Subscription selection with central cloud intelligence

Abstract

The present invention relates to a method for selecting suitable subscriptions to be loaded onto an eUICC of devices from an IoT fleet among candidate subscriptions, said method comprising: collecting teacher data related to MNO subscriptions and their associated favorite and forbidden PLMNs from field deployed devices and / or laboratory devices hosting samples for a central cloud intelligence, and associating said teacher data with the candidate subscriptions in a database, said method further comprising: receiving information about the radio environment of the device requiring a subscription, processing the received information about the radio environment regarding the teacher data available in the database and the candidate subscriptions, determining an adapted subscription among the candidate subscriptions according to this processing, and triggering a download of the determined subscription to the device requiring the subscription.

Description

[Technical field]

[0001] FIELD OF THEINVENTION The present invention relates to a method for selecting subscriptions to be loaded onto IoT devices from among candidate subscriptions implemented by a central cloud intelligence.

[0002] The invention also relates to a central cloud intelligence for implementing said method. [Background technology]

[0003] 2. Background of the Invention The present invention addresses the situation where an enterprise customer wants to load subscriptions into each field device from a fleet of cellular IoT devices. Such a situation can be done using a bootstrap profile in case of initial provisioning or using an already preloaded operational profile in case of a Mobile Network Operator (MNO) switch. This situation typically occurs when a service provider or an Original Equipment Manufacturer (OEM) is requested to select a field device, e.g. a smart meter, and download its subscriptions.

[0004] In such situations, customers may face subscription download errors, which will cause additional costs and service outages / degradation of the associated devices. The larger the IoT device fleet, the greater the cost impact may be.

[0005] For fleets of metering or highly distributed, sometimes high value devices, this situation can be very detrimental and you need to ensure the correct subscriptions are downloaded to each device in the fleet.

[0006] An existing solution would be to obtain information from the operators in an exhaustive manner regarding their roaming partners and RATs. With all this information, a database can be established and maintained to record such information. However, such a solution presents the drawback of being static and dependent on contact with the operator and its files and contracts.

[0007] Therefore, further alternative and advantageous solutions would be desirable in the art. Summary of the Invention [Problem to be solved by the invention]

[0008] Summary of the Invention The present invention aims to reduce or eliminate the risk of loading subscriptions that will not work once deployed to an individual device. [Means for solving the problem]

[0009] The invention in its broadest sense is defined as a method for selecting subscriptions to be loaded onto an embedded universal integrated circuit card of an Internet of Things device from among candidate subscriptions, said method comprising the steps of: collecting teacher data related to mobile network operator subscriptions and their associated favorite and / or banned PLMNs from field-deployed Internet of Things devices and / or from Internet of Things lab devices hosting a sample of subscriptions; -Associating those teacher data with candidate subscriptions in the database; and Including, The method comprises: receiving information about the wireless environment of an Internet of Things device that requires a subscription; - Processing the received information about the wireless environment regarding teacher data and candidate subscriptions in a database; - determining from among the candidate subscriptions a subscription that is adapted in response to this process; and - triggering a download of the determined subscription to a device requiring the subscription; Further includes:

[0010] The principle of the present invention is to train a central cloud intelligence based on teacher data collected from field and / or in-lab devices. Such field devices are typically deployed for production purposes, while the in-lab devices can be dedicated blueprint devices to run some experiments. It should be noted here that Mobile Network Operators (hereafter MNOs) are physical without distinction and therefore fully own their PLMN equipment or virtual, i.e. Mobile Virtual Network Operators (MVNOs), using partially or fully the Public Land Mobile Networks (PLMNs) of their Mobile Network Operator partners.

[0011] After the education of the cloud intelligence, as a prerequisite for deciding the subscription, the central cloud intelligence needs to know the radio environment of the individual device, which is typically a list of PLMNs that the device knows, with associated Radio Access Technologies (RATs), power strength and signal quality parameters. Such parameters are typically RSSI, RSCP, RSRP, ECN0, RS-SINR as defined in the 3GPP standard.

