DYNAMIC DEVICE CLUSTERING SYSTEM AND METHOD

Abstract

A method and apparatus forms clusters of co-located devices by correlating measurable quantities that are observed by the devices. Devices, both fixed and mobile, may be clustered according to spatially defined locations by receiving a plurality of observations of measurable metrics from the devices; scoring the plurality of observations to define a cluster state at each of the spatially defined locations, wherein the cluster state comprises a plurality of measurable quantities; assigning the devices to a cluster of a plurality of clusters; after assigning the devices to the cluster, receiving additional observations of measurable metrics from the devices; and autonomously updating the cluster states in based on the additional observations.

Description

Claims

1. A computer-implemented method of clustering devices according to spatially defined locations, comprising: receiving a plurality of observations of measurable metrics from the devices;scoring the plurality of observations to define a cluster state at each of the spatially defined locations, wherein the cluster state comprises a plurality of measurable quantities;assigning the devices to a cluster of a plurality of clusters;after assigning the devices to the cluster, receiving additional observations of measurable metrics from the devices; andautonomously updating the cluster states in based on the additional observations.

2. The computer-implemented method of claim 1, wherein scoring the plurality of observations further comprises evaluating the observations for consistency with the cluster state.

3. The computer-implemented method of claim 1, wherein a first observation of the plurality of observations comprises a location of a device making the first observation and scoring the first observation further comprises: calculating an uncertainty ellipsoid of the location;integrating the uncertainty ellipsoid through bounds of the spatially defined location of a cluster; anddetermining a probability of inclusion of the device making the first observation in one or more clusters of the plurality of clusters.

4. The computer-implemented method of claim 1, wherein a second observation of the plurality of observations comprises a wireless signals fingerprint of a device making the second observation and scoring the second observation further comprises: using a Bloom filter to count a number of emitters in the wireless signals fingerprint.

5. The computer-implemented method of claim 4, wherein the Bloom filter further comprises a Counting Down Bloom Filter, F, initialized to contain all zero counts and adding a measurement set, M, to F comprises: computing a raw_score = count(M in F);updating a mean_score as an exponential average wherein mean_score = α * mean_score + (1 - α) * raw_score;deprecating all non-zero counts in the filter by 1; andinserting the elements of M into F by computing their respective hash indices and setting the value of F(index) to max_count;wherein M is a measurement set, mean_score is initialized to 1, α is set to a desired decimal value between 0 and 1 and max_count.

6. The computer-implemented method of claim 5, wherein determining the score of a measurement set M in F comprises: computing the raw_score = count(M in F);computing and returning an actual_score as:if raw_score <= mean_score then actual_score = p * raw_score/mean_score else actual_score = (1 - p)*(raw_score - mean_score)/ (1 - mean_score) + p; where p ∈ [0, 1] and is a transition score.

7. The computer-implemented method of claim 1, wherein the measurable metric comprises an atmospheric condition.

8. The computer-implemented method of claim 1, wherein the measurable metric comprises acoustic wave signatures.

9. The computer-implemented method of claim 1, wherein each observation further comprises a set of measurable metrics and scoring an observation against a cluster state yields a vector of scores for each measurable metric.

10. The computer-implemented method of claim 9, wherein a number of measurable metrics in an observation is less than the number of measurable metrics in the cluster state.

11. The computer-implemented method of claim 9, wherein a neural network classifier is trained to reduce the vector of scores to assign a device associated with an observation to the spatially defined location associated with a highest scoring cluster state.

12. A system comprising processing circuitry for clustering devices according to spatially defined locations, the processing circuitry performing a method of: receiving a plurality of observations of measurable metrics from the devices;scoring the plurality of observations to define a cluster state at each of the spatially defined locations, wherein the cluster state comprises a plurality of measurable quantities;assign the devices to a cluster of a plurality of clusters;after assigning the devices to the cluster, receive additional observations of measurable metrics from the devices; andautonomously update the cluster states in based on the additional observations.

13. The system of claim 12, wherein scoring the plurality of observations further comprises evaluating the observations for consistency with the cluster state.

14. The system of claim 12, wherein a first observation of the plurality of observations comprises a location of a device making the first observation and scoring the first observation further comprises: calculating an uncertainty ellipsoid of the location;integrating the uncertainty ellipsoid through bounds of the spatially defined location of a cluster; anddetermining a probability of inclusion of the device making the first observation in one or more clusters of the plurality of clusters.

15. The system of claim 12, wherein a second observation of the plurality of observations comprises a wireless signals fingerprint of a device making the second observation and scoring the second observation further comprises: using a Bloom filter to count a number of emitters in the wireless signals fingerprint.

16. The system of claim 15, wherein the Bloom filter further comprises a Counting Down Bloom Filter, F, initialized to contain all zero counts and adding a measurement set, M, to F comprises: computing a raw_score = count(M in F);updating a mean_score as an exponential average wherein mean_score = α * mean_score + (1 - α) * raw_score;deprecating all non-zero counts in the filter by 1; andinserting the elements of M into F by computing their respective hash indices and setting the value of F(index) to max_count;wherein M is a measurement set, mean_score is initialized to 1, α is set to a desired decimal value between 0 and 1 and max_count.

17. The system of claim 16, wherein determining the score of a measurement set M in F comprises: computing the raw_score = count(M in F);computing and returning an actual_score as:if raw_score <= mean_score then actual_score = p * raw_score/mean_score else actual_score = (1 - p)*(raw_score - mean_score)/ (1 - mean_score) + p; where p ∈ [0, 1] and is a transition score.

18. The system of claim 12, wherein each observation further comprises a set of measurable metrics and scoring an observation against a cluster state yields a vector of scores for each measurable metric.

19. The system of claim 18, wherein a number of measurable metrics in an observation is less than the number of measurable metrics in the cluster state.

20. The system of claim 18, wherein a neural network classifier is trained to reduce the vector of scores to assign a device associated with an observation to the spatially defined location associated with a highest scoring cluster state.