Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/04—Arrangements for maintaining operational condition
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
  • H04L41/5003—Managing SLA; Interaction between SLA and QoS
  • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/06—Testing, supervising or monitoring using simulated traffic

Definitions

• the field of the invention is that of Vehicle- to- Everything (V2X) communication. More particularly, the invention relates to the use of a quality of service (in English “ Quality of Service ”, or QoS) prediction model for adapting to a change in quality of service of user equipment in a network of V2X communications.
• a quality of service in English “ Quality of Service ”, or QoS
• V2X Vehicle-to-everything communication
• external entities such as another vehicle, road infrastructures, pedestrians or a network of extended communication (eg Internet).
• V2X communication is usually deployed on the basis of two technologies: Wi-Fi (for example according to the IEEE 802.11p standard), and the mobile network (for example the 4G network, or 5G).
• Wi-Fi for example according to the IEEE 802.11p standard
• the mobile network for example the 4G network, or 5G.
• V2X communication is deployed on the existing mobile network.
• V2X communication makes it possible to ensure and improve road safety, reduce fuel consumption and improve the experience between drivers and other road users, such as cyclists and pedestrians.
• V2X communication can be used in various ways, e.g. for cooperative driving, traffic jam warning, collision avoidance, hazard warning, autonomous driving, driver assistance, infotainment...
• the quality of service requirements related to each of these uses can be very different and have a significant impact on the telecommunications standards to be used to provide the adequate service. Indeed, safe and efficient driving, especially of automated vehicles, can be affected by sudden changes in the quality of service provided.
• the invention meets this need by proposing a method for predicting a variation in a quality of service in a V2X communication network comprising at least one base station, to which at least one user equipment is connected.
• the process includes: - an identification of at least one key performance indicator (KPI) representative of the quality of service for said at least one user equipment, called the key performance indicator of interest, - a prediction by deep learning from past values of at least one secondary key performance indicator, collected for said at least one base station, of a future value of said at least one key performance indicator of interest, - a transmission, if necessary, of an information notification of the variation in quality of service intended for said at least one user equipment, according to the predicted value.
• KPI key performance indicator
• the invention is based on an entirely new and inventive approach to QoS variation prediction in a V2X type communication network. More particularly, in order to satisfy the conditions of security and user quality of experience of a network based on V2X type communication, the method according to the invention implements a prediction of a variation in quality of service (QoS ).
• QoS quality of service
• This QoS prediction makes it possible in particular to anticipate variations in QoS and to warn, by sending an information notification, the user equipment of this variation. The equipment can then, upon receipt of the notification, anticipate and adapt its behavior to the change to come.
• QoS prediction is then useful for the user devices of the V2X network to be informed in advance of any upcoming changes in the available quality of service, in order to allow V2X applications to take the appropriate measures.
• measures are, for example, the adaptation or complete shutdown of applications that cannot be operated safely under the expected quality of service conditions (for example: autonomous driving).
• the deep learning stage can accurately predict QoS variations.
• the prediction by deep learning also takes into account a distance measurement of said at least one user equipment item to said at least one base station.
• the method further comprises a selection, from a set of secondary key performance indicators, of said at least one secondary key performance indicator taken into account for the prediction, a value of said at least one selected secondary key performance indicator influencing a value of the key performance indicator of interest.
• the QoS prediction implements in particular a step of selecting a set of factors (also called secondary KPIs) for at least one key performance indicator (KPI) of interest to be monitored for a case of use of the V2X communication network (e.g. remote operation).
• KPI key performance indicator
• different KPIs of interest are to be monitored according to the different use cases of V2X communication.
• the KPI(s) of interest depend on other secondary KPIs that will influence it.
• This selection of a relevant set of secondary KPIs, for a KPI of interest makes it possible to reduce the prediction time and increase its accuracy. Indeed, in an environment as dynamic as a V2X communication network, this selection makes it possible to eliminate secondary KPIs that would bias the QoS prediction.
• the prediction by deep learning implements an algorithm of the LSTM type.
