AI wireless network

Artificial Intelligence (AI) technology is poised to impact business and personal communications and transactions almost universally in the coming years and beyond, and at no point is it’s value more apparent than with the potential of an AI wireless network. AI can provide neural capacity to networks that will streamline operations, troubleshoot issues, and transform the user experience.

Wireless network user needs are rapidly changing with the expansion of big data analytics, network capacity, and open source technologies. Traditional wireless operation and management processes are becoming obsolete. This is happening for three reasons. First, Wifi has become the primary internet access technology, and as such, it must be predictable, reliable, and measurable. Second, mobile users increasingly rely on personalized wireless services based on contextual data such as location services. Third, managed cloud services are becoming standard for core business practices, including HR, finance, and sales.

This has optimally positioned the industry for the inclusion of AI wireless network integration points. The following strategy elements must be considered to meet existing and future user needs.

Data Collection

AI sustainability relies on access to enormous amounts of relevant data. In fact, it builds its intellectual capacity through data collection and analysis, so the more data available, both relevant and diverse, the more quickly it can grow. Data from the WiFi/BLE domain is sent ot the cloud, where it is analyzed by AI algorithms.

Contextual Services

To identify and monitor data trends, metadata from BLE and mobile apps must be available, including client behavior, location, from various device types, operating systems, applications, and beyond. This enables contextual services within AI wireless network platforms.

Domain-specific Design Intent Metrics

Using design intent metrics, or specific data categories that organize and monitor wireless user experience, AI can enable problem solving across IT, healthcare, entertainment and beyond. The AI model is trained using small segments of problems via domain-specific knowledge.

Data Science Toolbox

The above mentioned domain-specific metadata is next fed into larger neural networks and unsupervised machine learning environments in order to achieve actionable insight.

Security Anomaly Detection

A powerful tool in the AI arsenal is the ability to detect unexpected network activity that could potentially be an existing or even a day-zero threat. This capacity includes the use of location technology to locate accidental and malicious rogue devices.

Virtual Wireless Assistant

With the ability to enable collaborative filtering in AI, such as that utilized by Google ads or Amazon recommendations, large data sets can be sorted and turned into meaningful action and knowledge. In other words, it can function as a virtual wireless expert on any subject, however complex.

The potential to enable predictable, reliable, and measurable wireless operations through AI wireless network integration is both a simple and cost-effective option toward available to wireless users.

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