WO2022041116A1

WO2022041116A1 - Management for background da ta transfer (bdt) based on dynamic tariff da ta

Info

Publication number: WO2022041116A1
Application number: PCT/CN2020/112101
Authority: WO
Inventors: Fengpei Zhang
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2020-08-28
Filing date: 2020-08-28
Publication date: 2022-03-03

Abstract

The present disclosure is related to a method for managing background data transfer (BDT) for a user equipment (UE) based on dynamic tariff data and associated network nodes. The method comprises: receiving, from a second network element, dynamic tariff data; performing at least one of: determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data; or receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE.

Description

MANAGEMENT FOR BACKGROUND DA TA TRANSFER (BDT) BASED ON DYNAMIC TARIFF DA TA

Technical Field

The present disclosure is related to the field of telecommunication, and in particular, to management for background data transfer (BDT) based on dynamic tariff data.

Background

The Internet of Things (IoT) is a network of physical objects, such as vehicles, machines, home appliances, that use sensors and Application Programming Interfaces (APIs) to connect and exchange data over the Internet. The IoT depends on a whole host of technologies, such as APIs that connect devices to the Internet. Other key IoT technologies may include Big Data management tools, predictive analytics, Artificial Intelligence (AI) and machine learning, the cloud, and radio communication, etc.

In a typical IoT application, there are a large number of non-time-critical data needed to be transferred between IoT devices and their associated application server. A few typical examples include:

● Firmware Update: Application server may push a new firmware file to IoT devices in order to introduce new features or fix known issues. A firmware file is regarded as "big" data comparing with telemetry data.

● Logging Data: IoT Devices usually generate logs for events and errors. Those logging data will provide the application server with more insight views if transferred to the server side and analyzed by data analytics techniques.

● High Definition (HD) Sensors: HD sensors are critical for some use cases such as autonomous driving cars. HD sensors will generate large volume of data. For example, cameras installed on a vehicle alone will generate 20 to 40 Mbps, while radars installed on a vehicle will generate between 10 and 100 Kbps. According to Intel′s prediction, an Autonomous Driving Car may generate 4TB data per day.

Therefore, it is a major challenge for the owner or the operator of the IoT devices to transmit such a large amount of data at a lower cost.

Summary

According to a first aspect of the present disclosure, a method at a first network element for managing background data transfer (BDT) for a user equipment (UE) based on dynamic tariff data is provided. The method comprises: receiving, from a second network element, dynamic tariff data; performing at least one of: determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data; or receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE.

In some embodiments, the method further comprises: triggering the BDT transmission for the UE in response to determining, at the first network element, that the BDT transmission is to be initiated for the UE. In some embodiments, before the step of receiving, from a second network element, dynamic tariff data, the method further comprises: transmitting, to the second network element, a subscribe dynamic tariff data request for subscribing dynamic tariff data from the second network element; and receiving, from the second network element, a subscribe dynamic tariff data response indicating success of the subscription. In some embodiments, the step of receiving, from a second network element, dynamic tariff data comprises: receiving, from the second network element, a dynamic tariff data publication indication request which notifies the first network element of availability of the latest dynamic tariff data; retrieving, from a location specified by a uniform resource identifier (URI) comprised in the dynamic tariff data publication indication request, the latest dynamic tariff data. In some embodiments, the method further comprises: transmitting, to the second network element, a dynamic tariff data publication indication response which acknowledges the dynamic tariff data publication indication request. In some embodiments, the step of determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data comprises: determining whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE.

In some embodiments, the determination is made by an artificial intelligence model which is trained by an artificial intelligence algorithm based on the historical trajectory data and the retrieved latest dynamic tariff data. In some embodiments, the artificial intelligence algorithm is Q-Learning algorithm. In some embodiments, the artificial intelligence model is configured as follows: the artificial intelligence model′s agent is the UE; the artificial intelligence model′s environment is a geographical area in which the UE is moving, the geographical area covering one or more cells of a telecommunications network to which the UE is communicatively connected and each of the one or more cells having an associated dynamic tariff data; the artificial intelligence model′s actions comprise instructing the UE to start or stop the BDT transmission; the artificial intelligence model′s states comprise at least one of day of week, remaining data amount to be communicated with the UE, and tariff for each cell; and the artificial intelligence model′s reward function uses the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.

In some embodiments, the step of determining whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE comprises: determining the current states of the artificial intelligence model based on the current time and date, the current location of the UE, remaining data amount to be communicated with the UE, and/or tariff for each cell; and determining whether the BDT transmission is to be initiated for the UE by the trained artificial intelligence model based on the determined states.

In some embodiments, before the step of receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE, the method further comprises: training an artificial intelligence model by an artificial intelligence algorithm based on historical trajectory data and the retrieved latest dynamic tariff data; and transmitting, to the UE, the trained artificial intelligence model to enable the UE to determine whether the BDT transmission is to be initiated at least partially based on the trained artificial intelligence model. In some embodiments, the artificial intelligence algorithm is Q-Learning algorithm. In some embodiments, the artificial intelligence model is configured as follows: the artificial intelligence model′s agent is the UE; the artificial intelligence model′s environment is a geographical area in which the UE is moving, the geographical area covering one or more cells of a telecommunications network to which the UE is communicatively connected and each of the one or more cells having an associated dynamic tariff data; the artificial intelligence model′s actions comprise instructing the UE to start or stop the BDT transmission; the artificial intelligence model′s states comprise at least one of day of week, remaining data amount to be communicated with the UE, and tariff for each cell; and the artificial intelligence model′s reward function uses the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.

In some embodiments, the step of triggering the BDT transmission for the UE in response to determining that the BDT transmission is to be initiated for the UE comprises: transmitting, to the second network element, a set chargeable party request, which comprises flow information identifying a flow for the UE, to sponsor the traffic of the flow; and receiving, from the second network element, a set chargeable party response to indicate that a sponsorship transaction is activated for the flow. In some embodiments, the UE is a vehicle, the first network element is a connected vehicle system serving the vehicle, and the second network element is a service capability exposure function (SCEF) or a network exposure function (NEF) .

According to a second aspect of the present disclosure, a first network element is provided. The first network element comprises: a processor; a memory storing instructions which, when executed by the processor, cause the processor to perform the any method of the first aspect.

According to a third aspect of the present disclosure, a method at a second network element for facilitating background data transfer (BDT) for a user equipment (UE) based on dynamic tariff data is provided. The method comprises: receiving, from a third network element, general dynamic tariff data; processing the general dynamic tariff data to generate specific dynamic tariff data for a first network element; and triggering the first network element to retrieve the specific dynamic tariff data from the second network element.

In some embodiments, before the step of receiving, from a third network element, general dynamic tariff data, the method further comprises: receiving, from the first network element, a subscribe dynamic tariff data request for subscribing specific dynamic tariff data from the second network element; authenticating the first network element for the subscription of the specific dynamic tariff data; and transmitting, to the first network element, a subscribe dynamic tariff data response indicating success of the subscription in response to the success of the authentication. In some embodiments, the subscribe dynamic tariff data request comprises a field indicating a filter, and the step of processing the general dynamic tariff data to generate specific dynamic tariff data for a first network element further comprises: applying the filter to the general dynamic tariff data to generate the specific dynamic tariff data.

In some embodiments, before the step of receiving, from a third network element, general dynamic tariff data, the method further comprises: periodically receiving, from the third network element, a first dynamic tariff data publication indication request indicating that a latest general dynamic tariff data is available; and retrieving, from a location specified by a uniform resource identifier (URI) comprised in the first dynamic tariff data publication indication request, the latest general dynamic tariff data. In some embodiments, the method further comprises: transmitting, to the third network element, a first dynamic tariff data publication indication response which acknowledges the first dynamic tariff data publication indication request.

In some embodiments, the step of triggering the first network element to retrieve the specific dynamic tariff data from the second network element comprises: enabling the latest specific dynamic tariff data to be downloadable at a location specified by a uniform resource identifier (URI) ; transmitting, to the first network element, a second dynamic tariff data publication indication request comprising the URI. In some embodiments, the method further comprises: receiving, from the first network element, a second dynamic tariff data publication indication response which acknowledges the second dynamic tariff data publication indication request. In some embodiments, the UE is a vehicle, the first network element is a connected vehicle system serving the vehicle, and the second network element is an SCEF or an NEF. In some embodiments, the method further comprises: receiving, from the first network element, a set chargeable party request, which comprises flow information identifying a flow for the UE, to sponsor the traffic of the flow; and transmitting, to the first network element, a set chargeable party response to indicate that a sponsorship transaction is activated for the flow.

According to a fourth aspect of the present disclosure, a second network element is provided. The second network element comprises: a processor; a memory storing instructions which, when executed by the processor, cause the processor to perform the any method of the third aspect.

According to a fifth aspect of the present disclosure, a computer program comprising instructions is provided. The instructions, when executed by at least one processor, cause the at least one processor to carry out the method of any method of the first aspect and the third aspect.

