At&t Mobility Ii LLC

Johnathan Bradley Webb

Stephen Thomas Hardin

Charles Carey

Thomas Becker

Jordan Alexander

Zachary Orlynn Morsbach

Internet-of-things (IOT) devices can be identified based on specific behavioral patterns when their identification data is unknown. Previously identified IOT devices with similar behavioral patterns can be used as a baseline from which to compare data that is available about unknown IOT devices. For example, an IOT device can be pooled with a group of IOT devices based on the frequency with which they connect to a wireless network. Additionally, a confidence level of the unknown device being associated with the group of IOT devices can be generated based on such comparison data.

Facilitation of predictive internet-of-things device identification

The disclosed subject matter provides for traffic analysis employing timed fingerprint location information. In an aspect, TFL information can be associated with location characteristics for a UE. These location characteristics can describe the motion of the UE. As such, with proper analysis, the motion of the UE can be correlated to traffic patterns. Transportation analytics can employ TFL information to provide real time or near real time traffic information, forecast traffic conditions, or automate response to traffic conditions based on TFL information. Whereas TFL can provide advantages over other types of location information systems, leveraging TFL information in traffic analysis can reflect these advantages. Further, whereas TFL information can be gathered from nearly all modern and many legacy mobile devices, large volumes of TFL information can be employed in related transportation analytics.

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Katy, TX, United States of America

Johnathan Bradley Webb