Analysis of the six technical elements that should be included in the wireless network AI strategy

Thanks to the development of artificial intelligence (AI), organizations can retrofit their wireless networks with predictable, reliable and measurable WiFi.

Today, artificial intelligence is popular all over the world. It is widely believed that artificial intelligence will become the next technology to subvert the industry.

In the coming years, artificial intelligence will affect every aspect of our lives, including transportation, medical care and financial services.

According to market research firm Gartner, by 2020, artificial intelligence will be popularized in almost all new software products and services, and this technology will become one of the top five CIO investment priorities of more than 30%.

Among them, one area where AI shows great value is wireless networks. Using machine learning turns WLANs into neural networks, which simplifies operations, speeds troubleshooting, and provides unprecedented visibility into the user experience.

However, we are still only in the early days of AI application in the wireless network field. Coming soon is a true virtual wireless assistant that proactively identifies and resolves problems and predicts future events quickly and reliably.

Research laboratories and universities have been studying artificial intelligence for many years. But until recently, due to advances in computing power, big data, and open source technologies, this technology proved its strength in practical applications.

It is not unreasonable for CIOs to use artificial intelligence in their respective wireless strategies. Wireless networks are at a turning point, and the traditional way of deploying, operating, and managing WiFi networks is no longer sufficient. Three basic market transformation factors in wireless networks also make AI indispensable.

First of all, WiFi has become the main Internet access technology. It's more important than ever, so it must be more predictable, reliable, and measurable.

At the same time, wireless network troubleshooting is more difficult than ever, given the large number of mobile device types, applications and operating systems, plus a large number of mobile users and wireless IoT devices. This shift requires a better understanding of the end-to-end experience of mobile users and the need for new automated management tools to replace manual and cumbersome tasks with automation and programmability.

Second, mobile users are increasingly accustomed to using wireless devices that are personalized by mobile devices that utilize relevant information such as location. Enterprises view location as a key way to bring value to business operations through a new perspective on mobile user behavior.

Third, companies are moving IT, which supports sales, human resources, and finance, to hosted cloud services to increase efficiency and better align internal IT skills with core business. Even security, storage and other critical infrastructure elements are rapidly moving to the cloud. However, wireless networks are slow in this transition, and more than 90% of the WLAN market is still delivered through local controllers. Moving the wireless network to the cloud provides CIOs with a more scalable and resilient infrastructure that is easy to operate and provides a specific action plan for data flowing through the wireless network.

Without the right wireless network AI strategy, IT can't meet the stringent needs of current wireless network users. The following are the six technical elements that this strategy should include.

01

Insight into data collection

Just as all the best wines start with grapes, any meaningful AI solution starts with a lot of high quality data.

Artificial intelligence continues to gain intelligence through data collection and analysis, and the more data it collects, the more intelligent it becomes. Therefore, it is important to be able to collect data from the Wi-Fi / BLE domain in real time from each device and then send that information to the cloud, which the AI ​​algorithm can analyze immediately.

02

Contextual service

Enterprises adopting BLE and mobile applications in their wireless network strategy will also acquire data from mobile devices to provide high-precision location services for contextual services. They need to be able to aggregate global metadata. In other words, not only to collect data to gain insight into specific customer behavior and location information, but also to gain insights and analysis on device types, operating systems, applications, and more. This is critical for benchmarking and monitoring trends, and early detection of macro issues can be proactively addressed.

03

Design intent indicators for specific areas

Whether trying to build a system that can participate in the American puzzle game Jeopardy, or help doctors diagnose cancer, or assist IT administrators in diagnosing wireless problems, artificial intelligence solutions require tagged data based on domain-specific knowledge to break down the problem into usable Train a small part of the AI ​​model. This can be achieved by using design intent metrics, which are structured data categories used to classify and monitor wireless user experiences.

04

Data science toolbox

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

05

Safety anomaly detection

By detecting anomalous network activity at each level in the network, the AI ​​platform can accurately detect existing threats and initial threats. In addition, location technology can be used to accurately locate accidental or malicious illegal devices and provide access to location resources.

06

Virtual wireless assistant

Most people will experience collaborative filtering when they choose to unveil a movie on Netflix or when they shop from Amazon, they will receive other similar movies or item recommendations. In addition to recommendations, collaborative filtering can also be used to categorize large amounts of data and apply it to AI solutions.

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

Imagine virtual wireless assistants combining high-quality data, domain expertise, and grammar (metrics, classification, roots, associations, rankings, etc.) to provide predictive advice on how to avoid problems and provide specific answers on how to solve existing problems. Action plan. That's a system that can learn the nuances of wireless networks and answer questions like "What's wrong?" and "Why?" AI makes these things a reality.

Thanks to the development of artificial intelligence (AI), companies can retrofit their wireless networks with predictable, reliable and measurable WiFi for simple and cost-effective wireless operation, delivering an amazing new wireless experience. Location service.