Abstract: A plurality of sensors are positioned in a home. Sensor fire data is delivered to a remote server, and the sensor fire data is segmented into single-state blocks broken up by door opening and closing events. The door opening and closing events represent potential state changes in the home where the number of people present in the home may have changed. The raw data from the sensor fires are then processed into adjacent sensor fires and used to populate adjacency matrices and frequency distributions. That information is subjected to a statistical goodness-of-fit test, which reveals a probability indicating the likelihood a given data block should be attributed with the single or multiple person state. The single versus multiple person state is passed along with these data to the remainder of the data analyses, which can then be properly aware of which data should be treated with due suspicion.

Abstract: A remote sensing system operable to collect sensor timing data comprising a central data hub for detecting the occurrence of an event and processing information about the detected event, the remote sensing system comprising a central data hub, at least one sensor having a simple clock operable to sense an event and transmit data to central data hub. By way of calculating the data transmitted by the at least one sensor, the central data hub can determine the time at which each event occurred and place a plurality of sensed events in sequence.

Abstract: A remote sensing system operable to collect sensor timing data comprising a central data hub for detecting the occurrence of an event and processing information about the detected event, the remote sensing system comprising a central data hub, at least one sensor having a simple clock operable to sense an event and transmit data to central data hub. By way of calculating the data transmitted by the at least one sensor, the central data hub can determine the time at which each event occurred and place a plurality of sensed events in sequence.

Abstract: A plurality of sensors are positioned in a home. Sensor fire data is delivered to a remote server, and the sensor fire data is segmented into single-state blocks broken up by door opening and closing events. The door opening and closing events represent potential state changes in the home where the number of people present in the home may have changed. The raw data from the sensor fires are then processed into adjacent sensor fires and used to populate adjacency matrices and frequency distributions. That information is subjected to a statistical goodness-of-fit test, which reveals a probability indicating the likelihood a given data block should be attributed with the single or multiple person state. The single versus multiple person state is passed along with these data to the remainder of the data analyses, which can then be properly aware of which data should be treated with due suspicion.