System and method for implementing sensing and communication functions in telecommunications systems

Abstract

A system, method, and device for performing sensing and communication functions in a telecommunications system are provided. According to the embodiment, the system may include at least one sensor node and at least one server node. The at least one sensor node may be configured to acquire one or more sensing data and to provide one or more sensing data to one or more network functions. Furthermore, the at least one sensor node may include at least one 3GPP node, at least one non-3GPP node, or a combination thereof. The at least one server node may include memory storage for storing instructions for performing sensing functions and at least one processor communicatively connected to the memory storage. The at least one processor may be configured to execute instructions for receiving one or more sensing data from one or more network functions, processing one or more sensing data to generate one or more sensing results, and outputting one or more sensing results to one or more network functions.

Description

Technical Field

[0001] Cross - reference to related applications This application claims priority to Indian Provisional Patent Application No. 202341037445, titled "Implementation of Sensing and Communication Functions in 5G and 6G Systems", filed with the Indian Patent Office on May 31, 2023, the disclosure of which is hereby incorporated by reference in its entirety.

[0002] Systems, methods, devices, etc. that conform to exemplary embodiments of the present disclosure relate to sensing technologies in telecommunications systems, and more particularly, to the implementation of sensing and communication functionality in one or more telecommunications systems such as 5G systems and 6G systems.

Background Art

[0003] Telecommunications systems are constantly evolving to meet the changing needs of users, businesses, and society. The global rapid commercialization of advanced telecommunications technologies (such as 5G systems, etc.) not only enhances the communication capabilities of telecommunications systems (e.g., provisioning telecommunications systems with faster and smaller latency), but also enables more devices (such as automobiles, buildings, electrical appliances, etc.) to be utilized as network nodes within the telecommunications system, and the devices can be connected to each other and interoperate with each other.

[0004] The involvement of nodes that can provide sensing functionality (e.g., nodes that can detect the surroundings and exchange observed values via communication), etc., due to the increase in the types and kinds of network nodes in telecommunications systems, makes it possible to transform telecommunications systems into intelligent systems, where network nodes are communicatively connected to each other in a smart way.

[0005] To enhance telecommunications networks, the concept of integrating sensing technologies into telecommunications systems has been introduced over time. For example, the concept of provisioning Integrated Sensing Communications (ISAC) in telecommunications systems such as 5G and 6G systems has been proposed. Generally, ISAC refers to the integration and implementation of sensing and communication technologies into integrated telecommunications systems. Ideally, by utilizing ISAC in telecommunications systems, it is possible to leverage telecommunications infrastructure to provide sensing-related functions or services that address various target areas and applications, such as autonomous / assisted driving, vehicle-to-vehicle and vehicle-to-infrastructure (V2X), unmanned aerial vehicles (UAVs), 3D map reconstruction, smart cities, smart homes, factories, healthcare, and the maritime sector. Therefore, the objective of integrating sensing technologies into telecommunications systems is to reduce the resources required to provision sensing-related services, improve spectral and energy efficiency, and reduce hardware and signaling costs.

[0006] Nevertheless, while the concept and purpose of utilizing sensing capabilities using communication technologies (e.g., ISACs) in telecommunications networks or systems have been introduced into related technologies, specific system architectures and approaches for integrating and implementing sensing technologies into telecommunications systems remain unclear and unspecified to this day.

[0007] Considering the above, it is necessary to define the system architecture and procedures related to the implementation of sensing technology in telecommunications systems. For example, it is necessary to define the system architecture and procedures for hosting or deploying sensing functionality, acquiring sensing data, communicating sensing data between nodes in the network, and processing and analyzing sensing data.

[0008] The information provided above is intended solely to enhance the general context of this disclosure and should not be construed as an endorsement or any form of suggestion of forming prior art already known to those skilled in the art. [Overview of the project]

[0009] Exemplary embodiments of this disclosure provide system architectures, system configurations, and procedures for efficiently and effectively implementing sensing functionality in one or more telecommunications systems.

[0010] According to the embodiment, a system can be provided. The system may have at least one sensor node and at least one server node. At least one sensor node may be configured to acquire one or more sensing data and to provide one or more sensing data to one or more network functions. Furthermore, at least one sensor node may include at least one 3GPP® node, at least one non-3GPP node, or a combination thereof. At least one server node may include memory storage for storing instructions for performing sensing functions and at least one processor communicably connected to the memory storage. At least one processor may be configured to execute instructions for receiving one or more sensing data from one or more network functions, processing one or more sensing data to generate one or more sensing results, and outputting one or more sensing results to one or more network functions.

[0011] According to the embodiment, a method can be provided. The method may be executed by at least one processor of at least one server node of the system when it executes an instruction to perform a sensing function. The method may include receiving one or more sensing data from one or more network functions, processing one or more sensing data to generate one or more sensing results, and outputting one or more sensing results to one or more network functions. The one or more sensing data may be provided to one or more network functions by at least one sensor node, and the at least one sensor node may include at least one 3GPP node, at least one non-3GPP node, or a combination thereof.

[0012] Additional embodiments are some of which are described below, some of which are evident from the description, and some may be realized by the practice of the embodiments presented in this disclosure.

[0013] The features, advantages, and importance of exemplary embodiments of this disclosure will be described below with reference to the attached drawings, where similar reference numerals indicate similar elements. [Brief explanation of the drawing]

[0014] [Figure 1] This is a block diagram of a general system architecture for implementing sensing functions in a telecommunications system, according to one or more embodiments.

[0015] [Figure 2] This is a block diagram of an exemplary system configuration for performing a sensing function, according to one or more embodiments.

[0016] [Figure 3] This is a block diagram of another exemplary system configuration for performing a sensing function, according to one or more embodiments.

[0017] [Figure 4] FIG. A diagram of an exemplary environment in which the sensing functions described herein, and related systems and / or methods, according to one or more embodiments, may be implemented.

[0018] [Figure 5] FIG. A block diagram of a container-based server node, according to one or more embodiments.

[0019] [Figure 6] FIG. A block diagram of an example of general components of a server node, according to one or more embodiments. [[ID=ID=18]]

[0020] [Figure 7] FIG. A diagram of an exemplary configuration of exemplary components of a server node, according to one or more embodiments.

[0021] [Figure 8] FIG. A flowchart of an exemplary method for managing sensing data, according to one or more embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0022] The following detailed description of the exemplary embodiments refers to the accompanying drawings. The same reference numerals in different drawings can identify the same or similar elements.

[0023] The foregoing disclosure provides illustrations and descriptions, but is not intended to be exhaustive or to limit the implementation forms to the exact forms disclosed. Modifications and variations are possible in light of the above disclosure or may be obtained from the implementation of the implementation forms. Furthermore, one or more features or components of one implementation form may be incorporated into another implementation form (or one or more features of another implementation form), or may be combined with another implementation form (or one or more features of another implementation form). In addition, in the description of the operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be executed (at least partially) simultaneously, and the order of one or more operations may be interchanged.

[0024] It will be apparent that the systems and / or methods described herein can be implemented in different forms of hardware, firmware, or combinations of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to the disclosed implementation forms. Therefore, the operations and behaviors of the systems and / or methods are described herein without reference to specific software code. It should be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0025] Even if specific combinations of features are disclosed herein, these combinations are not intended to limit the disclosure of possible implementation forms. In fact, many of these features may be combined in ways not specifically disclosed herein.

[0026] Any elements, actions, or instructions used herein should not be construed as important or essential unless expressly stated otherwise. Furthermore, where used herein, the articles “a” and “an” are intended to refer to one or more items and may be used interchangeably with “one or more.” When only one item is intended, the term “one” or similar language is used. Also, where used herein, terms such as “has,” “have,” “having,” “include,” and “including” are intended to be non-restrictive. Additionally, the phrase “based on” is intended to mean “at least partially based on” unless otherwise specified. Furthermore, expressions such as “at least one of [A] and [B]” or “at least one of [A] or [B]” should be understood as referring to only A, only B, or both A and B.

[0027] Furthermore, it should be understood that the terms “sensing function,” “sensing functionalities,” and “sensing and communication functionalities” as described herein can refer to the same concept and may be used interchangeably herein.

[0028] Exemplary embodiments of this disclosure provide system architectures, system configurations, and procedures for efficiently and effectively implementing sensing functionality in one or more telecommunications systems.

[0029] According to the embodiments, a system architecture for implementing sensing functionality is provided. Specifically, exemplary embodiments of the present disclosure provide a system having at least one server node for storing or hosting a dedicated sensing function (SF) (also referred herein as “sensing and communication function”, “SACF”, or collectively “SF / SACF”) and at least one sensor node for detecting and acquiring sensing data.

[0030] The server node may execute SF to acquire sensing data from the sensor node via the telecommunications system infrastructure and may manage the sensing data independently of the operation of the sensor node. Furthermore, the server node may process the sensing data to generate or produce one or more sensing results and may output one or more sensing results to at least one target node or target location via the telecommunications system infrastructure. A description of an exemplary system architecture for implementing sensing functionality is provided below with reference to Figure 1.

[0031] Since the implementation of SF in the exemplary embodiments of this disclosure utilizes the infrastructure of a telecommunications system, it may utilize existing network infrastructure, minimizing the cost of implementing SF. Furthermore, the time required for developing and deploying new sensing services may be reduced.

[0032] Furthermore, the implementation of SF in the exemplary embodiments of this disclosure separates the sensing data measurement operation from the sensing data management operation. That is, the sensor node only needs to measure and acquire sensing data without managing the sensing data, and the server node only needs to manage the sensing data (e.g., store, process, etc.). Thus, power consumption in each of the sensor node and server node can be optimized. For example, power consumption in the sensor node can be significantly reduced because sensing data processing is performed in the server node. The sensor node only needs to periodically acquire sensing data (e.g., signal information) and provide it to the server node, without needing to know how the data will be processed or the target location to which the sensing results should be transmitted. Furthermore, the sensing data management method may be coordinated or harmonized without requiring a user to physically visit the location where the sensor node is located.

[0033] According to the embodiment, the SF may interoperate with one or more network functions of the core network of the telecommunications system when communicating sensing information such as sensing data and sensing results. The SF may be hosted or deployed on a server node near the component (may be one) where one or more network functions are deployed, and the server node is typically a server in a regional data center or a server in a central data center. Alternatively, the SF may be hosted or deployed on a server near the sensor node and / or near a node where sensing results can be utilized. Such a server may be located in an edge data center, and therefore such a server may be referred to herein as an “edge server”. An example of a system configuration in which the SF interoperates with one or more network functions is described below with reference to Figures 2 and 3.

[0034] Embodiments of the SF in the exemplary embodiments of this disclosure enable interaction and interoperability between the SF and core network functions(s), thereby enabling efficient communication of sensing information (e.g., sensing data, sensing results, etc.) and allowing core network functions(s) to use the sensing information to enhance network performance.

[0035] According to the embodiments, the SF may be hosted or deployed in a cloud server or cloud computing environment. A description of an exemplary environment in which the SF may be implemented is provided below with reference to Figure 4. Furthermore, the SF may be containerized and deployed on a container-based server. Thus, implementations of the SF in the exemplary embodiments of this disclosure can take advantage of the benefits of containerization, such as high scalability, reliability, portability, and resource efficiency. A description of an example of a server node in which the SF may be implemented is provided below with reference to Figures 5-7, and a description of an exemplary operation that may be performed by the server node(s) is provided below with reference to Figure 8.

[0036] Ultimately, exemplary embodiments of this disclosure provide system architectures, system configurations, and operations for implementing and utilizing SF in telecommunications systems, enabling telecommunications systems to effectively, efficiently, and seamlessly integrate sensing operations and provide sensing services.

[0037] The features, advantages, and importance of the exemplary embodiments described above are only a part of this disclosure and are not intended to be exhaustive or to limit the scope of this disclosure.

[0038] Further descriptions of the features, components, configuration, operation, and implementation of exemplary implementations of this disclosure, as well as the associated technical advantages, are provided below.

[0039] General system architecture Figure 1 illustrates a block diagram of a general system architecture 100 for implementing sensing functions in a telecommunications system, according to one or more embodiments.

[0040] As shown in Figure 1, the system architecture 100 may include one or more server nodes 110, one or more sensor nodes 120, and one or more users 130. The server node(s) 110, sensor node(s) 120, and user(s) 130 may be connected to each other via wireless and / or wired connections and may be configured to interoperate with each other.

