SECURE WIFI USING PREDICTIVE ANALYTICS

• US 20170156087A1
• Filed: 02/13/2017
• Published: 06/01/2017
• Est. Priority Date: 07/21/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: GANCI, JR., JOHN M., ROE, JI YOUNG, BOSTICK, JAMES E., STARKS, KIMBERLY G.
• Granted Patent: US 9,860,796 B2
• Time in Patent Office: Days
• CPC Class Codes: H04W 28/18 Negotiating wireless communication parameters
• CPC Class Codes: H04W 36/0022 for transferring data sessions between adjacent core network technologies
• CPC Class Codes: H04W 36/165 for reducing network power consumption H04W36/18 - H04W36/22 take precedence
• CPC Class Codes: H04W 36/26 by agreed or negotiated communication parameters
• CPC Class Codes: H04W 48/17 Selecting a data network PoA [Point of Attachment]
• CPC Class Codes: H04W 84/12 WLAN [Wireless Local Area Networks]
• CPC Class Codes: H04W 88/06 adapted for operation in multiple networks or having at least two operational modes, e.g. multi-mode terminals

Abstract

Aspects automatically direct data requests from a specific application of a mobile programmable device to a designated WiFi network pathway without interaction from a mobile device user in response to determining that an initial data network pathway does not meet a satisfactory service threshold for forwarding the data requests to an event web server. Aspects also predict total attendance within a venue of the event and within a communication range of a designated WiFi service node by device users as function of real-time levels of social media following activity associated with the event and an available venue occupancy, a historical attendance at similar events within similar venues, and a time of the event. Aspects predicting a bandwidth requirement for usage of a WiFi pathway of the WiFi node for requests as a function of the predicted attendance.

Claims

1. A computer-implemented method for determining WiFi network bandwidth needed in support of WiFi pathway service at an event, the method comprising executing on a processor the steps of:

predicting a total attendance within a venue of the event and within a communication range of a designated WiFi service node by users of mobile programmable devices that are each operating a first application, wherein the predicting is a function of a determined real-time level of social media following activity associated with said event and an input that is selected from the group consisting of an available occupancy of the venue, a historical attendance of the users of the units of the mobile programmable device at similar events within similar venues, and a relation of a time of the event to a time period known to have an impact on event attendance relative to other times; and

predicting a bandwidth requirement for usage of a first WiFi pathway of the designated WiFi node for a plurality of requests by the units of the mobile programmable device as a function of the predicted attendance.

2. The method of claim 1, further comprising:

integrating computer-readable program code into a computer system comprising a processor, a computer readable memory in circuit communication with the processor, and a computer readable storage medium in circuit communication with the processor; and

wherein the processor executes program code instructions stored on the computer-readable storage medium via the computer readable memory and thereby performs the steps of predicting the total attendance and predicting the bandwidth requirement.

3. The method of claim 2, wherein the computer-readable program code is provided as a service in a cloud environment.

4. The method of claim 1, further comprising:

predicting data usage of the units of the mobile programmable device of the predicted attendance as a function of historic data usage relevant to the predicted attendance, historic data usage relevant to the venue for the event, and of the determined real-time level of social media following activity associated with said event; and

wherein the step of predicting the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as the function of the predicted attendance is further a function of the predicted data usage of the units of the mobile programmable device of the predicted attendance.

5. The method of claim 4, wherein the step of predicting the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as the function of the predicted attendance is further a function of projected data sizes of the requests.

6. The method of claim 5, wherein the venue spans a geographic space that is larger than the communication range of the designated WiFi service node, and the designated WiFi service node is one of a plurality of WiFi service nodes that are deployed within the venue geographic space and spaced to provide non-overlapping communication ranges that in aggregate provide comprehensive WiFi coverage of the venue, the method further comprising:

dynamically deploying, to meet a real-time determination of an increase in demand for bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of the predicted attendance and as a function of the determined real-time level of social media following activity associated with said event, an additional item that is selected from the group consisting of an additional first WiFi pathway by the WiFi node providing the first WiFi pathway, and an additional WiFi node.

7. The method of claim 6, further comprising:

determining the real-time determination of the increase in demand for the bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the designated WiFi node as a function of a projected percentage of favored requests relative to all requests passed by designated WiFi node, and the projected data sizes of the favored requests.

8. The method of claim 6, further comprising:

determining the relation of the time of the event to the time period known to have the impact on event attendance as whether the time of the event is within the time period known to have the impact on event attendance; and

wherein the time period known to have the impact on event attendance is selected from the group consisting of a business day, a school calendar, and an annual season.

9. A system, comprising:

a processor;

a computer readable memory in circuit communication with the processor; and

a computer-readable storage medium in circuit communication with the processor and the computer readable memory;

wherein the processor, when executing program instructions stored on the computer readable storage medium via the computer readable memory;

predicts a total attendance within a venue of an event and within a communication range of a designated WiFi service node by users of mobile programmable devices that are each operating a first application, wherein the predicting is a function of a determined real-time level of social media following activity associated with said event and an input that is selected from the group consisting of an available occupancy of the venue, a historical attendance of the users of the units of the mobile programmable device at similar events within similar venues, and a relation of a time of the event to a time period known to have an impact on event attendance relative to other times; and

predicts a bandwidth requirement for usage of a first WiFi pathway of the designated WiFi node for a plurality of requests by the units of the mobile programmable device as a function of the predicted attendance.