According to a sixth aspect of the present disclosure, a carrier is provided. The carrier contains the computer program of the fourth aspect, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

According to a seventh aspect of the present disclosure, a system for managing background data transfer (BDT) for one or more user equipments (UEs) based on dynamic tariff data is provided. The system comprises: the one or more UEs; a first network element of the second aspect; a second network element of the fourth aspect; and a third network element configured to providing the second network element with a latest general tariff data periodically.

In some embodiments, the one or more UEs are vehicles, the first network element is a connected vehicle system serving the vehicles, the second network element is an SCEF or an NEF, and the third network element hosts a dynamic tariff service.

With the first network element, the second network element, and methods performed at the network elements as described above, a global optimization for reducing UEs′ data transmission cost may be achieved, while a better network utilization may be achieved for network operators.

Brief Description of the Drawings

The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several embodiments in accordance with the disclosure and therefore are not to be considered limiting of its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings.

Fig. 1 is a diagram illustrating an exemplary IoT network in which a dynamic-tariff-data based background data transfer (BDT) mechanism according to an embodiment of the present disclosure may be applicable.

Fig. 2 is a diagram illustrating an exemplary message flow between various nodes in an exemplary telecommunication network for negotiating a BDT policy according to an embodiment of the present disclosure.

Fig. 3 is a diagram illustrating an exemplary message flow between various nodes in an exemplary telecommunication network for activating a BDT policy according to an embodiment of the present disclosure.

Fig. 4 is a diagram illustrating an exemplary system for managing BDT based on dynamic tariff data according to an embodiment of the present disclosure.

Fig. 5 is a diagram illustrating an exemplary message flow between various nodes for managing BDT transmission based on dynamic tariff data according to an embodiment of the present disclosure.

Fig. 6 is a diagram illustrating an exemplary message flow between various nodes for a network-initiated BDT procedure according to an embodiment of the present disclosure.

Fig. 7 is a diagram illustrating another exemplary message flow between various nodes for a UE-initiated BDT procedure according to another embodiment of the present disclosure.

Fig. 8 is a diagram illustrating an exemplary IoT network in which a dynamic-tariff-data based background data transfer (BDT) mechanism according to an embodiment of the present disclosure is applied.

Fig. 9 is a flow chart of an exemplary method at a first network element for managing BDT for a user equipment (UE) based on dynamic tariff data according to an embodiment of the present disclosure.

Fig. 10 is a flow chart of an exemplary method at a second network element for facilitating BDT for a UE based on dynamic tariff data according to an embodiment of the present disclosure.

Fig. 11 schematically shows an embodiment of an arrangement which may be used in a first network element and/or a second network element according to an embodiment of the present disclosure.

Fig. 12 is a diagram illustrating how a reinforcement algorithm works according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, the present disclosure is described with reference to embodiments shown in the attached drawings. However, it is to be understood that those descriptions are just provided for illustrative purpose, rather than limiting the present disclosure. Further, in the following, descriptions of known structures and techniques are omitted so as not to unnecessarily obscure the concept of the present disclosure.

Those skilled in the art will appreciate that the term "exemplary" is used herein to mean "illustrative, " or "serving as an example, " and is not intended to imply that a particular embodiment is preferred over another or that a particular feature is essential. Likewise, the terms "first" and "second, " and similar terms, are used simply to distinguish one particular instance of an item or feature from another, and do not indicate a particular order or arrangement, unless the context clearly indicates otherwise. Further, the term "step, " as used herein, is meant to be synonymous with "operation" or "action. " Any description herein of a sequence of steps does not imply that these operations must be carried out in a particular order, or even that these operations are carried out in any order at all, unless the context or the details of the described operation clearly indicates otherwise.

Conditional language used herein, such as "can, " "might, " "may, " "e.g., " and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or states. Thus, such conditional language is not generally intended to imply that features, elements and/or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or states are included or are to be performed in any particular embodiment. Also, the term "or" is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term "or" means one, some, or all of the elements in the list. Further, the term "each, " as used herein, in addition to having its ordinary meaning, can mean any subset of a set of elements to which the term "each" is applied.

The term "based on" is to be read as "based at least in part on. " The term "one embodiment" and "an embodiment" are to be read as "at least one embodiment. " The term "another embodiment" is to be read as "at least one other embodiment. " Other definitions, explicit and implicit, may be included below. In addition, language such as the phrase "at least one of X, Y and Z, " unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limitation of example embodiments. As used herein, the singular forms "a" , "an" , and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" , "comprising" , "has" , "having" , "includes" and/or "including" , when used herein, specify the presence of stated features, elements, and/or components etc., but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof. It will be also understood that the terms "connect (s) , " "connecting" , "connected" , etc. when used herein, just mean that there is an electrical or communicative connection between two elements and they can be connected either directly or indirectly, unless explicitly stated to the contrary.

Of course, the present disclosure may be carried out in other specific ways than those set forth herein without departing from the scope and essential characteristics of the disclosure. One or more of the specific processes discussed below may be carried out in any electronic device comprising one or more appropriately configured processing circuits, which may in some embodiments be embodied in one or more application-specific integrated circuits (ASICs) . In some embodiments, these processing circuits may comprise one or more microprocessors, microcontrollers, and/or digital signal processors programmed with appropriate software and/or firmware to carry out one or more of the operations described above, or variants thereof. In some embodiments, these processing circuits may comprise customized hardware to carry out one or more of the functions described above. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

Although multiple embodiments of the present disclosure will be illustrated in the accompanying Drawings and described in the following Detailed Description, it should be understood that the disclosure is not limited to the disclosed embodiments, but instead is also capable of numerous rearrangements, modifications, and substitutions without departing from the present disclosure that as will be set forth and defined within the claims.

Further, please note that although the following description of some embodiments of the present disclosure is given in the context of 5 ^th Generation New Radio (5G NR) , the present disclosure is not limited thereto. In fact, as long as BDT management based on dynamic tariff data is involved, the inventive concept of the present disclosure may be applicable to any appropriate communication architecture, for example, to Global System for Mobile Communications (GSM) /General Packet Radio Service (GPRS) , Enhanced Data Rates for GSM Evolution (EDGE) , Code Division Multiple Access (CDMA) , Wideband CDMA (WCDMA) , Time Division -Synchronous CDMA (TD-SCDMA) , CDMA2000, Worldwide Interoperability for Microwave Access (WiMAX) , Wireless Fidelity (Wi-Fi) , Long Term Evolution (LTE) , etc. Therefore, one skilled in the arts could readily understand that the terms used herein may also refer to their equivalents in any other infrastructure. For example, the term "User Equipment" or "UE" used herein may refer to a mobile device, a mobile terminal, a mobile station, a user device, a user terminal, a wireless device, a wireless terminal, an IoT device, a vehicle, or any other equivalents. For another example, the term "gNB" used herein may refer to a base station, a base transceiver station, an access point, a hot spot, a NodeB (NB) , an evolved NodeB (eNB) , a network element, or any other equivalents. Further, the term "node" used herein may refer to a UE, a functional entity, a network entity, a network element, a network equipment, or any other equivalents.

Further, following 3GPP documents are incorporated herein by reference in their entireties:

- 3GPP TS 23.203 V16.2.0 (2019-12) , 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Policy and charging control architecture (Release 16) ;

- 3GPP TS 23.682 V16.6.0 (2020-03) , 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Architecture enhancements to facilitate communications with packet data networks and applications (Release 16) ; and

- 3GPP TS 29.122 V16.5.0 (2020-03) , 3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; T8 reference point for Northbound APIs (Release 16) .

As mentioned above, it is a major challenge for the owner or the operator of the IoT devices to transmit such a large amount of data at a lower cost. Therefore, IoT device owners or operators are eager to keep the data traffic cost lower and they don′t mind shifting such data traffic to non-busy hours or locations. On the other hand, Communication Service Providers (CSPs) are eager to balance loads of different areas at different times in their networks to increase the return on investment (ROI) . To maximize the usage of non-busy hours or locations, they don′t mind offering lower tariff during the non-busy hours or at the non-busy locations to their customers.

To address the needs from both sides, a mechanism called "Background Data Transfer (BDT) " is proposed, which allows an application server to initiate a background data transfer for non-time-critical data to a set of mobile UEs in a specific time window and optionally a specific geographical area.

Fig. 1 is a diagram illustrating an exemplary IoT network 10 in which a dynamic-tariff-data based background data transfer (BDT) mechanism according to an embodiment of the present disclosure may be applicable. As shown in Fig. 1, the IoT network 10 may comprise multiple cells 100-1 through 100-7 which are served by one or more gNBs. Further, one or more IoT devices (for example, smart water meters 115-1 through 115-8 and a vehicle 110) may be present and served by the cells 100-1 through 100-7, respectively. Please note that: although seven cells 100-1 through 100-7, eight smart water meters 115-1 through 115-8 and a vehicle 110 are shown in Fig. 1, the present disclosure is not limited thereto. In some other embodiments, a different number of cells and/or a different number and/or type of IoT devices may be provided.

