The IoT not only collects analysis data but also enhances itself

Nowadays it is the information age, and the one who gets the data is the world. However, it is not enough just to have "yes" data. The "accuracy" of data and "analysis" of data are also crucial. Einstein also said: "It's not necessarily useful, but it's not necessarily useful."

"Data" and "information" are not the same thing. "Data" refers to a pile of unprocessed raw measurement results. We must analyze it and take its essence to its dregs in order to obtain useful information. Therefore, we often say that "information overload" is actually wrong, and "data" may be overloaded, but the more "information", the better. The data itself is not necessarily useful because if it is not properly screened, the data may be as false news, and it can lead us astray.

The IoT not only collects analysis data but also enhances itself

Over the past decade, our data volume has exploded. The "New York Times" reported that in 2005 the total amount of global data reached 130 billion GB. Today's companies often deal with the data recorded in PB. With the rapid growth of data sources, the speed of data acquisition is also increasing. The progress of science and technology is so rapid. According to @HistoricalPics Twitter, a 5MB hard drive was over 2,000 pounds in weight in 1956, and IBM used a plane to transport it! Looking down at a large mobile phone, you couldn't help but feel compelled.

With the measurement of people's activities and sensors, the data types are also increasing. And we must remember that the data is only useful after it has been analyzed and turned into information.

The advantage of the Internet of Things is that it can acquire and organize data in real time. If the architecture is correct, the Internet of Things can turn data into useful information to determine what to do next.

KrisTian J. Hammond once said in a Harvard Business Review: "Most of the time, we all know what we want to get from the data: we know what needs to be analyzed, what relevance we need to look for, and how we need to compare. The data can be given to a machine that can do the job, and then let it tell us the results in a human way, using natural language, so that we can quickly and easily extract a lot of useful information from the data - but now We haven't achieved it. By supplementing the power of the machine, we can fully automate the gold rush from the data and let the cold numbers become perceptual cognition.

How to discover the meaning of data?

Before the Internet of Things, it was very difficult to analyze a wide variety of sensor data. Through the Internet of Things technology, we can put the data obtained by the machine into automatic analysis of the data pool to determine what the next step needs to do with the data and the program. The Internet of Things not only collects and analyzes data, it also enhances itself.

Before introducing specific steps, we first clarify two common terms used in the discussion of data transmission: "Northbound" and "Southbound". "Northbound data" refers to data sent from the device through the gateway to the cloud, typically telemetry data, or command and control requests. "Southbound data" is sent from the cloud to the gateway, or from the cloud to the device through the gateway, usually command and control information (such as software updates, request, change configuration parameters, etc.).

The following are the ways to use the north and south travel channels to find useful information from the probe data:

The first step: the sensor sends northbound telemetry data. Depending on the architecture, this data is preprocessed and sent to a data store (such as a gateway) located near the sensor.

Step 2: Perform a certain amount of data analysis on the temporary node of the gateway, where you can process the data (such as summary data, or convert the data to prepare for deep analysis of the data center or cloud). Then, comparing the information processed at the gateway with the previous precise result is to perform correlation matching in the history information. The pattern of discovery can serve as a basis for our actions. But in addition to discovering known patterns, you also want to find something you don't know and want to discover new relevance and conclusions. For example, you may not know that when the temperature falls below 10°C, doctors will increase prescriptions for anti-influenza by 30%, while sales of chicken soup and tissue will also increase within 10 days. You may not have noticed these connections before, but now that you have an Internet of Things, you can use these to make new business decisions.

Step 3: With the new information, you can create a rule. For example, when the sensor finds that the temperature has dropped below 10°C, the warehouse will send chicken soup and paper towels near the dock. In this way, you turn information into rules that can be monitored, managed, and enforced.

Step 4: Finally put the established rules into practice. It is an iterative process as shown.

What is the benefit of open source?

Open source software projects provide standardized toolkits (eg, Camel, Drools) that you can use to manipulate and manipulate data. Apache Camel is a Java-based routing and mediation engine with an enterprise integration model that can handle data. It enables the development of networking solutions through "out-of-the-box" information mediation, routing, and data transformation. I think it's best to use Apache Camel in IoT through Eclipse IoT workgroup projects (like Eclipse Kapua, Kura).

Drools in the JBoss community is a business rules management system with built-in rule templates that you can use to specify what actions should be taken under what circumstances. Drools uses well-defined DSLs (Domain Specific Languages) to implement the rules required for the Internet of Things and the scalability required to optimize the rules engine. It also comes with a GUI called Workbench that allows developers to create and edit rules very simply.

Converting data into useful information is at the core of all IoT work, and this can be achieved through open source software, which helps accelerate the implementation of IoT.

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