10. The system of claim 9, wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and thereby further:

predicts data usage of the units of the mobile programmable device of the predicted attendance as a function of historic data usage relevant to the predicted attendance, historic data usage relevant to the venue for the event, and of the determined real-time level of social media following activity associated with said event; and

predicts the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of the predicted attendance and of the predicted data usage of the units of the mobile programmable device of the predicted attendance.

11. The system of claim 10, wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and thereby predicts the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of projected data sizes of the requests.

12. The system of claim 11, wherein the venue spans a geographic space that is larger than the communication range of the designated WiFi service node, and the designated WiFi service node is one of a plurality of WiFi service nodes that are deployed within the venue geographic space and spaced to provide non-overlapping communication ranges that in aggregate provide comprehensive WiFi coverage of the venue;

and

wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and thereby dynamically deploys, to meet a real-time determination of an increase in demand for bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of the predicted attendance and as a function of the determined real-time level of social media following activity associated with said event, an additional item that is selected from the group consisting of an additional first WiFi pathway by the WiFi node providing the first WiFi pathway, and an additional WiFi node.

13. The system of claim 12, wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and thereby determines the real-time determination of the increase in demand for the bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the designated WiFi node as a function of a projected percentage of favored requests relative to all requests passed by designated WiFi node, and the projected data sizes of the favored requests.

14. The system of claim 12, wherein the processor executes the program instructions stored on the computer-readable storage medium via the computer readable memory and thereby further determines the relation of the time of the event to the time period known to have the impact on event attendance as whether the time of the event is within the time period known to have the impact on event attendance;

and

wherein the time period known to have the impact on event attendance is selected from the group consisting of a business day, a school calendar, and an annual season.

15. A computer program product for determining WiFi network bandwidth needed in support of WiFi pathway service at an event, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising instructions that, when executed by a computer processor, cause the computer processor to;

predict a total attendance within a venue of an event and within a communication range of a designated WiFi service node by users of mobile programmable devices that are each operating a first application, wherein the predicting is a function of a determined real-time level of social media following activity associated with said event and an input that is selected from the group consisting of an available occupancy of the venue, a historical attendance of the users of the units of the mobile programmable device at similar events within similar venues, and a relation of a time of the event to a time period known to have an impact on event attendance relative to other times; and

predict a bandwidth requirement for usage of a first WiFi pathway of the designated WiFi node for a plurality of requests by the units of the mobile programmable device as a function of the predicted attendance.

16. The computer program product of claim 15, wherein the computer readable program code instructions, when executed by the computer processor, further cause the computer processor to:

predict data usage of the units of the mobile programmable device of the predicted attendance as a function of historic data usage relevant to the predicted attendance, historic data usage relevant to the venue for the event, and of the determined real-time level of social media following activity associated with said event; and

predict the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of the predicted attendance and of the predicted data usage of the units of the mobile programmable device of the predicted attendance.

17. The computer program product of claim 16, wherein the computer readable program code instructions, when executed by the computer processor, further cause the computer processor to predict the bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of projected data sizes of the requests.

18. The computer program product of claim 17, wherein the venue spans a geographic space that is larger than the communication range of the designated WiFi service node, and the designated WiFi service node is one of a plurality of WiFi service nodes that are deployed within the venue geographic space and spaced to provide non-overlapping communication ranges that in aggregate provide comprehensive WiFi coverage of the venue;

and

wherein the computer readable program code instructions, when executed by the computer processor, further cause the computer processor to dynamically deploy, to meet a real-time determination of an increase in demand for bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the WiFi node for the plurality of the requests by the units of the mobile programmable device as a function of the predicted attendance and as a function of the determined real-time level of social media following activity associated with said event, an additional item that is selected from the group consisting of an additional first WiFi pathway by the WiFi node providing the first WiFi pathway, and an additional WiFi node.

19. The computer program product of claim 18, wherein the computer readable program code instructions, when executed by the computer processor, further cause the computer processor to determine the real-time determination of the increase in demand for the bandwidth for the first WiFi pathway above the predicted bandwidth requirement for usage of the first WiFi pathway of the designated WiFi node as a function of a projected percentage of favored requests relative to all requests passed by designated WiFi node, and the projected data sizes of the favored requests.

20. The computer program product of claim 18, wherein the computer readable program code instructions, when executed by the computer processor, further cause the computer processor to determine the relation of the time of the event to the time period known to have the impact on event attendance as whether the time of the event is within the time period known to have the impact on event attendance;

and

wherein the time period known to have the impact on event attendance is selected from the group consisting of a business day, a school calendar, and an annual season.