Home Telecoms 6 Key AI Features of a Wireless Networking Strategy

6 Key AI Features of a Wireless Networking Strategy

With artificial intelligence being a huge buzz nowadays, there appears to be a consensus that the technology could influence almost every aspect of life in the near future. In fact, Gartner forecasted that by 2020, artificial intelligence present in virtually every new software service and product. The prediction also stated that artificial intelligence could be a top-five investment priority for over 30 % of CIOs.

AI is also displaying great value in wireless networking. Machine learning can convert WLANs into neural networks, which not only simplify operations but also accelerate troubleshooting and offer exceptional visibility into the experience of the users. CIOs ought to consider embracing AI in their wireless plans since the conventional way of installing, managing and operating WiFi networks will no longer be sufficient.

IT may fail to keep up with the latest strict wireless user needs, especially without the ideal wireless AI plan. Here are six technology features such a strategy ought to have:

1. Contextual Services

Businesses that are incorporating mobile and BLE apps into their wireless s plan are also bringing data obtained from mobile gadgets to provide on high-accuracy location services in a bid to facilitate contextual services. However, they require aggregating all the global metadata across their customers. This process entails gathering data for ideas on specific customer behaviors and location information as well as obtaining analytics and insights across applications, operating systems and device types among others.

2. Data Science Toolbox

With the problem being subdivided into domain-specific portions of metadata, the metadata is currently ready to be applied to the big data and machine learning world. Various methods including neural networks and unsupervised/ supervised machine learning ought to be used to provide actionable insight and analyze data.

3. Domain-specific Design Intent Metrics

AI solutions require labeled data, which is based on domain-specific knowledge in a bid to break down the problem into pieces that can be used in training the AI models. Utilizing design intent metrics can assist in achieving this task.

4. Virtual Wireless Assistant

Many people experience collaborative filtering when they purchase an item on Amazon or select a movie on Netflix and, in turn, get recommendations for other alike items or movies. Aside from such recommendations, collaborative filtering also comes in handy when sorting through large volumes of data. It helps to put a face to artificial intelligence (AI) solution.

5. Security Irregularity Detection

An AI-powered platform can accurately spot day-zero and existing threats by identifying unusual network activities at each level of the network. Also, location technology can be used to find malicious devices.

6. The Potential to Gather Data for Insight

All meaningful AI solutions should begin with large volumes of quality data. AI technology builds its intelligence constantly by collecting and analyzing data. This means that the more diverse the data, the more the technology becomes smarter or knowledgeable. Hence, collecting data in the BLE/WiFi domain from each device in real-time is vital. Once such data is sent to the cloud, AI algorithms can be able to analyze it instantly.

Source ITProPortal


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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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