[0041] Generally, a sensor node(s) 120 may be configured to perform one or more sensing operations to measure or acquire one or more sensing data, and to provide one or more sensing data to a server node(s) 110 for management (e.g., storage / backup, further processing, etc.). A server node(s) 110 may be configured to receive one or more sensing data from a sensor node(s) 120, process the sensing data to generate one or more sensing results, and provide one or more sensing results to a user(s) 130 and / or to the sensor node(s) 120 for use.

[0042] At least one server node 110 may have one or more servers that may have one or more components (e.g., storage) configured to store or host at least one sensing function (SF) 110-1, and may have one or more components (e.g., processors) configured to run or utilize at least one SF 110-1 to manage (e.g., receive, store, process) one or more sensing data. A description of exemplary components that may be included in server node 110 is provided below with reference to Figures 6 and 7, and a description of exemplary operations that may be performed by the server node is provided below with reference to Figure 8.

[0043] According to the embodiment, the server node(s) 110 may include one or more edge servers(s) (also referred to herein as “edge nodes”), which are servers(s) deployed or implemented in one or more edge data centers located near target devices(s) rather than in a central data center. For example, the server node(s) 110 may be located or implemented in one or more edge data centers near sensor nodes(s) 120 and / or near users(s) 130. By implementing the server node(s) 110 (and SF 110-1 included therein) at the edge, sensing-related data can be acquired, processed, and / or delivered with reduced latency, thereby improving the performance and reliability of applications or services that require real-time or near real-time sensing data processing and low latency, such as autonomous vehicle navigation and disaster management.

[0044] Additionally or alternatively, server node 110 may include one or more centralized servers (which may be referred to as “central nodes”), which are servers deployed or implemented in one or more central data centers. The centralized servers may be communicatively connected to a plurality of servers configured to store, host, or deploy a plurality of network functions or network services of a telecommunications system.

[0045] The sensing function (SF) 110-1 described herein (which may be described as a sensing and communication function (SACF), or any other appropriate term) may refer to a dedicated network function for acquiring, storing, processing, managing, and outputting sensing-related information. The SF 110-1 may be defined in a software-based form, such as a computer executable instruction, algorithm, or software application program. Additionally or alternatively, the SF 110-1 may be defined in a form such as a virtualized network function (vNF), an element of software-defined networking (SDN), etc.

[0046] By introducing and implementing SF 110-1 on server node(s) 110, sensing functionality that was previously performed on dedicated hardware sensors may be defined in virtualization or software form, and sensing processes that were previously limited on one or more dedicated hardware sensors may be separated and implemented on different nodes that may be located in different locations (e.g., acquisition of sensing data performed on sensor node(s) 120 and processing of sensing data performed on server node(s) 110). Therefore, sensing data can be acquired from more sensor nodes scattered over a wider area, expanding the size of the sensing area and providing opportunities for new sensing services.

[0047] According to the embodiment, SF 110-1 may be hosted, deployed, or implemented on a cloud server or cloud server cluster, such as a hybrid cloud server / hybrid cloud cluster. A description of an exemplary cloud environment for implementing SF 110-1 is provided below with reference to Figure 4.

[0048] Alternatively or additionally, SF 110-1 may be containerized and hosted, deployed, or implemented on one or more container-based servers or platforms in the form of containers, pods, and / or microservices. For example, server node(s) 110 may include one or more container-based nodes (e.g., Kubernetes (K8s) nodes), and SF 110-1 may be isolated and distributed across multiple containers according to the operation and functionality of SF 110-1. Deploying SF 110-1 as a containerized application allows implementations of SF 110-1 to leverage the benefits of containerization, such as high portability, scalability, and resource efficiency. An example description of a container-based server node for implementing SF 110-1 is provided below with reference to Figure 5.

[0049] According to the embodiment, the server node(s) 110 may be configured to utilize SF 110-1 together with one or more additional network functions. For example, the server node(s) 110 may be configured to house / host the one or more additional network functions, and / or to interoperate with one or more devices / nodes that host the one or more additional network functions as needed. The one or more additional network functions may include network functions(s) of the core network of a telecommunications system, such as an LTE evolved packet core (EPC) network, a 5G core network, or a 6G core network.

[0050] In non-limiting examples, one or more additional network functions that SF 110-1 can interoperate with include: Access and Mobility Management Function (AMF), Session Management Function (SMF), Network Exposure Function (NEF), User Plane Function (UPF), Network Slice Selection Function (NSSF), Network Slice-Specific Authentication and Authorization Function (NSSAAF), Authentication Server Function (AUSF), Network Repository Function (NRF), Policy Control Function (PCF), Unified Data Management (UDM), Service Communication Proxy (SCP), Application Function (AF), Network Slice Acceptance Control Function (NSACF), Edge Application Server Discovery Function (EASDF), Non-3GPP Interworking Function (N3IWF), Trusted 3GPP Gateway Function (TNGF), Unified Data Repository (UDR), Unstructured Data Storage Function (UDSF), Short Message Service Function (SMSF), and 5 It may include one or more of the following: G-device identity register (5G-EIR), location management function (LMF), gateway mobile location center (GMLC), security edge protection proxy (SEPP), network data analysis function (NWDAF), wireline access gateway function (W-AGF), UE radio function management function (UCMF), trusted WLAN interworking function (TWIF), data collection coordination function (DCCF), messaging framework adapter function (MFAF), analytical data repository function (ADRF), multicast broadcast session management function (MB-SMF), multicast broadcast user plane function (MB-UPF), multicast / broadcast service function (MBSF), multicast / broadcast service forwarding function (MBSTF), time-sensing communication time synchronization function (TSCTSF), 5G direct discovery name management function (5G DDNMF), time-dependent network application function (TSN AF), and non-seamless WLAN offload function (NSWOF).It will be understood that one or more additional network functions may have any other suitable network functions defined or specified in one or more specifications provided by the Third Generation Partnership Project (3GPP) standardization body, without departing from the scope of this disclosure.

[0051] SF 110-1 may be configured to communicate with each of the one or more additional network functions described above via their respective dedicated interfaces. As will be further described below, exemplary embodiments of the present disclosure provide a dedicated service-based interface (SBI) "Nsf" for exposing the functionality of a sensing function (e.g., SF 110-1) to one or more of the one or more additional network functions described above. Through the Nsf interface, SF 110-1 may communicate with and interoperate with other network functions.

[0052] Similarly, each of the one or more additional network functions described above may be communicatively connected to SF 110-1 via its own dedicated interface. For example, in a service-based architecture (SBA), each of the above network functions may expose its respective functionality (e.g., network capabilities, resources, information, etc.) via a dedicated SBI (e.g., NEF may expose functionality via the Nnef interface, AMF may expose functionality via the Namf interface, SMF may expose functionality via the Nsmf interface, etc.). In addition to SBIs, some of the one or more additional network functions described above may expose functionality via interfaces defined by reference point representations. For example, the AMF may communicate with the user equipment (UE) via the N1 interface, the AMF may communicate with the access network (or related components, e.g., gNodeB) via the N2 interface, the access network (or related components) may communicate with the UPF via the N3 interface, the SMF may communicate with the UPF via the N4 interface, the UPF may communicate with the data network (DN) via the N6 interface, and multiple UPFs (e.g., intermediate I-UPF and UPF session anchors) may communicate with each other via the N9 interface, and so on.

[0053] An example use case in which SF 110-1 is used with NEF is provided below with reference to Figure 2, and an example use case in which SF 110-1 is used with AMF, SMF, and UPF is provided below with reference to Figure 3.

[0054] In some embodiments, the functionality of SF 110-1 may be separated into multiple categories depending on each function(s). For example, the functionality of SF 110-1 may be separated into a first category associated with control plane-related functions(s) and a second category associated with user plane-related functions(s). Control plane-related functions(s) may include sensing functions(s) that control the operation of one or more sensing services and manage the initiation, termination, and synchronization of one or more sensing tasks (e.g., authorization of sensing entities, network traffic assigned to sensing services, etc.). User plane-related functions(s) may include sensing functions(s) for managing the transmission and communication of sensing data and sensing results, ensuring the efficient and reliable transfer of information from one node to another, etc.

[0055] In some embodiments, SF 110-1 may be hosted or deployed on multiple server nodes 110. In this regard, SF 110-1 may be hosted or deployed on multiple server nodes 110 according to, for example, functional categories. For example, a first part of SF 110-1 associated with control plane-related functions may be hosted or deployed on a first server node 110, and a second part of SF 110-1 associated with user plane-related functions may be hosted or deployed on a second server node 110. In some implementations, the first part of SF 110-1 may be hosted or deployed on an edge server, and the second part of SF 110-1 may be hosted or deployed on a central server. Furthermore, SF 110-1 may be hosted or deployed on multiple server nodes 110 according to the network operation in which each function interoperates. For example, the second part of SF 110-1 (which relates to user plane-related functions) may be hosted on the server that stores or hosts the UPF (or a server near the server that stores or hosts the UPF). In this way, SF 110-1 can be implemented with high reliability and resilience.

[0056] Considering the above, virtualizing the sensing functionality to SF 110-1 and separating the functionality of SF 110-1 allows the virtualized sensing functionality to be dynamically and flexibly implemented according to different network configurations and / or network requirements, while simultaneously enabling centralized management of sensing tasks and sensing information. As a result, the sensing functionality can be implemented easily, flexibly, and optimally.

[0057] Continuing to refer to Figure 1, one or more sensor nodes 120 may include one or more devices, etc., which may be configured to measure, monitor, capture, acquire, (temporarily or for a set period of time) store, and / or transmit one or more sensing data.

[0058] According to one embodiment, one or more sensor nodes 120 may include at least one sensing transceiver that can be configured to transmit and receive one or more sensing data. According to another embodiment, one or more sensor nodes 120 may include at least one sensing transmitter that can be configured to transmit one or more sensing data (e.g., in the form of signals) and at least one sensing receiver that can be configured to receive one or more sensing data.

[0059] According to the embodiment, one or more sensor nodes 120 may include one or more instruments, apparatus, devices, etc., which can be configured to emit one or more signals (in the form of radio waves, etc.) and capture information of one or more response signals or return signals to acquire one or more sensing data. One or more response signals may be reflected, refracted, and / or diffracted versions of one or more emitted signals (which may be referred to herein as “reflected signals,” “refracted signals,” and “diffracted signals,” respectively).

[0060] One or more sensor nodes may be compatible with, or comply with, one or more features and / or requirements specified in one or more specifications provided by the 3GPP standardization body. For example, one or more sensor nodes 120 may include one or more 3GPP-based user equipment (UEs), such as computing devices (e.g., desktop computers, laptop computers, tablet computers, handheld computers, smart devices, servers, etc.), mobile phones (e.g., smartphones, wireless phones, etc.), wearable devices (e.g., smart glasses or smartwatches), SIM-based devices (e.g., vehicles that can utilize V2X technology, etc.), and / or any other suitable devices or equipment that can be configured to emit and transmit one or more signals in the form of 3GPP radio signals and / or via one or more 3GPP interfaces. Additionally or alternatively, one or more sensor nodes 120 may include one or more 3GPP-based network nodes, such as one or more 3GPP-based access networks (e.g., 5G New Radio (NR) radio access networks (RANs), 6G RANs, etc.), one or more 3GPP-based base stations associated with the access networks (e.g., LTE e-node B, 5G g-node B, etc.), and / or any other type of network device or equipment. The term “3GPP sensor node” as used below may refer to one or more of the 3GPP-based sensor nodes provided above.

[0061] According to the embodiment, one or more sensor nodes 120 may also include one or more devices, apparatus, etc., which may have one or more associated hardware sensors. One or more hardware sensors may include an accelerometer for measuring and capturing data related to the acceleration / deceleration of an object; an image sensor (e.g., a camera) for detecting and capturing image data around or near an object; a LiDAR (Light Detection and Ranging) sensor for detecting and capturing data related to light in one or more optical spectra, such as the visible spectrum, infrared spectrum, ultraviolet spectrum, and / or any other optical spectrum; a sound sensor (e.g., a microphone) for detecting and capturing sound data around or near an object; a temperature sensor for measuring and capturing data related to the temperature around or near an object; a position sensor (e.g., a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), etc.) for measuring and capturing data related to the position, location, and / or orientation of an object; a contact sensor (e.g., a pressure detector, an impact detector, etc.) for detecting and capturing data between a part of an object and other objects; an air sensor for measuring and capturing data related to the air around or near an object (e.g., oxygen level, pollution level, humidity level, etc.); and any other suitable type of sensor. Therefore, the sensor node described above may be configured to acquire one or more sensing data without requiring the use of 3GPP radio signals, and may be referred to herein as a "non-3GPP sensor node".

