Abstract: Example implementations relate to a traffic management system. For example, the traffic management system may receive data from a plurality of connected vehicles. The traffic management system may determine the relative positions of non-connected vehicle to the connected vehicles and may cluster connected vehicles and non-connected vehicles into a flock. The traffic management system may determine a flock traffic pattern and a nature of the flock traffic pattern, based on data received from the connected vehicles.

Abstract: In example implementations, an apparatus is provided. The apparatus includes an interface, an inheritance engine, an anomaly detection module and a processor. The interface communicates with a sensor array. The inheritance engine is used to create a model of the sensor array based on information collected from the each one of a plurality of nodes in the sensor array over the interface. The anomaly detection engine is used to monitor the sensor array in accordance with the model to detect an anomaly and initiate a corrective action to correct the anomaly in two or more of the plurality of nodes within the sensor array simultaneously. The processor is in communication with the inheritance engine and the anomaly detection engine is used to execute instructions associated with the inheritance engine and the anomaly detection engine.

Abstract: Examples associated with data validation are disclosed. One example includes a sensor attribute data store having information describing attributes of a set of distributed sensors. A pattern data store stores information describing patterns indicating anomalous sensor activity. A data aggregation module flags data received from a tested sensor as anomalous data when the anomalous data exceeds a variance level described by an attribute of the tested sensor. A data validation module validates the anomalous data by comparing the anomalous data to the patterns indicating anomalous sensor activity. A learning module updates the pattern indicating anomalous sensor activity based on a result received from the validation logic after the validation logic validates data received from the tested sensor.

Abstract: Example implementations relate to a traffic management system. For example, the traffic management system may receive data from a plurality of connected vehicles. The traffic management system may determine the relative positions of non-connected vehicles to the connected vehicles and may cluster connected vehicles and non-connected vehicles into a flock. The traffic management system may determine a flock traffic pattern and a nature of the flock traffic pattern, based on data received from the connected vehicles.

Abstract: Examples associated with data validation are disclosed. One example includes a sensor attribute data store having information describing attributes of a set of distributed sensors. A pattern data store stores information describing patterns indicating anomalous sensor activity. A data aggregation module flags data received from a tested sensor as anomalous data when the anomalous data exceeds a variance level described by an attribute of the tested sensor. A data validation module validates the anomalous data by comparing the anomalous data to the patterns indicating anomalous sensor activity. A learning module updates the pattern indicating anomalous sensor activity based on a result received from the validation logic after the validation logic validates data received from the tested sensor.

Abstract: Examples disclosed herein relate, among other things, to a non-transitory machine-readable storage medium encoded with instructions executable by a processor of a computing device to cause the computing device to obtain data from a plurality of sensors of at least a first type and a second type. The instructions may also cause the computing device to determine, for each sensor of the second type, whether the data obtained from the sensor of the second type is reliable based at least on the data obtained from a sensor of the first type that is associated with the sensor of the second type.