Abstract:
The disclosed subject matter provides for fraud analysis for a location aware transaction. In an aspect, location information can be associated with historical fraud events. The location of user equipment can be analyzed against historical fraud information to facilitate determination of a fraud factor value. The fraud factor value can reflect a determination related to the likelihood of fraud occurring in the present transaction based on the historic fraud events at the same or similar location as the present location of the user equipment. The determination can be based on fraud rules. Further aspects provide for abstraction of the fraud factor to limit exposure of personal information associated with wireless carrier subscribers in fraud analysis for a location aware transaction.
Abstract:
Providing for network repair prioritization as a function of impact on network services is described herein. By way of example, impact of a given service outage on surrounding network infrastructure and associated terminals can be analyzed and estimated. The impact can be characterized at least in part by changes in loading to surrounding network equipment, as well as related quality and performance metrics. Network impact estimates and loading changes can be derived through mobile device position data for an impacted area and identifying overlapping coverage areas, and signal characteristics of the surrounding network infrastructure. Measured or predicted changes in network performance can be scored to provide relative priorities for allocating limited service personnel in repairing base station errors. Personnel resources can therefore be deployed in a manner that more accurately reflects customer service goals of a network provider.
Abstract:
The disclosed subject matter provides for employing timed fingerprint location information in location analytics. Timed fingerprint location information can provide a location for a user equipment. The location of the user equipment can be compared to a location analytics rule related to the location of a user equipment. Where the location satisfies a condition of the location analytics rule, the user equipment can be associated with a compliance status. Compliance, or noncompliance, can initiate further action. Further action can include reporting the compliance status, verifying the identity of a user associated with the user equipment, enforcing the location analytics rule, etc. Enforcing the location analytics rule can include alerts, fines, reporting to an authority figure or agency, etc.
Abstract:
A user equipment (UE) location in a wireless network can be determined by leveraging geometric calculations for an overlaid bin grid framework mapping the wireless network area to store differential values for each frame of the bin grid framework for each pair of relevant NodeBs. A timing offset can be determined, such that when a time value from a target UE is accessed, the location can be quickly determined with minimal real time computation. In an aspect, the time value from an idle-state target UE can be accessed. The target UE time value can be searched among pre-computed differential value data sets indexed by relevant NodeB site pairs to return sets of frames that can facilitate converging on a location for the target UE. Intersecting frames can represent the geographic location of the UE in the wireless network. Further, the data can be leveraged to correct timing in the network.
Abstract:
Adaptive radio area network (RAN) coverage is described. The azimuth, elevation or carrier-channel power of an antenna of a RAN can be adjusted to adapt the coverage area of the RAN. Monitoring scheduled and unscheduled changes in the characteristics of the coverage area can facilitate determining an adaptation response to adapt the coverage area. This can be performed in a closed-loop and can facilitate optimization of the coverage area with regard to predetermined optimization characteristics. UEs can be employed as mobile reporting components to measure coverage characteristics as a function of the position of the UE in the RAN. The UE can report measurements and the location information of the UE. The location information can be timed fingerprint location information.
Abstract:
Methods, systems, and apparatuses for comparing multi-dimensional datasets are provided. A multi-dimensional dataset comparison includes receiving a plurality of datasets, each including a plurality of coordinates, wherein a subset of coordinates defines a geo-fence. For a coordinate within a geo-fence of one of the plurality of datasets, determining analogous coordinates in each of the other datasets, the analogous coordinates defining a coordinate input set, and performing in parallel an operation on the coordinate input set to determine whether an entry is present at a coordinate of the coordinate input set.
Abstract:
Systems and methods disclosed herein can implement a femtocell calibration solution that uses the known location of the femtocell to calibrate timing based locating systems. The calculated time differences of different signals sent between macrocells and a mobile device can be used to solve for a reference time difference that accounts for the timing differences of the unsynchronized macrocells. The reference time difference can then be used to solve for the location of another mobile device if the calculated time differences between that mobile device and the macrocells are known. The solution can include taking many measurements of the calculated time difference at the first mobile device in order to average them to get a more accurate reference time difference. The solution can further include ceasing measurements at the first mobile device when the mobile device is no longer within range of the femtocell.
Abstract:
The disclosed subject matter provides for sharing timed fingerprint location information. In an aspect, timed fingerprint location information can be associated with a location of a user equipment. This timed fingerprint location information can be shared with other devices. As such, with proper analysis, these other devices can employ the shared timed fingerprint location information to determine their location. In an aspect, the other devices can determine that they are located at the same location as the user equipment. However, a level of error can be inherent in the location determined from shared timed fingerprint location information. In some embodiments, this error can be compensated for.
Abstract:
Providing for identifying and ticketing mobile network communication errors according to geographic position of the errors is described herein. By way of example, communication errors, such as dropped calls, can be tracked and recorded as a function of position of a mobile terminal affected by a dropped call. A number of these errors within a given location is compared with historic error data to determine statistically anomalous instances of communication errors. Upon identifying such an anomaly, an error ticket can be generated that identifies a geographic region associated with the error. Particularly, the geographic region can be independent of radio access network infrastructure, which is conventionally used as a means of locating events within a mobile network. A geographic error ticket can, in some aspects of the subject disclosure, facilitate discovery and troubleshooting of errors that originate at least in part from unknown or unanticipated sources.
Abstract:
The disclosed subject matter provides for employing timed fingerprint location information in location analytics. Timed fingerprint location information can provide a location for a user equipment. The location of the user equipment can be compared to a location analytics rule related to the location of a user equipment. Where the location satisfies a condition of the location analytics rule, the user equipment can be associated with a compliance status. Compliance, or noncompliance, can initiate further action. Further action can include reporting the compliance status, verifying the identity of a user associated with the user equipment, enforcing the location analytics rule, etc. Enforcing the location analytics rule can include alerts, fines, reporting to an authority figure or agency, etc.