Referring to Fig. 1, the CSP or the operator of the IoT network 10 may provide its users or subscribers with a tariff discount. For example, in the cell 100-1, a 30%discount is offered during the day and a 50%discount is offered at night by the CSP in view of the traffic caused by the IoT devices and/or other devices served by the cell 100-1 (e.g. the smart water meter 115-1) . Similarly, in the cell 100-2, no discount is offered during the day and a 30%discount is offered at night by the CSP in view of the traffic caused by the IoT devices and/or other devices served by the cell 100-2 (e.g. the smart water meters 115-2 and 115-3) . For another example, in the cell 100-6, a 90%discount is offered during the day and a 90%discount is offered at night by the CSP.

As shown in Fig. 1, the tariff of the cells 100-1 through 100-7 may vary depending on time and/or locations. One reason for this may be due to different network traffic caused by different numbers and/or types of IoT devices at different times. For example, there are two smart water meters 115-2 and 115-3 in the cell 100-2 which generate more network traffic than that generated by the only one smart water meter 115-1. Further, since the network load in the cell 100-6 may be much lower than those of the cell 100-1 and 100-2, the tariff rate of the cell 100-6 may be also much lower than those of the cell 100-1 and 100-2.

With the different tariff data for the cells 100-1 through 100-7, the IoT devices served by these cells may exploit the discounted tariff to reduce their cost for data transmission, for example, by calling a BDT procedure when entering into a cell with a lower tariff, as will be described below.

In some embodiments, a BDT procedure may consist of two parts: a policy negotiation procedure and a policy activation procedure. Next, a description of how to negotiate and activate a BDT policy for an IoT device or a UE will be given with reference to Fig. 2 and Fig. 3, respectively.

Fig. 2 is a diagram illustrating an exemplary message flow between various nodes in an exemplary telecommunication network for negotiating a BDT policy according to an embodiment of the present disclosure. In the embodiment shown in Fig. 2, the telecommunication network may be a 5G NR network which may comprise a Policy & Charging Enforcement Function (PCEF) 202, a Subscription Profile Repository (SPR) 204, one or more Policy & Charging Rules Functions (PCRFs) 206, an SCEF 208, and an SCS (Services Capability Server) /AS (Application Server) 210. However, these nodes are given only for the purpose of illustration, and the present disclosure is not limited thereto. In some other embodiments, more nodes, less nodes, and/or different nodes may be involved in a similar procedure for negotiating a BDT policy.

As shown in Fig. 2, the procedure for negotiating a BDT policy is started at step 212 where the SCS/AS 210 may send a background data transfer request message to the SCEF 208 to negotiate a BDT policy on behalf of a group of UEs. The message may comprise some parameters, such as SCS/AS identifier, volume per UE, number of UEs, desired time window. The "volume per UE" parameter describes the volume of data the SCS/AS 210 expects to be transferred per UE. The "number of UEs" parameter describes the expected amount of UEs participating in the BDT. The "desired time window" parameter describes the time interval during which the SCS/AS 210 wants to realize the data transfer. Optionally, the SCS/AS 210 can provide a geographic area information.

At step 214, the SCEF 208 may authorize the SCS/AS 210′s request received in the step 212, for example, based on one or more of the parameters carried in the request. If the request is authorized, at step 216, the SCEF 208 may select any of the available PCRFs 206 and trigger a negotiation for future BDT procedure with the selected PCRF 206, and the negotiation may further involve the SPR 204. Further, the SCEF 208 may forward the parameters provided by the SCS/AS 210 to the PCRF 206 during the negotiation. The PCRF 206 may respond to the SCEF 208 with the possible transfer policies and a reference ID which is associated with the policies.

At step 218, the SCEF 208 may forward the reference ID and the transfer policies to the SCS/AS 210 by sending a background data transfer response message which includes the reference ID and possible transfer policies. The SCS/AS 210 may store the reference ID for the future interaction with the PCRF 206, for example, for the interaction with the PCRF 206 when the policy is to be activated as shown in Fig. 3.

If more than one transfer policy was received, at step 220, the SCS/AS 210 may select one of them and send another background data transfer request message including the parameters "SCS/AS Identifier" and "selected transfer policy" , to inform the SCEF 208 and PCRF 206 about the selected transfer policy. At step 222, the SCEF 208 may confirm the transfer policy selection to the SCS/AS 210 by sending a background data transfer response message.

Next, at step 224, the SCEF 208 may continue the negotiation for future background data transfer procedure with the PCRF 206 and also the SPR 204. The PCRF 206 may store the reference ID and the new transfer policy in the SPR 204.

At step 226, the SCS/AS 210 (acting as an application function (AF) ) may contact the same or a different PCRF 206 for each individual UE (via the Rx interface) , the SCS/AS 210 shall provide the Reference ID. Alternatively, the SCS/AS 210 may activate the selected transfer policy via the SCEF 208, for each UE in the group, by using the "set the chargeable party at session set-up" (shown in Fig. 3) or "change the chargeable party during the session" procedure to provide the Reference ID to the same or different PCRF 206. The PCRF 206 may correlate the SCS/AS 210 or SCEF 208′s request with the transfer policy retrieved from the SPR 204 via the reference ID. The PCRF 206 may finally trigger Policy & Charging Control (PCC) procedures to provide the respective policing and charging information to the PCEF 202 for the background data transfer of this UE.

Fig. 3 is a diagram illustrating an exemplary message flow between various nodes in an exemplary telecommunication network for activating a BDT policy according to an embodiment of the present disclosure. In the embodiment shown in Fig. 3, the telecommunication network may be a 5G NR network which may comprise a Policy & Charging Enforcement Function (PCEF) 202, a Policy & Charging Rules Function (PCRF) 206, an Online Charging System (OCS) 207, an SCEF 208, and an SCS/AS 210. However, these nodes are given only for the purpose of illustration, and the present disclosure is not limited thereto. In some other embodiments, more nodes, less nodes, and/or different nodes may be involved in a similar procedure for activating a previously negotiated BDT policy. Further, please note that similar nodes in Fig. 2 and Fig. 3 are given similar reference numerals for ease of understanding.

Referring to Fig. 3, when setting up a connection between the AS 210 and a UE, for example, when a UE is intended to initiate a BDT transmission, the SCS/AS 210 may request to become the chargeable party for a session of the UE to be set up by sending a set chargeable party request message to the SCEF 208 at step 302. The message may include parameters, such as SCS/AS identifier, description of the application flows, sponsor information, sponsoring status, reference ID, and optionally a usage threshold. The parameter "sponsoring status" indicates whether sponsoring is started or stopped, i.e. whether the 3rd party service provider is the chargeable party or not. The reference ID parameter identifies a previously negotiated transfer policy for BDT, such as the negotiated policy shown in Fig. 2.

At step 304, the SCEF 208 may authorize the SCS/AS 210′s request to sponsor the application traffic and store the sponsor information together with the SCS/AS Identifier. If the authorization is not granted, step 306 may be skipped and the SCEF 208 may reply to the SCS/AS 210 with a result value indicating that the authorization failed.

At step 306, the SCEF 208 may interact with the PCRF 206 by triggering a PCRF initiated IP-CAN Session Modification as described in clause 7.4.2 of TS 23.203 and provide IP filter information, sponsored data connectivity information (as defined in TS 23.203) , reference ID (if received from the SCS/AS 210) and Sponsoring Status (if received from the SCS/AS 210) to the PCRF 206.

The PCRF 206 determines whether the request is allowed and notifies the SCEF 208 if the request is not authorized. If the request is not authorized, SCEF 208 responds to the SCS/AS 210 in step 308 with a Result value indicating that the authorization failed.

As specified in 3GPP TS 23.203, the PCRF 206 may determine the PCC rule (s) for the specified session including charging control information. Charging control information shall be set according to the sponsoring status (if received over Rx) , i.e. either indicating that the 3rd party service provider is the chargeable party or not. The PCC rule (s) for the specified session shall then be provided to the PCEF 202. In the case of online charging and depending on operator configuration, the PCEF 202 may request credit when the first packet corresponding to the service is detected or at the time the PCC rule was activated. Further, the PCRF 206 may notify the SCEF 208 that the request is accepted.

At step 308, the SCEF 208 may send a set chargeable party response message to the SCS/AS 210 to indicate whether the request is granted or not.

With the solution shown in Fig. 2 and Fig. 3, one or more IoT devices (such as the smart water meters 115-1 through 115-8 and the vehicle 110 shown in Fig. 1) may utilize the policy negotiated by their SAS/AS 210 to conduct their own BDT transmissions, thereby reducing the cost for non-time-critical data transmission.

However, such a BDT solution is based on a centralized BDT policy negotiation and selection mechanism before the actual BDT transmission occurs. Therefore, there are some limitations for highly mobile devices such as the vehicle 110 shown in Fig. 1.

To be specific, an application server (e.g. the AS 210) basically uses a query-and-answer mechanism in the negotiation and therefore it does not really know the most optimal time periods and locations globally for background data transfers. Further, since the time window specified in the selected policy is applicable to a group of UEs specified in the negotiation, the AS 210 may assume that this group of UEs shall be able to do background data transfer during the period at the specified location. This assumption may be true for fixed IoT devices or UEs, such as the smart water meters 115-1 through 115-8. However, for a highly mobile UE, such as the vehicle 110, it is not always possible for the vehicle 110 to conduct a BDT transmission during a specified time period at a specified location. To guarantee that, the AS 210 has to specify a relatively long time window and/or a relatively large area in a policy. On the other hand, from the CSP′s point of view, the network utilization is not optimal with such a policy and therefore the tariff corresponding to the policy is probably not optimal either. Alternatively, the AS 210 simply could not guarantee that, e.g., if a car parks in an underground parking lot with very poor signal strength at that time. In such a case, the cost for the vehicle′s data transmission cannot not be as optimal as it expected.

