Abstract: The disclosure provides methods and systems for securing internet of thing systems. One method includes receiving, at a computing device, a token, wherein the token comprises a cryptographically signed list of rights that the computing device is authorized to request. The method also includes requesting, using the computing device, an action of a receiving device in an industrial location, wherein requesting the action comprises sending the token with the request to cause the receiving device to authenticate the user of the computing device and confirm the user is authorized to perform the action.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: Daily activities of mobile data may be represented as and processed as an image consisting of days of the week versus time of day. The images may be rapidly processed from raw data, but also may be readily analyzed using image processing techniques. The daily activities may be a composite of several images, each of which may represent observations for a particular dimension. The dimension may represent a type of activity, a physical location, a labeled location, or some other aspect. The image having time of day versus day of week may show relationships or patterns that may occur from one day to the next, which may otherwise be difficult to detect.

Abstract: Machine learning techniques may be applied to determining a mode of transportation for a trajectory of a sequence of user locations. The mode of transportation, such as walking, bicycling, riding in a car or bus, riding in a train, or other mode, may be determined by creating a training set of data, then using classification mechanisms to classify trajectories by mode of transport. The training set may be generated by tracking then verifying a user's transportation mode. In some cases, a user may manually input or verify their transportation mode, while in other cases, a user's transportation mode may be determined through other data sources.

Abstract: A trajectory may be derived from noisy location data by mapping candidate locations for a user, then finding a match between successive locations. Location data may come from various sources, including telecommunications networks. Telecommunications networks may give location data based on observations of users in a network, and such data may have many inaccuracies. The observations may be mapped to physical constraints, such as roads, pathways, train lines, and the like, as well as applying physical rules such as speed analysis to smooth the data and identify outlier data points. A trajectory may be resampled or interpolated to generate a detailed set of trajectory points from a sparse and otherwise ambiguous dataset.

Abstract: A user's location may be estimated by applying a probability function to raw user location data taken from various telephony or wireless systems. The probability function may estimate a user's location based on a training dataset that may be generated a priori to the analysis. A training dataset may be generated or updated by analyzing queries made with global positioning system (GPS) data to extract a device's GPS location. The probability function may be generated in part from physical maps. The estimated location may improve location accuracy, especially when attempting to map a user's location with accuracies that may be much smaller than a cell of a cellular telephony system.

Abstract: A drilling system includes a surface system comprising a control panel. The drilling system further includes a pressure control equipment configured to be operatively coupled to the control panel, wherein the control panel comprises at least one intrusion prevention system (IPS) enabled device configured to provide for one or more IPS functions.

Abstract: Daily activities of mobile data may be processed as images. The image processing techniques may include classifying, pattern matching, and other automated analyses. Even when the images contain such highly condensed and summarized versions of the underlying raw data, very meaningful classification, pattern matching, and other analyses may be performed quickly and efficiently. Some analysis techniques may involve processing mobility data into individual dimensions, then prioritizing the dimensions based on activity observations. Other analysis techniques may involve processing mobility data into predefined dimensions.

Abstract: Mobility observations may be analyzed to create so-called mobility genes, which may be intermediate data forms from which various analyses may be performed. The mobility genes may include a trajectory gene, which may describe a trajectory through which a user may have travelled. The trajectory gene may be analyzed from raw location observations and processed into a form that may be more easily managed. The trajectory genes may be made available to third parties for analysis, and may represent a large number of location observations that may have been condensed, smoothed, and anonymized. By analyzing only trajectories, a third party may forego having to analyze huge numbers of individual observations, and may have valuable data from which to make decisions.

Abstract: An industrial internet of things gateway boot method is described wherein installation, operation and maintenance phases are controlled to limit the chance of a malicious attack on a connected network.

Abstract: A visit mobility gene may be generated from analyzing raw location observations and may be made available for further analysis. The visit mobility gene may include summarized statistics about a certain location or location type, and in some cases may include ingress and egress travel information for visitors. The visit mobility gene may be made available to third parties for further analysis, and may represent a concise, rich, and standardized dataset that may be generated from several sources of mobility data.

