Mist Systems

Equipment Overview

[Prior to 2020]

The basic features and capabilities of commercial-grade wireless LAN controller software, including the Mist cloud-based wireless LAN controller system, are generally consistent between all the major wireless network manufacturers in the market. Mist Systems stands alone in that it has taken the first major step beyond the conventional cloud-controller model by integrating a true artificial intelligence (A.I.) engine into their cloud platform. The Mist Systems self-learning A.I. model is embodied in a cloud-based software suite called "Marvis" which uses machine learning algorithms to adapt in real-time to changes in user or mobile application behavior and 802.11-related characteristics of the WLAN plus providing a natural-language query system for the WLAN network administrator to ask questions and get information and recommendations about performance and problems.

All Mist access points not only have internal 802.11 WiFi radios but also have a Bluetooth transceiver radio built in as well. All Mist access point provide complete support for Bluetooth Low Energy (BLE) implementations which are built in to all Android and iOS mobile devices. This Mist capability, called "vBLE" (Virtual Bluetooth Low Energy) eliminates the requirements for installation of hardware BLE beacon transmitters throughout a site as is normally associated with BLE deployment and implementation. BLE-enabled sites provide support for BLE applications on mobile devices that enhance the user experience through push notifications (in retail, for example) or turn-by-turn directions to a location within a corporate campus or large office or medical facility. 

The Marvis A.I. engine uses BLE location tracking data to determine user movement trends and per AP user density allowing real-time, proactive and automatic WLAN reconfiguration as necessary to accommodate predicted future trends in user and application behavior as well as alerting and notification when issues are anticipated. Unlike a conventional wireless LAN cloud controller that can only detect threshold-related problems after they occur, Mist Marvis in harmony with BLE, can predict mobile device problems before they occur ensuring predictable and reliable WiFi and a consistent user experience.

The Mist Systems engineering team at Connect802 has completed the Mist factory authorized training program and have obtained their "Mist Master" certification. This means that your questions about the Mist Learning WiFi System, Marvis Artificial Intelligence, Mist Systems 802.11 and Bluetooth Low Energy (BLE) access points, plus use cases and deployment, can be answered in a way that makes sense and provides a complete perspective. Please get in touch with Connect802 to discuss Mist Systems and schedule a live demonstration of the Mist Learning WiFi System cloud user interface and its artificial intelligence features.

Using an interface that is like a chat window, the Mist Virtual Network Assistant allows you to make natural language queries ("NLP" - Natural Language Processing) and interact with the artificial intelligence engine. You ask a question, the Virtual Network Assistant responds with an answer or with a request for additional details.

This aspect of the Mist Learning WLAN system provides statistics, reports, and monitoring results for all aspects of the WLAN. If you’re charged with meeting the requirements of a particular Service Level Agreement, the WiFi Assurance Service is your tool to confirm that the service level expectations are being met (or, if they’re not, the degree to which they have fallen short).

Users with mobile devices can have location-based information pushed to their handheld device. This could be turn-by-turn directions in a building to reach a particular office, a retail floorplan with the location of a desired product or area indicated (again, with turn-by-turn directions provided).

Immediately locate high value resources with a graphic push-pin shown on a floorplan image. The could range from a portable X-ray machine in a hospital to a golf cart on the golf course.

The scanning mode of the Mist access points continuously monitors the airspace for threats and interference and sends alerts when an abnormal behavior or condition is observed. Dual-band RF scanning provides automatic optimization of the network configuration based on real-time user behavior, device density, RF noise and interference, and application requirements.

The Mist platform captures 802.11 data, management, and control packets (including TCP/IP protocols and data) and streams them to the cloud when major WLAN performance or failure issues are detected. This eliminates the problem that occurs when a technician tries to troubleshoot a problem after-the-fact and the problem is either not reproducible or it occurs entirely randomly at uncertain intervals.

A dedicated dual-band radio collects data for Mist’s patent-pending Proactive Analytics and Correlation Engine (PACE) which leverages machine learning with an artificial intelligence engine to understand, track, and analyze the user experience. Problem and performance issues are correlated, and the root cause of a problem is automatically detected. These metrics are used to monitor service level expectations and provide proactive recommendations in a plain English format. No more trying to guess as to what network statistic or measurement is, or isn’t, significant and in need for further analysis. No more trying to isolate a specific network characteristic or reported metric to find the one that is related to a problem or performance issue.

The Mist platform has a unique Proactive Analytics and Correlation Engine (PACE), which provides the foundation for artificial intelligence (A.I.) data collection and analysis in the WiFi / BLE domain. PACE collects over 100 connection-related user and location states in near real-time from every wireless device. This state information is sent to the Mist cloud, where machine learning artificial intelligence algorithms are used for real-time analysis. In addition, the Mist environment uses design intent metrics to classify and monitor the wireless user experience using artificial intelligence (A.I.). For example, Mist lets you set, monitor, and enforce your own Service Level Expectations (SLE) for various key WiFi metrics such as “Time to Connect”, “Successful Connections”, “Throughput”, “Coverage”, “Capacity”, “Roaming”, and “AP Uptime”. These are then used to quantify the WiFi performance of clients, Access Points, and entire locations. 

For example, you can define a throughput SLE of 30 Mbps for all users in your main campus. PACE will tell you exactly what percentage of the time this SLE is being hit, which users are not getting this level of service, and which device types/operating systems/ applications are consistently causing problems. In addition, it can predict if this SLE will be achieved in the future based on current conditions. Mist’s architecture allows for the capacity and performance to aggregate global metadata across customers. 

Not only is Mist capable of collecting data for insight into a specific client behavior and location information, it can provide insights and analytics across device types, operating systems, applications, and more. This is key for baselining and monitoring trends, and identifying macro issues early so they can be addressed proactively. For example, client roaming time, hardware radio performance and device throughput can all be analyzed to identify global issues, such as a performance degradation when a new client operating system version is released.