Learning locations of interest using IoT devices
In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.
1. A computer implemented method for learning places of interest for one or more users, the method comprising:
learning and storing location information of one or more Internet of Things (IoT) devices;
detecting a location where no movement of the of the one or more IoT devices has occurred over a pre-determined duration of time;
determining whether the detected location is classified as a learned location of interest based on a predefined criteria for determining the location of interest; and
clustering the learned location of interest with lower radius of separation into bigger groups with a larger radius of separation using a clustering algorithm based on location information of the learned location of interest for the one or more IoT devices using a pre-defined criteria for clustering the learned location of interest into bigger groups,
wherein the clustering algorithm uses a modal value of the detected location by popularity of visits to determine center of the cluster when aggregating nearby locations.
2. The computer-implemented method of claim 1 , wherein detecting a location where no movement of the one or more IoT devices has occurred comprises analyzing the location information of the at least one mobile device.
3. The computer-implemented method of claim 2 , wherein the location information of one or more IoT devices comprises latitude and longitude of the one or more IoT devices.
4. The computer-implemented method of claim 2 , wherein the location information of one or more IoT devices further comprises any one or more of: number of visits by the one or more IoT devices, day of the visits, date of the visits, time of the visits, duration of the visits.
5. The computer-implemented method of claim 1 , wherein the location information of the one or more IoT devices is obtained by using any one or more of:
GPS technology, radio frequency identification (RFID) technology, near field communication (NFC) technology, cell tower identification technology, short range communication technology including any of: UWB, Wi-Fi, ZigBee, Bluetooth technology, or a combination thereof.
6. The computer-implemented method of claim 1 , wherein the pre-determined criteria for determining the location of interest comprises any one or more of: time of the day, number of visits by the one or more IoT devices.
7. The computer-implemented method of claim 1 further comprising augmenting the learned location of interest with other relevant data.
8. The computer-implemented method of claim 7 , wherein augmenting the learned location of interest with other relevant data comprises associating the learned location with one or more of known address of a business, activity of a certain type, monument and place of worship.
9. The computer-implemented method of claim 1 , wherein the pre-defined criteria for clustering the learned location of interest into bigger groups comprises any one or more of, existence of a location as a known location, proximity of newly discovered locations to the known location based on radius of proximity, time of visit of the one or more IoT devices and duration of stay of the one or more IoT devices.
10. A system for learning places of interest for of one or more users, the system comprising one or more Internet of Things (IoT) devices, a data processing system and a user interface, wherein the data processing system further comprises:
a location-aware database, wherein the database learns and stores location information of the users using one or more IoT devices;
a processor, wherein the processor detects a location where no movement of the one or more IoT devices has occurred for a pre-determined duration of time, and determines whether the detected location is classified as a learned location of interest based on a pre-defined criteria for determining the location of interest; and
a clustering engine, wherein the clustering engine forms the learned location of interest with lower radius of separation into bigger groups with a larger radius of separation of learned places based on location information for the users using one or more IoT devices using a pre-defined criteria for clustering the learned location of interest into bigger groups,
wherein the clustering engine uses a modal value of the detected location by popularity of visits to determine center of the cluster when aggregating nearby.
11. The system of claim 10 , wherein detecting a location where no movement of the one or more IoT devices has occurred comprises analyzing the location information of the users using one or more IoT devices by an analytics engine.
12. The system of claim 11 , wherein the location information of the one or more IoT devices comprises latitude and longitude of the at least one of the one or more of IoT devices.
13. The system of claim 11 , wherein the location information of the one or more IoT devices further comprises any one or more of: number of visits by the one or more IoT devices, day of the visits, date of the visits, time of the visits, duration of the visits.
14. The system of claim 10 , wherein the location information of the one or more IoT devices is obtained by using any one or more of: GPS technology, radio frequency identification (RFID) technology, near field communication (NFC) technology, cell tower identification technology, short range communication technology including any of: UWB, Wi-Fi, ZigBee, Bluetooth technology, or a combination thereof.
15. The system of claim 10 , wherein the pre-determined criteria for determining the location of interest comprises any one or more of: time of the day, number of visits by the one or more IoT devices.
16. The system of claim 10 further comprising a data augmentation engine, wherein the data augmentation engine augments the learned location of interest with other relevant data.
17. The system of claim 16 , wherein augmenting the learned location of interest with other relevant data comprises associating the learned location with one or more of known address of: a business, activity of a certain type, monument and place of worship.
18. The system of claim 10 , wherein the pre-defined criteria for clustering the learned location of interest into bigger groups comprises any one or more of: existence of a location as a known location, proximity of newly discovered locations to the known location based on radius of proximity, time of visit of the one or more IoT devices and duration of stay of the one or more IoT devices.
19. A non-transitory computer-readable medium having executable instructions stored therein for learning places of interest for one or more users, that when executed, cause one or more processors corresponding to a system having a storage database, a data processing system including a processor, a database and a user interface to perform operations comprising:
learning and storing location information of one or more
Internet of Things (IoT) devices to a storage database;
detecting a location where no movement of the one or more IoT devices has occurred for a pre-determined duration of time;
determining whether the detected location is classified as a learned location of interest based on a pre-defined criteria for determining the location of interest; and
clustering the learned location of interest with lower radius of separation into bigger groups with a larger radius of separation using a clustering algorithm based on location information of the learned location of interest for the users using one or more IoT devices using a pre-defined criteria for clustering the learned location of interest into bigger groups,
wherein the clustering algorithm uses a modal value of the detected location by popularity of visits to determine center of the cluster when aggregating nearby locations.
20. The non-transitory computer-readable medium of claim 19 , wherein detecting a location where no movement of the one or more IoT devices has occurred comprises analyzing the location information of the one or more IoT devices.
21. The non-transitory computer-readable medium of claim 20 , wherein the location information of one or more IoT devices comprises latitude and longitude of the one or more IoT devices.
22. The non-transitory computer-readable medium of claim 20 , wherein the location information of one or more IoT devices further comprises any one or more of: number of visits by the one or more IoT devices, day of the visits, date of the visits, time of the visits, duration of the visits.
23. The non-transitory computer-readable medium of claim19 , wherein the location information of the one or more IoT devices is obtained by using any one or more of: GPS technology, radio frequency identification (RFID) technology, near field communication (NFC) technology, cell tower identification technology, short range communication technology including any of: UWB, Wi-Fi, ZigBee, Bluetooth technology, or a combination thereof.
24. The non-transitory computer-readable medium of claim 19 , wherein the pre-determined criteria for determining the location of interest comprises any one or more of: time of the day, number of visits by the users using one or more IoT devices.
25. The non-transitory computer-readable medium of claim 19 further comprising augmenting the learned location of interest with other relevant data.
26. The non-transitory computer-readable medium of claim 25 , wherein augmenting the learned location of interest with other relevant data comprises associating the learned location with one or more of: known address of a business, activity of a certain type, monument and place of worship.
27. The non-transitory computer-readable medium of claim 19 , wherein the pre-defined criteria for clustering the learned location of interest into bigger groups comprises any one or more of: existence of a location as a known location, proximity of newly discovered locations to the known location based on radius of proximity, time of visit of the one or more IoT devices and duration of stay of the one or more IoT devices.