[0012] The present invention helps the central cloud intelligence responsible for dispatching MNO subscriptions to a fleet of cellular devices that include embedded Universal Integrated Circuit Cards (eUICCs) with greater relevance and efficiency. MNOs have the habit of pushing lists of favorite and forbidden PLMNs to SIM cards. Such a mechanism allows MNOs to influence the PLMNs selected by devices, as defined in the 3GPP standards. The uniqueness of the present invention is that it leverages such information by pulling it into an MNO-independent central cloud intelligence, so that a service provider using such a central cloud intelligence can use them as training data to download the most suitable MNO subscriptions to each of its devices. In effect, it implements a kind of roaming partner map in a centralized, non-MNO-dependent manner. Such a mechanism then allows service providers to maximize the relevance of MNO subscription selection for each of their devices.

[0013] The present invention, through its education mechanism, helps to create a full fleet of devices that benefit from the experience of a small number of devices, thanks to its tunable teaching data collection methods.

[0014] According to an advantageous feature, the teacher data relating to MNO subscriptions and their associated favourite and forbidden PLMNs comprises some standard 3GPP information to be read from the MNO profile in the eUICC.

[0015] Standard 3GPP information includes: -Home PLMN, - equivalent PLMN, -Priority PLMN, -Ban PLMN.

[0016] They are Home PLMN, PLMNs equivalent to Home PLMN, Preferred PLMNs, and such list includes PLMNs that should be used as PLMN selectors from the operator and user point of view, and forbidden PLMNs.

[0017] According to an advantageous feature, collecting the training data is periodic. This allows the server to decide to read the training data at regular intervals, for example once a month.

[0018] According to another feature, collecting the training data occurs after a given number of periodic subscriptions have been successfully downloaded to the device.

[0019] This feature allows you to collect, for example, every 1000 devices that have successfully downloaded the same type of subscription.

[0020] According to another feature, collecting the training data is performed following a network registration failure experienced by the IoT device, such network registration failure instructing an update of a forbidden PLMN list of an associated subscription in an embedded universal integrated circuit card of the failed IoT device.

[0021] This feature allows for exemplary real-time updates of subscription-teaching data associations in the event of a device failure.

[0022] According to another feature, collecting training data is performed from devices registered with a given mobile network operator following an update of a subscriber identity module file triggered by the given mobile network operator.

[0023] This feature makes it possible to take into account any kind of update, including mass updates, of eUICCs having all subscriptions with a given mobile network operator.

[0024] Collecting training data may be a combination of the above features. According to an advantageous characteristic, the method comprises, in advance, for collecting training data, -Access to hashes of training data; and - Comparing the received hash value with a previous hash value calculated on the last updated teacher data for the given subscription; Further comprising: Collecting training data is triggered only if the hash value differs from the previous hash value.

[0025] Due to this feature, the use of the hashing mechanism avoids the cloud intelligence server from reading the entirety of such teacher data if the teacher data is identified as being the same as some previously downloaded teacher data.

[0026] Using the above features, the present invention proposes a dynamic way to create and maintain a frequently evolving MNO roaming partner map in a central cloud intelligence database, composed of supervised data. Due to the short periodicity, it can actually move quasi-continuously. Thus, the present invention provides a dynamic way to constantly select and download the most suitable MNO subscription to each of the IoT fleet devices.

[0027] According to a first embodiment, receiving information regarding the radio environment of a device requiring a subscription is preceded by scanning network frequencies for the device requiring a subscription to identify available PLMNs and associated RATs that make up the radio environment and transmitting this information regarding the radio environment to a central cloud intelligence.

[0028] In this embodiment, a specific report from the individual device itself is generated with the help of a scan. The information about the device's radio environment as contained in the report is then processed by cloud intelligence and combined with the MNO roaming partner map to determine the best subscription to download for this particular device.

[0029] Advantageously and classically, the radio environment may also include signal power and quality information associated with discovered PLMNs and associated RATs.

[0030] According to a second embodiment, receiving information about the radio environment of a device requiring subscription is preceded by scanning network frequencies for a device companion of the device requiring subscription to identify available PLMNs, associated RATs that make up the radio environment, and transmitting this information about the radio environment to a central cloud intelligence.

[0031] In this embodiment, the specific report is generated by a device companion close to the target device, for example, it can be a smartphone with an application specific to the present invention that is close and paired with the IoT device for which the subscription is to be provisioned.