To avoid coarse-grained negotiation for a group of UEs mentioned above, the AS 210 could also do fine-grained negotiation for small group of UEs or even individual UE, but considering the enormous number of IoT devices, it will be extremely complex for both the AS 210 and the whole network 10.

In summary, negotiation beforehand in the above BDT procedure is not suitable for highly mobile devices to do background data transfer simply because individual, dynamic context and behaviors are not considered at all.

Therefore, another improved solution to implement BDT which is different from the above BDT procedure shown in Fig. 2 and Fig. 3 is provided. This solution is optimized for highly mobile IoT devices, e.g., connected vehicles, autonomous driving cars, or drones, etc. The general idea of the improved solution is to leverage AI algorithms to schedule BDT transmission for a UE based on historical trajectory behaviors of a vehicle (or in general, a highly mobile UE or IoT device) and the dynamic time-based and/or location-based tariff data exposed by the network operators, thereby minimizing the cost of the data transmission for the UE. The improved solution will be described in details with reference to Fig. 4 and Fig. 5.

Fig. 4 is a diagram illustrating an exemplary system for managing BDT based on dynamic tariff data according to an embodiment of the present disclosure. The system may comprise one or more connected vehicles 410-1 through 410-3 (collectively, vehicle 410) , a connected vehicle system 420, a SCEF/NEF 430, and a dynamic tariffing service 440. The connected vehicles 410 may be vehicles with network connectivity that communicate bi-directionally with other devices outside the vehicles 410. In some embodiments, a connected vehicle 410 may send telemetry data or events, and receive commands or notifications. The connected vehicle system 420 may be a platform provides a set of services to connected vehicles 410, such as telematics service, infotainment service, geofencing service, remote diagnostic service etc. The connected vehicles 410 may communicate with the connected vehicle system 420 through network connectivity in order to access those services. The SCEF/NEF 430 may be a function which provides a means to securely expose the service capabilities and/or data provided by a telecommunication network (e.g. the IoT network 10) . In other words, the SCEF/NEF 430 described in this embodiment may not be the very same functional entity described in Fig. 2, Fig. 3, or in any 3GPP documents. The dynamic tariffing service 440 may be a service which offers a mobile network user a discount based on the amount of capacity available at current time and location in the network.

In a typical application scenario, the connected vehicles 410 may be vehicles of a certain brand which are produced by a vehicle company, and the vehicle company may provide services, such as Advanced Driving Assistance System (ADAS) , to the drivers of the connected vehicles 410 sold to their customers. Such services may require the connected vehicles 410 to communicate with the connected vehicle system 420 for navigation data, firmware upgrades, entertainment data (e.g. music, movie, etc. ) , etc. To reduce the cost for such communications, the vehicle company or the connected vehicle system 420 may obtain tariff data from a node of the network, such as SCEF/NEF 430 and the tariff data may be obtained from the dynamic tariffing service 440.

In some embodiments, the dynamic tariffing service 440 may periodically collect radio utilization data from Radio Access Network (RAN) , periodically calculate dynamic tariffs at least based on the collected radio utilization data, and provision the latest calculated dynamic tariff data to a charging system. However, the present disclosure is not limited thereto. In some other embodiments, the dynamic tariffing service 440 may also calculate and publish dynamic tariff in response to a request, for example, a request from the SCEF/NEF 430.

Next, a method for managing BDT transmission based on dynamic tariff data will be described with reference to Fig. 5 in view of Fig. 4.

As shown in Fig. 5, the method may start at step 502 where the connected vehicle system 420 may send a "Subscribe Dynamic Tariff Data Request" message to the SCEF/NEF 430 to subscribe dynamic tariff data change. Parameters in the request message may comprise but not limited to: Application Server identifier (asId) , filter, and a notificationDestination URI, where asId is the identification of the connected vehicle system 420 and the filter at least contains a list of location areas. At step 504, the SCEF/NEF 430 may authenticate the connected vehicle system 420, for example, based on the asId, and check the corresponding authorization. At step 506, upon a successful authentication, the SCEF/NEF 430 may send a "Subscribe Dynamic Tariff Data Response" message to the connected vehicle system 420 with a subscriptionId. With the steps 502 through 506, the connected vehicle system 420 may successfully subscribe the latest tariff data from the SCEF/NEF 430. With the subscription of latest tariff data, the connected vehicle system 420 may be notified of the latest tariff data which is used for training its AI model. With the obtained latest tariff data, the trained AI model may be capable of determining whether a BDT transmission is to be started/stopped when the connected vehicle 410 is located at a certain place at a certain time. Further, with the filter of the subscription request, the connected vehicle system 420 may process the tariff data of interest only and save its processing power and time.

At step 508, the dynamic tariffing service 440 may periodically refresh the dynamic tariff data and then sends a "Dynamic Tariff Data Publication Indication Request" message to the SCEF/NEF 430. At step 510, upon receipt of the request, the SCEF/NEF 430 may send a "Dynamic Tariff Data Publication Indication Response" message to the dynamic tariffing service 440. At step 512, the SCEF/NEF 430 may download or otherwise obtain the new dynamic tariff data from the dynamic tariffing service 440. For example, the SCEF/NEF 430 may download the latest general dynamic tariff data from a location specified in the "Dynamic Tariff Data Publication Indication Request" message received at step 508. With the steps 508 through 512, the SCEF/NEF 430 may obtain the latest general dynamic tariff data. The term "general dynamic tariff data" used herein refers to the dynamic tariff data for all the locations (or all the cells) and all the time periods. With the obtained latest general tariff data, the SCEF/NEF 430 may generate specific tariff data which is specific for each subscriber (e.g., the connected vehicle system 420) . In this way, each of its subscribers may obtain its own tariff data of interest, and save its processing power and time. Further, with a centralized tariff data publisher, such as the SCEF/NEF 430 and/or dynamic tariffing service 440, different subscribers may share the same tariff publishing service, and reduce their costs further.

Further, please note that although Fig. 5 shows that the steps 502 through 506 occur before the steps 508 through 512, the present disclosure is not limited thereto. In some other embodiments, these steps may be performed in a different order, for example, the steps 502 through 506 may be performed after the steps 508 through 510, or they are performed in an interleaved manner or concurrently. In fact, the steps 502 through 506 are somehow independent to the steps 508 through 512.

After the step 512, at step 514, the SCEF/NEF 430 may check available subscriptions (for example, the subscription from the connected vehicle system 420 may be one of them) and generate application server specific data based on the filters in application server subscriptions, respectively. To be specific, the SCEF/NEF 430 may generate specific dynamic tariff data from the general dynamic tariff data by using the filter specified in the subscription request received from the connected vehicle system 420 at step 502. In other words, the general dynamic tariff data is tailored for the connected vehicle system 420 for its specific use, for example, for a specified time period and/or for a specified location.

At step 516, the SCEF/NEF 430 may send a "Dynamic Tariff Data Publication Indication Request" message to the connected vehicle system 420 with subscriptionId and downloadUrl. At step 518, the connected vehicle system 420 may send a "Dynamic Tariff Data Publication Indication Response" message to the SCEF/NEF 430 to acknowledge the request. At step 520, the connected vehicle system 420 may download the latest specific dynamic tariff data from a location specified by the downloadUrl. With the steps 516 through 520, the connected vehicle system 430 may obtain the latest specific dynamic tariff data.

At step 520, the connected vehicle system 420 may process the new specific dynamic tariff data such that these data may be used for training an AI model which may determine whether a BDT transmission is to be started for a specific UE (or vehicle 410) or not. The details of the processing will be described below.

At step 524, the connected vehicle system 420 may provide an indication to a connected vehicle 410 based on at least one of the following factors: the historical trajectory behavior of the connected vehicle 410, the current trip planning of the connected vehicle 410, and the latest specific dynamic tariff data.

At an optional step 526, the connected vehicle system 420 may optionally trigger a standard 3GPP chargeable party procedure to sponsor the data traffic for the connected vehicle 410 before the BDT transmission for the connected vehicle 410 is started. In this way, the cost of BDT transmission for the connected vehicle 410 may be sponsored by the connected vehicle system 420 or another party and thus further reduced.

At step 528, the BDT transmission for the connected vehicle 410 may be started, paused, or stopped by the connected vehicle system 420, the connected vehicle 410 itself, or both.

With a such a procedure, a connected vehicle 410 may conduct its own BDT transmissions based on its own historical trajectory behavior, current trip planning, and/or latest dynamic tariff data, thereby achieving a global optimization in terms of time and/or location.

Further, some exemplary formats of some messages used above are given below for the illustration purpose only.

