The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Embodiments herein provide a method for performing a routing ID (RID) update in user equipment (UE) in a wireless communication network.

In one embodiment of the present invention, a method for enabling a UDM (Unified Data Management) to perform a registration related procedure of an AMF (Access and Mobility Management Function) in a wireless communication system comprises the steps of: receiving, by the UDM, a message related to serving AMF registration of a UE, which includes access type information and ID (Identity) information, from a first AMF; transmitting, by the UDM, a deregistration related message to a second AMF when the second AMF exists, wherein the second AMF is registered as a serving AMF of the UE and related to the access type information.

In one embodiment of the present invention, a method for enabling a UDM (Unified Data Management) to perform a registration related procedure of an AMF (Access and Mobility Management Function) in a wireless communication system comprises the steps of: receiving, by the UDM, a message related to serving AMF registration of a UE, which includes access type information and ID (Identity) information, from a first AMF; transmitting, by the UDM, a deregistration related message to a second AMF when the second AMF exists, wherein the second AMF is registered as a serving AMF of the UE and related to the access type information.

A home appliance configured to be registered in a server, the home appliance includes: a wireless fidelity (Wi-Fi) module, based on the home appliance being registered in the server, communicatively connected to an access point (AP) and configured to periodically operate in an AP mode through a virtual interface, and a controller configured to control the Wi-Fi module to periodically operate in the AP mode. The Wi-Fi module is configured to generate a beacon signal to search for an unregistered home appliance based on the Wi-Fi module being operated in the AP mode.

Tracking devices can be associated with safe zones, smart zones, and high risk zones. Safe zones correspond to regions where a likelihood that a tracking device is lost within the safe zone is lower than outside the safe zone. High risk zones correspond to regions where a likelihood that a tracking device is lost within the high risk zone is higher than outside the high risk zone. Smart zones correspond to an expected tracking device, mobile device, or user behavior. Home areas are geographic regions in which a user resides, and travel areas are geographic regions in which a user does not reside. A tracking device can be configured to operate in a mode selected based on a presence of the tracking device within a safe zone, a smart zone, a high risk zone, a home area, or a travel area.

A wireless communication network serves User Equipment (UEs) over a Third Generation Partnership Project (3GPP) Network Exposure Function (NEF). The wireless communication network comprises a non-3GPP Interworking Function (IWF) and the 3GPP NEF. The non-3GPP IWF receives NEF Application Programming Interface (API) calls that have UE data from the UEs over non-3GPP access nodes. The non-3GPP IWF transfers the NEF API calls that have the UE data to the 3GPP NEF. The 3GPP NEF receives the NEF API calls that have the UE data from the non-3GPP IWF. The 3GPP NEF exposes the UE data to an Application Functions (AF) in response to the NEF API calls.

The present disclosure provides a system for intelligently sampling information, such as location, activities, etc. on device. Sampling and uploading of background context is optimized using machine learning, such that battery usage is reduced, and quality of metrics based on the reported information is maintained or improved. A policy is generated based on the machine learning, the policy dictating how scanning and upload rates should change in response to conditions on the device.

The present disclosure provides a system for intelligently sampling information, such as location, activities, etc. on device. Sampling and uploading of background context is optimized using machine learning, such that battery usage is reduced, and quality of metrics based on the reported information is maintained or improved. A policy is generated based on the machine learning, the policy dictating how scanning and upload rates should change in response to conditions on the device.

The present disclosure provides a system for intelligently sampling information, such as location, activities, etc. on device. Sampling and uploading of background context is optimized using machine learning, such that battery usage is reduced, and quality of metrics based on the reported information is maintained or improved. A policy is generated based on the machine learning, the policy dictating how scanning and upload rates should change in response to conditions on the device.

The present disclosure provides a system for intelligently sampling information, such as location, activities, etc. on device. Sampling and uploading of background context is optimized using machine learning, such that battery usage is reduced, and quality of metrics based on the reported information is maintained or improved. A policy is generated based on the machine learning, the policy dictating how scanning and upload rates should change in response to conditions on the device.