[0062] According to the embodiment, one or more sensor nodes 120 may include multiple 3GPP sensor nodes, multiple non-3GPP sensor nodes, or a mixture of 3GPP and non-3GPP sensor nodes. In some implementations, at least some of the multiple sensor nodes 120 may be located in different geographical locations from the other part of the multiple sensor nodes 120, the server nodes 110, and / or the users 130.

[0063] Furthermore, the above-mentioned sensor nodes may interoperate with each other. For example, the UE may send one or more acquired sensing data to the gNodeB, and the gNodeB may send one or more sensing data provided by the UE to the server node(s) 110 along with the one or more sensing data acquired by the gNodeB. As another example, the image sensor may interoperate with the sound sensor to present one or more sensing data in the form of a video file before sending one or more sensing data to the server node(s) 110.

[0064] Alternatively, multiple sensor nodes may simply provide acquired sensing data to a server node(s) 110, which may be configured to process the sensing data acquired from the multiple sensor nodes using SF 110-1. For example, a server node(s) 110 may use SF 110-1 to receive one or more sensing data (which may be referred to herein as "3GPP sensing data") from one or more 3GPP sensor nodes, analyze the 3GPP sensing data, and generate one or more sensing results. Similarly, a server node(s) 110 may use SF 110-1 to receive one or more sensing data (which may be referred to herein as "non-3GPP sensing data") from one or more non-3GPP sensor nodes, analyze the non-3GPP sensing data, and generate one or more sensing results. According to one embodiment, the server node(s) 110 may be configured to use SF 110-1 to receive both 3GPP sensing data and non-3GPP sensing data, and to generate one or more sensing results based on the 3GPP sensing data and non-3GPP sensing data. For example, the server node(s) 110 may combine 3GPP sensing data with non-3GPP sensing thereon to generate a combined sensing dataset, and then analyze the combined sensing dataset to generate one or more combined sensing results. Additionally or alternatively, the server node(s) 110 may use SF 110-1 to generate one or more sensing results based on one of the 3GPP sensing data and non-3GPP sensing data, and may use another of the 3GPP sensing data and non-3GPP sensing data to verify and / or enhance the generated one or more sensing results.

[0065] In some implementations, when a server node(s) 110 provides or exposes one or more sensing results (for example, to a user(s) 130), it may be configured to include sensing context information with one or more sensing results. The sensing context information may include information about the context or conditions under which the sensing result was derived, such as the location of the capture of the relevant sensing data, the time of the capture of the relevant sensing data, and information about the sensor node(s) 120 (e.g., ID, location, type) that acquired the relevant sensing data.

[0066] According to one embodiment, one or more sensing data provided by one or more sensor nodes 120 may include sensing support information that can be used by server nodes 110 to derive one or more sensing results. For example, the sensing support information may include map information, area information, UE IDs associated with or adjacent to the sensing target, UE location information, UE speed information, etc.

[0067] Continuing to refer to Figure 1, the user(s) 130 may include one or more systems, one or more devices, one or more machines, and / or any other suitable system or node that can be configured to receive one or more sensing results from the server node(s) 110 and subsequently utilize one or more sensing results.

[0068] In some embodiments, user(s) 130 may include systems, devices, nodes, etc., associated with one or more trusted or authorized third parties. A trusted third party(s) may refer to a service provider (e.g., a security service provider, a weather forecasting service provider, a healthcare service provider, etc.) that can utilize one or more sensing results to provide one or more related services to one or more users or subscribers.

[0069] Additionally or alternatively, user(s)130 may include devices, systems, equipment, etc., associated with one or more authorities (e.g., fire departments, police, road conditions management departments, natural disaster response departments, hospital / health emergency departments, etc.), which may use one or more sensing results to provide a response to or take action on one or more incidents or events.

[0070] Furthermore, user(s) 130 may include one or more network functions (or one or more components that deploy one or more network functions) that can utilize one or more sensing results to enhance network performance. Furthermore, user(s) 130 may include one or more storage media (e.g., cloud servers / server clusters, data repositories, etc.) that can be configured to store, host, publish, etc., one or more sensing results. Thus, one or more sensing results may be accessed and retrieved for use as needed.

[0071] Considering the above, sensing functionality can be efficiently and effectively implemented in one or more telecommunications systems. Nodes in a network system may intelligently interoperate to acquire one or more sensing data, process one or more sensing data to generate one or more sensing results, and communicate one or more sensing data and one or more sensing results. In this way, exemplary embodiments of the present disclosure may effectively and efficiently implement, leverage, and be compatible with the concept of integrated sensing communications (e.g., ISAC), thereby providing optimized and enhanced network performance and useful sensing services.

[0072] It will be understood that the configuration shown in Figure 1 is only one possible embodiment, and the scope of this disclosure should not be limited thereto. Specifically, the system architecture may include one or more additional components, or fewer components than those shown, and may be configured in a manner different from that shown. For example, Figure 1 shows that sensor node(s) 110 may provide sensing results to sensor node(s) 120, but in some embodiments, the sensing results may not necessarily be provided to sensor node(s) 120.

[0073] Example configuration for conducting SF (Structure Firmware) As described above with reference to Figure 1, according to the embodiment, the sensing function (SF / SACF) may be implemented and used together with one or more network functions of one or more core networks of one or more telecommunications systems (e.g., network functions(s) of a 5G core network). The following describes exemplary use cases related thereto with reference to Figures 2 and 3.

[0074] Figure 2 shows an exemplary system configuration 200 for implementing sensing functionality in one or more embodiments. One or more components in system configuration 200 may be the same as one or more components described above with reference to Figure 1. For example, the server node(s) 210 (and associated SF 210-1), sensor node(s) 220, and / or trusted third party(s) 230 in Figure 2 may be the same as the server node(s) 110 (and associated SF 110-1), sensor node(s) 120, and user(s) 130 in Figure 1, respectively. Thus, it will be understood that the features described in relation to the above components may be applicable to one another unless otherwise stated.

[0075] Furthermore, while Figure 2 illustrates that server node 210 includes both SF 210-1 and network exposure function (NEF) 210-2, it will be understood that SF 210-1 and NEF 210-2 may be hosted or stored on different server nodes without departing from the scope of this disclosure. For example, SF 210-1 may be deployed or hosted on a first server node (e.g., one or more edge server nodes), and NEF 210-2 may be deployed or hosted on a second server node (e.g., one or more central server nodes).

[0076] In the exemplary use case shown in Figure 2, a system / device associated with a trusted third party 230 (an example of a trusted third party is provided above with reference to Figure 1) is used as an example of a user(s). In practice, it will be understood that the trusted third party 230 can be replaced with any other suitable system / device that can be communicatively connected to the SF 210-1 via the NEF 210-2.

[0077] Generally, a server node(s) 210 may be configured to communicate with a sensor node(s) 220 and receive one or more sensing data from there. An exemplary configuration and operation for communicating with several exemplary sensor nodes is provided below with reference to Figure 3. Thus, a server node(s) 210 may utilize SF 210-1 to process the received sensing data and generate or produce one or more sensing results. The server node(s) 210 may then utilize or interoperate with NEF 210-2, thereby communicating with a trusted third party(s) 230 and subsequently providing one or more sensing results to the trusted third party(s) 230.

[0078] Specifically, upon generating one or more sensing results, the server node(s) 210 may utilize or interoperate with the NEF 210-2 to determine which target location or target node requires or requests one or more sensing results, and send the one or more sensing results to it. In this regard, the NEF 210-2 may refer to a network function that utilizes a network architecture (e.g., a 3GPP network architecture such as a 5G network architecture or a 6G network architecture) to enable network exposure or service exposure within the network. In other words, the use of the NEF 210-1 makes network functions such as data and network services readily available to relevant users (e.g., network operators, service providers, and trusted third parties), thereby making network data and resources accessible to different ecosystems and enabling the enhancement and strengthening of different sensing services and applications.

[0079] In the example in Figure 2, it is assumed that server node(s) 210 utilizes NEF 210-2 to determine that the sensing results (generated based on sensing data provided by sensor node(s) 220) were needed or requested by a trusted third party(s) 230. Information (e.g., sensing results) may be exchanged and communicated between SF 210-1 and NEF 210-2 via an SBI associated with NEF 210-2, such as an Nnef interface. NEF 210-2 may also be part of the network functionality of one or more 3GPP-defined networks (e.g., a 5G core network, a 6G core network, etc.). Alternatively or additionally, the above information may be exchanged and communicated between SF 210-1 and NEF 210-2 via an SBI associated with SF 210-1. In this regard, while the SBI related to the SF in an exemplary embodiment is described herein as "Nsf," it will be understood that such SBI may have any other appropriate labeling without departing from the scope of this disclosure. For example, if the SF of this disclosure is adopted as part of an ISAC, such SBI may have a label such as "Nisac." As another example, if the virtualized sensing function is described as "SACF," such SBI may have a label such as "Nsacf."

[0080] For this purpose, the sensing function SF 210-1 may be implemented and utilized to interoperate with NEF 210-2 and one or more trusted third parties 230 in the network. For example, SF 210-1 may process sensing data to determine available spectral bands, and the sensing results may include such information. Thus, NEF 210-2 may expose or provide spectral availability information to one or more trusted third parties 230, for example, via at least one application programming interface (API). One or more trusted third parties 230 may then utilize the above information to manage the relevant spectra (e.g., selecting the optimal frequency band for operation), thereby optimizing wireless communication with the network.

[0081] As another example, SF 210-1 may process sensing data to determine the location information of nodes associated with one or more end users, and may include such information in (one or more) sensing results. Thus, NEF 210-2 may, for example, expose or provide location information to one or more trusted third parties 230 via at least one API. One or more trusted third parties 230 may then use the location information to deliver personalized and / or context-based services to one or more end users based on their relevant locations.

[0082] In consideration of the foregoing, exemplary embodiments of this disclosure may implement and utilize a sensing function (SF / SACF) which may interoperate with a network exposure function (NEF) of a telecommunications network system and efficiently and effectively provide or disclose sensing information to trusted third parties.

[0083] Figure 3 shows a block diagram of another exemplary system configuration 300 according to one or more embodiments. One or more components in system configuration 300 may be the same as one or more components described above with reference to Figures 1 and / or 2. For example, the server node(s) 310 (and associated SF 310-1) in Figure 3 may be the same as the server node(s) 110 (and associated SF 110-1) in Figure 1, and / or the server node(s) 210 (and associated SF 210-1) in Figure 2. Furthermore, the user equipment (UE) 320-1 and access network (AN) 320-2 in Figure 3 are examples of the sensor node(s) 120 in Figure 1 and / or the sensor node(s) 220 in Figure 2. Furthermore, the data network (DN) 320-3 in Figure 3 may have the roles of sensor node(s) 120 in Figure 1, sensor node(s) 220 in Figure 2, user(s) 130 in Figure 1, and / or trusted third party(s) 230 in Figure 2. Thus, it will be understood that the features described in relation to the above components may be applicable to one another unless otherwise stated.

[0084] Generally, a server node(s) 310 may be configured to utilize SF 311-1 along with several network functions to communicate with UE 320-1, AN 320-2, and DN 320-3. These network functions may include access and mobility management functions (AMF) 311-2, session management functions (SMF) 311-3, and user plane functions (UPF) 312-1. One or more of these network functions may be part of the network functions of one or more core networks (e.g., a 5G core network, a 6G core network, etc.).

[0085] According to the embodiment, SF 311-1, AMF 311-2, and SMF 311-3 may form part of the control plane 311, and UPF 312-1 may form part of the user plane 312. Alternatively or additionally, part of SF 311-1 may be associated with the user plane 312.

[0086] Furthermore, while Figure 3 shows that a server node(s) 310 has SF 311-1, AMF 311-2, SMF 311-3, and UPF 312-1, it will be understood without departing from the scope of this disclosure that SF 311-1 and AMF 311-2, SMF 311-3, and / or UPF 312-1 may be hosted or stored on different server nodes. For example, SF 311-1 may be deployed or hosted on a first server node (e.g., one or more edge server nodes), and AMF 311-2, SMF 311-3, and UPF 312-1 may be deployed or hosted on a second server node (e.g., one or more central server nodes), etc.

[0087] According to one embodiment, a server node(s) 310 may be configured to utilize the SF 311-1 to communicate with the UE 320-1 and AN 320-2 via the AMF 311-2. In this regard, the AMF 311-2 may act as an entity responsible for the access, mobility, and session management functions of the UE 320-1, ensuring an efficient and secure connection between the UE 320-1, the server node(s) 310, and the AN 320-2.

