With the soaring number of mobile and IoT devices and onslaught of new apps that businesses are faced with on their wireless network, it is time for innovation that can help IT scale and meet these new requirements. Thankfully, AI and modern cloud platforms built with microservices are evolving to meet these needs, and more and more businesses are realizing that AI is a core component to enable a learning and insightful WLAN. AI can help bring efficiency and cost savings to IT through automation while providing deep insights into user experience, or service-level enforcement. It can also enable new location-based services that bring enormous value to businesses and end users.
At the core of a learning WLAN is the AI engine, which provides key automation and insight to deliver services like Wi-Fi assurance, natural language processing-based virtual network assistants, asset location, user engagement and location analytics.
There are four key components to building an AI engine for a WLAN: data, structure and classify, data science and insight. Let’s take a closer look.
Just like wine is only as good as the grapes, the AI engine is only as good as the data gathered from the network, applications, devices and users. To build a great AI platform, you need high-quality data — and a lot of it.
To address this well, one needs to design purpose-built access points that collect pre- and post-connection states from every wireless device. They need to collect both synchronous and asynchronous data. Synchronous data is the typical data you see from other systems, such as network status. Asynchronous data is also critical, as it gives the user state information needed to create user service levels and detect anomalies at the edge.
This information, or metadata, is sent to the cloud, where the AI engine can then structure and classify this data.
Next, the AI engine needs to structure the metadata received from the network elements in a set of AI primitives.
The AI engine must be programmed with wireless network domain knowledge so that the structured metadata can then be classified properly for analysis by the data science toolbox and ultimately deliver insights into the network.
Various AI primitives, structured as metrics and classifiers, are used to track the end-to-end user experience for key areas like time to connect, throughput, coverage, capacity and roaming. By tracking when these elements succeed, fail or start to trend in a direction, and determining the reason why, the AI engine can give the visibility needed to set, monitor and enforce service levels.
Once the data has been collected, measured and classified, the data science can then be applied. This is where the fun begins.
There are a variety of techniques that can be used, including supervised and unsupervised machine learning, data mining, deep learning and mutual information. They are used to perform functions like baselining, anomaly detection, event correlation and predictive recommendations.
For example, time-series data is baselined and used to detect anomalies, which is then combined with event correlation to rapidly determine the root cause of wireless, wired and device issues. By combining these techniques together, network administrators can lower the mean-time-to-repair issues, which saves time and money and maximizes end-user satisfaction.
Mutual information is also applied to Wi-Fi service levels to predict network success. More specifically, unstructured data is taken from the wireless edge and converted into domain-specific metrics, such as time to connect, throughput and roaming. Mutual information is applied to the service-level enforcement metrics to determine which network features are the most likely to cause success or failure as well as the scope of impact.
In addition, unsupervised machine learning can be used for highly accurate indoor location. For received signal strength indicator-based location systems, there is a model needed that maps RSSI to distance, often referred to as the RF path loss model. Typically, this model is learned by manually collecting data known as fingerprinting. But with AI, path loss can be calculated in real time using machine learning by taking RSSI data from directional BLE antenna arrays. The result is highly accurate location data that doesn’t require manual calibration or extensive site surveys.
AI-driven virtual assistants
The final component of the AI engine is a virtual assistant that delivers insights to the IT administrator as well as feeds that insight back into the network itself to automate the correction of issues, ultimately becoming a “self-healing network.”
The use of a natural language processor is critical to simplify the process for administrators to extract insights from the AI engine without needing to hunt through dashboards or common language interpreter commands as legacy systems that lack AI do. This can drive up the productivity of IT teams while delivering a better user experience for employees and customers.
Wireless networks are more business-critical than ever, yet troubleshooting them is more difficult every day due to an increasing number of different devices, operating systems and applications. AI engines are a must-have for businesses that need to keep up with soaring numbers of new devices, things and apps in today’s connected world.
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