Extracting Client Presence Cycles from Access Point Measurements
AssigneeHewlett Packard Enterprise Development LP
InventorsAtaur Rehman, Manaswini Lakshmikanth Sugatoor
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Abstract
Access points in different areas of a site detect client devices present in their areas. To be detected, the clients need only be enabled for a wireless technology (for example, Wi-Fi); they need not activate location services or any specific application. A network management interface may control the access points to monitor the number of clients in different areas at different times and store the results to a database. Algorithms may operate on the stored data to discover predictable cycles of client presence such as working and nonworking days or peak and nonpeak hours.
Claims20
A client presence monitoring system, comprising:
- a first access point to sense a presence of all clients using a wireless communication technology in a first area;
- a persistent data structure containing stored presence data from a plurality of clients at a plurality of times;
- and
- a network management interface to collect incoming presence data sensed by the first access point and store part of the incoming presence data in the persistent data structure;
- a processor to analyze the incoming presence data, the stored presence data, or both to extract periodic cycles of client presence over time.
A method of determining client presence cycles, comprising:
- in response to a manual command or timer event, collecting identifiers of all clients sensed by an access point;
- saving the identifiers with a timestamp in a data structure;
- repeating the collecting and saving for a predetermined length of time to create a data set in the data structure;
- detecting periodic components in the data set;
- deriving true periods of the periodic components;
- removing outliers from the data set to isolate most common behavior;
- and
- characterizing the skewness and kurtosis of peaks in the most common behavior.
A non-transitory machine-readable storage medium containing instructions that, when executed, cause a machine to perform actions comprising:
- collecting time-stamped data on client presence in an area via an access point located in the area;
- storing the time-stamped data in a persistent data structure;
- detecting when the persistent data structure contains data representing a threshold length of time;
- transforming the time-stamped data into a periodogram;
- autocorrelating the periodogram;
- inverse-transforming the periodogram back to the time domain;
- baselining the data to remove outliers;
- and
- analyzing skewness and kurtosis.
Dependencies are inferred from the claim text.