1. A mobile device comprising:
one or more non-transitory computer-readable media having instructions; and
one or more processors coupled with the one or more non-transitory computer-readable media and configured to execute the instructions to cause the mobile device to:
send a non-access stratum (NAS) signaling message to a network controller, the NAS signaling message to include a location update request, a tracking area update request, or a service request;
determine whether the NAS signaling message had a low-priority indicator; and
determine whether to start a backoff timer with an extended wait time (EWT) value based on whether the NAS signaling message had a low-priority indicator, wherein said determine whether to start the backoff timer includes start the backoff timer with the EWT value upon a determination that the NAS signaling message did include a low-priority indicator or ignore the EWT value upon a determination that the NAS signaling message did not include a low-priority indicator.
2. The mobile device of claim 1, wherein the one or more processors are further configured to execute the instructions to:
receive the EWT value in a radio resource control (RRC) reject message.
3. The mobile device of claim 1, wherein the one or more processors are further configured to execute the instructions to:
receive the EWT value in a radio resource control (RRC) connection release message.
4. The mobile device of claim 3, wherein the one or more processors are further configured to execute the instructions to:
receive the EWT value from lower layers.
5. The mobile device of claim 4, wherein the one or more processors are further configured to execute the instructions to:
determine, upon receipt of the EWT value, whether a back-off timer associated with the mobile device is running; and
determine whether to start the backoff timer with the received EWT value based at least in part on the determination of whether the back-off timer is running.
6. The mobile device of claim 5, wherein the mobile device comprises a machine type communication (MTC) device.
7. An mobile device comprising:
one or more non-transitory computer-readable media having instructions; and
one or more processors coupled with the one or more non-transitory computer-readable media and configured to execute the instructions to cause the mobile device to:
process a message, received from a network controller, that instructs the mobile device to release a connection, the message to include an extended wait time (EWT) value;
determine whether a procedure is ongoing; and
determine whether to start a backoff timer with the EWT value based on the determination of whether a procedure is ongoing,
wherein the procedure is an attach procedure, a tracking area update procedure, a location update procedure, or a service request procedure; and
wherein the one or more processors are configured to ignore the EWT value upon a determination that the EWT value was received when the procedure is not ongoing.
8. The mobile device of claim 7, wherein the procedure is a location update procedure or a service request procedure.
9. The mobile device of claim 8, wherein the one or more processors are configured to:
start the backoff timer with the EWT value upon a determination that the EWT value was received when the procedure is not ongoing.
10. The mobile device of claim 9, wherein the EWT value is received from lower layers.
11. The mobile device of claim 10, wherein the EWT value is received for a circuit-switched domain.
12. The mobile device of claim 11, wherein the mobile device comprises a mobile station having a touchscreen user interface.
13. A computer-implemented method comprising:
sending, by a mobile device, a message to a network controller;
receiving, by the mobile device, an extended wait time value;
determining, by the mobile device, whether the message included a low-priority indicator; and
determining, by the mobile device, whether to start a backoff timer with the received extended wait time value based on the determining of whether the message included a low-priority indicator, wherein said determining whether to start the backoff timer includes starting the backoff timer with the EWT value upon a determination that the NAS signaling message did include a low-priority indicator or ignoring the EWT value upon a determination that the NAS signaling message did not include a low-priority indicator.
14. The method of claim 13, wherein the network controller is associated with a wireless communication network.
15. At least one non-transitory computer-readable storage medium having instructions stored thereon that, when executed on a mobile device, cause the mobile device to:
send a request message to a network controller;
receive, from the network controller, a response message including an extended wait time (EWT) value;
determine whether the request message had a low-priority indicator; and
determine whether to start the backoff timer with the received EWT value based on the determination of whether the request message had a low-priority indictor, wherein said determine whether to start the backoff timer includes start the backoff timer with the EWT value upon a determination that the NAS signaling message did include a low-priority indicator or ignore the EWT value upon a determination that the NAS signaling message did not include a low-priority indicator.
16. The non-transitory computer-readable storage medium of claim 15, wherein the mobile device is a machine-type communication (MTC) device.
17. The non-transitory computer-readable storage medium of claim 16, wherein the network controller is associated with a wireless communication network.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method of characterizing relationships among spatio-temporal events, the method comprising:
receiving information specifying the spatio-temporal events and associated categories from one or more sources; and
building, using a processor, a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets, each of the two or more SL and TL sets defining a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG, the respective (SL,TL)-neighborhood of each of the spatio-temporal events being a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories being a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.
2. The method according to claim 1, wherein, for each of the spatio-temporal boundaries associated with the two or more SL and TL sets, the building the DAG includes considering a maximum number of connections given by:
N
\ue8a0
(
N
–
1
)
2
,
wherein
N is a number of the categories with associated spatio-temporal events within the respective spatio-temporal boundary.
