 
  class="com.vanrish.AppUtils" method="encode(String)">
   <java:args ><![CDATA[#[{plainString:"mystringval"}]]]></java:args>
</java:invoke-static>
Invoke-static module
4. Validate type – Validate type java module use instance of method from java. This module accepts “Accept subtypes” parameter which indicates if the operation should accept all subclasses of a class. By default it acceptSubtypes=“true” which means it will accept all sub class of main class but if it will set as false acceptSubtypes=“false” then during execution the operation throws an error (JAVA:WRONG_INSTANCE_CLASS) 
<java:validate-type doc:name="Validate type" doc:id="288c791c-50eb-4be0-b924-56481dfdc023"
 class="com.vanrish.Utils" instance="#[vars.appInst]" acceptSubtypes="false"/>
Validate-type module

Java in Mule flow diagram

java in Mule flow

Utils.java

package com.vanrish;

public class Utils{
	
}

AppUtils.java

package com.vanrish;
import java.util.Random;
import sun.misc.BASE64Encoder;
/**
 * @author rajnish
 */
public class AppUtils extends Utils {
	public static final BASE64Encoder encoder = new BASE64Encoder();
	//Constructor without Parameter
	public AppUtils(){
		System.out.println("Constructor with no parameter");
	}
	//Constructor with Parameter
  public AppUtils(String paramVal){
		System.out.println("Constructor with parameter value="+paramVal);
	}
	/**
	 * @param String
	 * @return
	 */
	public  String generateRandomNumber(String numVal) {
		Integer numNoRange = null;
		  Random rand = new Random();
		  if(numVal !=null) {
			  numNoRange = rand.nextInt(new Integer(numVal));
		  }else {
		   numNoRange = rand.nextInt();
		  }
	    return numNoRange.toString();
	}
	/**
	 * @param plainString
	 * @return
	 */
	public static String encode(String plainString)
	{
		String encodedString = encoder.encodeBuffer(plainString.getBytes());
		return encodedString;
	}
}

Mulesoft Code

<?xml version="1.0" encoding="UTF-8"?>
<mule xmlns:java="http://www.mulesoft.org/schema/mule/java" xmlns:db="http://www.mulesoft.org/schema/mule/db"
xmlns:ee="http://www.mulesoft.org/schema/mule/ee/core" xmlns:http="http://www.mulesoft.org/schema/mule/http" xmlns="http://www.mulesoft.org/schema/mule/core"
 xmlns:doc="http://www.mulesoft.org/schema/mule/documentation" 
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.mulesoft.org/schema/mule/core http://www.mulesoft.org/schema/mule/core/current/mule.xsd 
http://www.mulesoft.org/schema/mule/http http://www.mulesoft.org/schema/mule/http/current/mule-http.xsd
http://www.mulesoft.org/schema/mule/ee/core http://www.mulesoft.org/schema/mule/ee/core/current/mule-ee.xsd
http://www.mulesoft.org/schema/mule/hl7 http://www.mulesoft.org/schema/mule/hl7/current/mule-hl7.xsd
http://www.mulesoft.org/schema/mule/db
 http://www.mulesoft.org/schema/mule/db/current/mule-db.xsdhttp://www.mulesoft.org/schema/mule/java http://www.mulesoft.org/schema/mule/java/current/mule-java.xsd">

<http:listener-config name="HTTP_Listener_config" doc:name="HTTP Listener config" doc:id="af3ce281-bf68-4c7b-83fb-52b2d2506677" >

<http:listener-connection host="0.0.0.0" port="8081" />
</http:listener-config>
<flow name="helloworldFlow" doc:id="a13826dc-67a1-4cda-8133-bc16a59ddba2" >
  <http:listener doc:name="Listener" doc:id="82522c77-5c33-4003-820a-7a04b51c3001" config-ref="HTTP_Listener_config" path="helloworld"/>
  <logger level="INFO" doc:name="Logger" doc:id="b40bc107-1ab5-4a3f-8f20-d7dfb63e5acb" message="entering flow"/>
  <java:new doc:name="Instantiate appUtils" doc:id="c1854580-f4d4-4e5c-a34d-7ca185152d02"
constructor="AppUtils(String)" class="com.vanrish.AppUtils" target="appInst" 
 >
  <java:args ><! 
  [CDATA[#[{
      paramVal:"Hello world"
  }]]]></java:args>
  </java:new>