Message "Subscribe Dynamic Tariff Data Request"

Message "Subscribe Dynamic Tariff Data Response"

Message "Dynamic Tariff Data Publication Indication Request"

Message "Dynamic Tariff Data Publication Indication Response"

Attribute	Data Type	Description
none	none	none

Data type "Filter"

Attribute	Data Type	Description
locationArea	LocationArea	Indicate desired location area.
tariffRanges	arrage (Range)	Indicate desired tariff ranges

Data type "Range"

Data type "LocationArea"

The exposed Dynamic Tariff Data structure is given as follows:

Attribute	Data Type	Description
tariffDatas	array (TariffData)	The data structure of Dynamic Tariff Data.

Data type "TariffData"

Please note that the above messages and data types are examples for the purpose of illustration only, and therefore different messages and/or data types may be used in some other embodiments.

Next, detailed descriptions of different embodiments of the step 528 shown in Fig. 5 will be given with reference to Fig. 6 and Fig. 7, respectively.

Fig. 6 is a diagram illustrating an exemplary message flow between various nodes for a network-initiated BDT procedure according to an embodiment of the present disclosure. In the embodiment shown in Fig. 6, the telecommunication network may be a 5G NR network which may comprise a connected vehicle (or UE) 410, a Session Management Function (SMF) which is also a Charging Trigger Function (CTF) 450, a connected Vehicle system (or Application Server (AS) ) 420, a Charging History Function 460, and an Online Charging Service (OCS) /Offline Charging Service (OFCS) 470. However, these nodes are given only for the purpose of illustration, and the present disclosure is not limited thereto. In some other embodiments, more nodes, less nodes, and/or different nodes may be involved in a similar procedure for a network-initiated BDT procedure.

At the optional steps 602, the connected vehicle 410 may report its trajectory behaviors to the connected vehicle system 420 periodically or in response to the request from the connected vehicle system 420. In this way, the connected vehicle system 420 may accumulate the historical trajectory behaviors of the connected vehicle 410. In some embodiments, the trajectory behaviors may comprise the current location of the connected vehicle 410, such as, a geological address (e.g. latitude and longitude) , a cell identifier of a cell in which the connected vehicle 410 is located, a position relative to a known landmark, etc.

At step 604, the connected vehicle system 420 may determine whether a BDT transmission is to be started or not for the connected vehicle 410. In some embodiments, the connected vehicle system 420 may determine whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE. To be more specific, the determination may be made by an artificial intelligence model which is trained by an artificial intelligence algorithm based on the historical trajectory data (e.g. the historical trajectory behaviors accumulated at step 602 shown in Fig. 6) and the retrieved latest dynamic tariff data (e.g. the dynamic tariff data retrieved at step 520 shown in Fig. 5) .

In some embodiments, the artificial intelligence algorithm may be the Q-Learning algorithm, and the artificial intelligence model may be configured as follows: the artificial intelligence model′s agent may be the UE; the artificial intelligence model′s environment may be a geographical area in which the UE is moving, the geographical area covering one or more cells of a telecommunications network to which the UE is communicatively connected and each of the one or more cells having an associated dynamic tariff data; the artificial intelligence model′s actions may comprise instructing the UE to start or stop the BDT transmission; the artificial intelligence model′s states may comprise at least one of day of week, remaining data amount to be communicated with the UE, and tariff for each cell; and the artificial intelligence model′s reward function may use the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost. In some embodiments, the step of determining whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE may comprise: determining the current states of the artificial intelligence model based on the current time and date, the current location of the UE, remaining data amount to be communicated with the UE, and/or tariff for each cell; and determining whether the BDT transmission is to be initiated for the UE by the trained artificial intelligence model based on the determined states.

By using the dynamic tariff data together with the connected vehicle 410′s historical trajectory data, the connected vehicle system 420 may apply the AI algorithm to make smart decision on scheduling BDT transmission to minimize the cost. Further, in the embodiment of Fig. 6, although the Q-Learning algorithm is used, the present disclosure is not limited thereto. For example, other reinforcement learning algorithms can also be used for this purpose.

Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps someone to maximize some portion of the cumulative reward. This neural network learning method helps someone to learn how to attain a complex objective or maximize a specific dimension over many steps.

There are some important terms used in Reinforcement AI as follows:

● Agent: It is an assumed entity which performs actions in an environment to gain some reward.

● Environment (e) : A scenario that an agent has to face.

● Reward (R) : An immediate return given to an agent when he or she performs specific action or task.

● State (s) : State refers to the current situation returned by the environment.

● Policy (n) : It is a strategy which applies by the agent to decide the next action based on the current state.

● Value (V) : It is expected long-term return with discount, as compared to the short-term reward.

● Value Function: It specifies the value of a state that is the total amount of reward. It is an agent which should be expected beginning from that state.

● Model of the environment: This mimics the behavior of the environment. It helps you to make inferences to be made and also determine how the environment will behave.

● Model based methods: It is a method for solving reinforcement learning problems which use model-based methods.

● Q value or action value (Q) : Q value is quite similar to value. The only difference between the two is that it takes an additional parameter as a current action.

Please refer to Fig. 12 for a simple example which helps to illustrate the reinforcement learning mechanism.

Fig. 12 is a diagram illustrating how a reinforcement algorithm works according to an embodiment of the present disclosure. As shown in Fig. 12, please consider the scenario of teaching new tricks to a cat. As cats do not understand English or any other human language, we cannot tell her directly what to do. Instead, a different strategy is followed. To be specific, a situation is emulated, and the cat tries to respond in many different ways. If the cat′s response is the desired way, a fish (reward 1220) will be given to her. Now whenever the cat is exposed to the same situation, the cat executes a similar action with even more enthusiastically in expectation of getting more reward (fish) 1220. Therefore, that is like learning that cat gets from "what to do" from positive experiences. At the same time, the cat also learns what not do when faced with negative experiences.

In this case, the cat is an agent 1210 that is exposed to the environment, for example, a house. An example of a state could be the cat sitting, and a specific word is used for the cat to walk. The agent (or the cat) 1210 reacts by performing an action transition from one "state" to another "state. " For example, the cat 1210 goes from sitting to walking. The reaction of an agent 1210 is an action, and the policy is a method of selecting an action given a state in expectation of better outcomes. After the transition, they may get a reward or penalty in return.

There are three approaches to implement a Reinforcement Learning algorithm: value-based approaches, policy-based approaches, and model-based approaches. Q learning is a value-based method of supplying information to inform which action an agent should take. Since the Q-learning algorithm is known in the art, the details thereof are omitted for simplicity.

In our case, the model of BDT problem is shown as below:

● Agent: the connected vehicle 410 may be a logical agent. The connected vehicle system 420 may also be the representative of an agent on behalf of the connected vehicle 410.

● Environment: The environment may comprise at least one of:

■ The geographical area where the connected vehicle 410 drives through.

■ The distribution of cells and corresponding coverage areas in such geographical area.

■ The dynamic tariff data associated with cells and time period.

■ The connected vehicle 410′s trajectory data.

● Action: There are 2 actions: BDT transmission ON and OFF.

● State: the states includes

■ Day of Week: assume that the connected vehicle 410 may have different behaviors according to the day of week.

■ Remaining Data: in reality, the connected vehicle 410 shall not cache the data forever in order to save cost. Therefore, the algorithm shall encourage the connected vehicle 410 to send data to the cloud in time therefore remaining data is designed as one of the states.

■ Tariff: the tariff of a specific location and time.

● Reward: reward is designed as the cost has been saved. Firstly, according the historical trajectory data, the algorithm can calculate the possible lowest average tariff as the baseline. Secondly, the reward is the aggregated cost delta between baseline and current tariff.

Using the model defined above, the algorithm can be trained periodically or in response to any tariff data publication event, and eventually a Q-Table may be generated, for example:

With the determination by the AI model described above, a decision whether a BDT transmission is to be started/stopped may be made in view of tariff data at all locations and/or times and a potential travelling route of the connected vehicle 410. In other words, the decisions may be globally optimized.

Referring back to Fig. 6, after the connected vehicle system 420 determines whether the BDT transmission is to be started, the connected vehicle system 420 may transmit a BDT transmission indication to the connected vehicle 410 to start or stop its BDT transmission at step 606, and the connected vehicle 410 may accordingly start or stop the BDT transmission with the SMF/CTF 450 at step 608. With the BDT transmission indication, the BDT transmission from/to the connected vehicle 410 may be started at an appropriate time and/or location (for example, when entering the cell 100-6 at night etc. ) . In such a way, the cost for the BDT transmission may be reduced to a maximum degree, which cannot be achieved by an algorithm without such a global vision.

Upon detection of the start or stop of the BDT transmission, the SMF/CTF 450 may generate a charging event for this BDT session and send the charging event to the CHF 460 at step 610. The CHF 460 may generate a call detail record (CDR) based on this charging event. In some embodiments, a CDR may be generated based on multiple charging events accumulated during a time period rather than only one charging event. Anyway, the CDR may be transmitted from the CHF 460 to the OCS/OFCS 470 at step 612 for billing purpose.

In this way, the connected vehicle system 450 may dynamically determine whether the BDT transmission for the connected vehicle 410 based on the latest tariff data and the connected vehicle′s historical trajectory behaviors and/or current route planning, such that a maximal cost down for data transmission may be achieved.