3. The method according to claim 1, wherein the building the DAG, for each of the two or more SL and TL sets, includes beginning with a null set, generating one or more candidate DAGs based on adding one connection, connecting a respective predecessor category associated with predecessor events to a respective successor category associated with successor events, at each iteration, and retaining or discarding the one connection for each of the one or more candidate DAGs based on a pruning process prior to a next iteration.
4. The method according to claim 3, wherein the pruning process includes estimating a statistical significance of the one connection of each of the one or more candidate DAGs.
5. The method according to claim 4, wherein the estimating the statistical significance for each of the one or more candidate DAGs includes counting a number of support events for the respective one connection, the number of support events being a number of the respective successor events which are inside a volume representing the respective predecessor category (SL,TL)-neighborhood, and calculating an expected number of support events in the absence of a relationship between the respective predecessor category and the respective successor category.
6. The method according to claim 5, wherein the estimating the statistical significance for each of the one or more candidate DAGs includes computing a respective P-value based on the respective number of support events and the respective expected number of support events.
7. The method according to claim 5, wherein the calculating the expected number of support events includes estimating a density of the respective successor category.
8. The method according to claim 7, wherein the estimating the density of the respective successor category, for each of the one or more candidate DAGs for each of the two or more SL and TL sets, is done within a sub-region corresponding with an area within a total area for which the information is available.
9. A system to characterize relationships among spatio-temporal events, the system comprising:
an input interface configured to receive information specifying the spatio-temporal events and associated categories from one or more sources; and
a processor configured to build a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets, each of the two or more SL and TL sets defining a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG, the respective (SL,TL)-neighborhood of each of the spatio-temporal events being a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories being a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.
10. The system according to claim 9, wherein, for each of the spatio-temporal boundaries associated with the two or more SL and TL sets, the DAG includes a maximum number of connections given by:
N
\ue8a0
(
N
–
1
)
2
,
wherein
N is a number of the categories with associated spatio-temporal events within the respective spatio-temporal boundary.
11. The system according to claim 9, wherein, for each of the two or more SL and TL sets, the processor begins with a null set, generates one or more candidate DAGs based on adding one connection, connecting a respective predecessor category associated with predecessor events to a respective successor category associated with successor events, at each iteration, and retains or discards the one connection for each of the one or more candidate DAGs based on estimating a statistical significance of the one connection for each of the one or more candidate DAGs prior to a next iteration.
12. The system according to claim 11, wherein the processor estimates the statistical significance based on a count of a number of support events for the respective one connection, the number of support events being a number of the respective successor events which are inside a volume representing the respective predecessor category (SL,TL)-neighborhood, and a calculation of an expected number of support events in the absence of a relationship between the respective predecessor category and the respective successor category.
13. The system according to claim 12, wherein the processor estimates the statistical significance for each of the one or more candidate DAGs based on a computation of a respective P-value based on the respective number of support events and the respective expected number of support events.
14. The system according to claim 12, wherein the processor calculates the expected number of support events based on estimating a density of the respective successor category.
15. The system according to claim 14, wherein the processor estimates the density of the respective successor category for each of the one or more candidate DAGs for each of the two or more SL and TL sets within a sub-region corresponding with an area within a total area for which the information is available.
16. A computer program product comprising instructions that, when processed by a processor, cause the processor to implement a method of characterizing relationships among spatio-temporal events, the method comprising:
obtaining, from one or more sources, information specifying the spatio-temporal events and associated categories; and
building a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets, each of the two or more SL and TL sets defining a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG, the respective (SL,TL)-neighborhood of each of the spatio-temporal events being a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories being a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.
17. The computer program product of claim 16, wherein, for each of the spatio-temporal boundaries associated with the two or more SL and TL sets, the building the DAG includes considering a maximum number of connections given by:
N
\ue8a0
(
N
–
1
)
2
,
wherein
N is a number of the categories with associated spatio-temporal events within the respective spatio-temporal boundary.
18. The computer program product according to claim 16, wherein the building the DAG, for each of the two or more SL and TL sets, includes beginning with a null set, generating one or more candidate DAGs based on adding one connection, connecting a respective predecessor category associated with predecessor events to a respective successor category associated with successor events, at each iteration, and retaining or discarding the one connection for each of the one or more candidate DAGs based on a pruning process prior to a next iteration.
19. The computer program product according to claim 18, wherein the pruning process includes estimating a statistical significance of the one connection of each of the one or more candidate DAGs, the estimating the statistical significance for each of the one or more candidate DAGs including counting a number of support events for the respective one connection, the number of support events being a number of the respective successor events which are inside a volume representing the respective predecessor category (SL,TL)-neighborhood, and calculating an expected number of support events in the absence of a relationship between the respective predecessor category and the respective successor category.
20. The computer program product according to claim 19, wherein the calculating the expected number of support events includes estimating a density of the respective successor category, the estimating the density of the respective successor category, for each of the one or more candidate DAGs for each of the two or more SL and TL sets, being done within a sub-region corresponding with an area within a total area for which the information is available.