[0088] AN 320-2 may include a radio access network (RAN) having at least one base station (e.g., eNodeB, gNodeB, etc.), at least one radio unit (e.g., remote radio unit (RRU), etc.), at least one antenna system (e.g., distributed antenna system (DAS), etc.), at least one radio network controller, and any other suitable components that can be configured to acquire and / or transmit one or more sensing data.

[0089] During operation, the server node(s) 310 may be configured to utilize the SF 311-1 to receive one or more sensing data from the UE 320-1 and / or AN 320-2. For example, when the UE 320-1 acquires sensing data, it may access the server node(s) 310 and send the acquired sensing data to the server node(s) 310. In this regard, the AMF 311-2 may be used to manage the access of the UE 320-1 before the UE 320-1 provides the sensing data to the server node(s) 310. For example, if the AMF 311-2 is used to authenticate and authorize the access of the UE 320-1, the server node(s) 310 may receive sensing data from the UE 320-1 and then further utilize the SF 311-1 to manage the sensing data. Communication between UE 320-1 and AMF 311-2 may be conducted via the N1 interface, and communication between SF 311-1 and AMF 311-2 may be conducted via the SBI associated with AMF 311-2 (which may be called the "AMF interface" or "Namf") and / or the SBI associated with SF 311-1 (e.g., Nsf).

[0090] AN 320-2 (or one or more associated components) is intended to be able to access server node(s) 310 in a similar manner via the use of AMF 311-2. In this regard, communication between AN 320-2 (or one or more associated components) and AMF 311-2 may be performed via the N2 interface. Furthermore, UE 320-1 and AN 320-2 may be connected to communicate with each other via any suitable means or channel.

[0091] Considering the above, the AMF 311-2 may act as the entity responsible for the access, mobility, and session management functions of the UE 320-1, ensuring efficient and secure connectivity between the UE 320-1, the server node(s) 310, and the AN 320-2. Furthermore, via the AMF 311-2, the server node(s) 310 may receive sensing data and provide one or more sensing results (or related information) to the UE 320-1 and the AN 320-2, thereby enhancing coordination between the UE 320-1 and the AN 320-2.

[0092] For example, a server node(s) 310 may determine information about available spectral bands in real time or near real time based on acquired sensing data and provide this information to UE 320-1 or AN 320-2 via AMF 311-2. Thus, UE 320-1 may use the spectral information to select the optimal frequency band for communication in order to optimize connectivity with the network. Similarly, AN 320-2 may use the spectral information to help UE 320-1 connect to the identified or selected optimal frequency band.

[0093] In consideration of the foregoing, exemplary embodiments of the present disclosure may implement and utilize sensing functions (e.g., SF 311-1) to optimize the allocation and utilization of network resources, improve quality of service, and enhance network performance according to sensing information, thereby enabling interoperability with access management functions (AMF 311-2) of the telecommunications network system.

[0094] Continuing to refer to Figure 3, in some embodiments, the server node(s) 310 may be configured to utilize the SF 311-1 to communicate with the AN 320-2 and DN 320-3 via interoperability with the SMF 311-3 and UPF 312-1.

[0095] A data network (DN) 320-3 may include one or more networks, such as 3GPP networks and / or non-3GPP networks, that are involved in data transmission within a telecommunications network ecosystem. For example, a data network refers to infrastructure that enables the exchange of data between different entities, such as UEs, core network functions, and external networks. This infrastructure includes both core networks (e.g., 3GPP nodes) and non-3GPP networks (external networks accessed via the core network).

[0096] Furthermore, the data network may also include non-3GPP networks such as the internet, private enterprise networks, or other external networks. These non-3GPP networks may be connected to the core network via interworking functions and gateways, enabling the exchange of data between the core network and the external networks.

[0097] On the other hand, Session Management Function (SMF) 311-3 may refer to a network function responsible for managing and controlling session-related aspects of nodes in a network (e.g., UEs). Key functions of SMF 311-3 may include, for example, session establishment (e.g., allocation of network resources, configuration of necessary control and bearer paths, establishment of session context), separation of user plane and control plane (e.g., delegating user plane data processing to user plane functions (UPFs) while interacting with control plane functions (CPFs) to handle control plane signaling and management), policy and quality of service (QoS) enforcement (e.g., enforcing policies and QoS requirements defined by network operators or service providers, ensuring that appropriate QoS levels are applied to user sessions, ensuring that policies related to data usage, traffic management, and service differentiation are enforced), and network slice management (e.g., coordinating the allocation of network resources, policies, and QoS parameters specific to network slices associated with a particular session).

[0098] Furthermore, User Plane Function (UPF) 312-1 may refer to a network function responsible for processing user plane data within the network. Key functions of UPF 312-1 may include, for example, data routing and forwarding (e.g., receiving user data packets from the SMF and routing them to their intended destinations), providing QoS management (e.g., enforcing QoS policies and managing the allocation of network resources to meet specified QoS requirements for user data traffic, and controlling the delivery of data packets based on QoS parameters defined by the SMF 310-3), performing data processing tasks (e.g., data traffic shaping, data packet inspection, content filtering), and interworking with data networks (e.g., facilitating interaction and integration with external 3GPP networks and / or non-3GPP networks, and processing the adaptation and transformation of protocols, formats, or interfaces to enable seamless communication between networks). Communication between the SMF 310-3 and UPF 310-4 may occur via the N4 interface.

[0099] In summary, the SMF 311-3 and UPF 312-1 may be utilized by the server node(s) 310 to establish and manage user sessions, ensure proper QoS enforcement, handle data routing and forwarding, and enable efficient and secure data transmission, thereby enabling efficient communication of sensing data and / or sensing results between nodes. Communication between the SF 311-1 and the SMF 311-3 may occur via the SBI associated with the SMF 311-3 (which can be called the "SMF interface" or "Nsmf"), and / or via the SBI associated with the SF 311-1 (e.g., Nsf). Communication between the SMF 311-3 and the UPF 312-1 may also occur via the N4 interface. In addition, one or more sensing results (provided by the SF 311-1 based on acquired sensing data) may be utilized by the SMF 311-3 and UPF 312-1 to perform one or more actions to improve network performance.

[0100] For example, a server node(s) 310 may be configured to use SF 311-1 to acquire sensing data from sensor nodes(s) (e.g., UE 320-1, AN 320-2, etc.) and generate sensing results based on the acquired sensing data. According to the embodiment, the sensing results may include information about real-time network conditions such as network traffic and signal quality. Therefore, a server node(s) 310-1 may be configured to run SF 311-1 and transmit the sensing results to an SMF 311-3, which may use the aforementioned information to dynamically adjust QoS parameters such as priority or bandwidth allocation for a particular session. Furthermore, the SMF 311-3 may communicate with UPF 312-1 to update QoS settings to ensure that appropriate QoS requirements are met for data transmission within the DN 320-3.

[0101] As another example, the sensing results may include information related to changes in network conditions (e.g., changes in signal strength, changes in available network paths, etc.). In this regard, the SMF 311-3 may utilize the above information to manage data routing and forwarding. For example, the SMF 311-3 may communicate with the UPF 312-1 to dynamically adapt data forwarding paths within the DN, ensuring efficient and reliable data transmission based on the network conditions detected by the SMF 311-1.

[0102] In consideration of the foregoing, exemplary embodiments of the present disclosure may implement and utilize sensing functions (e.g., SF) to optimize network performance, ensure efficient resource utilization, and improve network reliability, wherein the sensing functions may interoperate with session management functions (SMF) and user plane functions (UPF) of a telecommunications network system.

[0103] In consideration of the foregoing, exemplary embodiments of the present disclosure may implement sensing functionality (e.g., SF, SACF, etc.) that interoperates with one or more network functionality of a telecommunications network, thereby enhancing network performance using the sensing functionality or services.

[0104] Examples of implementation environments As described above, according to one or more embodiments, the sensing function (SF / SACF) may be implemented on one or more server nodes. According to the embodiments, one or more server nodes may include cloud servers or cloud server clusters, and the sensing function may be performed in a cloud environment.

[0105] Figure 4 shows a diagram of an exemplary environment 400 in which the sensing functions and related systems and / or methods described herein may be implemented. As shown in Figure 4, the environment 400 may include a plurality of nodes 410, a server platform 420, and a network 430. The devices in the environment 400 may be interconnected by wired connections, wireless connections, or a combination of wired and wireless connections.

[0106] Multiple nodes 410 may include one or more sensor nodes and one or more users as described above. Therefore, for the sake of brevity, redundant explanations related to them may be omitted below.

[0107] The server platform 420 has one or more servers that can receive, generate, store, process, and / or provide information. A description of exemplary embodiments of one or more servers is provided below with reference to Figures 5 to 7. In some implementations, the server platform 420 may include a cloud server or a group of cloud servers. In some implementations, the server platform 420 may be designed modularly so that specific software components can be swapped in or swapped out as needed. Thus, the server platform 420 may be easily and / or quickly reconfigured for different applications.

[0108] In some implementations, the server platform 420 may be hosted in a cloud computing environment 422, as shown in the figures. In particular, the implementations described herein are described assuming that the server platform 420 is hosted in a cloud computing environment 422, but in some implementations, the platform 420 may not be cloud-based (i.e., it may be implemented outside a cloud computing environment), or it may be partially cloud-based.

[0109] The cloud computing environment 422 has an environment that hosts the server platform 420. The cloud computing environment 422 may provide services such as computing, software, data access, and storage that do not require the end user to know the physical location and configuration of the system and / or device that hosts the server platform 420. As shown in the figure, the cloud computing environment 422 may include a group of computing resources 424 (collectively referred to as "computing resources 424" and individually referred to as "computing resources 424").

[0110] The computing resource 424 may include one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, the computing resource 424 may host a server platform 420. The cloud resource may include instances that compute and run on the computing resource 424, storage provided on the computing resource 424, data transfer devices provided by the computing resource 424, and so on. In some implementations, the computing resource 424 may communicate with other computing resources 424 via wired connections, wireless connections, or a combination of wired and wireless connections.

[0111] As further shown in Figure 4, a single computing resource 424 has a group of cloud resources, such as one or more applications ("APP") 424-1, one or more virtual machines ("VM") 424-2, virtualized storage ("VS") 424-3, and one or more hypervisors ("HYP") 424-4.

[0112] Application 424-1 may include one or more software applications that can be provided to or accessed by node 410. Application 424-1 may eliminate the need to install and run software applications on node 410. For example, application 424-1 may include any other software that can be provided through software associated with server platform 420 and / or cloud computing environment 422. In some implementations, one application 424-1 may send and receive information to and from one or more other applications 424-1 via virtual machine 424-2.

[0113] The virtual machine 424-2 may include a software implementation of a machine (e.g., a computer) that runs programs like a physical machine. Depending on its application and the degree to which the virtual machine 424-2 matches an actual machine, the virtual machine 424-2 may be either a system virtual machine or a process virtual machine. A system virtual machine may provide a complete system platform that supports the execution of a complete operating system ("OS"). An exemplary description of an OS is provided below with reference to Figure 7. A process virtual machine may run a single program and may support a single process. In some implementations, the virtual machine 424-2 may run on behalf of a user (e.g., a user associated with node 410) and may manage the infrastructure and / or configuration of the cloud computing environment 422, such as data management, synchronization, or long-term data transfer.

[0114] Virtualized storage 424-3 may include one or more storage systems and / or one or more devices that use virtualization techniques within a storage system or device of a single computing resource 424. In some implementations, in the context of a storage system, the types of virtualization may include block virtualization and file virtualization. Block virtualization may refer to extracting (or separating) logical storage from physical storage so that the storage system can be accessed regardless of the physical storage or heterogeneous structure. Separation may allow the administrator of the storage system to have flexibility in how the administrator manages the storage for end users. File virtualization may eliminate the dependency between data accessed at the file level and the location where the file is physically stored. This may enable optimization of storage usage, server consolidation, and / or performance of non-disruptive file migration.

[0115] The hypervisor 424-4 may provide hardware virtualization technology that enables multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer such as computing resource 424. The hypervisor 424-4 may present a virtual operating platform to the guest operating system and may manage the execution of the guest operating system. Multiple instances of various operating systems may share virtualized hardware resources.

[0116] Network 430 may include one or more wired networks and / or wireless networks. For example, Network 430 may include cellular networks (e.g., fifth-generation (5G) networks, sixth-generation (6G) Network Long-Term Evolution (LTE) networks, third-generation (3G) networks, code division multiple access (CDMA) networks, etc.), public land mobile networks (PLMN), local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs), telephone networks (e.g., public switched telephone networks (PSTNs)), private networks, ad-hoc networks, intranets, the Internet, fiber optic-based networks, etc., and / or combinations of these or other types of networks.

