Computer-implemented system for predicting connectivity quality applied to mobility and optimization process.

Abstract

The invention relates to a connectivity quality prediction system for mobile environments, designed to anticipate and optimize bandwidth management for communication applications. Based on real-time analysis of network distribution and availability data, the system dynamically adapts the data flow to maintain stable and seamless communication. This system offers an innovative solution to current connectivity limitations encountered in mobile environments, such as air, rail, and road transport, by providing enhanced network quality predictability.

Description

Title of the invention: Computer-implemented system for predicting connectivity quality applied to mobility and optimization method. TECHNICAL FIELD OF THE INVENTION

[0001] The present invention relates to the field of telecommunications, and more particularly, the predictability of connectivity, in mobile conditions. STATE OF THE ART

[0002] In a society where connectivity plays a central role in personal and professional communications, knowledge of network quality has become essential. The state of the art offers predictive systems for network quality in "fixed" geographical areas.

[0003] However, the rise of teleworking, the increase in the volume of business travel and, in general, the growing need to stay connected in all circumstances, implies the need to anticipate the quality of this connectivity in "mobile" conditions, that is to say in a context of movement (travel using air, rail, sea transport systems, motorized locomotion devices, or any other form of transport).

[0004] However, this mobility is hampered by the irregularity of network distribution, which poses significant challenges in terms of connectivity predictability. Network availability can also have a significant impact on connectivity quality, particularly in environments such as public transportation, where network saturation occurs when many users simultaneously share the same resources.

[0005] Under these conditions, users are therefore regularly confronted with network instability or overload, complicating the maintenance of smooth communications. These challenges are particularly critical for activities related to videoconferencing, data transfers, WebRTC technologies, or telecommunications in general, which require a stable and high-performance connection. It therefore becomes essential to anticipate these fluctuations, interruptions, and network saturations. SUBJECT AND SUMMARY OF THE INVENTION

[0006] To address these challenges, the present invention proposes a computer-implemented system for predicting connectivity quality "under mobile conditions," thus enabling the user to plan their calls or travel based on these connectivity quality predictions. The invention is complemented by a system for adapting the flow of exchanged data in depending on the accessible and available network bandwidth, in order to maintain optimized connectivity.

[0007] The solution includes several features to achieve this objective: • Predictive connectivity quality system, • based on • network distribution • network availability # • established from: • stored historical data • data collected in real time # # • Connectivity quality optimization system, based on: • A dynamic adjustment system for the delivered data flow (“OUTPUT”) based on the quality of connectivity (“INPUT”) estimated by the system described above. • A user priority management system, particularly suited to air transport systems (equipped with Modem Manager "Modman"), designed to control the distribution of the connection on board. Thus, in the event of overload or any other form of network disruption, the system coordinates access to the connection in order to optimize user connectivity. # • Proactive User Support System, including: • A network quality prediction visualization interface to plan or adjust telecommunications and / or travel for better management of their communication experience. • A consulting service that offers users connection slots tailored to connectivity quality forecasts. This feature allows for distributed use of network resources and optimized connectivity during periods of high demand. • A real-time alert notification system that informs users of variations in network quality that may impact their connectivity. • A computer-implemented telecommunications system, designed to integrate all the functionalities described above, within an interactive digital environment DETAILED DESCRIPTION OF THE INVENTION

[0008] [008 The invention proposes a complete system for predicting and optimizing the Connectivity quality in mobile environments, such as trains or air transport systems. The main goal is to provide a smooth and stable communication experience, even under conditions where the network is subject to significant fluctuations. The system relies on several interactive modules that predict network quality in advance and adjust data flows accordingly, while informing users of connectivity variations.

[0009] This solution addresses the recurring problem of connectivity interruptions during travel, whether in air, rail, maritime, or motorized transport systems. Unlike existing systems, which simply measure network quality in real time, this invention goes further by offering a predictive approach combined with an adaptive approach.

[0010] The invention comprises several integrated modules. First, a predictive system that anticipates connectivity quality based on parameters such as network distribution and availability. Second, an adaptive system that dynamically optimizes the throughput of transmitted and received data, for example in telecommunications, based on the forecasts provided by the predictive system. A connection distribution system designed to ensure the continuity of the most urgent communications. Finally, a telecommunications system that integrates, among other things, telecommunications functionalities, visualization of connectivity quality predictions, user alerts in case of unforeseen events, and the proposal of appropriate solutions.

[0011] The predictive system anticipates network quality by analyzing stored historical data as well as collected real-time data. It takes into account parameters such as network distribution and network availability.

