Multiscale Graph-Based Framework for Efficient Multi-Sensor Integration and Event Detection

We present a general framework for integrating disparate sensors to dynamically detect events. Events are often observed as multiple, asynchronous, disparate sensors' activations in time. The challenge is to reconcile them to infer that a process of interest is underway or has occurred. We abstractly model each sensor as a value-attributed time interval over which it takes values that are relevant to a known process of interest. Process knowledge is incorporated in the detection scheme by defining sensor neighborhood intervals that overlap with temporally neighboring sensor neighborhood intervals in the process. The sensor neighborhoods are represented as nodes of an interval graph, with edges between nodes of overlapping sensor neighborhoods. Sensor activity is then interpreted via this process model by constructing an interval graph time series, for relevant sensor types and process-driven neighborhoods, and looking for subgraphs that match those of the process model graph. A time series that dynamically records the number of sensor neighborhoods overlapping at any given time is used to detect temporal regions of high sensor activity indicative of an event. Multiscale analysis of this time series reveals peaks over different time scales. The peaks are then used to efficiently triage underlying interval subgraphs of sensor activity to examine them for relational patterns similar to the process model graph of interest. Thus, our framework synergistically uses relational as well as scale information to dynamically and efficiently triage sensors related to a process. Multiple processes of interest may be efficiently detected and tracked in parallel using this approach.