[0032] The invention also relates to a cloud infrastructure having a central cloud intelligence for implementing the method of the invention, said cloud infrastructure having connections for collecting teacher data related to MNO subscriptions of a Mobile Network Operator and their associated favourite and banned PLMNs from field deployed devices and / or laboratory devices hosting a sample of subscriptions, The cloud infrastructure further has a processing module for processing the received information about the wireless environment regarding the teacher data in the database and the candidate subscriptions when the cloud infrastructure receives information about the wireless environment from an Internet of Things device requiring a subscription, and associating the teacher data in the database with the candidate subscriptions to determine a subscription adapted in response to this processing from among the candidate subscriptions, and the cloud infrastructure then triggers download of the determined subscription to the device requiring the subscription.

[0033] Such a cloud infrastructure implements the inventive central cloud intelligence for managing subscription selection for a fleet of devices in a reliable and relevant manner, which allows the central cloud intelligence to filter out subscriptions that do not fit and advantageously also filter out subscriptions that are at risk, i.e., those without an explicit PLMN in the favorites list.

[0034] The present invention also relates to an Internet of Things device having an embedded universal integrated circuit card into which candidate subscriptions are downloaded, said device having a cellular module or chipset supporting network scanning or being connectable to a device companion, said cellular module or chipset supporting network scanning or said device companion adapted to scan network frequencies to identify available PLMNs and associated RATs that make up the wireless environment, said device further having a transmit / receive cellular module or chipset for transmitting this information about its wireless environment to a central cloud intelligence of the present invention and for receiving the downloaded subscriptions.

[0035] The present invention therefore also relates to a "chip-on-board" solution, which therefore also relates to devices of the IoT that use the chipset directly without a cellular module.

[0036] Such devices are therefore provided with subscriptions that are adapted to their wireless environment, thus reducing the risk of downloading the wrong subscription.

[0037] Advantageously, the device further has agent software within the embedded universal integrated circuit card for reading teacher data related to mobile network operator subscriptions and their associated favorite and forbidden PLMNs and transmitting the read teacher data to a central cloud intelligence.

[0038] Using such agent software, the devices are adapted to feed further teacher data to the central cloud intelligence on a periodic or regular basis, which dynamically renders the database in the central cloud intelligence. This is particularly beneficial as MNO roaming partner maps are tedious to establish manually and almost impossible to maintain due to their high update cadence.

[0039] To the accomplishment of the foregoing and related ends, the one or more embodiments comprise the features hereinafter fully described and particularly pointed out in the claims.

[0040] BRIEF DESCRIPTION OF THE DRAWINGS The following description and the annexed drawings set forth in detail certain illustrative aspects and illustrate some of the various ways in which the principles of the embodiments may be employed. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings, and the disclosed embodiments are intended to include all such aspects and their equivalents. [Brief description of the drawings]

[0041] [Figure 1] 1 shows a schematic representation of the context of the present invention; [Diagram 2] 1 shows a schematic diagram of the general functioning of the method of the present invention. [Diagram 3] FIG. 2 is a time diagram illustrating a collection of experiences according to the present invention. [Figure 4] 1 illustrates the use of the present invention to select a subscription. [Figure 5A] 4 illustrates implementation options for the subscription selection method of the present invention. [Figure 5B] 4 illustrates implementation options for the subscription selection method of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0042] Detailed Description of the Invention Embodiments In order to more fully understand the present invention, the present invention will be described in detail with reference to the accompanying drawings. The detailed description illustrates and describes what are considered to be preferred embodiments of the present invention. It should be understood, of course, that various modifications and changes in form or details can be easily made without departing from the scope of the present invention. Therefore, the present invention may not be limited to the exact forms and details shown and described herein, and is not intended to be limited to less than the entirety of the invention disclosed herein and claimed below. The same elements are indicated by the same reference numerals in different drawings. For clarity, only those elements and steps that are useful for understanding the present invention are shown and described in the drawings.

[0043] Fig. 1 shows diagrammatically an environment in which the invention is exemplarily implemented. The invention concerns a central cloud intelligence CCI implemented in at least one server connected to several devices D1 to Di.

[0044] Of these several devices, at least two, here D1 and D2, are loaded with subscriptions from Mobile Network Operator A (hereafter MNO A) and Mobile Network Operator B (hereafter MNO B), respectively, and are therefore sending training data TDA and TDB to the central cloud intelligence CCI. The training data TDA, TDB is information related to subscriptions such as home PLMN, equivalent PLMN, etc., and their associated RATs if applicable. Such information is classically found in a file on the SIM card.