• LSTM-type algorithm is particularly advantageous for predicting QoS variations over time. Indeed, this type of algorithm makes it possible to obtain precise temporal predictions, in particular by weighting the collected data by giving more importance to recent temporal data compared to older temporal data.
• the LSTM type algorithm is implemented as a sliding window on said past values of said at least one selected secondary key performance indicator.
• the prediction is implemented from past values of at least one secondary key performance indicator, collected for a plurality of neighboring base stations of said network.
• the invention also relates to a device for predicting variation in a quality of service in a V2X communication network comprising at least one base station to which at least one user equipment is connected.
• the prediction device is configured to: - identifying at least one key performance indicator (KPI) representative of the quality of service for said at least one user device, called the key performance indicator of interest, - predicting by deep learning from past values of at least one secondary key performance indicator, collected for said at least one base station, of a future value of said at least one key performance indicator of interest, - emitting, if necessary, an information notification of said variation in quality of service to said at least one user equipment item, as a function of said predicted value.
• KPI key performance indicator
• the device is configured to predict the future value by deep learning also taking into account a distance measurement of said at least one user equipment item to said at least one base station.
• the device is further configured to select, from a set of secondary key performance indicators, said at least one secondary key performance indicator taken into account for the prediction, a value of said at least a selected secondary key performance indicator influencing a value of said key performance indicator of interest.
• the prediction device is integrated into said at least one base station.
• the prediction device is integrated into network equipment configured to implement the prediction from past values of at least one secondary key performance indicator, collected for a plurality of base neighbors of the network.
• the prediction device when the prediction device is integrated into an operational subsystem (OSS), it is possible to collect KPI data (of interest or secondary) on a set of base stations located in a given geographical area.
• KPI data of interest or secondary
• the invention also relates to a computer program product comprising program code instructions for implementing a prediction method as described previously, when it is executed by a processor.
• the invention also relates to user equipment connected to at least one base station of a V2X communication network.
• This equipment includes: - a communication module configured to receive, if necessary, an information notification of a variation in quality of service predicted according to a prediction method as described above;
• user equipment in a V2X communication network can anticipate the QoS variation by receiving a notification from a prediction device and relayed by a base station.
• the V2X application via an adaptation module, reacts upstream of the QoS change upon receipt of the information notification.
• the user equipment thanks to the V2X application, has already adapted its behavior.
• the reception of an information notification of variation of quality of service before the actual change of QoS makes it possible to guarantee the security of the users of a V2X communication network.
• such a communication module is advantageously configured to transmit a distance measurement from the user equipment to the base station. It is thus possible to make a spatio-temporal prediction of the variation of QoS for the user equipment.
• Such recording medium can be any entity or device capable of storing the program.
• the medium may include a storage medium, such as a ROM, for example a CD ROM or a microelectronic circuit ROM, or else a magnetic recording medium, for example a mobile medium (memory card) or a hard drive or SSD.
• such a recording medium may be a transmissible medium such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means, so that the program computer it contains is executable remotely.
• the program according to the invention can in particular be downloaded onto a network, for example the Internet network.
• the recording medium may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute the steps or to be used in the execution of the method for predicting a variation in quality of service in a network aforementioned V2X communication system.
• the present technique is implemented by means of software and/or hardware components.
• module or “device” may correspond in this document to a software component, a hardware component or a set of hardware and software components.
• a software component corresponds to one or more computer programs, one or more sub-programs of a program, or more generally to any element of a program or software capable of implementing a function or a set of functions, as described below for the module concerned.
• Such a software component is executed by a data processor of a physical entity (terminal, server, router, etc.) and is likely to access the hardware resources of this physical entity (memories, recording media, communication bus , electronic input/output cards, user interfaces, etc.).
• resources means all sets of hardware and/or software elements supporting a function or service, whether unitary or combined.
• a hardware component corresponds to any element of a hardware assembly (or hardware) able to implement a function or a set of functions, according to what is described below for the module concerned. It can be a hardware component that can be programmed or has an integrated processor for executing software, for example an integrated circuit, a smart card, a memory card, an electronic card for executing firmware ( “firmware” in English), etc.