[0012] The adaptive system adjusts the data flow (“output”) in real time according to the predicted connectivity quality (“input”). This mechanism optimizes the maintenance of communications according to needs and available resources.

[0013] The computer-implemented connectivity optimization systems and methods (predictive and adaptive systems) are implemented, among other things, within a telecommunications software platform. This telecommunications software platform has the advantage of adapting the connection flow to the forecasts. established networks, thus ensuring continuity of telecommunications in mobile conditions, marked by fluctuation in connection quality.

[0014] This telecommunications software platform integrates a system for visualizing connectivity quality predictions, a system for alerting users to occasional variations, and a system for recommending connection slots. This system is designed to enable proactive telecommunications planning, making it possible to avoid periods of low connectivity, complemented by a recommendation system aimed at limiting network saturation issues.

[0015] The telecommunications software platform is compatible with various "connected" devices, such as smartphones, tablets or laptops.

[0016] The "inputs" are collected via the user interface of the telecommunications software platform. The telecommunications software platform receives and processes this information.

[0017] The system can be integrated into other types of platforms, such as connectivity management systems, network management automation systems to improve bandwidth distribution in high-density user environments, etc.

[0018] The user priority management system, particularly suited to air transport (equipped with Modem Manager "Modman"), allows, in the event of connection overload, for the coordination of onboard connection access. This priority management system favors connection access for any user whose access is considered a priority and declared as such ("input"). This priority management system can be supplemented by a "boost mode" whereby the system allocates (when the network is available) more bandwidth to users who have subscribed to a specific option.

[0019] The invention is based on a computer-implemented system, based on a predictive analysis algorithm, based on advanced mathematical models, based on machine-learning or statistical modeling techniques, allowing the collection, processing and interpretation of large quantities of stored historical data, as well as data collected in real time.

[0020] The computer-implemented system therefore consists of several modules: 1. Network data collection module: This module is responsible for retrieving the data. 2. Predictive analytics module: This module analyzes real-time data trends and compares them to historical models to anticipate variations. For example, it can predict areas where coverage will be poor based on the route and weather conditions. 3. Communication adaptation module: When network quality degradation is expected, this module automatically adjusts communication parameters to limit interruptions or drops in quality. These calculations make it possible to anticipate variations in connectivity quality in space and time and to adjust data flows dynamically, thus guaranteeing optimal quality of service for users.

[0021] The predictive network distribution system indicates network coverage. It is based on several key variables. These key variables include, in particular, the geographical distribution of antennas or access points, the historical performance of network infrastructure, interruptions due to maintenance or outages...

[0022] The predictive network availability system indicates the state of network saturation. It is based on several key variables. These key variables include the density of connected users in a given area, performance variations during peak hours, etc.

[0023] The predictive system relies on both historical and real-time data to refine its forecasts. Historical data makes it possible to detect patterns in network behavior (periods of saturation, areas of poor coverage), while real-time data provides a current and accurate view of connectivity conditions. These two data sources complement each other to anticipate future disruptions with high accuracy, thus improving the system's reliability.

[0024] Historical data includes information such as: • Available bandwidth (satellite communication systems for air or maritime transport; terrestrial mobile networks - 4G or 5G - for rail transport, motorized vehicles, public transport or any other form of land transport), • The number of simultaneous users, • Saturation rates, • Recorded breakdowns, • Geographical areas where network coverage was insufficient, • Data from specific APIs depending on the modes of transport used (air, rail or maritime transport systems, motorized locomotion devices. This data is collected across network infrastructures and analyzed over extended periods to identify trends and recurring patterns that influence the quality of connectivity.

[0025] Real-time data includes unforeseen, point-in-time information such as: • Delays in transportation, • The battery percentage of users' devices • Weather conditions • Network saturation (congestion zones (accidents, works, peak hours, traffic jams, etc.) for motorized transport devices, ticket reservation rates, number of people connected to a public transport network, etc.), • User movement (via the accelerometer), • GPS location, • The mobile operator • Network terminal identifiers This data is collected and compared to historical models in order to provide an accurate view of current connectivity conditions

[0026] This data is collected at varying frequencies depending on the type of data (for example, GPS data is collected in real time, while network information is updated according to availability). It is stored in a centralized database and analyzed via predictive algorithms to anticipate network quality.

