Systems and methods for probabilistic semantic sensing in a sensory network are disclosed. The system receives raw sensor data from a plurality of sensors and generates semantic data including sensed events. The system correlates the semantic data based on classifiers to generate aggregations of semantic data. Further, the system analyzes the aggregations of semantic data with a probabilistic engine to produce a corresponding plurality of derived events each of which includes a derived probability. The system generates a first derived event, including a first derived probability, that is generated based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum to enable at least one application to perform a service based on the plurality of derived events.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving raw sensor data from a plurality of sensors; generating semantic data based on the raw sensor data, the semantic data including a plurality of sensed events and further including a first plurality of classifiers including a first classifier including a semantic datum representing an event detected based on the receiving of the raw sensor data, a second classifier including a probability representing a confidence of an associated semantic datum, a third classifier identifying a location of a sensor that is utilized to sense an associated semantic datum, and a fourth classifier identifying a location of an associated semantic datum; correlating the semantic data based on a second plurality of classifiers to generate a plurality of aggregations of semantic data, the second plurality of classifiers being selected from the first plurality of classifiers; analyzing the plurality of aggregations of semantic data with a probabilistic engine to produce a corresponding plurality of derived events, each of which includes a derived probability, the analyzing including generating a first derived event including a first derived probability that is generated based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum; and enabling at least one application to perform a service based on the plurality of derived events. 2. The method of claim 1, wherein the plurality of sensors includes a first sensor located on a first node in a light sensory network that includes a plurality of nodes. 3. The method of claim 1, wherein the raw sensor data includes visual data, audio data, and environmental data and wherein the event represented by the semantic datum includes detecting a person, detecting a vehicle, detecting an object, and detecting an empty parking space. 4. The method of claim 1, wherein the third classifier includes a spatial coordinate of a first sensor and wherein the plurality of classifiers includes a fifth classifier including a temporal coordinate that is associated with the third classifier. 5. The method of claim 4, wherein the fourth classifier includes a spatial coordinate of a first event represented by the semantic datum and wherein the plurality of classifiers includes a sixth classifier including a temporal coordinate that describes a time the first sensor was utilized to detect a first event represented by the semantic datum and wherein the first event is an occupancy state of empty for a parking spot. 6. The method of claim 1, wherein the first plurality of classifiers includes a seventh classifier including an application identifier that is utilized to identify the at least one application from a plurality of applications, wherein each application from the plurality of applications is utilized to perform a different service. 7. The method of claim 1, the correlating the semantic data includes at least one of correlating to generate abstract graphs based on classifiers that match and correlating to generate abstract graphs based on classifiers that fuzzy match. 8. The method of claim 1, wherein the correlating the semantic data includes correlating based on mathematical relationships between spatial and temporal coordinates and wherein the spatial and temporal coordinates involve at least one of a sensor itself, a detection of an event, and a combination of both. 9. The method of claim 1, wherein the plurality of sensed events include a first sensed event, a second sensed event and a third sensed event, and wherein the first sensed event includes a first classifier that describes an occupancy state of empty for a first parking spot, the second sensed event includes a first classifier that describes an occupancy state of empty for the first parking spot and a third sensed event describes an occupancy state of empty for the first parking spot and wherein the plurality of aggregated semantic data includes a first aggregated semantic data that aggregates the first, second and third sensed events. 10. A system comprising: a plurality of sensing engines, implemented by one or more processors, that are configured to receive raw sensor data from a plurality of sensors, the plurality of sensing engines are further configured to generate semantic data based on the raw sensor data, the semantic data including a plurality of sensed events and further including a first plurality of classifiers including a first classifier including a semantic datum representing an event detected based on the receiving of the raw sensor data, a second classifier including a probability representing a confidence of an associated semantic datum, a third classifier identifying a location of a sensor that is utilized to sense an associated semantic datum, and a fourth classifier identifying a location of an associated semantic datum; and an correlation engine, implemented by one or more processors, that is configured to correlate the semantic data based on a second plurality of classifiers to generate a plurality of aggregations of semantic data, the correlation engine is configured to select the second plurality of classifiers from the first plurality of classifiers; and a probabilistic engine, implemented by one or more processors, that is configured to analyze the plurality of aggregations of semantic data to produce a corresponding plurality of derived events, each of which including a derived probability, the plurality of derived events including a first derived event including a first derived probability, the probabilistic engine generating the first derived event based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum, the probabilistic engine is further configured to communicate the plurality of derived events to an interface to enable at least one application to perform a service based on the plurality of derived events. 11. The system of claim 10, wherein the plurality of sensors includes a first sensor located on a first node in a light sensory network that includes a plurality of nodes. 12. The method of claim 10, wherein the raw sensor data includes visual data, audio data, and environmental data and wherein the event represented by the semantic datum includes a detection of a person, a detection of a vehicle, a detection of an object, and a detection of an empty parking space. 13. The system of claim 10, wherein the third classifier includes a spatial coordinate of a first sensor and wherein the plurality of classifiers includes a fifth classifier including a temporal coordinate that is associated with the third classifier. 14. The system of claim 13, wherein the fourth classifier includes a spatial coordinate of a first event represented by the semantic datum and wherein the plurality of classifiers includes a sixth classifier including a temporal coordinate that describes a time the first sensor was utilized to detect a first event represented by the semantic datum and wherein the first event is an occupancy state of empty for a parking spot. 15. The system of claim 10, wherein the probabilistic engine is configured to analyze, based on user inputs, wherein the user inputs include a desired accuracy and a user preference. 16. The system of claim 10, wherein the probabilistic engine is configured to analyze based on a weight that is assigned to a second classifier and wherein the probabilistic engine alters the weight over time. 17. The system of claim 10, wherein the plurality of derived events includes a first derived event and wherein the probabilistic engine analyzes based on a first threshold and wherein the first threshold defines a minimum level of raw sensor data that is utilized to produce the first derived event. 18. The system of claim 17, wherein the probabilistic engine alters the first threshold over time. 19. The system of claim 10, wherein the at least one application includes a parking location application, a surveillance application, a traffic application, retail customer application, a business intelligence application, an asset monitoring application, an environmental application, an earthquake sensing application. 20. A machine-readable medium having no transitory signals storing a set of instructions that, when executed by a processor, causes a machine to perform operations comprising: receiving raw sensor data from a plurality of sensors; generating semantic data based on the raw sensor data, the semantic data including a plurality of sensed events and further including a first plurality of classifiers including a first classifier including a semantic datum representing an event detected based on the receiving of the raw sensor data, a second classifier including a probability representing a confidence of an associated semantic datum, a third classifier identifying a location of a sensor that is utilized to sense an associated semantic datum, and a fourth classifier identifying a location of an associated semantic datum; correlating the semantic data based on a second plurality of classifiers to generate a plurality of aggregations of semantic data, the second plurality of classifiers being selected from the first plurality of classifiers; analyzing the plurality of aggregations of semantic data with a probabilistic engine to produce a corresponding plurality of derived events, each of which include a derived probability, the analyzing including generating a first derived event including a first derived probability that is generated based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum; and enabling at least one application to perform a service based on the plurality of derived events.