• the prediction device and the aforementioned corresponding computer program have at least the same advantages as those conferred by the method for securing an exchange according to the present invention.
• FIG. 1 there depicts an exemplary environment of a Vehicle-to-All communication network within a base station network coverage according to one embodiment of the invention
• FIG. 1 there schematically illustrates an example of architecture of a device for predicting a variation in quality of service in a V2X communication network, according to one embodiment of the invention.
• the general principle of the invention is based on the use of a spatio-temporal prediction model of the change in the quality of service in a V2X communication network.
• the invention allows V2X communication applications embedded in user equipment (for example: autonomous vehicles, smart phone, connected watch, etc.) to be informed of an upcoming QoS change and to react to it by upstream.
• user equipment for example: autonomous vehicles, smart phone, connected watch, etc.
• the spatio-temporal prediction of QoS for example along a specific route and/or in a specific time frame, makes it possible to adjust the behavior of user equipment connected to the V2X network at the application level (for example: change at the level of vehicle automation, change of vehicle speed, transfer to driver, display of alert notification, etc.). It is thus possible to guarantee the security of users of the V2X communication network, as well as the quality of the user experience.
• QoS prediction helps to provide early notifications about predicted QoS changes to interested consumers.
• V2X application e.g.: vehicle tele-operation, infotainment, etc.
• QoS variation adaptation module e.g.: vehicle tele-operation, infotainment, etc.
• These prior notifications when the predictions are sufficiently reliable, are then sent with a notice period before the new predicted QoS is felt. This notice period depends on the specific application and use case, but should be long enough to give the application time to adapt to future QoS.
• This V2X communication network comprises various user equipment: vehicles V (V 1 , V 1' , V 2 , V 3 or V 4 ) connected to the V2X network, road infrastructures I, smart phones or watches connected pedestrians or cyclists P... and network equipment such as, for example, mobile network infrastructures N (base station or operational subsystem OSS).
• vehicles V V 1 , V 1' , V 2 , V 3 or V 4
• road infrastructures I smart phones or watches connected pedestrians or cyclists P...
• network equipment such as, for example, mobile network infrastructures N (base station or operational subsystem OSS).
• the connected vehicles are for example autonomous vehicles, such as autonomous cars or trucks, or any type of vehicle, such as for example boats, or planes.
• Connected vehicles can also be non-autonomous vehicles, but can still connect to the V2X network and exchange data with other user equipment or with a V2X application management server in a wide area network.
• “user equipment” of a V2X communication network is subsequently designated as any equipment that can connect to the V2X network and exchange data (for example: autonomous car, smartphone, bicycle navigator, road infrastructure, etc. ).
• the user equipment includes in particular a communication module configured to transmit and receive data.
• a user equipment is able to transmit its position to other equipment or to a base station, and to receive notifications, in particular from the base station.
• Road infrastructures I can be traffic lights, streetlights, traffic or billboards, etc.
• V2X communication network allows different specific types of communication, such as V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), V2N (Vehicle-to-Network) or V2P (Vehicle-to-Vehicle) communication.
• V2V Vehicle-to-Vehicle
• V2I Vehicle-to-Infrastructure
• V2N Vehicle-to-Network
• V2P Vehicle-to-Vehicle communication.
• Pedestrian an infrastructure of relay antennas, or N base stations, makes it possible, via the deployment of a mobile network, for example 5G, to relay these communications.
• an autonomous vehicle V 1 can establish communication with another vehicle V 1′ which is connected to the V2X network.
• This second vehicle V 1' can also be an autonomous vehicle or not.
• the autonomous vehicle V 1 can communicate with the connected vehicle V 1′ in front of it, for example requesting a transfer of data from the on-board camera in V 1′ to see if it can carry out an overtaking maneuver. It is thus possible to improve the perception of the autonomous vehicle V 1 by benefiting from data collected (for example via a camera) by one or more other vehicles connected to the V2X network.