[0117] The number and arrangement of devices and networks shown in Figure 4 are provided as an example. In practice, there may be more or fewer devices and / or networks, different devices and / or networks, or devices and / or networks arranged differently than those shown in Figure 4. Furthermore, two or more devices shown in Figure 4 may be implemented within a single device, or a single device shown in Figure 4 may be implemented as multiple distributed devices. In addition, or instead, one set of devices in environment 400 (e.g., one or more devices) may perform one or more functions that are described as being performed by another set of devices in environment 400.

[0118] In some embodiments, the sensing function (SF / SACF) of the exemplary embodiments described herein may be implemented or deployed on the server platform 420 described above in the form of a virtualized network function (VNF). In this regard, it is intended that the terms “virtual,” “virtualized,” etc., as described herein are merely intended to specify the nature of a machine (and its associated elements and resources) provided in virtual or software form. In this regard, “virtual machine,” “virtualized storage,” etc., as described above should not be limited to any particular type of virtual machine or virtual element. Thus, it will be understood that the sensing function (SF / SACF) may be defined or presented in the form of a containerized network function, and that function may be provided in the form of a container. A description of an exemplary implementation configuration for implementing the sensing function in the form of a containerized sensing function is provided below with reference to Figure 5.

[0119] For this purpose, by virtualizing and implementing the sensing functions (SF / SACF) within the server platform 420, which is isolated from node 410 (e.g., sensor nodes), the operations of the sensing functions, such as processing, storing, and managing sensing data and sensing results, may be performed on a node separate from the sensor node that acquires the sensing data, and by users who utilize the sensing results to provide sensing services.

[0120] As a result, power consumption at sensor nodes can be reduced, which can be particularly advantageous for sensor nodes operating on an independent power source (e.g., a battery). Furthermore, resources for sensing operations (e.g., processing power, memory, storage, etc.) can be easily managed and dynamically scaled up or down on demand, optimizing resource allocation and utilization, and enabling efficient and effective execution of sensing operations, especially those involving complex processing and requiring interoperability between multiple sensor nodes. In addition, sensing data and sensing results can be easily and reliably managed because they are managed collectively by the sensing function. Moreover, the sensing data and sensing results may be easily cloned or backed up to provide redundancy, and access to the sensing data and sensing results may be permitted and authenticated only to trusted entities.

[0121] Examples of containerized sensing functionality As described above, according to one or more embodiments, the sensing function (SF / SACF) may be implemented in the form of a containerized network function. Below are some configuration examples for realizing a containerized sensing function.

[0122] Figure 5 shows a block diagram of exemplary components of a server node 500 according to one or more embodiments. Node 500 may correspond to any of the server nodes (or more) in Figures 1 to 3 (e.g., node 120-1), and may be configured to implement the server platform in Figure 4.

[0123] In this exemplary embodiment, the sensing function may be defined in software form, for example, through containerization (or any other suitable technique). Thus, the containerized sensing function may be deployed on the server node 500 in the form of a container, and the functionality of the sensing function may be performed or achieved through the execution or orchestration of a container associated with the sensing function.

[0124] As shown in Figure 5, the server node 500 may include multiple containers 511-512 and 521-522. The containerized sensing function may be decomposed or distributed among the multiple containers 511-512 and / or 521-522. For example, the functionality of the sensing function may be separated into control plane-related functions and user plane-related functions, the control plane functions may be distributed among containers 511-512, and the user plane functions may be distributed among containers 521-522. Additionally or alternatively, the sensing function may also be separated according to the type of operation, such as operations for receiving sensing data, operations for processing sensing data, and operations for storing sensing data.

[0125] According to one embodiment, the server node 500 may include a Kubernetes (K8s) node, and the user plane containers may be grouped or aggregated into pods (for example, containers related to a first function of the sensing function are included in the first pod 510, containers related to a second function of the sensing function are included in the second pod 520, etc.).

[0126] Multiple pods within server node 500 may share the same resources (e.g., CPU, memory, etc.) provided by server node 500. Resources allocated to sensing functions may be managed by adjusting the pods and / or containers associated with the sensing functions. For example, resources may be scaled up by increasing the number of associated containers and / or pods, or scaled down by decreasing the number of associated containers and / or pods.

[0127] The configuration shown in Figure 5 is simplified for illustrative purposes and should be understood not to limit the scope of this disclosure. Specifically, in practice, the server node 500 may include any suitable components for hosting and running multiple pods without departing from the scope of this disclosure, the number of pods may be more than two, and the number of containers contained in each pod may be more than two. Furthermore, it should be understood that the containerized sensing functionality may be hosted or deployed on multiple nodes in a manner similar to that described above. Furthermore, it should be understood that multiple nodes may have the same container (or pod) to provide network redundancy and thereby improve network availability.

[0128] In consideration of the foregoing, exemplary embodiments of this disclosure may leverage the advantages of containerization when implementing sensing functions (SF / SACF). For example, since sensing functions may be efficiently scaled on demand and may be easily replicated and orchestrated across multiple nodes, implementing containerized sensing functions provides improved scalability, thereby enabling efficient resource utilization and seamless scaling.

[0129] Furthermore, containerized sensing functions can be rapidly instantiated, migrated, and updated, enabling faster time to market for new sensing services and functions. Additionally, the functionality of sensing functions may be managed by coordinating the associated containers, allowing for independent development, testing, and deployment of sensing functions.

[0130] In addition, implementing containerized sensing capabilities can improve resource utilization efficiency, leverage container-specific security features to enhance system security, provide improved portability and interoperability, and enable seamless integration with different systems or platforms.

[0131] Exemplary components of a server node As described above, according to one or more embodiments, the sensing function (SF / SACF) may be implemented in one or more server nodes. Below, exemplary components of a server node(s) for performing the sensing function and related exemplary configurations are provided.

[0132] Figure 6 shows a block diagram of exemplary components of a server node 600 according to one or more embodiments. The server node 600 may correspond to any of the server nodes in Figures 1 to 3 and Figure 5, and may be configured to implement the server platform in Figure 4.

[0133] As shown in Figure 6, the server node 600 may have at least one communication interface 610, at least one storage 620, and at least one processor 630, but it will be understood that, without departing from the scope of this disclosure, the server node 600 may have more or fewer components than those shown in Figure 6, and / or may be arranged in a different manner than those shown in Figure 6.

[0134] The communication interface 610 may include at least one transceiver-like component (e.g., transceivers, separate receivers and transmitters, buses, etc.) that enables the components of the server node 600 to communicate with each other via wired connections, wireless connections, or a combination of wired and wireless connections, and / or with one or more components outside the server node 600.

[0135] For example, the communication interface 610 may connect the processor 630 to the storage 620, thereby enabling them to communicate and interoperate with each other when performing one or more operations. In another example, the communication interface 610 may connect the server node 600 (or one or more components contained therein) to one or more sensor nodes and / or one or more users, thereby enabling them to communicate and interoperate with each other.

[0136] In one or more embodiments, the communication interface 610 may include one or more application programming interfaces (APIs) that enable the server node 600 (or one or more components contained therein) to communicate with one or more software applications (e.g., software applications deployed on sensor nodes, software applications deployed on one or more users, virtualization network functions, etc.). The communication interface 610 may also include at least one input / output interface, at least one network interface, and at least one storage interface (more described below with reference to Figure 7).

[0137] The storage 620 may include one or more storage media suitable for storing data, information, and / or computer executable instructions. According to the embodiment, the storage 620 may include at least one memory storage such as random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) for storing information and / or instructions for use by the processor 630.

[0138] Additionally or alternatively, storage 620 may include, along with a corresponding drive, hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact discs (CDs), digital multipurpose discs (DVDs), floppy disks, cartridges, magnetic tapes, and / or other types of non-temporary computer-readable media.

[0139] According to the embodiment, the storage 620 may be configured to store raw data, metadata, and other information obtained from one or more sensor nodes and from one or more users. Additionally or alternatively, the storage 620 may be configured to store one or more pieces of information relating to one or more operations performed by the processor 630. For example, the storage 620 may store one or more sensing results generated or produced by at least one processor 630, information about the nodes involved in the operations performed by the processor 630, information about the operation history performed by the processor 630, and so on.

[0140] Furthermore, storage 620 may store sensing functions (SF / SACF) and one or more pieces of information relating thereto (e.g., computer-readable instructions for performing the sensing functions). For example, server node 600 may include a cloud server, and the sensing functions may be defined in the form of a cloud-native application running on at least one OS within the cloud server. In addition, storage 620 may similarly store executable instructions for performing one or more network functions described herein, such as NEF, AMF, SMF, and UPF. Furthermore, storage 620 may include memory or a storage medium for storing a collection of program or database components, such as user interfaces, operating systems, web browsers, and mobile applications (more on these later with reference to Figure 7).

[0141] In some implementations, the storage 620 may include multiple storage media, and the storage 620 may be configured to store copies or duplicates of at least a portion of the information on the multiple storage media in order to provide redundancy and back up the information or related data.

[0142] The processor 630 may include at least one processor that can be programmed or configured to perform the functions or operations described herein. According to embodiments, the processor 630 may be configured to receive one or more signals and / or instructions to trigger the performance of one or more operations (e.g., via a communication interface 610, etc.). Furthermore, the processor 630 may be implemented in hardware, firmware, or a combination of hardware and software. For example, the processor 630 may include at least one general-purpose or dedicated processing unit, such as a central processing unit (CPU), graphics processing unit (GPU), acceleration processing unit (APU), microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), integrated system (bus) controller, memory management control unit, floating-point unit, digital signal processing unit, and / or at least one of other types of processing or computing units.

[0143] In some embodiments, the processor 630 may be configured to perform sensing functions stored in at least one storage medium or memory storage (e.g., storage 620) to perform one or more actions or operations as described herein. For example, the processor 630 may perform sensing functions to request, collect, and / or acquire one or more sensing data from one or more sensor nodes (e.g., as described above with reference to Figures 1 to 4), process the acquired sensing data to generate or produce one or more sensing results, store the sensing data and / or sensing results, and provide one or more sensing results to one or more users (e.g., as described above with reference to Figures 1 to 4). In addition, the processor 630 may be configured to perform sensing functions to interoperate with one or more network functions (e.g., network functions (or more) of the core network of a network system such as NEF, AMF, SMF, UPF, etc.). In some embodiments, the processor 630 may be configured to perform sensing functions to interoperate with one or more network functions (e.g., network functions (or more) of the core network of a network system such as NEF, AMF, SMF, UPF, etc.). A description of exemplary operations that can be performed by processor 630 is provided below with reference to Figure 8.

[0144] Figure 7 shows an exemplary configuration of exemplary components of a server node 700 according to one or more embodiments. One or more components of server node 700 may be part of one or more components of server node 600 in Figure 6. For example, the I / O interface 711, storage interface 712, and network interface 713 in server node 700 may be part of the communication interface 610 in server node 600, the memory 720 in server node 700 may be part of the storage 620 in server node 600, and the processor 730 in server node 700 may be part of the processor 630 in server node 600. Thus, it will be understood that the features of server node 600 and server node 700 described herein may be mutually applicable unless otherwise specified. Furthermore, in order to simplify the explanation, redundant descriptions may be omitted below.

[0145] Referring to Figure 7, the processor 730 may be configured to communicate with one or more input devices 740 and one or more output devices 750 via the I / O interface 711. The input devices 740 may include, but are not limited to, devices such as a keyboard, mouse, touchscreen, sensor, microphone, scanner, camera, and fingerprint scanner. The one or more output devices 750 may include, but are not limited to, devices such as a speaker and an electronic screen. The I / O interface 711 may use, but is not limited to, communication protocols / methods such as audio, analog, digital, stereo, IEEE-1393, serial bus, Universal Serial Bus (USB), infrared, PS / 2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), High Definition Multimedia Interface (HDMI®), radio frequency (RF) antenna, S-video, Video Graphics Array (VGA), IEEE 802.n / b / g / n / x, Bluetooth, and cellular (e.g., Code Division Multiple Access (CDMA), High Speed ​​Packet Access (HSPA+), Global Mobile Communication System (GSM), Long-Term Evolution (LTE), WiMAX, etc.). The server node 700 may communicate with input device 740 and output device 750 via the I / O interface 711.

[0146] In some embodiments, the processor 730 may be configured to communicate with the network 760 via a network interface 713. The network interface 713 may connect one or more components of the server node 700 to the network 760 in a communicable manner. The network interface 713 may use, but is not limited to, a connection protocol having direct connection, Ethernet (e.g., twisted-pair 10 / 100 / 1000-base T), Transmission Control Protocol / Internet Protocol (TCP / IP), Token Ring, IEEE 802.11a / b / g / n / x, etc.

