Method and apparatus of sharing information related to status

Abstract

The present invention relates to method and device for sharing state related information among a plurality of electronic devices and, more particularly, to method and device for predicting the state of a device on the basis of information shared among a plurality of electronic devices. In order to attain the purpose, a method for sharing state related information of a device, according to an embodiment of the present invention, comprises the steps of: generating a state model of a device on the basis of state related data; selecting one or more parameters for determining the state of the device on the basis of the generated state model; and transmitting the one or more selected parameters to at least one other device.

1 . A method performed by a device of a base station (BS), the method comprising:
 obtaining local data and a state model for local learning to predict a state of the device;   performing the local learning to update the state model using the local data based on a machine learning algorithm;   obtaining information for determining the state of the device using the updated state model, based on the local learning; and   transmitting, to a remote device through communication circuitry of the device, first state related information including the information for determining the state of the device using the updated state model,   wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the BS for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.   
     
     
         2 . The method of  claim 1 , further comprising:
 receiving, from the remote device through the communication circuitry of the device, second state related information for predicting the state of the device.   
     
     
         3 . The method of  claim 2 , wherein the performing of the local learning comprises:
 performing the local learning to update the state model based on the second state related information.   
     
     
         4 . The method of  claim 2 , wherein the performing of the local learning further comprises:
 updating the state model based on the first state related information and the second state related information.   
     
     
         5 . The method of  claim 1 ,
 wherein the information for determining the state of the device comprises at least one parameter for updating the state model among a plurality of parameters for the state model, and   wherein the number of the at least one parameter is smaller than a total number of the plurality of parameters for the state model.   
     
     
         6 . The method of  claim 5 ,
 wherein the information for determining the state of the device comprises weight information of the at least one parameter, and   wherein the at least one parameter and the weight information are used to determine the state of the BS based on the updated state model in the remote device.   
     
     
         7 . The method of  claim 1 , wherein the transmitting of the first state related information comprises:
 determining whether the remote device belongs to a shared group for the device; and   in case that the remote device belongs to the shared group, transmitting the first state related information to the remote device.   
     
     
         8 . The method of  claim 1 , wherein the at least one error comprises at least one of a communication error, a memory error, a fan error, a memory full error, a central processing unit (CPU) full error, or a digital signal processing (DSP) error. 
     
     
         9 . The method of  claim 1 , further comprising:
 receiving, from the remote device through the communication circuitry of the device, information associated with the state model for the local learning in the device.   
     
     
         10 . The method of  claim 1 , wherein the local data comprises sensor data. 
     
     
         11 . A device of a base station (BS), comprising:
 communication circuitry; and   a processor configured to:
 obtain local data and a state model for local learning to predict a state of the device, 
 perform the local learning to update the state model using the local data based on a machine learning algorithm, 
 obtain information for determining the state of the device using the updated state model based on the local learning, and 
 control the communication circuitry to transmit, to a remote device, first state related information including the information for determining the state of the device using the updated state model, 
   wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the BS for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.   
     
     
         12 . The device of  claim 11 , wherein the processor is further configured to control the communication circuitry to receive, from the remote device, second state related information for predicting the state of the device. 
     
     
         13 . The device of  claim 12 , wherein, to perform the local learning, the processor is configured to:
 perform the local learning to update the state model based on the second state related information.   
     
     
         14 . The device of  claim 12 , wherein, to perform the local learning, the processor is configured to:
 update the state model based on the first state related information and the second state related information.   
     
     
         15 . The device of  claim 11 ,
 wherein the information for determining the state of the device comprises at least one parameter for updating the state model among a plurality of parameters for the state model, and   wherein the number of the at least one parameter is smaller than a total number of the plurality of parameters for the state model.   
     
     
         16 . The device of  claim 15 ,
 wherein the information for determining the state of the device comprises weight information of the at least one parameter, and   wherein the at least one parameter and the weight information are used to determine the state of the BS based on the updated state model in the remote device.   
     
     
         17 . The device of  claim 11 , wherein, to transmit the first state related information, the processor is configured to:
 determine whether the remote device belongs to a shared group for the device, and   in case that the remote device belongs to the shared group, transmit the state related information to the remote device.   
     
     
         18 . The device of  claim 11 , wherein the at least one error comprises at least one of a communication error, a memory error, a fan error, a memory full error, a central processing unit (CPU) full error, or a digital signal processing (DSP) error. 
     
     
         19 . The device of  claim 11 , wherein the processor is further configured to control the communication circuitry to receive, from the remote device, information associated with the state model for the local learning in the device. 
     
     
         20 . An electronic device comprising:
 a memory configured to store instructions,   wherein, when the instructions are executed on a device of a base station (BS), the instructions cause the device to:
 obtain local data and a state model for local learning to predict a state of the device, 
 perform the local learning to update the state model using the local data based on a machine learning algorithm, 
 obtain information for determining the state of the device using the updated state model based on the local learning, and 
 transmit, to a remote device through communication circuitry of the device, first state related information including the information for determining the state of the device using the updated state model, and 
   wherein the state of the device is associated with at least one of a power consumption of the device, a resource usage of the device related to frequency or time resources, an abnormal operation of the BS for detecting at least one error occurring in the device, or a network throughput performance of at least one terminal.