• a connected vehicle V 2 (autonomous or not) can establish communication with a road infrastructure I, for example a traffic light, which then warns the vehicle V 2 of a change in the color of the light.
• a road infrastructure I for example a traffic light
• a connected vehicle V 4 can communicate with an infrastructure of the mobile network, or base station N, either for access to the V2X communication network for communication with other network equipment, or for access with a wide area network WAN ( Wide Area Network ) for communication with a V2X application management server, for example for connection to the autonomous driving V2X service, or another V2X service.
• This V2X application management server SERV_V2X is for example located in the network of the operator and then manages one or more different V2X applications.
• a connected vehicle V 3 can establish communication with a pedestrian, or cyclist, via a V2P type communication.
• This technology differs from the three others presented by the fact that it establishes a link between a living being (the pedestrian) and an object (the vehicle). Communication between a connected vehicle and a pedestrian (or cyclist) is done for example via a connected watch, or smartphone etc.
• KPIs Key performance indicators
• - accessibility it groups together the measures that allow operators to collect information relating to the accessibility of mobile services for the subscriber (for example the KPI used is the success rate or " Success Rate " representing the number of times where the user manages to connect to the service),
• - integrity it measures the high or low quality of a service while the subscriber is using it (e.g. latency or data rate),
• - mobility it measures the number of times a service has been interrupted or abandoned during the transfer of a subscriber or his mobility from one base station to another,
• EE energy efficiency
• KPIs must be taken into account.
• the use case is infotainment
• data rate is relevant.
• the user's safety is at stake, as for example in the case of tele-operation
• several KPIs are to be taken into account: data rate, latency and reliability.
• the required QoS may vary. Table 1 below presents examples of KPIs to be taken into account for different use cases and examples of reactions of the V2X application when the QoS of these KPIs is not respected.
• KPIs to predict for QoS change Examples of potential reactions of the V2X application during a QoS change Remotely operated driving data rate, latency, reliability change the route, park the vehicle, pass the baton to a nearby driver, modify the set or properties of the sensors, change the tele-operation mode (for example, switch from maneuvering to providing a trajectory) .
• alert on dangerous areas reliability inform the user of the availability of an alert service, modify the speed or the route.
• Table 1 only presents examples of KPIs for certain use cases. It should be noted that this list is not exhaustive, and that other KPIs may be taken into account.
• the quality of service of a KPI (such as latency, or data rate) may be influenced by one or more other factors, also referred to below as secondary KPIs (for example: user density for a given base station, the quality of the mobile network etc.).
• a connected and automated vehicle V which is remotely controlled by a tele-operated driving application (in English “ tele-operated driving ” or ToD) is described.
• the software application must be able to receive sufficient quality video data from the vehicle's on-board cameras, as well as vehicle status data such as speed and direction.
• tele-operation commands must be transmitted with a latency of the order of 20 ms or less for example, because a greater delay can lead to a lack of responsiveness.
• the network must provide QoS specific to the tele-operation V2X application, especially in terms of minimum data rate (uplink) and maximum latency (downlink).
• the connected and automated vehicle V drives for example with a minimum data rate of 20 Mbps, and a minimum latency of the order of 20 ms.
• a QoS variation prediction device Disp_V2X, implements a step of predicting a QoS variation by taking into account the different KPIs for the teleoperation use case.
• This QoS prediction step is presented below in connection with FIGS. 3A and 4A.
• the Disp_V2X prediction device performs a QoS prediction for each KPI to be monitored for the given use case.
• the Disp_V2X prediction device can be located directly at the level of a network infrastructure, such as the base station N.
• this prediction device Disp_V2X can be located for example in a V2X application management server of the operator's network, at the level of the operational subsystem OSS
• the prediction device Disp_V2X when the prediction device Disp_V2X is located in a SERV_V2X management of the operator, the latter has access to the data of several base stations.
• the prediction device Disp_V2X determines that according to the trajectory that the vehicle V is following, in 20 seconds, the data download rate should drop below the 20 Mbps threshold and the latency below the threshold 20ms, for 30 seconds.
