6 AI Elements You Need for a Wireless Network Strategy

Thanks to advances in artificial intelligence (AI), organizations can transform their wireless networks with predictable, reliable, and measurable WiFi.

Today, artificial intelligence is all the rage around the world. It is widely believed that artificial intelligence will be the next industry-disrupting technology; in the next few years, artificial intelligence will affect every aspect of our lives, including transportation, healthcare, and financial services. According to market research firm Gartner, by 2020, artificial intelligence will be prevalent in almost all new software products and services, and this technology will become one of the top five investment priorities for more than 30% of CIOs.

One area where AI is showing great value is in wireless networks. Using machine learning, WLANs can be transformed into neural networks, streamlining operations, speeding up troubleshooting, and providing unprecedented visibility into the user experience.

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However, we are only at the beginning of AI applications in wireless networking. On the horizon is a true virtual wireless assistant that can proactively identify and resolve issues and predict future events quickly and reliably.

Artificial intelligence has been studied in research labs and universities for years, but only recently, thanks to advances in computing power, big data, and open source technology, has the technology proven itself in real-world applications.

It makes sense for CIOs to use artificial intelligence in their wireless strategies. Wireless networking is at a turning point, and the traditional way of deploying, operating and managing WiFi networks is no longer sufficient. Three fundamental market shifts in wireless networking also make AI indispensable.

First, WiFi is increasingly becoming the primary Internet access technology. It is more important than ever, so it must be more predictable, reliable, and measurable. At the same time, given the large number of mobile device types, applications, and operating systems, coupled with the large number of mobile users and wireless IoT devices, wireless network troubleshooting is more difficult than ever. This shift requires better understanding of the end-to-end experience of mobile users and new automated management tools to replace manual, tedious tasks through automation and programmability.

Second, mobile users are increasingly accustomed to personalized wireless services on their mobile devices that leverage relevant information such as location. Enterprises see location as a key way to bring value to business operations through new insights into mobile user behavior.

Third, enterprises are moving IT that supports sales, HR, and finance to managed cloud services to improve efficiency and better align internal IT skills with the core business. Even security, storage, and other critical infrastructure elements are moving rapidly to the cloud. However, wireless networks have been slow to make this transition, with more than 90% of the WLAN market still delivered through local controllers. Moving wireless networks to the cloud provides CIOs with a more scalable and resilient infrastructure that is easy to operate and can provide specific actions based on the data flowing through the wireless network.

Without the right wireless AI strategy, IT will be unable to meet the stringent demands of today’s wireless users. Here are six technical elements that this strategy should include.

1. Gathering Data for Insight

Just as all the best wines start with grapes, any meaningful AI solution starts with a large amount of high-quality data. AI continuously gains intelligence through data collection and analysis, and the more data it collects, the smarter it becomes. Therefore, it is critical to be able to collect data in the Wi-Fi/BLE domain from each device in real time and then send this information to the cloud where AI algorithms can analyze it immediately.

2. Contextual Services

Enterprises that adopt BLE and mobile applications in their wireless network strategies will also obtain data from mobile devices to provide high-precision location services to enable contextual services. They need to be able to aggregate global metadata. That is, not only collect data to gain insights into specific customer behaviors and location information; but also gain insights and analysis on device types, operating systems, applications, etc. This is critical for benchmarking and monitoring trends, and to detect macro issues early so that they can be proactively addressed.

3. Domain-Specific Design Intent Indicators

Whether trying to build a system that can play Jeopardy, help doctors diagnose cancer, or assist IT administrators in diagnosing wireless problems, AI solutions require labeled data based on specific domain knowledge to break down the problem into small parts that can be used to train AI models. This can be achieved by using design intent indicators, which are structured data categories used to classify and monitor wireless user experience.

4. Data Science Toolbox

After the problem is divided into domain-specific metadata chunks, it will be introduced to the field of machine learning and big data. Various techniques such as supervised/unsupervised machine learning and neural networks should be used to analyze the data and provide specific actions.

5. Security Anomaly Detection

By detecting abnormal network activity at every level in the network, the AI ​​platform can accurately detect existing and initial threats. In addition, positioning technology can be used to accurately locate unauthorized devices, whether accidental or malicious, and provide location resource access.

6. Virtual Wireless Assistant

Most people have experienced collaborative filtering when they choose a movie on Netflix or when they buy something from Amazon and receive recommendations for other similar movies or items. In addition to recommendations, collaborative filtering can also be used to classify large amounts of data and apply it to AI solutions.

In wireless networks, this approach can be used to transform all data and analysis into meaningful solutions or actions, similar to a virtual wireless expert, to help solve complex problems.

Imagine a virtual wireless assistant that combines high-quality data, domain expertise, and grammar (metrics, classifications, root causes, correlations, rankings, etc.) to provide predictive recommendations on how to avoid related problems and specific courses of action on how to resolve existing problems. A system that learns the nuances of wireless networks and can answer questions like "What went wrong?" and "Why did it happen?" AI makes this possible.

Thanks to advances in artificial intelligence (AI), businesses of all types can transform their wireless networks with predictable, reliable and measurable WiFi, simple and cost-effective wireless operations, and location-based services that deliver amazing new wireless experiences.