[0147] Network 760 may be similar to Network 430 in Figure 4. Additionally or alternatively, Network 760 may be implemented as one or more of various types of networks, such as an intranet or local area network (LAN), or a closed area network (CAN). Furthermore, Network 760 may be either a dedicated network or a shared network, which represents an association of different types of networks that communicate with each other using various protocols, such as Hypertext Transfer Protocol (HTTP), CAN protocol, Transmission Control Protocol / Internet Protocol (TCP / IP), and Wireless Application Protocol (WAP). Furthermore, Network 760 may include various network devices, such as routers, bridges, servers, computing devices, and storage devices.

[0148] In some embodiments, the processor 730 may be configured to communicate with memory 720 (e.g., RAM, ROM, etc., as described above with reference to Figure 6) via a storage interface 712. The storage interface 712 may connect to memory 720 having, but not limited to, memory drives, removable disk drives, etc., using connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1393, Universal Serial Bus (USB), Fibre Channel, Small Computer System Interface (SCSI). Memory drives may further include drums, magnetic disk drives, magneto-optical drives, optical drives, redundant arrays of independent disks (RAID), solid-state memory devices, solid-state drives, etc. Further descriptions of memory 720 are provided by reference to “Storage 620” and “Computer-Readable Media” as described herein.

[0149] Memory 720 may store or deploy a set of program or database components having, but not limited to, at least one user interface 721, at least one operating system 722, at least one web browser 723, and at least one software application 724. The software application 724 may be associated with a sensing function (SF / SACF) of the Disclosure, which, when executed by the processor 730, can enable the processor 730 to perform one or more operations associated therewith. In some embodiments, memory 720 may store user / application data such as data, variables, and records, as described herein. Such a database may be implemented as a fault-tolerant, relational, scalable, and secure database such as Oracle or Sybase.

[0150] Operating system 722 may facilitate resource management and operation of server node 700. Examples of operating systems include, but are not limited to, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (e.g., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (e.g., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM® OS / 2®, MICROSOFT® WINDOWS (XP®, VISTA® / 7 / 8, 10, 11, etc.), APPLE® IOS®, GOOGLE® ANDROID®, BLACKBERRY® OS, etc.

[0151] The user interface 721 may facilitate the display, execution, interaction, manipulation, or operation of program components through text or graphic functions. For example, the user interface may provide computer interaction interface elements such as cursors, icons, checkboxes, menus, scrollers, windows, and widgets on a display system operably connected to the server node 700. A graphical user interface (GUI) may be used, but is not limited to, Apple® Macintosh® operating systems, Aqua®, IBM® OS / 2®, Microsoft® Windows® (e.g., Aero, Metro, etc.), or web interface libraries (e.g., ActiveX®, Java®, Javascript®, AJAX, HTML, Adobe® Flash®, etc.).

[0152] Web browser 723 may be a hypertext browsing application such as MICROSOFT® INTERNET EXPLORER®, GOOGLE®, CHROME®, MOZILLA® FIREFOX®, or APPLE® SAFARI®. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browser 723 may utilize features such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, and Application Programming Interfaces (APIs).

[0153] In some embodiments, the server node 700 may implement a mail server storage program component. The mail server may be an Internet mail server such as Microsoft Exchange. The mail server may utilize functions such as Active Server Pages (ASP), ACTIVEX®, ANSI® C++ / C#, MICROSOFT® .NET, CGI SCRIPTS, JAVA®, JAVASCRIPPT®, PERL®, PHP, PYTHON®, and WEBOBJECTS®. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® Exchange, Post Office Protocol (POP), and Simple Mail Transfer Protocol (SMTP). In some embodiments, the server node 700 may implement a mail client storage program component. The email client may be an email viewing application such as APPLE®MAIL, MICROSOFT®ENTOURAGE®, MICROSOFT®OUTLOOK®, or MOZILLA®THUNDERBIRD®.

[0154] For this purpose, exemplary embodiments of the present disclosure may provide one or more server nodes on which a sensing function (SF / SACF) may be implemented and deployed. Thus, one or more server nodes (or one or more processors associated therewith) may be configured to perform one or more operations for managing sensing data by executing a sensing function (or computer executable instructions associated therewith). A description of some exemplary operations that may be performed by the server node(s) of the present disclosure is provided below with reference to Figure 8.

[0155] Examples of operation using sensing functions As described above, the server node(s) of the exemplary embodiments of this disclosure may utilize or perform sensing functions (SF / SACF) to perform one or more operations. A description of some exemplary operations related thereto is provided below.

[0156] Figure 8 shows a flowchart of an exemplary method 800 for managing sensing data according to one or more embodiments. One or more operations of method 800 may be performed by at least one processor of at least one server node. Specifically, at least one processor may be configured to utilize or execute sensing functions (e.g., SF / SACF) or computer-readable / computer-executable instructions (which may be referred to herein as “instructions for performing sensing functions”) stored in one or more storage media in order to perform the one or more operations described above.

[0157] Generally, at least one processor may execute sensing functions (or related instructions) to receive one or more sensing data, process one or more sensing data to generate one or more sensing results, and output one or more sensing results.

[0158] Specifically, in operation S810, at least one processor may receive one or more sensing data from one or more sensor nodes. As described above, one or more sensor nodes may include one or more user devices (UEs), one or more access networks (ANs), one or more data networks (DNs), one or more users, and any other suitable nodes or devices that can be configured to monitor ambient conditions and acquire sensing data from them. One or more sensor nodes may be further classified into 3GPP sensor nodes and non-3GPP sensor nodes configured to acquire 3GPP sensing data and non-3GPP sensing data, respectively.

[0159] According to the embodiment, the sensing data may include 3GPP sensing data. Specifically, the sensing data may include information about the signal emitted by the sensor node(s), as well as information about response signals such as reflected signals (e.g., a reflected version of the emitted signal), refraction signals (e.g., a refraction version of the emitted signal), and diffraction signals (e.g., a diffraction version of the emitted signal). For example, the sensing data may include the emission angle of the emitted signal, the reflection angle of the reflected signal, the refraction angle of the refraction signal, and / or the diffraction angle of the diffraction signal, the frequencies of the emitted signal, reflected signal, refraction signal, and / or diffraction signal, the phases of the emitted signal, reflected signal, refraction signal, and / or diffraction signal, the time of flight (ToF) of the emitted signal, reflected signal, refraction signal, and / or diffraction signal, and the amplitudes of the emitted signal, reflected signal, refraction signal, and / or diffraction signal.

[0160] Additionally or alternatively, the sensing data may include information relating to the quality of the emitted and responsive signals. For example, the sensing data may include information on the signal-to-noise ratio (SNR) of the emitted, reflected, refracted, and / or diffracted signals, the signal-to-interference plus noise ratio (SINR) of the emitted, reflected, refracted, and / or diffracted signals, the signal intensity of the emitted signal, the received signal intensity indicator (RSSI) of the reflected, refracted, and / or diffracted signals, the signal variation (e.g., fading) of the reflected, refracted, and / or diffracted signals, the delay time of the reflected, refracted, and / or diffracted signals, the jitter of the reflected, refracted, and / or diffracted signals, and the bit error rate (BER) of the reflected, refracted, and / or diffracted signals.

[0161] Additionally or alternatively, the sensing data may include non-3GPP sensing data. Specifically, the sensing data may include information related to accelerometers (e.g., acceleration of an object, deceleration of an object, etc.), information related to image sensors (e.g., image data), information related to LiDAR sensors (e.g., optical spectral data), information related to sound sensors (e.g., audio data), information related to temperature sensor data (e.g., temperature data), information related to position sensors (e.g., position data, location data, orientation data), information related to contact sensors (e.g., contact data), information related to air sensors (e.g., air quality data), and so on.

[0162] According to the embodiment, at least one processor may receive sensing data directly from one or more sensor nodes via an API. Additionally or alternatively, at least one processor may receive sensing data via one or more network functions (or one or more components that perform one or more network functions). Specifically, one or more sensor nodes may acquire one or more sensing data and provide it to one or more network functions, and at least one processor may be configured to communicate with one or more network functions to receive one or more sensings from there.

[0163] For example, sensing data may be provided to the server node by the UE and / or base station (e.g., gNodeB, eNodeB, etc.). In this regard, assuming that the UE and / or base station have not yet established a connection with the server node, the sensing data may first reach the Access Management Function (AMF) (or one or more components that perform the AMF) along with, for example, an access request (or any other appropriate information). Thus, the AMF performs access management (e.g., granting access, establishing access, etc.) and, once it manages access, may provide the sensing data to at least one processor that performs the sensing function (e.g., via a Namf interface, etc.).

[0164] As another example, assuming that the UE and / or base station have established a connection with a server node, the UE may send sensing data to the base station, which may send the sensing data to the User Plane Function (UPF) (or one or more components that perform the UPF) (e.g., via the N3 interface, etc.). The UPF may then send the sensing data to the Session Management Function (SMF) (or one or more components that perform the SMF) (e.g., via the N4 interface, etc.), which may then send the sensing data to at least one processor that performs the sensing function (e.g., via the Nsmf interface, etc.).

[0165] As yet another example, sensing data may be provided by one or more nodes that are communicatively connected to the data network (DN) associated with the server node. For example, a trusted third-party device may be communicatively connected to the DN via a 3GPP network or a non-3GPP network. In this case, the device may provide sensing data to the DN, and the DN may route the sensing data to the UPF (or one or more components that perform the UPF) (e.g., via an N6 interface, etc.). Thus, the UPF may send the sensing data to the SMF (or one or more components that perform the SMF) (e.g., via an N4 interface, etc.), and the SMF may then send the sensing data to at least one processor that performs the sensing function (e.g., via an Nsmf interface, etc.).

[0166] According to one embodiment, at least one processor may be configured to continuously (or periodically) perform sensing functions in order to continuously (or periodically) request sensing data from one or more sensor nodes. For example, at least one processor may continuously (or periodically) generate one or more request messages containing information about the requested sensing data, and may continuously (or periodically) provide the request messages to one or more sensor nodes via one or more API calls. Thus, at least one processor may obtain the latest sensing data in real time or near real time. In some implementations, at least one processor may be configured to receive one or more sensing data from multiple sensor nodes. Multiple sensor nodes may be deployed or positioned at multiple locations that form a sensing area.

[0167] It will be understood that at least one processor may also be configured to acquire one or more sensing data from sensor nodes in any other suitable manner, without departing from the scope of this disclosure.

[0168] Continuing to refer to Figure 8, upon receiving one or more sensing data, in operation S820, at least one processor may be configured to process the one or more sensing data to generate one or more sensing results. For example, assuming that the one or more sensing data consists of one or more 3GPP sensing data, at least one processor may derive one or more network condition information, such as signal quality, spectral and / or bandwidth availability, potential network interference, and areas with (or a probability of) network congestion, based on the one or more 3GPP sensing data (e.g., signal quality data).

[0169] According to the embodiment, at least one processor may detect the intrusion of an object based on one or more 3GPP sensing data. Specifically, at least one processor may determine a change in the emitted signal by comparing information from the emitted signal with information from at least one response signal (e.g., a reflected signal, a refraction signal, a diffraction signal, etc.).

[0170] For example, at least one processor may determine whether the emitted signal has changed by comparing the angle of the emitted signal with the angle of the reflected signal and determining whether the angle of the emitted signal is different from the angle of the reflected signal. Thus, based on the determination that the emitted signal has changed, at least one processor may determine whether the change in the emitted signal satisfies one or more conditions. For example, at least one processor may determine whether the change is within a threshold and / or within any other suitable conditions. One or more conditions may be specified or defined by a sensing function (SF / SACF). At least one processor may then include the result of the determination in one or more sensing results.

[0171] As a non-limiting use case, one or more sensor nodes may be base stations deployed on a road to detect pedestrian intrusion, or devices in a home security system deployed inside a house to detect intrusion into a UE or residence. In this regard, one or more sensor nodes may be configured to continuously (or periodically) emit signals to the surrounding environment and to continuously (or periodically) measure, detect, and acquire information related to the response signals (e.g., reflected signals, refracted signals, diffracted signals, etc.). Information on emitted signals and response signals may be provided to a server node (in the form of sensing data) in the same manner as described above.

