Six AI elements required for wireless network strategy and ai elements for wireless network strategy

Six AI elements required for wireless network strategy and ai elements for wireless network strategy

With the development of artificial intelligence (AI), organizations can transform their wireless networks through predictable, reliable, and measurable WiFi.

Today, artificial intelligence is everywhere. It is widely believed that artificial intelligence will become the next technology to subvert the industry. In the next few years, artificial intelligence will affect all aspects of our lives, including transportation, medical care, and financial services. According to Gartner, a market research institute, artificial intelligence will be popularized in almost all new software products and services by 2020, in addition, this technology will become one of the five major investment priorities of over 30% CIOs.

Among them, one of the areas where AI shows great value is wireless networks. Machine Learning allows you to convert a WLAN to a neural network to simplify operations, accelerate troubleshooting, and provide unprecedented visibility into user experience.

However, we are only in the early stages of application of AI In the wireless network field. What is coming soon is a real virtual wireless assistant that can actively identify and solve problems and quickly and reliably predict future events.

For many years, research laboratories and universities have been studying artificial intelligence. However, since the recent advances in computing capabilities, big data, and open-source technologies, this technology has proved its strength in practical applications.

It is not unreasonable for CIOs to use artificial intelligence in their wireless strategies. Wireless networks are at a turning point. The traditional methods of deploying, operating, and managing Wi-Fi networks cannot meet the current requirements. The three fundamental market changes in wireless networks also make AI indispensable.

First, WiFi becomes the main internet access technology. It is more important than ever before, so it must be more predictable, reliable, and measurable. At the same time, in view of a large number of mobile device types, applications and operating systems, coupled with a large number of mobile users and wireless Iot devices, wireless network troubleshooting is more difficult than ever. This change requires a better understanding of the end-to-end experience of mobile users and new automated management tools to replace manual cumbersome tasks with automation and programmability.

Second, mobile users are increasingly accustomed to using personalized wireless services on mobile devices, which take advantage of information such as location. Enterprises regard location as a key way to bring value to business operations through a new perspective of mobile user behavior.

Third, enterprises are moving IT services that support sales, human resources, and finance to hosting cloud services to improve efficiency and ensure better consistency between internal IT skills and core business. Even security, storage, and other key infrastructure elements are rapidly moving to the cloud. However, wireless networks are progressing slowly in this transition, and over 90% of the WLAN market is still delivered through local controllers. Transferring a wireless network to the cloud provides CIOs with a more scalable and elastic infrastructure, which is easy to operate and provides specific action plans through data flowing through the wireless network.

Without the correct wireless network AI strategy, IT cannot meet the strict requirements of current wireless network users. The following are the six major technical elements of this strategy.

I. Data collection insights

Just as all the best wines start with grapes, any meaningful AI solution begins with a large amount of high-quality data. Artificial Intelligence continuously gains intelligence through data collection and analysis. The more data it collects, the more intelligent it becomes. Therefore, it is critical that AI algorithms can immediately analyze data in Wi-Fi/BLE domains from each device and then send the information to the cloud.

Ii. Context Service

Enterprises that adopt BLE and mobile applications in the wireless network strategy will also obtain data from mobile devices to provide high-precision location services for contextual services. They need to be able to aggregate global metadata. That is to say, we should not only collect data to gain insight into specific customer behavior and location information, but also gain insights and analysis on the device type, operating system, and application. This is critical to benchmarking and monitoring trends, and early detection of macro problems can proactively address these problems.

Iii. design intent indicators for specific fields

Whether IT's trying to build a system that can participate in the American intelligence game Jeopardy, help doctors diagnose cancer, or help IT administrators diagnose wireless problems, artificial Intelligence solutions all need to break down the problem into a fraction of the data that can be used to train AI models based on labeled data in specific fields. This can be achieved through the use of design intent indicators, which are structured data categories used to classify and monitor wireless user experience.

Iv. Data Science toolbox

After the problem is divided into metadata blocks in a specific field, it will be introduced into the machine learning and big data fields. Various technologies, such as supervised/unsupervised machine learning and neural networks, should be used for data analysis and specific action plans.

V. Security exception detection

By detecting abnormal network activity at each level in the network, the AI platform can accurately detect existing and initial threats. In addition, location technology can be used to accurately locate unexpected or malicious illegal devices and provide Location Resource Access.

Vi. Virtual wireless Assistant

Most people who choose to unveil a movie on Netflix or shop from Amazon will receive recommendations for other similar movies or items and will experience collaborative filtering. In addition to recommendations, collaborative filtering can also be used to classify a large amount of data and apply it to AI solutions.

In wireless networks, this method can be used to convert all data and analysis into meaningful solutions or actions. Similar to virtual wireless experts, it helps solve complex problems.

Imagine how a virtual wireless assistant combines high-quality data, domain expertise, and syntax (measurement, classification, root cause, association, and ranking) to provide predictive advice on how to avoid problems, and provide specific action plans for solving existing problems. It is a way to learn the nuances of wireless networks and answer questions similar to "What's wrong ?" And "why ?" System. AI has turned all of these into reality.

With the development of artificial intelligence (AI), enterprises can transform their wireless networks through predictable, reliable, and measurable WiFi, it provides simple and cost-effective wireless operations and a location service that provides an amazing new wireless experience.