A network-initiated BDT procedure is described above with reference to Fig. 6. However, a BDT procedure may also be initiated by a UE. Next, a UE-initiated BDT procedure will be described with reference to Fig. 7. The major difference between Fig. 6 and Fig. 7 is that the decision of whether a BDT transmission is to be started or stopped is made at the connected vehicle 410, rather than the connected vehicle system 420 as shown in Fig. 6.

Fig. 7 is a diagram illustrating another exemplary message flow between various nodes for a UE-initiated BDT procedure according to another embodiment of the present disclosure. The nodes shown in Fig. 7 are substantially similar to those in Fig. 6, and therefore the detail description thereof is omitted for simplicity.

Similar to step 602 shown in Fig. 6, at the optional steps 702, the connected vehicle 410 may report its trajectory behaviors to the connected vehicle system 420 periodically or in response to the request from the connected vehicle system 420. In this way, the connected vehicle system 420 may accumulate the historical trajectory behaviors of the connected vehicle 410. In some embodiments, the trajectory behaviors may comprise the current location of the connected vehicle 410, such as, a geological address (e.g. latitude and longitude) , a cell identifier of a cell in which the connected vehicle 410 is located, a position relative to a known landmark, etc.

At step 704, the connected vehicle system 420 may train an artificial intelligence model by an artificial intelligence algorithm based on historical trajectory data and the retrieved latest dynamic tariff data. For example, the AI model may be similar to that used in the embodiment of Fig. 6. In some other embodiments, the AI model may be trained at the connected vehicle 410 rather than the connected vehicle system 420. However, due to limited power and processing capability of the connected vehicle 410, the training process of the AI model may be preferably executed on the network side, such as the connected vehicle system 420.

At step 706, the trained artificial intelligence model may be transmitted to the connected vehicle 410 to enable the connected vehicle 410 to determine whether the BDT transmission is to be initiated at least partially based on the trained artificial intelligence model.

At step 708, the connected vehicle 410 may use the received trained AI model to determine whether a BDT transmission is to be enabled or not for the connected vehicle 410, and at step 710, the connected vehicle 410 may send a BDT transmission indication to the connected vehicle system 420 to indicate its desire for BDT transmission. However, in some other embodiments, this indication is not necessarily sent to the connected vehicle system 420. In fact, this indication may be not sent at all or sent to another network function which is in charge of the BDT transmission, for example, the PCEF 202 shown in Fig. 2. With the determination at the connected vehicle 410, a more accurate travelling plan may be accounted of (e.g., when the driver of the connected vehicle 410 inputs his/her intended destination and/or route to the connected vehicle 410) , and a more deterministic cost down for the BDT transmission may be achieved.

After that, the

steps

712, 714, and 716 are substantially similar to the

steps

608, 610, and 612, respectively, and the description thereof is omitted for simplicity.

In this way, the connected vehicle 410 may dynamically determine whether the BDT transmission for the connected vehicle 410 based on the latest tariff data and the connected vehicle′s historical trajectory behaviors and/or current route planning, such that a maximal cost down for data transmission may be achieved in a manner similar to Fig. 6.

Next, a specific example in which the inventive BDT management based on dynamic tariff data is applied will be described with reference to Fig. 8.

Fig. 8 is a diagram illustrating an exemplary IoT network 80 in which a dynamic-tariff-data based background data transfer (BDT) mechanism according to an embodiment of the present disclosure is applied. Similar to Fig. 1, the network 80 may comprise multiple cells 800-1 through 800-7 and a vehicle 810, and the tariff thereof is kept same. Further, the smart water meters 115-1 through 115-8 are omitted for simplicity.

As shown in Fig. 8, the vehicle 810 may have a relatively fixed path P from the cell 800-1, through the cells 800-2, 800-3, 800-4, 800-5, 800-6, and finally to the cell 800-7. For example, this path P may be a daily commute path between one′s home and office, and therefore the AI model trained based on the historical trajectory behaviors of the vehicle 810 may be aware of the presence of the cell 800-6 which is the all-time low tariff cell with a 90%discount of tariff. Therefore, when the vehicle 810 enters into the cell 800-1 with a discount of 30%during the day and 50%at night, the AI model (either at the connected vehicle system or the vehicle 810) will determine that this is not the optimal cell in which the BDT transmission should be conducted and tell the vehicle 810 not to start the BDT transmission. For the cells 800-2, 800-3, 800-4, and 800-5, the same decision is made by the AI model until the vehicle 810 enters into the cell 800-6. Once the vehicle 810 enters into the cell 800-6, the AI model may determine that the BDT transmission should be started until it detects the vehicle 810 leaves the cell 800-6 for the cell 800-7 in which the BDT transmission is stopped by the AI model. In some other embodiments, if the amount of data is greater than that can be transmitted in the cell 800-6, the BDT transmission may be started by the trained AI model in other cells, such as the cells 800-1, 800-3, 800-5, and/or 800-7 depending on the time when the vehicle 810 travels in these cells.

By contrast, if no historical trajectory behaviors or current route planning of the vehicle 810 is used, then the AI model or another algorithm will be not aware of the presence of the cell 800-6 on the path of the vehicle 810. In such a case, the vehicle 810 may start its BDT transmission upon entering the cell 800-2 since its tariff is lower than that of the cell 800-1, resulting in non-global-optimization of cost down.

Further, although an event-driven decision mode is described with reference to Fig. 8, a periodical decision mode may be used in some other embodiments. In the periodical decision mode, the vehicle 810 or its connected vehicle system may periodically determine whether a BDT transmission should be started or not. In some embodiments, the periodicity may be 5 or 10 minutes. Furthermore, in some other embodiments a combination of the event-driven decision mode and the periodical decision mode may be used.

In some embodiments, the connected vehicle system may periodically trigger the real-time decision sub-procedure. It may prepare the needed states (e.g., day of week, remaining data, and tariff data) according to current time and location of the connected vehicle 810. The connected vehicle system may input the states into the trained AI model (e.g., Q-Table mentioned above) and get the results. If the best action on current states is ″BDT On″ , then the connected vehicle system will inform the connected vehicle 810 to either start BDT transmission or keep the on-going one. Otherwise, the best action on current states is ″BDT Off″ . In such a case, the connected vehicle system will inform the connected vehicle 810 to either stop the on-going BDT transmission or not start any.

Fig. 9 is a flow chart of an exemplary method 900 at a first network element for managing BDT for a user equipment (UE) based on dynamic tariff data according to an embodiment of the present disclosure. The method 900 may be performed at a first network element (e.g., the connected vehicle system 420 shown in Fig. 4) for managing BDT for a UE based on dynamic tariff data. The method 900 may comprise step S910 and Step S920. However, the present disclosure is not limited thereto. In some other embodiments, the method 900 may comprise more steps, less steps, different steps, or any combination thereof. Further the steps of the method 900 may be performed in a different order than that described herein. Further, in some embodiments, a step in the method 900 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 900 may be combined into a single step.

The method 900 may begin at step S910 where dynamic tariff data may be received from a second network element. At step S920, at least one of determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data or receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE is performed.

In some embodiments, the method 900 may further comprise: triggering the BDT transmission for the UE in response to determining, at the first network element, that the BDT transmission is to be initiated for the UE. In some embodiments, before the step of receiving, from a second network element, dynamic tariff data, the method 900 may further comprise: transmitting, to the second network element, a subscribe dynamic tariff data request for subscribing dynamic tariff data from the second network element; and receiving, from the second network element, a subscribe dynamic tariff data response indicating success of the subscription. In some embodiments, the step of receiving, from a second network element, dynamic tariff data may comprise: receiving, from the second network element, a dynamic tariff data publication indication request which notifies the first network element of availability of the latest dynamic tariff data; retrieving, from a location specified by a uniform resource identifier (URI) comprised in the dynamic tariff data publication indication request, the latest dynamic tariff data. In some embodiments, the method 900 may further comprise: transmitting, to the second network element, a dynamic tariff data publication indication response which acknowledges the dynamic tariff data publication indication request. In some embodiments, the step of determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data may comprise: determining whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE.

In some embodiments, the determination may be made by an artificial intelligence model which is trained by an artificial intelligence algorithm based on the historical trajectory data and the retrieved latest dynamic tariff data. In some embodiments, the artificial intelligence algorithm may be the Q-Learning algorithm. In some embodiments, the artificial intelligence model may be configured as follows: the artificial intelligence model's agent may be the UE; the artificial intelligence model's environment may be a geographical area in which the UE is moving, the geographical area covering one or more cells of a telecommunications network to which the UE is communicatively connected and each of the one or more cells having an associated dynamic tariff data; the artificial intelligence model's actions may comprise instructing the UE to start or stop the BDT transmission; the artificial intelligence model's states may comprise at least one of day of week, remaining data amount to be communicated with the UE, and tariff for each cell; and the artificial intelligence model's reward function may use the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.

In some embodiments, the step of determining whether the BDT transmission is to be initiated for the UE based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE and current trip planning of the UE may comprise: determining the current states of the artificial intelligence model based on the current time and date, the current location of the UE, remaining data amount to be communicated with the UE, and/or tariff for each cell; and determining whether the BDT transmission is to be initiated for the UE by the trained artificial intelligence model based on the determined states.

