System and approach of wireless sensor auto routing using machine learning
A system relating to using machine learning and associated technologies for auto routing of wireless sensors to one of the multiple radio frequency (RF) portals/gateways which in turn may be interfaced/wired to control panel(s). A deployed machine learning algorithm may incorporate current parameters and historical data, such as signal strength, sensor association, critical sensor response types and disruption in the network, to determine sensor routing for one of the appropriate RF portal/gateway which in turn is interfaced to control panel(s).
1 . An apparatus for sensor routing comprising:
a control panel;
one or more transceivers connected to the control panel; and
one or more sensors connected to each transceiver; and
wherein:
the control panel comprises:
a security applications module connected to the one or more transceivers; and
a machine learning module connected to the security applications module; and
wherein:
sensor data are provided from the one or more sensors via the one or more transceivers to the security applications module; and
the sensor data go from the security applications module to an algorithm of the machine learning module where a pattern study is formed;
based on the pattern study, a chart that indicates which transceiver of the one or more transceivers that the sensor was associated with most of the time during its operational history;
a weightage table from the chart is derived for each association of the sensor with the transceiver; and
the weightage table is referred to for each time while there is a routing of the sensor from one transceiver to another, which avoids an unnecessary abrupt routing of one of the one or more sensors to a transceiver of the one or more transceivers.
2 . The apparatus of claim 1 , wherein the one or more sensors are wireless sensing devices.
3 . The apparatus of claim 1 , wherein if a transceiver is having a communication failure, then the sensor or sensors associated with the transceiver are routed to another transceiver.
4 . The apparatus of claim 1 , wherein the one or more sensors are selected from a group of sensors comprising a door contact sensor, PIR sensor, shock sensor, temperature sensor, smoke sensor, sounders, microphone, imaging sensor, light sensor, smell sensor, touch sensor, finger print sensor, RF sensor, iris sensor, current sensor, natural gas sensor, CO2 sensor, odor sensor, panic sensor, glass break sensor and chemical sensor.
5 . A system for sensor auto routing comprising:
a control panel;
one or more transceivers connected to the control panel; and
one or more sensors connected to the one or more transceivers; and
wherein:
the control panel comprises:
a security applications module connected to the one or more transceivers; and
a machine learning module connected to the security applications module; and
wherein:
sensor data are provided from the one or more sensors via the one or more transceivers to the security applications module; and
the sensor data go from the security applications module to an algorithm of the machine learning module where a pattern study is formed;
a schedule of an association of a sensor with a transceiver is developed based on the pattern study; and
the schedule indicates when a strength of a signal of the association of the sensor with a transceiver of the one or more transceivers is sufficient to automatically route the sensor to the transceiver.
6 . The system of claim 5 , wherein the one or more sensors are wireless sensing device.
7 . The system of claim 5 , wherein the control panel collects the pattern study which includes information about a selected sensor.
8 . The system of claim 7 , wherein the information comprises one or more items of a group comprising signal strength of the sensor; fault events, and a time of a day that the signal strength in units is most favorable relative to a maximum of the units.
9 . The system of claim 7 , wherein a weightage is derived from the information.
10 . The system of claim 7 , wherein the pattern study indicates which transceiver that sensor was associated with a most time over a predetermined period of time.
11 . The system of claim 5 , wherein a weightage table is developed from the pattern study, and the weightage table is used for routing the sensor from one transceiver to another transceiver to avoid abrupt routing of a sensor from one transceiver to another transceiver from exceeding a limitation of a number of sensors that a transceiver can operate.
12 . The system of claim 11 , wherein the routing of the sensor from one transceiver to another transceiver is automated and is a smartly agile routing of the sensor based on a machine learning pattern analysis.
13 . The system of claim 12 , wherein the weightage table is derived for an association of a sensor with a transceiver.
14 . The system of claim 13 , wherein the weightage table is referred to each time a sensor is routed from one transceiver to another transceiver.
15 . The system of claim 14 , wherein if a transceiver is having a communication failure, then a sensor or sensors associated with the transceiver are routed to another transceiver.
16 . A mechanism for sensor routing comprising:
one or more transceivers connected to a control panel; and
one or more sensors connected to the one or more transceivers; and
wherein:
the control panel comprises:
a security module connected to the one or more transceivers; and
a machine learning module connected to the security module; and
wherein:
sensor data are provided from the one or more sensors via the one or more transceivers to the security module; and
the sensor data go from the security module to an algorithm of the machine learning module where the sensor data are analyzed by the algorithm according to the type of response which indicates which sensors of the one or more sensors are auto routed on the basis of a priority response type, to a particular or selected transceiver, portal, cloud, gateway, or storage.
17 . The mechanism of claim 16 , wherein the basis of the priority response type is proportional to the criticality of the sensor data.
18 . The mechanism of claim 16 , wherein the control panel determines when to auto route a sensor.
19 . The mechanism of claim 16 , wherein one or more sensors are wireless for connections.
20 . The mechanism of claim 16 , wherein one or more sensors are wired for connections.