• the prediction device Disp_V2X via the base station N, informs the V2X application of the vehicle V of the non-compliance with the data download rate and the latency by issuing a notification information on the variation of QoS.
• this notification notably includes the predicted values of the KPIs of interest, such as latency and data rate.
• this notification can also include the duration during which the change of QoS takes place, that is to say the duration during which the values of KPIs are not respected.
• This notification can be sent for example via the 5G communication network, Wifi (in English “ Wireless Fidelity ”) or Lifi (in English “ Light Fidelity ”)... Depending on the use case and the level of security required, this notification is sent with sufficient notice to allow the application to make the necessary changes.
• step E23 the V2X application on board the vehicle V takes an appropriate action/countermeasure, via an adaptation module. This allows the vehicle V to slow down and adapt its speed to allow the remote operation to continue under the future quality of service conditions. If the quality of service to come is not at all sufficient for tele-operation, the reaction of the vehicle V can, in the worst case, bring the vehicle V to a controlled and safe stop.
• step E24 the violation of the data rate and the latency then takes effect, but the necessary measures have already been taken by the V2X application of the vehicle V.
• the example related to only presents an example of V2N type communication in the context of tele-operation.
• the prediction method presented above can be applied to all the types of V2X communication previously presented.
• the method according to the invention makes it possible to ensure the security of the users by anticipating future QoS changes and by informing the user equipment of this change before it occurs.
• one of the KPIs to consider for the tele-operation use case is latency, i.e. the time required for packets to data are transmitted from the V2X management server SERV_V2X to the base station N, or from the base station N to the connected and automated vehicle V.
• this first embodiment of the invention it is sought to make a temporal prediction of the QoS for a KPI, such as latency, at the level of a base station.
• This same temporal prediction model can be applied to any other relevant KPI in the context of tele-operation or other use cases.
• the QoS temporal prediction is defined as the prediction over time of a QoS change taking into account at least one KPI for a given base station or for a piece of equipment. given user. It should be noted that when the Disp_V2X prediction device is located in an operator's management server, at the OSS level, it is possible to predict the QoS for several base stations and therefore to effectively manage situations of handover of the user equipment during its movement.
• the prediction device Disp_V2X collects, via the base station, a set of secondary KPIs, or factors , which have a direct impact on the latency at base station N (e.g. connected subscriber density, mobile network quality). These secondary KPIs data are for example collected with a fine granularity of 15 min for the base station N. These data are then preprocessed to eliminate the null values, for example according to a standard statistical processing in which the null values are modified by the average or the median of the values of the KPI considered.
• each base station in the network has different useful indicators (KPIs), for example depending on the density of users connected at different times of the day or season. .
• a secondary KPI selection phase is applied.
• the objective of this phase is to reduce the number of secondary input KPIs, in order to process only the most significant secondary KPIs, or the most relevant in predicting the future values of the KPI(s) of interest.
• this step makes it possible to reduce the execution time of the prediction, but also to increase the precision of the prediction by removing the insignificant secondary KPIs which can bias the prediction.
• the prediction model then comprises a learning step E32.
• a “Sequence to Sequence” (or Seq2Seq) model.
• This learning model transforms a sequence into another sequence (sequence transformation). It does this by using a recurrent neural network (or RNN for “ Recurrent Neural Network ”).
• the first sequence contains the history of the values of secondary KPIs taken into account, i.e. selected in the previous step, which is transformed into an output sequence which goes from the current sample to the horizon of prediction.
• the Seq2Seq algorithm is trained to match an input sequence of historical observations with an output sequence based on the prediction horizon. This process is repeated as a sliding window over the training data set.
• the Seq2Seq model used in this prediction model is based on an LSTM ( Long Short-Term Memory ) algorithm.
• LSTM Long Short-Term Memory
• the use of a learning algorithm of the LSTM type makes it possible to obtain precise predictions.
• This type of algorithm is particularly advantageous for dynamic environments, such as V2X communication.