[0045] This is also shown in Figure 2, where the teacher data TDA and TDB from devices loaded with different subscriptions from MNOs A and B are sending their teacher data to the central cloud intelligence CCI. The teacher data TDA and TDB are stored in the central cloud intelligence CCI's central cloud intelligence database (CCI DB) associated with the candidate subscriptions. The CCI DB then contains some kind of up-to-date roaming partner map with the list of favorite and forbidden PLMNs for subscriptions from MNOs A and B.

[0046] The devices D3 to Di are devices located in different radio environments RE3 to REi that are scanned by each device D3 to Di. The scanned radio environments REi are then information provided to a central cloud intelligence CCI, as shown diagrammatically in Figures 1 and 2.

[0047] The radio environment REi of each device is then used by the central cloud intelligence CCI and combined with the database CCI DB to determine adapted subscriptions AS3 to ASi which are downloaded respectively to the devices D3 to Di as shown in FIG.

[0048] In an advantageous embodiment of the invention, all devices D1 to Di are then used to supply the central cloud intelligence CCI with further updated data constituting further teacher data TD1 to TDi.

[0049] Figure 3 shows a time diagram of the learning phase by the central cloud intelligence CCI. The device D involved in the supply of the central cloud intelligence CCI comprises at least a cellular module or chipset and an embedded universal integrated circuit card eUICC on which a subscription is to be provisioned. Based on such subscription, the device D selects a public land mobile network PLMN and performs an attachment procedure on it, where a subscription already exists in the eUICC for the needs of the learning phase of the central cloud intelligence CCI. The device D in the field or in the laboratory can access the information stored in the relevant SIM profile installed in the eUICC. An agent software (AG) running on the device D can read the list of favorite and forbidden PLMNs associated with the subscription and transmit such teaching data to the central cloud intelligence CCI.

[0050] The device further comprises a Modem Stack (MS) as an interface between the eUICC and a PLMN. Such a PLMN may be a Home PLMN, where the device D is connected to a network belonging to the issuer of the subscription, or a Visited PLMN, if the device is roaming.

[0051] The agent software AG is further connected to a central cloud intelligence CCI and processes information relating to the invention.

[0052] In a first step S1, the modem stack MS reads the subscriptions stored therein in the module eUICC and accesses the PLMN lists, i.e. lists of forbidden PLMNs, preferred PLMNs and home PLMNs. Using such lists, the modem stack MS selects the most suitable PLMN and proceeds with the attachment procedure in step S2. In case of a failure due to a certain cause, the selected PLMN can be declared "forbidden" and the modem stack MS updates the list of PLMNs in the eUICC profile accordingly and restarts the PLMN selection and attachment procedure with other available PLMNs. In such a case, the eUICC profile of this device D contains some new useful information that becomes new teaching data for the CCI in the next steps. Such an update can also be performed with other lists of PLMNs as known by the eUICC.

[0053] The successive attachment procedures of the device D to different PLMNs indeed result in an update of the SIM PLMN list constituting the teaching data TD of the invention. The dashed procedure is therefore an example of a procedure for performing an update of the PLMN list.

[0054] The agent AG then reads the updated PLMN lists in step S4, these updated PLMN lists being teacher data TD, and transmits the updated PLMN lists associated with the active eUICC subscriptions to the central cloud intelligence CCI in step S5, which then stores these lists in association with the type of subscription of the corresponding device in the database CCI DB in step S6.

[0055] According to the invention, the central cloud intelligence CCI can read such information from the devices at different times depending on the implementation. Such reading can be done when a new type of subscription is first installed in an eUICC of the device fleet. Optionally, a hashing mechanism can be arranged to allow the server to avoid reading several times during the device Di step 5 the same teacher data TD that is published in a further later reading by the same device D or published by different devices D for the same type of subscription. In such a case, it is advantageous for the server to first systematically read the hash in order to determine whether the server should read the complete teacher data TD related to the subscription.

[0056] Thus, the server may decide to read the training data once at regular time intervals, e.g. monthly, and / or once on a regular subscription that has been successfully downloaded to the device, e.g. every 1000 successful downloads.

[0057] Thus, the present invention allows leveraging the same PLMN list as that used by the modem stack of device D during 3GPP PLMN selection. This is illustrated in FIG.