[0172] Therefore, at least one processor on a server node may use a sensing function to analyze sensing data, for example, to determine whether the emission angle of one or more emitted signals differs from the reflection angle of one or more reflected signals. In this regard, if the emission angle of at least one emitted signal differs to some extent from the reflection angle of at least one reflected signal, it indicates that a possible intrusion event has occurred (e.g., a burglar may have entered the house, or a pedestrian or wild animal may have entered the road). Subsequently, based on the determination that the angles differ from each other, at least one processor may determine whether the difference in the angles falls within a threshold (and / or any other suitable condition) defined by the sensing function, and may include a result (e.g., true / false) in one or more sensing results.

[0173] According to one embodiment, at least one processor may compare the signal quality of one or more emitted signals with the signal quality of one or more reflected signals, and determine whether one or more emitted signals have changed by determining whether the signal quality of one or more emitted signals differs from the signal quality of one or more reflected signals. Therefore, based on the determination that one or more emitted signals have changed, at least one processor may determine, in the same manner as described above, whether the change in one or more emitted signals satisfies one or more conditions. The result of the determination may be included in one or more sensing results.

[0174] As a non-limiting use case, one or more sensor nodes may be base stations deployed near a river to detect flood events (or any other suitable events). In this regard, one or more sensor nodes may be configured to continuously (or periodically) acquire one or more sensing data from the environment and continuously (or periodically) provide one or more sensing data to a server node, in a manner similar to that described above.

[0175] Therefore, at least one processor on the server node may use the sensing function to analyze one or more sensing data to determine whether the signal quality of one or more emitted signals differs from the signal quality of one or more reflected signals. In this regard, if the signal quality of one or more emitted signals differs to some extent from the signal quality of one or more reflected signals, it indicates that there may be an anomaly in signal quality attention and that an event (e.g., heavy rain) may have occurred. Subsequently, based on the determination that the signal qualities differ from each other, at least one processor may determine whether the difference is within a threshold (and / or any other suitable condition) defined by the sensing function, and may include a result (e.g., true / false) in one or more sensing results.

[0176] According to the embodiment, one or more sensing data received by at least one processor (in operation S810) may include both 3GPP sensing data and non-3GPP sensing data. In this regard, at least one processor may generate one or more sensing results based on the 3GPP sensing data and non-3GPP sensing data. For example, at least one processor may combine 3GPP sensing data with non-3GPP sensing data to generate a combined sensing dataset, and may analyze the combined sensing dataset to generate one or more combined sensing results. As another example, at least one processor may generate one or more sensing results based on one of the 3GPP sensing data and non-3GPP sensing data, and may use another one of the 3GPP sensing data and non-3GPP sensing data to verify and / or enhance the generated one or more sensing results. As yet another example, at least one processor may combine one first portion of 3GPP sensing data with non-3GPP sensing data to generate a combined sensing dataset, analyze the combined sensing dataset to generate one or more combined sensing results, and use another second portion of 3GPP sensing data and non-3GPP sensing data to validate and / or enhance the generated one or more combined sensing results. It will be understood that at least one processor may also process 3GPP sensing data and / or non-3GPP sensing data in any other suitable way to generate one or more sensing results without departing from the scope of this disclosure.

[0177] For example, in a flood event sensing use case, at least one processor may combine 3GPP sensing data with non-3GPP sensing data, such as data from humidity sensors, pressure sensors, and acoustic rain sensors, to generate a combined sensing dataset, which may then be processed to generate sensing results for determining flood events. Furthermore, at least one processor may validate the generated (one or more) sensing results in a manner similar to that described above.

[0178] Continuing to refer to Figure 8, once one or more sensing data have been processed and one or more sensing results have been generated, in operation S830, at least one processor of the server node may be configured to perform a sensing function to output one or more sensing results. For example, at least one processor may perform a sensing function to determine one or more target nodes or one or more target locations to which one or more sensing results should be sent, and may send one or more sensing results to each target node or target location. One or more target nodes / one or more target locations may include, for example, one or more users (e.g., one or more trusted third parties, one or more UEs, one or more network functions, etc.).

[0179] According to the embodiment, at least one processor may directly transmit one or more sensing results to a target node / target location via an API. Additionally or alternatively, at least one processor may output one or more sensing results to one or more network functions (or one or more components that perform one or more network functions).

[0180] For example, at least one processor may provide one or more sensing results to one or more of the NEF, AMF, and SMF (e.g., via an Nsf interface, etc.) (or one or more components that perform the above network functions), and the above network functions may appropriately route one or more sensing results to their respective target node(s) / target location(s). For example, assuming that the target node(s) / target location(s) are associated with a trusted third party, at least one processor may provide one or more sensing results to the NEF (or one or more components that perform the NEF), and the NEF may appropriately and securely transmit one or more sensing results to the trusted third party. Similarly, at least one processor may provide one or more sensing results to the AMF (or one or more components that perform the AMF), and the AMF may transmit one or more sensing results to a target UE, target AN, etc. Furthermore, at least one processor may provide one or more sensing results to the SMF (or one or more components performing the AMF), and the SMF may transmit one or more sensing results to the UPF (or one or more components performing the UPF). The UPF may then forward one or more sensing results to target AN, target DN, etc.

[0181] According to the embodiment, at least one processor may register information about sensing functions (SF / SACF), such as functionality and capabilities, with a network repository function (NRF) and update it continuously (or periodically). Accordingly, one or more network functions (e.g., NEF, AMF, SMF, etc.) may discover a sensing function (or one or more pieces of information related thereto) by utilizing the NRF discovery mechanism. For example, the one or more network functions may continuously (or periodically) send requests to the NRF for the latest information related to the sensing function. In response, the NRF may provide the requested information to the one or more network functions. In this way, one or more network functions may discover and communicate with a sensing function for communicating one or more sensing data and / or one or more sensing results. In this regard, one or more network functions may continuously (or periodically) send requests for one or more sensing results to at least one processor executing the sensing function via their respective interfaces (e.g., their respective SBIs, etc.). Therefore, at least one processor may, in response to a request, provide one or more sensing results to one or more network functions (for example, via an NSF interface).

[0182] Method 800 may terminate after outputting one or more sensing results. Alternatively, Method 800 may return to operation S810 so that at least one processor of the server node can execute operations S810 to S830 again for at least a certain period of time. In this way, at least one processor may continuously (or periodically) receive one or more updated sensing data, process one or more updated sensing data to generate one or more updated sensing results, and output one or more updated sensing results.

[0183] It should be understood that the features described above are only a part of possible embodiments and are not intended to be exhaustive or to limit the scope of this disclosure. Specifically, at least one processor may be configured, without departing from the scope of this disclosure, to utilize a sensing function to communicate one or more sensing data and one or more sensing functions with any other suitable network function in any other suitable manner.

[0184] For this purpose, exemplary embodiments of the present disclosure provide a server node with sensing functions deployed or hosted therein as a dedicated network function for managing sensing-related processes and tasks. Thus, the server node (or at least one associated processor) can, by utilizing the sensing functions (or associated instructions), communicate with one or more sensor nodes to acquire one or more sensing data, process one or more sensing data, and generate one or more sensing results. Thus, further operations or actions can be performed based on one or more sensing results.

[0185] For example, based on sensing a home intrusion event, one or more sensing results may be provided to security authorities. Another example is that based on sensing a pedestrian or wild animal entering a road, one or more sensing results may be provided to a vehicle near the road to warn the driver. Further non-exclusive use cases may include rainfall monitoring, flood sensing, sensing for traffic management at tourist attractions, sensing for contactless sleep monitoring services, and automotive driver assistance and navigation.

[0186] In addition to sensing services, the network performance of telecommunications systems may also be improved, enhanced, or further developed. For example, one or more sensing results may be used to optimize bandwidth / spectral selection and utilization, to provide accurate beamforming, to enable fast beam fault recovery, or to achieve less overhead for tracking channel status information (CSI).

[0187] Various embodiments The exemplary embodiments described above with reference to Figures 1 to 8 are merely examples of possible embodiments of the present disclosure and are not intended to limit or restrict the scope of the present disclosure.

[0188] Specifically, the foregoing disclosures provide examples and explanations, but are not intended to be exhaustive or to limit implementations to the exact forms disclosed. Modifications and variations are possible in light of the foregoing disclosures or may be derived from the practice of the implementations.

[0189] Some embodiments may relate to devices (e.g., server nodes), systems, methods, and / or computer-readable media in integration at any possible level of technical detail. Furthermore, one or more of the above-described components may be implemented as instructions stored in computer-readable media and executable by at least one processor (and / or may have at least one processor). The computer-readable media may consist of one or more computer-readable non-temporary storage media having computer-readable program instructions for causing a processor to perform an action.

[0190] A computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may, but is not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the aforementioned. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital multipurpose discs (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punched cards or grooved raised structures on which instructions are recorded, and any suitable combination of the aforementioned. When used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmitting media (e.g., light pulses passing through optical fiber cables), or electrical signals transmitted over wires.

[0191] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include transmission copper cables, transmission optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage on a computer-readable storage medium within each computing / processing device.

[0192] Computer-readable program code / instructions for performing an operation may be either assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk and C++, and procedural programming languages ​​such as the C programming language or similar programming languages.

[0193] Computer-readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network having a local area network (LAN) or wide area network (WAN), or a connection to an external computer may be made (for example, via the Internet using an Internet service provider). In some embodiments, for example, electronic circuits having programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may execute computer-readable program instructions and personalize the electronic circuit by utilizing the state information of the computer-readable program instructions to perform a manner or operation.

[0194] These computer-readable program instructions may be provided to a general-purpose computer, a dedicated computer, or a processor of another programmable data processing device to generate a machine such that instructions executed by the processor of the computer or other programmable data processing device form means for performing functions / operations specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored on a computer-readable storage medium on which the instructions are stored, which can be instructed to function in a particular manner to constitute a product having instructions that perform the modes of functions / operations specified in one or more blocks of a flowchart and / or block diagram.

[0195] Computer-readable program instructions can also be loaded into a computer, another programmable device, or another device to generate a computer implementation process that performs a series of operational steps on the computer, another programmable device, or another device, resulting in the instructions being executed on the computer, another programmable device, or another device performing functions / operations specified in one or more blocks of a flowchart and / or block diagram or both.

[0196] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media in various implementation forms. In this regard, each block in a flowchart or block diagram may represent a module, segment, or part of an instruction set having one or more executable instructions for implementing a specific logical function. Methods, computer systems, and computer-readable media may have additional blocks, fewer blocks, different blocks, or different block arrangements compared to those shown in the figures. In some alternative implementation forms, the functions described in the blocks may be performed in a different order than shown in the figure. For example, two blocks shown consecutively may actually be executed simultaneously or substantially simultaneously, or blocks may sometimes be executed in reverse order depending on the associated functionality. It should also be noted that each block in a block diagram and / or flowchart, as well as combinations of blocks in a block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or a combination of dedicated hardware and computer instructions.

[0197] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or combinations of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to the implementation form. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It will be understood that software and hardware may be designed to implement the systems and / or methods based on the descriptions herein.