• Another advantage of LSTM is that it assigns more importance to recent values in time over older values. In one example, we know that the KPIs values at 6:00 a.m. are different from those at 12:00 p.m., but that the KPIs values at 11:00 a.m. are already closer to those at 12:00 p.m. Thus, when one wishes to predict the values of the KPIs at 12 o'clock, the LTSM algorithm gives more importance to the KPI values of 11 o'clock, than to those of 6 o'clock.
• a step E33 predicted values are compared with the values of KPIs collected. For this, we use the root mean squared error (or RMSE for " Root Mean Squared Error ”) as an evaluation measure.
• the accuracy rate of the QoS temporal prediction model according to the invention amounts to 79% accuracy for the latency at the level of a given base station.
• the use of the above model for the temporal prediction of QoS in a V2X communication network makes it possible to accurately predict QoS changes over time. It is then possible to warn, by issuing a notification to user equipment, of this change in QoS, sufficiently in advance to allow time for the V2X application to adapt.
• the position of the user equipment in the coverage of the base station is taken into account.
• a base station N makes it possible to create a V2X communication network (for example by using a 5G telecommunications network), comprising the connected vehicles Va, Vb, Vc.
• the base station N allows the exchange of data between user equipment and a Disp_V2X prediction device, for example in a network operator management server at the level of an operational subsystem (OSS).
• OSS operational subsystem
• the vehicles Va, Vb, and Vc are located at different positions in the network coverage C of the base station N.
• the vehicle Vc is the closest and the vehicles Va and Vb the farthest.
• the accuracy of the data transmitted by the user equipment, here the vehicles Va, Vb and Vc (for example: position of the vehicles) n is not the same, the same goes for the latency at the level of the user equipment.
• the information coming from the vehicle Vc, closer to the base station N is more precise than that coming from the vehicles Va and Vb which are further away.
• the latency at the level of the vehicle Vc is lower than that at the level of the more distant vehicles Va and Vb.
• the QoS within the network coverage of a base station is not the same depending on whether the user is more or less close to the latter.
• a spatio-temporal prediction of the QoS taking into account the QoS at the level of the base station N as presented in connection with the , and the distance of the user equipment to this base station N.
• the prediction device Disp_V2X uses a linear regression model (for example according to the formulas below) using the temporal prediction data obtained at the prediction step E21 of the according to the process described in connection with the , of one or more base stations in a given geographical area and the distance from the user equipment to these base stations on a chosen route:
• KPI 1 KPI 1 ,..., KPI p considered, for each of which n (n ⁇ 1) values are collected:
• n1 represent the n values collected from the first KPI 1 ,
• - x 1p ...x np represent the n values collected from the p-th KPI p considered.
• y is a KPI whose n values y 1 to y n are to be predicted.
• y' is the prediction of y, y' being calculated using a linear regression function based on p+1 linear regression coefficients ⁇ 0 , ⁇ 1 ,..., ⁇ p applied to an i-th value among n values collected for each of the p KPIs.
• spatio-temporal prediction of QoS change allows users of connected vehicles to request predicted information from a specific base station, based on their routes.
• the prediction device Disp_V2X comprises a random access memory RAM, a processing unit CPU equipped for example with a processor, and controlled by a computer program stored in a read only memory (for example a ROM memory or a hard disk). On initialization, the code instructions of the computer program are for example loaded into the random access memory RAM before being executed by the processor of the processing unit CPU.
• the Disp_V2X prediction device further comprises a memory MEM making it possible to store data from a V2X communication network, such as for example the identity of the equipment connected, the density of the equipment connected to the V2X network for each base station in a zone geographical given...
• the Disp_V2X prediction device also comprises a communication module COM for the reception/transmission of data originating from the user equipment of the V2X network via one or more base stations and the transmission of QoS change notifications.
• the corresponding program (that is to say the sequence of instructions) could be stored in a removable storage medium (such as for example a SD card, USB key, CD-ROM or DVD-ROM) or not, this storage medium being partially or totally readable by a computer or a processor.
• a removable storage medium such as for example a SD card, USB key, CD-ROM or DVD-ROM

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