Abstract: Estimating a location of a device at a particular point of time may incorporate one, two, or more different location data points. The location data points may be derived from communications networks, where there may be different mechanisms for determining location. As part of the location estimation, each cellular location in a cellular network may have a different error range associated with each cell, for example. The error range for each cell may be generated by collecting precise location data from Global Positioning System or other mechanism with high accuracy, and comparing that data to location data gathered from other sources. A database of error ranges for each cell and each location mechanism may be gathered and used to estimate the actual location of a device for a given time period.

Abstract: Real time status of a device's movements may be determined from a sequence of location observations. The status may be in the form of a state, which may be “stay”, “transit”, “pause”, and “unknown”. A state transition may occur from transit to stay when the device has remained within a predefined radius for a predefined time period. Prior to being labeled a “stay”, a device that may have ceased moving but has not stayed at that location for enough time may be labeled “pause”. For those devices in a “transit” state, a mode of transport may be determined. The real time analysis system may be a low-overhead mechanism by which new location observations may be received and processed. The resulting data may be used by traffic analysts to monitor congestion, for real time traffic data for commuters, and other uses.

Abstract: A knowledge model is derived from many different data sources, including activities of a person's mobile devices, physical location, and various media consumption habits. A graph may be built having various nodes representing concepts from the data sources and edges representing relationships between them. From the graph, various inferences may be made that can provide insight that could not otherwise be obtained. The knowledge model may be deployed as several services, including rich geolocation services, recommendation services, and other services. The services may be accessed through an application programming interface, which may be a paid service with various payment options.

Abstract: A dataset's uniqueness level may be calculated by analyzing a dataset to determine a uniqueness level. In cases where the uniqueness level may be too low for a particular purpose, meaning when the dataset may not provide enough anonymity, the dataset may be adjusted by recomputing the dataset with different resolutions of spatial data, temporal data, content data, and relationship data. By adjusting the resolution or accuracy of the data elements, the uniqueness level may thereby be adjusted. An error calculation may be determined by comparing the adjusted dataset to the original data, and the error value may represent the consistency of the data to the original data. The uniqueness level may be used as an assurance level of anonymity, which may be advertised when a dataset is sold or transferred to a third party for analysis.

Abstract: The disclosure provides methods and systems for securing internet of thing systems. One method includes receiving, at a computing device, a token, wherein the token comprises a cryptographically signed list of rights that the computing device is authorized to request. The method also includes requesting, using the computing device, an action of a receiving device in an industrial location, wherein requesting the action comprises sending the token with the request to cause the receiving device to authenticate the user of the computing device and confirm the user is authorized to perform the action.

Abstract: A visit mobility gene may be generated from analyzing raw location observations and may be made available for further analysis. The visit mobility gene may include summarized statistics about a certain location or location type, and in some cases may include ingress and egress travel information for visitors. The visit mobility gene may be made available to third parties for further analysis, and may represent a concise, rich, and standardized dataset that may be generated from several sources of mobility data.

Abstract: Mobility observations may be analyzed to create so-called mobility genes, which may be intermediate data forms from which various analyses may be performed. The mobility genes may include a trajectory gene, which may describe a trajectory through which a user may have travelled. The trajectory gene may be analyzed from raw location observations and processed into a form that may be more easily managed. The trajectory genes may be made available to third parties for analysis, and may represent a large number of location observations that may have been condensed, smoothed, and anonymized. By analyzing only trajectories, a third party may forego having to analyze huge numbers of individual observations, and may have valuable data from which to make decisions.

Abstract: Real time status of a device's movements may be determined from a sequence of location observations. The status may be in the form of a state, which may be “stay”, “transit”, “pause”, and “unknown”. A state transition may occur from transit to stay when the device has remained within a predefined radius for a predefined time period. Prior to being labeled a “stay”, a device that may have ceased moving but has not stayed at that location for enough time may be labeled “pause”. For those devices in a “transit” state, a mode of transport may be determined. The real time analysis system may be a low-overhead mechanism by which new location observations may be received and processed. The resulting data may be used by traffic analysts to monitor congestion, for real time traffic data for commuters, and other uses.