 <java:invoke doc:name="Invoke" doc:id="9348e2cf-87fe-4ff7-958c-f430d0421702" instance="#[vars.appInst]" class="com.vanrish.AppUtils" method="generateRandomNumber(String)">
<java:args ><![CDATA[#[{
numVal:null
}]]]></java:args>
</java:invoke>
<java:invoke-static doc:name="Invoke static" doc:id="bc3e110c-d970-47ef-891e-93fb3ffb61bd" class="com.vanrish.AppUtils" method="encode(String)">
<java:args ><![CDATA[#[{
  plainString:"mystringval"
}]]]></java:args>
</java:invoke-static>
<java:validate-type doc:name="Validate type" doc:id="288c791c-50eb-4be0-b924-56481dfdc023" class="com.vanrish.Utils" instance="#[vars.appInst]"/>
<set-payload value="Success" doc:name="Set Payload" doc:id="8b6c9c0b-07c8-4e17-b649-2c24d4da8bea" />
</flow>
</mule>
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APIs for IOT and FOG computing

IOT (Internet Of Things) is transforming whole business and bringing new revolution in all kinds of business. These IOT devices generating terabytes of data. To handle unprecedented volume, variety and velocity of data, IOT needs new kind of infrastructure to support whole IOT eco system. FOG computing is a part of IOT eco system to support large volume of data with quick response. I explained in my previous blog, how FOG computing is now becoming major role in IOT devices. FOG is intermediate platform to collaborate between Cloud computing and Edge computing(IOT) to transfer data. Fog can hold small number of data and less computing power. Large data is stored in cloud and heavy computing is done in Cloud.

API (Application Programming Interface) have major role to transfer data from edge device (IOT) to Fog node and from fog node to Cloud (Internet). API is helping to collaborate between edge device to Fog node and Fog node to Cloud. API is playing major role to maintain volume, variety and velocity of data in IOT infrastructure.

API works on HTTP/HTTPS protocol. APIs are light weight and simple. Enabling APIs take very small amount of resource. So, API can enable in small system and consume without losing too much resources.  This API property helps to transfer data from Edge device(IOT) to Fog node and from Fog node to Cloud. API is not part of mechanical role. API is responsible for the optimization of data transfer. Proper enabling of APIs between these nodes increase the efficiency and computational power to all IOT devices. Fog node is intermediate node between IOT device and cloud. So, Fog node will be responsible to receive data from edge(IOT) device and transfer these data to Cloud. Communication between Edge(IOT) device to Fog node is very frequent. Data provided by API is responsible for all intermediate and quick computation on FOG node.

Cloud is still big stake holder for holding all data and large computation from IOT device.  API is providing data to cloud from FOG node in certain interval for heavy computation. As Edge(IOT) system getting more complex Fog computation responsibility will increase and API will come on picture to provide more data to Fog and from fog node to cloud.

API Integration of IOT with Fog and Cloud computing.

These are few benefits by enabling APIs for IOT devices and Fog Nodes

  • API provides flexibility to connect any IOT device to FOG node and FOG node to cloud network.
  • API provides seamless connectivity between these systems.
  • API brings whole IOT system in one seamless environment So, it is very easy to debug these systems.
  • API is very easy to develop and deploy so it’s easy to maintain these systems.
  • Provisioning of IOT device has also become very easy by enabling API.
  • According to Gartner study, Security of IOT is one of big concern. API provides whole one seamless system and network to mitigate this risk.
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Fog Computing and Edge computing

IOT, Connect car, Automated car getting lot of traction in current word. All big companies want to be part of this process. All kind of sensors are installed in these vehicles. These sensors generate Terabytes of data and computing these data to run vehicle smoothly. Connected car or IOT based devices are completely based on computing power and quick response.

Sending data and computing these data in cloud could be catastrophic. Any network latency and processing delay might end with bad result. For example, your automated car is traversing through busy street. Suddenly a person comes in front of automated car. In this scenario, any network latency, slowness of computation and analysis effects the decision and subsequent action (Apply brake on car).

In IOT based device, any computing near to IOT device can reduce this risk. So how can we make this happen, if your all computing power are in cloud and data is in cloud.

Fog Computing & Edge computing

FOG Computing–In context of IOT, if intelligence pushes d down to the local area network (LAN) and compute these data in IOT gateway or FOG node will reduce network latency risk. Fogging or FogNetwork is decentralized computing and stores data in most logical and efficient place between IOT device and the cloud.

In FOG computing, data transported from IOT to Cloud need many steps.

  1. Signals from IOT is transported through wire to I/O point of device programmable automation controller(PLC). PLC execute control system program to automate system.
  2. Control system program sends data to protocol gateway, which convert this data into a protocol, understand internet systems such as MQTT or HTTP.
  3. At the end, data is send to fog node or IOT gateway on the LAN, which collects the data and preform analysis and computing on data. This even stores the data to transfer further to cloud network for later processing and intelligence.

Edge Computing — Edge computing refers to any computing infrastructure near to source of data (i.e. IOT device). So, Making IOT device smart and intelligent enough to take decision near to data gateway. The role of edge computing is to process data, store data in local device and transfer data to fog or cloud network. Above all processes are automated through PAC (Programmable automation controller) by executing board controlled system program. In edge computing, intelligence literally push to edge of network where our IOT device and outside network first connect to each other.