[0198] In consideration of the foregoing, various further aspects and features of the embodiments of this disclosure may be defined by the following items. Item [1]: A system comprising at least one sensor node configured to acquire one or more sensing data and provide one or more sensing data to one or more network functions, wherein the at least one sensor node may have at least one 3GPP node, at least one non-3GPP node, or a combination thereof, and at least one server node. The at least one server node has memory storage for storing instructions for performing sensing functions and at least one processor communicatively connected to the memory storage, wherein the at least one processor may execute instructions for receiving one or more sensing data from one or more network functions, processing one or more sensing data to produce one or more sensing results, and outputting one or more sensing results to one or more network functions. Item [2]: The system described in Item [1], wherein at least one 3GPP sensor node may have at least one 3GPP-based user equipment (UE), a 3GPP-based base station, or a combination thereof. Item [3]: A system described in any one of items [1]-[2], in which at least one non-3GPP sensor node may have a non-3GPP based UE, accelerometer, image sensor, sound sensor, radar, camera, temperature sensor, position sensor, contact sensor, air sensor, or a combination thereof. Item [4]: ​​A system described in any one of items [1] to [3], wherein at least one server node may have an edge server. Item [5]: A system described in any one of items [1] to [4], in which one or more network functions may include one or more of the following: network exposure functions (NEF), access and mobility management functions (AMF), session management functions (SMF), and user plane functions (UPF). Item [6]: The system according to any one of items [1] to [5], wherein the system may have at least two server nodes, the first of the at least two server nodes may have a sensing function, and the second of the at least two server nodes may have one or more network functions. Item [7]: A system according to any one of items [1] to [6], wherein one or more sensing data may have information relating to an emission signal and information relating to a response signal, and the response signal may have one or more of a reflected signal, a refracted signal, and a diffracted signal. Item [8]: The system described in Item [7], in which one or more sensing data may include one or more of the following: emission angle of an emitted signal, reflection angle of a reflected signal, refraction angle of a refraction signal, diffraction angle of a diffracted signal, frequency of an emitted signal, frequency of a reflected signal, frequency of a refraction signal, frequency of a diffracted signal, phase of an emitted signal, phase of a reflected signal, phase of a refraction signal, phase of a diffracted signal, time of flight (ToF) of an emitted signal, ToF of a reflected signal, ToF of a refraction signal, ToF of a diffracted signal, amplitude of an emitted signal, amplitude of a reflected signal, amplitude of a refraction signal, and amplitude of a diffracted signal. Item [9]: A system described in any one of items [7] to [8], in which one or more sensing data may have one or more of the following: signal-to-noise ratio (SNR) of the emitted signal, SNR of the reflected signal, SNR of the refraction signal, SNR of the diffraction signal, signal-to-interference plus noise ratio (SINR) of the emitted signal, SINR of the reflected signal, SINR of the refraction signal, SINR of the diffraction signal, signal intensity of the emitted signal, received signal intensity indicator (RSSI) of the reflected signal, RSSI of the refraction signal, RSSI of the diffraction signal, signal variation of the reflected signal, signal variation of the refraction signal, signal variation of the diffraction signal, delay time of the reflected signal, delay time of the refraction signal, delay time of the diffraction signal, jitter of the reflected signal, jitter of the refraction signal, jitter of the diffraction signal, bit error rate (BER) of the reflected signal, BER of the refraction signal, and BER of the diffraction signal. Item

[10] : The system according to any one of items [7] to [9], wherein at least one processor of at least one server node may be configured to execute instructions for processing one or more sensing data by comparing information of an emitted signal with information of a reflected signal in order to determine a change in the emitted signal; determining, based on the determination that the emitted signal has changed, whether the change in the emitted signal satisfies one or more conditions; and generating one or more sensing results based on the result of the determination. Item

[11] : A method, which is performed by at least one processor of at least one server node of a system, when an instruction for performing a sensing function is executed, the method comprising receiving one or more sensing data from one or more network functions, the one or more sensing data may be provided to one or more network functions by at least one sensor node, the at least one sensor node may have at least one 3GPP node, at least one non-3GPP node, or a combination thereof; processing one or more sensing data to generate one or more sensing results; and outputting one or more sensing results to one or more network functions. Item

[12] : The method according to Item

[11] , wherein at least one 3GPP sensor node may have at least one 3GPP-based user equipment (UE), a 3GPP-based base station, or a combination thereof. Item

[13] : The method described in any one of Items

[11] -

[12] , wherein at least one non-3GPP sensor node may have a non-3GPP based UE, accelerometer, image sensor, sound sensor, radar, camera, temperature sensor, position sensor, contact sensor, air sensor, or a combination thereof. Item

[14] : The method described in any one of items

[11] to

[13] , wherein at least one server node may have an edge server. Item

[15] : The method described in any one of items

[11] to

[14] , wherein one or more network functions include one or more of the following: network exposure functions (NEF), access and mobility management functions (AMF), session management functions (SMF), and user plane functions (UPF). Item

[16] : The method according to any one of items

[11] to

[15] , wherein the system may have at least two server nodes, the first of the at least two server nodes may have sensing capabilities, and the second of the at least two server nodes may have one or more networking capabilities. Item

[17] : A system according to any one of items

[11] to

[16] , wherein one or more sensing data may have information relating to an emission signal and information relating to a response signal, and the response signal may have one or more of a reflected signal, a refracted signal, and a diffracted signal. Item

[18] : The method according to Item

[17] , wherein one or more sensing data may have one or more of the following: emission angle of an emitted signal, reflection angle of a reflected signal, refraction angle of a refraction signal, diffraction angle of a diffracted signal, frequency of an emitted signal, frequency of a reflected signal, frequency of a refraction signal, frequency of a diffracted signal, phase of an emitted signal, phase of a reflected signal, phase of a refraction signal, phase of a diffracted signal, time of flight (ToF) of an emitted signal, ToF of a reflected signal, ToF of a refraction signal, ToF of a diffracted signal, amplitude of an emitted signal, amplitude of a reflected signal, amplitude of a refraction signal, amplitude of a diffracted signal. Item

[19] : The method according to any one of items

[17] to

[18] , wherein one or more sensing data may have one or more of the following: signal-to-noise ratio (SNR) of the emitted signal, SNR of the reflected signal, SNR of the refraction signal, SNR of the diffraction signal, signal-to-interference plus noise ratio (SINR) of the emitted signal, SINR of the reflected signal, SINR of the refraction signal, SINR of the diffraction signal, signal intensity of the emitted signal, received signal intensity indicator (RSSI) of the reflected signal, RSSI of the refraction signal, RSSI of the diffraction signal, signal variation of the reflected signal, signal variation of the refraction signal, signal variation of the diffraction signal, delay time of the reflected signal, delay time of the refraction signal, delay time of the diffraction signal, jitter of the reflected signal, jitter of the refraction signal, jitter of the diffraction signal, bit error rate (BER) of the reflected signal, BER of the refraction signal, and BER of the diffraction signal. Item

[20] : A system according to any one of items

[17] to

[19] , wherein processing one or more sensing data may include comparing information of an emitted signal with information of a reflected signal in order to determine a change in the emitted signal; determining whether the change in the emitted signal satisfies one or more conditions based on the determination that the emitted signal has changed; and generating one or more sensing results based on the results of the determination.

[0199] In light of the above teachings, it will be understood that many modifications and variations of this disclosure are possible. It will also be apparent that, within the scope of the attached clauses, this disclosure may be implemented in ways other than those specifically described herein.

Claims

1. A sensor node configured to acquire one or more sensing data and provide the one or more sensing data to one or more network functions, wherein the at least one sensor node has at least one 3GPP node, at least one non-3GPP node, or a combination thereof, It has at least one server node, The aforementioned at least one server node, Memory storage for storing instructions to implement sensing functions, The system has at least one processor that is communicatively connected to the memory storage, The aforementioned at least one processor, Receiving one or more sensing data from one or more network functions, Processing the one or more sensing data to generate one or more sensing results, The one or more network functions output the one or more sensing results, It is configured to execute the aforementioned instructions for performing the above actions. system.

2. The system according to claim 1, wherein the at least one 3GPP sensor node comprises at least one 3GPP-based user equipment (UE), a 3GPP-based base station, or a combination thereof.

3. The system according to claim 1, wherein the at least one non-3GPP sensor node has a non-3GPP-based UE, accelerometer, image sensor, sound sensor, radar, camera, temperature sensor, position sensor, contact sensor, air sensor, or a combination thereof.

4. The system according to claim 1, wherein the at least one server node has an edge server.

5. The system according to claim 1, wherein the one or more network functions include one or more of the following: network exposure function (NEF), access and mobility management function (AMF), session management function (SMF), and user plane function (UPF).

6. The system according to claim 1, wherein the system has at least two server nodes, the first of the at least two server nodes having the sensing function, and the second of the at least two server nodes having one or more network functions.

7. The system according to claim 1, wherein the one or more sensing data have information relating to an emission signal and information relating to a response signal, and the response signal has one or more of a reflection signal, a refraction signal, and a diffraction signal.

8. The system according to claim 7, wherein the one or more sensing data includes one or more of the emission angle of the emission signal, the reflection angle of the reflected signal, the refraction angle of the refraction signal, the diffraction angle of the diffraction signal, the frequency of the emission signal, the frequency of the reflected signal, the frequency of the refraction signal, the frequency of the diffraction signal, the phase of the emission signal, the phase of the reflected signal, the phase of the refraction signal, the phase of the diffraction signal, the time of flight (ToF) of the emission signal, the ToF of the reflected signal, the ToF of the refraction signal, the ToF of the diffraction signal, the amplitude of the emission signal, the amplitude of the reflected signal, the amplitude of the refraction signal, and the amplitude of the diffraction signal.

9. The system according to claim 7, wherein the one or more sensing data includes one or more of the following: the signal-to-noise ratio (SNR) of the emitted signal, the SNR of the reflected signal, the SNR of the refraction signal, the SNR of the diffraction signal, the signal-to-interference plus noise ratio (SINR) of the emitted signal, the SINR of the reflected signal, the SINR of the refraction signal, the SINR of the diffraction signal, the signal intensity of the emitted signal, the received signal intensity indicator (RSSI) of the reflected signal, the RSSI of the refraction signal, the RSSI of the diffraction signal, the signal fluctuation of the reflected signal, the signal fluctuation of the refraction signal, the signal fluctuation of the diffraction signal, the delay time of the reflected signal, the delay time of the refraction signal, the delay time of the diffraction signal, the jitter of the reflected signal, the jitter of the refraction signal, the jitter of the diffraction signal, the bit error rate (BER) of the reflected signal, the BER of the refraction signal, and the BER of the diffraction signal.

10. The at least one processor of the at least one server node is In order to determine the change in the emission signal, the information of the emission signal is compared with the information of the reflected signal, Based on the determination that the emission signal has changed, it is determined whether the change in the emission signal satisfies one or more conditions, To generate one or more sensing results based on the result of the determination, The system according to claim 7, configured to execute the instructions for processing one or more sensing data.

11. A method by which instructions for performing a sensing function are executed by at least one processor of at least one server node of the system, Receiving one or more sensing data from one or more network functions, wherein the one or more sensing data is provided to the one or more network functions by at least one sensor node, and the at least one sensor node has at least one 3GPP node, at least one non-3GPP node, or a combination thereof. To generate one or more sensing results, the one or more sensing data are processed, The one or more network functions output the one or more sensing results, A method having.

12. The method according to claim 11, wherein the at least one 3GPP sensor node comprises at least one 3GPP-based user equipment (UE), a 3GPP-based base station, or a combination thereof.

13. The method according to claim 11, wherein the at least one non-3GPP sensor node has a non-3GPP-based UE, accelerometer, image sensor, sound sensor, radar, camera, temperature sensor, position sensor, contact sensor, air sensor, or a combination thereof.

14. The method according to claim 11, wherein the at least one server node includes an edge server.

15. The method according to claim 11, wherein the one or more network functions include one or more of the following: network exposure function (NEF), access and mobility management function (AMF), session management function (SMF), and user plane function (UPF).

16. The method according to claim 11, wherein the system has at least two server nodes, the first of the at least two server nodes has the sensing function, and the second of the at least two server nodes has the one or more network functions.

17. The method according to claim 11, wherein the one or more sensing data have information relating to an emission signal and information relating to a response signal, and the response signal has one or more of a reflection signal, a refraction signal, and a diffraction signal.

18. The method according to claim 17, wherein the one or more sensing data includes one or more of the emission angle of the emission signal, the reflection angle of the reflection signal, the refraction angle of the refraction signal, the diffraction angle of the diffraction signal, the frequency of the emission signal, the frequency of the reflection signal, the frequency of the refraction signal, the frequency of the diffraction signal, the phase of the emission signal, the phase of the reflection signal, the phase of the refraction signal, the phase of the diffraction signal, the time of flight (ToF) of the emission signal, the ToF of the reflection signal, the ToF of the refraction signal, the ToF of the diffraction signal, the amplitude of the emission signal, the amplitude of the reflection signal, the amplitude of the refraction signal, and the amplitude of the diffraction signal.

19. The method according to claim 17, wherein the one or more sensing data includes one or more of the following: the signal-to-noise ratio (SNR) of the emitted signal, the SNR of the reflected signal, the SNR of the refraction signal, the SNR of the diffraction signal, the signal-to-interference plus noise ratio (SINR) of the emitted signal, the SINR of the reflected signal, the SINR of the refraction signal, the SINR of the diffraction signal, the signal intensity of the emitted signal, the received signal intensity indicator (RSSI) of the reflected signal, the RSSI of the refraction signal, the RSSI of the diffraction signal, the signal fluctuation of the reflected signal, the signal fluctuation of the refraction signal, the signal fluctuation of the diffraction signal, the delay time of the reflected signal, the delay time of the refraction signal, the delay time of the diffraction signal, the jitter of the reflected signal, the jitter of the refraction signal, the jitter of the diffraction signal, the bit error rate (BER) of the reflected signal, the BER of the refraction signal, and the BER of the diffraction signal.

20. Processing the one or more of the aforementioned sensing data is In order to determine the change in the emission signal, the information of the emission signal is compared with the information of the reflected signal, Based on the determination that the emission signal has changed, it is determined whether the change in the emission signal satisfies one or more conditions, To generate one or more sensing results based on the results of the determination, The method according to claim 17, having the following characteristics.

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