[0058] According to the invention, such a device D performs a scan of the radio environment RE that results in a list of available PLMNs, RATs, power strengths and signal quality parameters. This scan allows the device D to learn the active frequencies.

[0059] Such a scan of the radio environment RE can be performed according to two options, illustrated in Figures 5A and 5B.

[0060] In the first case, the device D is independent and proceeds to scan the radio environment: According to the invention, the agent AG requests the modem stack MS to start an informal network scan in step S7.

[0061] The informal scan is indicated by step S8 where available PLMNs, RATs, power strength and signal quality parameters are listed.

[0062] Then, in step S9, the agent AG reads the radio environment RE established in step S8. This is an example of a procedure for obtaining the radio environment RE of an individual device that needs to download a subscription. This allows the agent AG to send the radio environment RE to the central cloud intelligence CCI, as shown in FIG. 4.

[0063] In step S11, the central cloud intelligence CCI then processes information about the radio environment RE using the teacher data TD stored in the subscription database CCI DB associated with the candidate subscription. In this step S11, the central cloud intelligence CCI compares a list of available PLMNs / RATs recognized by the device with a list of PLMNs / RATs compatible with the candidate subscription. The central cloud intelligence CCI then eliminates subscriptions that may not match, such as those classified as prohibited PLMNs of the candidate subscription involving all PLMNs / RATs recognized by the device, according to the subscription database CCI DB. The central cloud intelligence CCI then can eliminate subscriptions that may present a risk of not matching, such as those that are not explicitly listed in the list of favorite PLMNs of the candidate subscription involving all PLMNs / RATs recognized by the device, i.e., home PLMNs, equivalent PLMNs, preferred PLMNs, etc., according to the subscription database CCI DB. The central cloud intelligence CCI then selects the best subscription from the remaining subscriptions based on some criteria for its selection, such as best signal strength, best price, etc. Finally, in step S12, the selected subscription AS is downloaded to the module eUICC of the device D.

[0064] 5B shows a second option of embodiment of the invention, where device D is not the most suitable equipment to perform a frequency scan and is not a candidate for determining the radio environment RE. For example, device D is a meter. It therefore depends on another device companion DC to perform a network analysis via a network scan. Thus, typically, a smartphone with an installed application APP is dedicated to the management of device D or at least to the activation of device D. This smartphone is a device companion DC with a telecommunication stack TS.

[0065] In step S7', the application APP requests the telecommunication stack TS to perform a network scan, which is executed by the telecommunication stack TS in step S8'. Then, in step S9', the application APP reads the radio environment in the telecommunication stack TS, which lists the available PLMNs, RATs, power strength and signal quality parameters. This is an example of a procedure for obtaining the radio environment RE of an individual device with the aid of the device companion DC.

[0066] The application APP can then provide the radio environment RE to the central cloud intelligence CCI in step S10, as shown in Fig. 3, which triggers the determination of the best subscription AS for the radio environment RE by the central cloud intelligence CCI in step S11 using the teacher data stored in the database CCI DB, which then triggers the download of the subscription AS in the device D in step S12.

[0067] Thus, according to the invention, one or a small number of devices collects inputs including home PLMNs, PLMNs equivalent to home PLMNs, preferred PLMNs that allow PLMN selection, and forbidden PLMNs from an eUICC hosting one sample of candidate subscriptions. This or these devices transmit such inputs to a central cloud intelligence CCI in charge of dispatching / delivering MNO subscriptions, whether this provisioning is performed in push or pull mode. The central cloud intelligence CCI thus educates a more relevant subscription selection algorithm in subscription selection for the remaining device fleet, as follows:

[0068] - Not selecting a subscription if a PLMN recognized by the device involved is on the forbidden PLMN list Select a subscription for which an equivalent PLMN is identified among the PLMNs known by the device involved -Select a subscription for which a preferred PLMN is identified among the PLMNs known to the device concerned.

[0069] Such an algorithm obtains the training data TD in the database CCI DB as a basis for knowing whether a subscription is adapted to a device with a certain radio environment RE or whether this subscription is not adapted. For example, the algorithm filters out subscriptions of MNOs whose PLMNs do not appear in the radio environment RE of the device for which the subscription is provisioned. It avoids erroneous subscription downloads in devices that do not have a network corresponding to that radio environment RE.

