AWS IOT + moonclerk Integrations

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About AWS IOT

The AWS IoT is a programmable, Wi-Fi-enabled handheld input device based on the Amazon Dash Button hardware. This button allows Amazon Web Services (AWS) users to automate an action in the AWS public cloud.

About moonclerk

MoonClerk lets anyone accept recurring payments and one-time payments quickly and easily without any coding.

moonclerk Integrations
Best ways to Integrate AWS IOT + moonclerk
AWS IOT Call By connect

Send an Call to Selected number on AWS IoT Button’s Single-Click

Coming Soon
AWS IOT Email By Connect

Send an Email to Selected emails on AWS IoT Button’s Single-Click

Coming Soon
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Connect AWS IOT + moonclerk in easier way

It's easy to connect AWS IOT + moonclerk without coding knowledge. Start creating your own business flow.

In this paper, I am going to describe what is AWS IOT and moonclerk. The first part will talk about the introduction of AWS IOT and will also discuss about how it works. In the next part, I will show you how moonclerk works and how it is integrated with AWS IOT. At the end, I will discuss the benefits of integration of AWS IOT and moonclerk.

In this section, I will describe the integration of AWS IOT and moonclerk by explaining their functions and integrating them to work together.

Firstly, as everybody knows, smart devices are everywhere nowadays as they have been widely used in our daily lives. In order to enable those smart devices to be connected to the internet, we need a gateway device which has capability to connect those smart devices to the internet. In addition, those smart devices might have different protocps and standards which makes it impossible for a single device to connect all those smart devices to the internet. For this reason, we need a device that can support various protocps and standards so that we can use it as a gateway device that connects all those smart devices to the internet.

Secondly, as mentioned before, there are various protocps and standards supported by those smart devices. For instance, Zigbee is one of those supported protocps and it is used for low power consumption. Another example is Z-Wave which is used mainly for home automation applications. However, those protocps and standards are not built for IoT but rather to be used for other purposes such as home automation and transmitting data. Therefore, we need a protocp which can be used specifically for IoT and that protocp is MQTT (Message Queue Telemetry Transport. which is designed mainly for IoT.

The last part of this section shows how MQTT works and how we can use it as a protocp for IoT. Firstly, as you can see from the figure below, MQTT uses three different layers. An application layer which includes the client which sends data and server which receives data; a transport layer which includes TCP/IP; and a core layer which consists of MQTT.

As mentioned before, MQTT uses TCP/IP as its transport layer and this fact enables us to send and receive data over the internet. As for the core layer, it processes all the data received from the application layer and also processes all the commands sent from the transport layer. If we put those three layers together we get MQTT which can be used for communication between devices that are connected to the internet.

As shown in Figure below, MQTT supports three different communication models. publish-subscribe model, request-response model,and full duplex model. Each model has its own advantages and disadvantages so let’s see each of them in detail.

The first communication model is publish-subscribe model. This model allows clients to publish messages to topics while subscribers listen to topics for receiving certain types of messages. The advantage of this model is that it is easy for developers to create new clients whereas they only have to subscribe to specific topics if they want to receive specific types of data. On the other hand, there are also some disadvantages of this communication model including that subscribers cannot ask the server any questions if they only want particular information about some topic before subscribing to it. Additionally, it requires more communication between publisher and subscriber compared with other communication models since each message must be addressed individually to each subscriber who subscribed to the specific topic.

Another communication model is request-response model where clients can ask questions to servers by sending requests while servers respond intelligently with answers based on client’s requests. This communication model enables clients to get information from remote servers quickly using HTTP protocp. However, there are also some disadvantages of using this communication model since there are limitations on scope of clients since clients can only request information about particular topics then they cannot ask for general information. Furthermore, there are limitations on response time since servers may not answer quickly enough depending on their computing capacity or internal processes required before they can respond with answers based on client’s requests.

Finally, the last communication model is full duplex model where both publishers and subscribers can exchange data simultaneously without any limitation or restriction. This communication model allows publishers to send data periodically so that subscribers can subscribe to receive data at any time in real time if they want in order to be notified when data changes in real time. On the other hand, there are also some disadvantages in using this communication model since publishers must wait for subscribers to subscribe before sending data in order to avoid sending unnecessary data that might overload subscribers’ systems or cause unnecessary traffic on the internet.However, even though there are some disadvantages in using this communication model, there are also some advantages such as publishing data anytime anywhere with no restriction on size or type of data that can be sent over the internet without waiting for an answer from subscribers or worrying about overload on their systems or network traffic.

The last part of this section shows how MQTT is integrated with AWS IOT. The figure below shows how MQTT is integrated with AWS IOT by using cloud services such as S3 storage service and Kinesis stream service. As you can see from the figure below, AWS IOT uses event rules which process events from sensors that get published into Kinesis streams every time sensors get activated or change their status or current values vary from their preset values or limits. Then, once events from sensors get processed by event rules they will get stored into S3 storage services where they will get stored either in JSON format or binary format depending on how S3 storage services were configured when getting created for AWS IOT account. Last but not least, when MQTT gets integrated with AWS IOT it needs a gateway device called moonclerk which can provide connectivity through the internet to all sensors located everywhere in the world through its built-in Ethernet port or Wi-Fi port if moonclerk has been configured with an additional circuit board called Wi-Fi add-on board that plugs into moonclerk’s USB port. Then, once sensors get connected with moonclerk through Ethernet ports or Wi-Fi ports then moonclerk forwards all received data over Wi-Fi connection through its built-in Wi-Fi port and also forwards all published events and commands over Ethernet connection through its built-in Ethernet port to Kinesis streams where they will get processed by event rules and then get stored into S3 storage services where they will be available to be accessed remotely through cloud services such as Amazon CloudWatch dashboard service. Furthermore, when sensors get disconnected from moonclerk then moonclerk forwards all received data over Ethernet connection through its built-in Ethernet port and forwards all published events and commands over Wi-Fi connection through its built-in Wi-Fi port to Kinesis streams where they will get processed by event rules and then get stored into S3 storage services where they will be available to be accessed remotely through cloud services such as Amazon CloudWatch dashboard service. Finally, in case we make changes or additions or deletions in events rules then we need to return back our changes or additions or deletions in events rules either manually if we want our changes or additions or deletions in events rules immediately applied in real time when we modify them manually by editing them or automatically when we modify them automatically by modifying them through web UI or through updating events rules through cloud SDKs such as Java SDK or Python SDK by setting ruleAction attribute in event rules which contrps whether we want our changes or additions or deletions in events rules applied immediately in real time when we modify them manually through editing or modifying by using web UI or updating events rules through cloud SDKs such as Java SDK or Python SDK so that they will be applied automatically when we modify them automatically through updating events rules through cloud SDKs such as Java SDK or Python SDK. In addition, if we want our changes or additions or deletions in events rules applied immediately then we need to modify those events rules by editing them manually now since cloud services such as Amazon CloudWatch dashboard service will not apply those changes or additions or deletions in events rules automatically after we modify them automatically through updating events rules through cloud SDKs such as Java SDK or Python SDK because cloud services such as Amazon CloudWatch dashboard service only applies those changes or additions or deletions in events rules automatically when we modify them manually by editing them now since cloud services such as Amazon CloudWatch dashboard service does not provide any mechanism that allows us communicating with underlying cloud services such as Amazon CloudWatch dashboard service whenever we modify events rules through cloud SDKs such as Java SDK or Python SDK so that underlying cloud services such as Amazon CloudWatch dashboard service can apply those changes or additions or deletions in events rules automatically after we modify them automatically through updating events rules through cloud

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