In some embodiments, before the step of receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE, the method 900 may further comprise: training an artificial intelligence model by an artificial intelligence algorithm based on historical trajectory data and the retrieved latest dynamic tariff data; and transmitting, to the UE, the trained artificial intelligence model to enable the UE to determine whether the BDT transmission is to be initiated at least partially based on the trained artificial intelligence model. In some embodiments, the artificial intelligence algorithm may be the Q-Learning algorithm. In some embodiments, the artificial intelligence model may be configured as follows: the artificial intelligence model's agent may be the UE; the artificial intelligence model's environment may be a geographical area in which the UE is moving, the geographical area covering one or more cells of a telecommunications network to which the UE is communicatively connected and each of the one or more cells having an associated dynamic tariff data; the artificial intelligence model's actions may comprise instructing the UE to start or stop the BDT transmission; the artificial intelligence model's states may comprise at least one of day of week, remaining data amount to be communicated with the UE, and tariff for each cell; and the artificial intelligence model's reward function may use the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.

In some embodiments, the step of triggering the BDT transmission for the UE in response to determining that the BDT transmission is to be initiated for the UE may comprise: transmitting, to the second network element, a set chargeable party request, which comprises flow information identifying a flow for the UE, to sponsor the traffic of the flow; and receiving, from the second network element, a set chargeable party response to indicate that a sponsorship transaction is activated for the flow. In some embodiments, the UE may be a vehicle, the first network element may be a connected vehicle system serving the vehicle, and the second network element may be an SCEF or an NEF.

Fig. 10 is a flow chart of an exemplary method 1000 at a second network element for facilitating BDT for a UE based on dynamic tariff data according to an embodiment of the present disclosure. The method 1000 may be performed at a second network element (e.g., the SCEF/NEF 430 shown in Fig. 4) for managing BDT for a UE based on dynamic tariff data. The method 1000 may comprise step S1010, S1020, and Step S1030. However, the present disclosure is not limited thereto. In some other embodiments, the method 1000 may comprise more steps, less steps, different steps, or any combination thereof. Further the steps of the method 1000 may be performed in a different order than that described herein. Further, in some embodiments, a step in the method 1000 may be split into multiple sub-steps and performed by different entities, and/or multiple steps in the method 1000 may be combined into a single step.

The method 1000 may begin at step S1010 where general dynamic tariff data is received from a third network element. At step S1020, the general dynamic tariff data is processed to generate specific dynamic tariff data for a first network element. At step S1030, the first network element is triggered to retrieve the specific dynamic tariff data from the second network element.

In some embodiments, before the step of receiving, from a third network element, general dynamic tariff data, the method 1000 may further comprise: receiving, from the first network element, a subscribe dynamic tariff data request for subscribing specific dynamic tariff data from the second network element; authenticating the first network element for the subscription of the specific dynamic tariff data; and transmitting, to the first network element, a subscribe dynamic tariff data response indicating success of the subscription in response to the success of the authentication. In some embodiments, the subscribe dynamic tariff data request may comprise a field indicating a filter, and the step of processing the general dynamic tariff data to generate specific dynamic tariff data for a first network element further comprises: applying the filter to the general dynamic tariff data to generate the specific dynamic tariff data.

In some embodiments, before the step of receiving, from a third network element, general dynamic tariff data, the method 1000 may further comprise: periodically receiving, from the third network element, a first dynamic tariff data publication indication request indicating that a latest general dynamic tariff data is available; and retrieving, from a location specified by a uniform resource identifier (URI) comprised in the first dynamic tariff data publication indication request, the latest general dynamic tariff data. In some embodiments, the method 1000 may further comprise: transmitting, to the third network element, a first dynamic tariff data publication indication response which acknowledges the first dynamic tariff data publication indication request.

In some embodiments, the step of triggering the first network element to retrieve the specific dynamic tariff data from the second network element may comprise: enabling the latest specific dynamic tariff data to be downloadable at a location specified by a uniform resource identifier (URI) ; transmitting, to the first network element, a second dynamic tariff data publication indication request comprising the URI. In some embodiments, the method 1000 may further comprise: receiving, from the first network element, a second dynamic tariff data publication indication response which acknowledges the second dynamic tariff data publication indication request. In some embodiments, the UE may be a vehicle, the first network element may be a connected vehicle system serving the vehicle, and the second network element may be an SCEF or an NEF. In some embodiments, the method 1000 may further comprise: receiving, from the first network element, a set chargeable party request, which comprises flow information identifying a flow for the UE, to sponsor the traffic of the flow; and transmitting, to the first network element, a set chargeable party response to indicate that a sponsorship transaction is activated for the flow.

Fig. 11 schematically shows an embodiment of an arrangement which may be used in a first network element and/or a second network element according to an embodiment of the present disclosure. Comprised in the arrangement 1100 are a processing unit 1106, e.g., with a Digital Signal Processor (DSP) or a Central Processing Unit (CPU) . The processing unit 1106 may be a single unit or a plurality of units to perform different actions of procedures described herein. The arrangement 1100 may also comprise an input unit 1102 for receiving signals from other entities, and an output unit 1104 for providing signal (s) to other entities. The input unit 1102 and the output unit 1104 may be arranged as an integrated entity or as separate entities.

Furthermore, the arrangement 1100 may comprise at least one computer program product 1108 in the form of a non-volatile or volatile memory, e.g., an Electrically Erasable Programmable Read-Only Memory (EEPROM) , a flash memory and/or a hard drive. The computer program product 1108 comprises a computer program 1110, which comprises code/computer readable instructions, which when executed by the processing unit 1106 in the arrangement 1100 causes the arrangement 1100 and/or the first network element and/or the second network element in which it is comprised to perform the actions, e.g., of the procedure described earlier in conjunction with Fig. 2, Fig. 3, Fig. 5, Fig. 6, and Fig. 7 or any other variant.

The computer program 1110 may be configured as a computer program code structured in computer program modules 1110A -1110B. Hence, in an exemplifying embodiment when the arrangement 1100 is used in the first network element, the code in the computer program of the arrangement 1100 includes: a reception module 1110A for receiving, from a second network element, dynamic tariff data; and a performing module 1110B for performing at least one of: determining whether a BDT transmission is to be initiated for the UE at least partially based on the received dynamic tariff data; or receiving, from the UE, an indication that a BDT transmission is to be initiated for the UE.

The computer program 1110 may be further configured as a computer program code structured in computer program modules 1110C -1110E. Hence, in an exemplifying embodiment when the arrangement 1100 is used in the second network element, the code in the computer program of the arrangement 1100 includes: a reception module 1110C for receiving, from a third network element, general dynamic tariff data; a processing module 1110D for processing the general dynamic tariff data to generate specific dynamic tariff data for a first network element; and a triggering module 1110E for triggering the first network element to retrieve the specific dynamic tariff data from the second network element.

The computer program modules could essentially perform the actions of the flow illustrated in Fig. 2, Fig. 3, Fig. 5, Fig. 6, and Fig. 7, to emulate the first network element or the second network element. In other words, when the different computer program modules are executed in the processing unit 1106, they may correspond to different modules in the first network element or the second network element.

Although the code means in the embodiments disclosed above in conjunction with Fig. 11 are implemented as computer program modules which when executed in the processing unit causes the arrangement to perform the actions described above in conjunction with the figures mentioned above, at least one of the code means may in alternative embodiments be implemented at least partly as hardware circuits.

The processor may be a single CPU (Central processing unit) , but could also comprise two or more processing units. For example, the processor may include general purpose microprocessors; instruction set processors and/or related chips sets and/or special purpose microprocessors such as Application Specific Integrated Circuit (ASICs) . The processor may also comprise board memory for caching purposes. The computer program may be carried by a computer program product connected to the processor. The computer program product may comprise a computer readable medium on which the computer program is stored. For example, the computer program product may be a flash memory, a Random-access memory (RAM) , a Read-Only Memory (ROM) , or an EEPROM, and the computer program modules described above could in alternative embodiments be distributed on different computer program products in the form of memories within the UE.

The present disclosure is described above with reference to the embodiments thereof. However, those embodiments are provided just for illustrative purpose, rather than limiting the present disclosure. The scope of the disclosure is defined by the attached claims as well as equivalents thereof. Those skilled in the art can make various alternations and modifications without departing from the scope of the disclosure, which all fall into the scope of the disclosure.

