DETECTING ABNORMAL BEHAVIOR IN SMART BUILDINGS
A method and system for detecting anomalous behavior in a smart building is disclosed. A method includes detecting a presence of a user at the smart building; retrieving a profile of the user; monitoring actions of the user in the smart building with respect to each of a plurality of aspects; comparing the actions to historical actions of the user stored in the profile; and determining that anomalous behavior exists with respect to the user.
1 . A computer-implemented method for detecting anomalous behavior in a smart building comprising:
detecting a presence of a user at the smart building;
retrieving a profile of the user;
monitoring actions of the user in the smart building with respect to each of a plurality of aspects;
comparing the actions to historical actions of the user stored in the profile; and
determining that anomalous behavior exists with respect to the user.
2 . The computer-implemented method of claim 1 , wherein:
the profile includes historical pattern of movement of the user within the smart building.
3 . The computer-implemented method of claim 2 , wherein:
determining that anomalous behavior exists comprises determining that the user's current pattern of movement is not consistent with the user's historical pattern of movement.
4 . The computer-implemented method of claim 1 , wherein:
the profile includes access-granting privileges; and
the anomalous behavior comprises an attempt to wrongly utilize access-granting privileges.
5 . The computer-implemented method of claim 1 , wherein:
determining that anomalous behavior exists with respect to the user comprises:
accessing a calendar of the user; and
comparing the calendar to the user's pattern of movement within the building.
6 . The computer-implemented method of claim 1 , wherein:
the profile includes preferences of the user with respect to one or more aspects; and
the anomalous behavior comprises the user implementing settings for one or more aspects that are not consistent with the profile.
7 . A computer system for detecting anomalous behavior in a smart building comprising:
a processor;
a memory;
computer program instructions configured to cause the processor to perform the following method:
detecting a presence of a user at the smart building;
retrieving a profile of the user;
monitoring actions of the user in the smart building with respect to each of a plurality of aspects;
comparing the actions to historical actions of the user stored in the profile; and
determining that anomalous behavior exists with respect to the user.
8 . The computer system of claim 7 , wherein:
the profile includes historical pattern of movement of the user within the smart building.
9 . The computer system of claim 8 , wherein:
determining that anomalous behavior exists comprises determining that the user's current pattern of movement is not consistent with the user's historical pattern of movement.
10 . The computer system of claim 7 , wherein:
the profile includes access-granting privileges; and
the anomalous behavior comprises an attempt to wrongly utilize access-granting privileges.
11 . The computer system of claim 7 , wherein:
determining that anomalous behavior exists with respect to the user comprises:
accessing a calendar of the user; and
comparing the calendar to the user's pattern of movement within the building.
12 . The computer system of claim 1 , wherein:
the profile includes preferences of the user with respect to one or more aspects; and
the anomalous behavior comprises the user implementing settings for one or more aspects that are not consistent with the profile.
13 . A computer-implemented method for detecting free-standing conversational groups of users in a smart building comprising:
detecting a presence of more than one user at the smart building;
determining an orientation and location for each user;
determining one or more free-standing conversational groups of users based on the orientation and location of each user;
monitoring interactions between and within the one or more free-standing conversational groups of users; and
tracking each free standing conversational group of users in real-time.
14 . The computer-implemented method of claim 13 , further comprising:
determining that anomalous behavior exists with respect to one of the one or more free-standing conversational group of users.
15 . The computer-implemented method of claim 13 , further comprising:
optimizing an emergency response plan based on the behavior of the one or more free-standing conversational groups of users.
16 . The computer-implemented method of claim 13 , wherein:
determining free-standing conversational groups comprises finding an O-space comprising an empty space surrounded by a plurality of users, wherein the plurality of users are orientated toward the O-space.
17 . The computer-implemented method of claim 13 , wherein:
monitoring interactions between and within the one or more free-standing conversational groups of users comprises forming a graph representing each of the users within one of the free-standing conversational group of users; and
determining an entropy or other global complexity measure for estimating the groupness related to the graph.
18 . The computer-implemented method of claim 17 , further comprising:
transforming the graph into a topological object that is a simplicial complex.
19 . The computer-implemented method of claim 18 , further comprising:
using a persistent homology algorithm to analyze the simplicial complex.
20 . The computer-implemented method of claim 19 , wherein:
analyzing the simplicial complexes comprises detecting temporary and persistent groups in circular motifs.