[0070] In the above detailed description, reference is made to the accompanying drawings which show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. Accordingly, the above detailed description is not to be taken in a limiting sense, and the scope of the invention is defined only by the appended claims, appropriately interpreted.

Claims

1. 1. A method for selecting subscriptions to be loaded onto an embedded universal integrated circuit card of an Internet of Things device from among candidate subscriptions, the method comprising: - collecting training data related to mobile network operator subscriptions and their associated favorite and / or forbidden PLMNs from field deployed Internet of Things devices and / or from Internet of Things lab devices hosting a sample of subscriptions; - Associating those teacher data with candidate subscriptions in a database; Including, The method comprises: - receiving information about the radio environment of an Internet of Things device requiring a subscription; - processing the received information about the wireless environment with respect to training data and candidate subscriptions in the database; - determining from among said candidate subscriptions a subscription that is adapted in response to this process; - triggering a download of the determined subscriptions to the devices requiring the subscriptions; The method further comprising:

2. 2. The method of claim 1, wherein the training data relating to mobile network operator subscriptions and their associated favorite and forbidden PLMNs includes certain standard 3GPP information to be read from the mobile network operator profile in the embedded universal integrated circuit card.

3. The standard 3GPP information includes at least - Home PLMN, - equivalent PLMN, -Priority PLMN, - Forbidden PLMN The method of claim 2 , comprising:

4. The method according to any one of claims 1 to 3, wherein the collecting of the training data is periodic.

5. 10. The method of any one of the preceding claims, wherein the collecting of training data occurs after a given number of periodic subscriptions have been successfully downloaded to the device.

6. 10. The method of claim 1, wherein the collecting of teacher data is performed after a network registration failure experienced by an IoT device, such network registration failure instructing an update of the forbidden PLMN list of the associated subscription in the embedded universal integrated circuit card of the failed IoT device.

7. 10. The method of any one of the preceding claims, wherein the collecting of teacher data is performed from devices registered with a given mobile network operator following an update of a subscriber identity module file triggered by the given mobile network operator.

8. In order to collect teacher data, - accessing a hash of the training data; - comparing the received hash value with a previous hash value calculated on the last updated training data for the given subscription; Further comprising: The method according to any one of claims 4 to 7, wherein the collecting of training data is triggered only if the hash value differs from the previous hash value.

9. 13. The method of claim 1, wherein the receiving of information about the radio environment of a device requiring a subscription is preceded by scanning network frequencies to identify available PLMNs and associated RATs that make up the radio environment for the device requiring a subscription and transmitting this information about the radio environment to the central cloud intelligence.

10. 9. The method of claim 1, wherein the receiving of information about the radio environment of a device requiring subscription is preceded by scanning network frequencies for a device companion of the device requiring subscription to identify available PLMNs and associated RATs that make up the radio environment and transmitting this information about the radio environment to the central cloud intelligence.

11. 11. A cloud infrastructure with central cloud intelligence for implementing the method of any one of claims 1 to 10, said cloud infrastructure having connections for collecting teacher data related to mobile network operator subscriptions and their associated favorite and forbidden PLMNs from field deployed devices and / or laboratory devices hosting a sample of subscriptions, said cloud infrastructure further having a processing module for, when said cloud infrastructure receives information about the radio environment from an Internet of Things device requiring a subscription, processing the received information about the radio environment regarding the teacher data in said database and candidate subscriptions and associating those teacher data in a database with candidate subscriptions in order to determine from among the candidate subscriptions a subscription that is adapted in response to this processing, said cloud infrastructure then triggering the download of the determined subscriptions to the device requiring a subscription.

12. 13. An Internet of Things device having an embedded universal integrated circuit card into which candidate subscriptions are downloaded, the device having a cellular module or chipset supporting network scanning or capable of being connected to a device companion, the cellular module or chipset supporting network scanning or the device companion adapted to scan the network frequencies to identify available PLMNs and associated RATs that make up the wireless environment, the device further having a transmit / receive cellular module or chipset for transmitting said information regarding the device's wireless environment to a central cloud intelligence as described in claim 11 and receiving the downloaded subscriptions.

13. 13. The Internet of Things device of claim 12, further comprising agent software within the embedded universal integrated circuit card for reading teacher data related to mobile network operator subscriptions and their associated favorite and forbidden PLMNs and transmitting the read teacher data to the central cloud intelligence.

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