Claims

A method (900) at a first network element (420, 1100) for managing background data transfer (BDT) for a user equipment (UE) (410, 810) based on dynamic tariff data, the method (900) comprising:

receiving (S910) , from a second network element (430, 1100) , dynamic tariff data;

performing (S920) at least one of:

determining (604) whether a BDT transmission is to be initiated for the UE (410, 810) at least partially based on the received dynamic tariff data; or

receiving (710) , from the UE (410, 810) , an indication that a BDT transmission is to be initiated for the UE (410, 810) .
The method (900) of claim 1, further comprising:

triggering (606) the BDT transmission for the UE (410, 810) in response to determining, at the first network element (420, 1100) , that the BDT transmission is to be initiated for the UE (410, 810) .
The method (900) of claim 1, wherein before the step of receiving, from a second network element (430, 1100) , dynamic tariff data, the method further comprises:

transmitting (502) , to the second network element (430, 1100) , a subscribe dynamic tariff data request for subscribing dynamic tariff data from the second network element (430, 1100) ; and

receiving (506) , from the second network element (430, 1100) , a subscribe dynamic tariff data response indicating success of the subscription.
The method (900) of claim 3, wherein the step of receiving, from a second network element (430, 1100) , dynamic tariff data comprises:

receiving (516) , from the second network element (430, 1100) , a dynamic tariff data publication indication request which notifies the first network element (420, 1100) of availability of the latest dynamic tariff data;

retrieving (520) , from a location specified by a uniform resource identifier (URI) comprised in the dynamic tariff data publication indication request, the latest dynamic tariff data.
The method (900) of claim 4, further comprising:

transmitting (518) , to the second network element (430, 1100) , a dynamic tariff data publication indication response which acknowledges the dynamic tariff data publication indication request.
The method (900) of claim 4, wherein the step of determining whether a BDT transmission is to be initiated for the UE (410, 810) at least partially based on the received dynamic tariff data comprises:

determining (604, 708) whether the BDT transmission is to be initiated for the UE (410, 810) based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE (410, 810) and current trip planning of the UE (410, 810) .
The method (900) of claim 6, wherein the determination is made by an artificial intelligence model which is trained by an artificial intelligence algorithm based on the historical trajectory data and the retrieved latest dynamic tariff data.
The method (900) of claim 7, wherein the artificial intelligence algorithm is Q-

Learning algorithm.
The method (900) of claim 7, wherein the artificial intelligence model is configured as follows:

the artificial intelligence model′s agent is the UE (410, 810) ;

the artificial intelligence model′s environment is a geographical area in which the UE (410, 810) is moving, the geographical area covering one or more cells of a telecommunications network to which the UE (410, 810) is communicatively connected and each of the one or more cells having an associated dynamic tariff data;

the artificial intelligence model′s actions comprise instructing the UE (410, 810) to start or stop the BDT transmission;

the artificial intelligence model′s states comprise at least one of day of week, remaining data amount to be communicated with the UE (410, 810) , and tariff for each cell; and

the artificial intelligence model′s reward function uses the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.
The method (900) of claim 9, wherein the step of determining whether the BDT transmission is to be initiated for the UE (410, 810) based on the received dynamic tariff data and at least one of historical trajectory behaviors of the UE (410, 810) and current trip planning of the UE (410, 810) comprises:

determining the current states of the artificial intelligence model based on the current time and date, the current location of the UE (410, 810) , remaining data amount to be communicated with the UE (410, 810) , and/or tariff for each cell; and

determining whether the BDT transmission is to be initiated for the UE (410, 810) by the trained artificial intelligence model based on the determined states.
The method (900) of claim 4, wherein before the step of receiving, from the UE (410, 810) , an indication that a BDT transmission is to be initiated for the UE (410, 810) , the method further comprises:

training an artificial intelligence model by an artificial intelligence algorithm based on historical trajectory data and the retrieved latest dynamic tariff data; and

transmitting, to the UE (410, 810) , the trained artificial intelligence model to enable the UE (410, 810) to determine whether the BDT transmission is to be initiated at least partially based on the trained artificial intelligence model.
The method (900) of claim 11, wherein the artificial intelligence algorithm is Q-Learning algorithm.
The method (900) of claim 11, wherein the artificial intelligence model is configured as follows:

the artificial intelligence model′s agent is the UE (410, 810) ;

the artificial intelligence model′s environment is a geographical area in which the UE (410, 810) is moving, the geographical area covering one or more cells of a telecommunications network to which the UE (410, 810) is communicatively connected and each of the one or more cells having an associated dynamic tariff data;

the artificial intelligence model′s actions comprise instructing the UE (410, 810) to start or stop the BDT transmission;

the artificial intelligence model′s states comprise at least one of day of week, remaining data amount to be communicated with the UE (410, 810) , and tariff for each cell; and

the artificial intelligence model′s reward function uses the cost saved for transmitting a certain amount of data using BDT in view of a baseline cost.
The method (900) of claim 1, wherein the step of triggering the BDT transmission for the UE (410, 810) in response to determining that the BDT transmission is to be initiated for the UE (410, 810) comprises:

transmitting (526) , to the second network element (430, 1100) , a set chargeable party request, which comprises flow information identifying a flow for the UE (410, 810) , to sponsor the traffic of the flow; and

receiving (526) , from the second network element (430, 1100) , a set chargeable party response to indicate that a sponsorship transaction is activated for the flow.
The method (900) of claim 14, wherein the UE (410, 810) is a vehicle, the first network element (420, 1100) is a connected vehicle system serving the vehicle, and the second network element (430, 1100) is a service capability exposure function (SCEF) or a network exposure function (NEF) .
A first network element (420, 1100) , comprising:

a processor (1106) ;

a memory (1108) storing instructions (1110) which, when executed by the processor (1106) , cause the processor (1106) to perform the method of any of claims 1-15.
A method (1000) at a second network element (430, 1100) for facilitating background data transfer (BDT) for a user equipment (UE) (410, 810) based on dynamic tariff data, the method comprising:

receiving (S1010) , from a third network element (440) , general dynamic tariff data;

processing (S1020) the general dynamic tariff data to generate specific dynamic tariff data for a first network element (420, 1100) ; and

triggering (S1030) the first network element (420, 1100) to retrieve the specific dynamic tariff data from the second network element (430, 1100) .
The method of claim 17, wherein before the step of receiving, from a third network element (440) , general dynamic tariff data, the method further comprises:

receiving (502) , from the first network element (420, 1100) , a subscribe dynamic tariff data request for subscribing specific dynamic tariff data from the second network element (430, 1100) ;

authenticating (504) the first network element (420, 1100) for the subscription of the specific dynamic tariff data; and

transmitting (506) , to the first network element (420, 1100) , a subscribe dynamic tariff data response indicating success of the subscription in response to the success of the authentication.
The method of claim 18, wherein the subscribe dynamic tariff data request comprises a field indicating a filter, and the step of processing the general dynamic tariff data to generate specific dynamic tariff data for a first network element (420, 1100) further comprises:

applying (514) the filter to the general dynamic tariff data to generate the specific dynamic tariff data.
The method of claim 18, wherein before the step of receiving, from a third network element (440) , general dynamic tariff data, the method further comprises:

periodically receiving (508) , from the third network element (440) , a first dynamic tariff data publication indication request indicating that a latest general dynamic tariff data is available; and

retrieving (512) , from a location specified by a uniform resource identifier (URI) comprised in the first dynamic tariff data publication indication request, the latest general dynamic tariff data.
The method of claim 20, further comprising:

transmitting (510) , to the third network element (440) , a first dynamic tariff data publication indication response which acknowledges the first dynamic tariff data publication indication request.
The method of claim 18, wherein the step of triggering the first network element (420, 1100) to retrieve the specific dynamic tariff data from the second network element (430, 1100) comprises:

enabling (514) the latest specific dynamic tariff data to be downloadable at a location specified by a uniform resource identifier (URI) ;

transmitting (516) , to the first network element (420, 1100) , a second dynamic tariff data publication indication request comprising the URI.
The method of claim 22, further comprising:

receiving (518) , from the first network element (420, 1100) , a second dynamic tariff data publication indication response which acknowledges the second dynamic tariff data publication indication request.
The method of claim 17, wherein the UE (410, 810) is a vehicle, the first network element (420, 1100) is a connected vehicle system serving the vehicle, and the second network element (430, 1100) is a service capability exposure function (SCEF) or a network exposure function (NEF) .
The method of claim 24, further comprising:

receiving (526) , from the first network element (420, 1100) , a set chargeable party request, which comprises flow information identifying a flow for the UE (410, 810) , to sponsor the traffic of the flow; and

transmitting (526) , to the first network element (420, 1100) , a set chargeable party response to indicate that a sponsorship transaction is activated for the flow.
A second network element (430, 1100) , comprising:

a processor (1106) ;

a memory (1108) storing instructions (1110) which, when executed by the processor (1106) , cause the processor (1106) to perform the method of any of claims 17-25.
A computer program (1110) comprising instructions which, when executed by at least one processor (1106) , cause the at least one processor (1106) to carry out the method of any of claims 1-15 and 17-25.
A carrier (1108) containing the computer program (1110) of claim 27, wherein the carrier (1108) is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.
A system for managing background data transfer (BDT) for one or more user equipments (UEs) (410, 810) based on dynamic tariff data, the system comprising:

the one or more UEs (410, 810) ;

a first network element (420, 1100) of claim 16;

a second network element (430, 1100) of claim 26; and

a third network element (440) configured to providing the second network element (430, 1100) with a latest general tariff data periodically.
The system of claim 29, wherein the one or more UEs (410, 810) are vehicles, the first network element (420, 1100) is a connected vehicle system serving the vehicles, the second network element (430, 1100) is a service capability exposure function (SCEF) or a network exposure function (NEF) , and the third network element (440) hosts a dynamic tariff service.