[0027] The dynamic data flow adjustment system is based on continuous analysis correlated with established network quality level threshold transitions (i.e., the "input"). Once the predictions are made, the system follows a decision-making process to adjust the communication parameters. Thus, when certain thresholds are exceeded, the system automatically adjusts the outputs by modulating the data rate sent or received in the context of telecommunication or any other form of data exchange. The threshold levels are established beforehand. For example, in the context of a videoconference: • When the connectivity quality level is above "threshold 1", the data flow remains at maximum (video + sound). • When the connectivity quality level falls below "threshold 1", the emitted data flow decreases (avatar + sound). • When the connectivity quality level falls below "threshold 2", the flow of transmitted data continues to decrease (photo + sound). • When the connectivity quality level falls below "threshold 3", the flow of transmitted data continues to decrease ("text to speech"). This helps to limit situations of connection saturation. In contexts where the priority management system is possible, "priority users" are the last to be impacted by this bandwidth regulation.

[0028] The priority management system allows for the prioritization of connections in real time, primarily in air transport systems or any other type of transport system equipped with a Modem Manager (or "Modman"). A Modman manages onboard connections and monitors available bandwidth based on the number of connected users. Passenger statuses, such as those with a premium option, are recorded in a central system that communicates directly with the Modman. The Modman modulates network access based on the quality of the available connection, prioritizing priority users. Priority users include, among others, cockpit members, flight crew, passengers with a premium option, etc.In the event of network degradation, the Modman adjusts bandwidth distribution according to recorded statuses, ensuring a quality experience for priority users.

[0029] Use case for priority management of connections: The need to prioritize connections can arise in several contexts. In air transport systems, this concerns passengers with premium service subscriptions. On trains, it might involve premium pass holders or passengers making business calls. In high-density user areas (train stations, airports), prioritizing connections ensures stable connectivity for essential users, such as flight attendants, maintenance crews, or security personnel.

[0030] The user interface provides access to several key pieces of information, including connectivity quality predictions, alerts in case of imminent network degradation, and network usage recommendations. The recommendation system suggests adjustments to the distribution of connections among users based on network distribution and availability, to prevent congestion. It also allows users to schedule calls or communications for times when connectivity is optimal.

[0031] The telecommunications application integrates predictive, adaptive, and interactive systems to offer a unique communication experience. Unlike other applications, it dynamically adjusts call quality based on network forecasts and allows users to schedule their communications according to periods of good connectivity. This provides greater communication stability in environments where connectivity is often subject to interruptions or fluctuations.

[0032] Example of a use case: A user on board an airplane wants to make a video call during their flight. Before starting the call, the application informs them that the connection quality will be poor for the next 30 minutes, but that an improvement is expected thereafter. The user can then choose to delay their call or continue in audio quality. If the user has subscribed to a premium option, the application guarantees sufficient bandwidth to maintain the video call by adjusting the connection priority.

[0033] The system improves communication stability in mobile environments where connectivity is often unpredictable. It enables proactive call and data management by optimizing network resource allocation in real time. Thanks to its predictive capabilities, it informs users of periods of high or low connectivity, allowing them to plan accordingly. Finally, it offers priority connection management, ensuring a high-quality experience for priority users.

[0034] Other types of communications and network infrastructure:

[0035] Besides WebRTC, the algorithm can be applied to several other types of communication:

[0036] 1. VoIP Calls: The invention can optimize the quality of VoIP calls, such as those carried out via services such as Skype or WhatsApp, by adjusting the allocated bandwidth according to the predicted network quality.

[0037] 2. Low-latency streaming: In environments where low latency is crucial (for example, for drone operators, remote surgeries or competitive online games), the algorithm can anticipate latency spikes and adjust parameters in real time to minimize interaction delays.

[0038] Integration into existing infrastructures:

[0039] The invention can be integrated into existing network infrastructures, such as network management systems (NMS) or current network protocols. Here are some examples: • Quality of Service (QoS): The algorithm can interact with Quality of Service (QoS) management systems to ensure dynamic allocation of network resources. For example, if a drop in network quality is predicted, the algorithm could interact with QoS protocols to prioritize critical traffic flows. • 5G Networks: In the context of 5G networks, which offer slicing capabilities, the algorithm could help to dynamically allocate network slices based on predictions, thus providing additional bandwidth to critical or priority users. • Cloud and distributed infrastructures: The algorithm can be deployed in cloud environments to process data at scale and enable accurate and rapid prediction. In distributed infrastructures, predictive information can be centralized in the cloud while being applied locally to end users.

[0040] Main advantages:

[0041] - Proactive prediction: The ability to anticipate network variations before they do not occur, thus ensuring better continuity of service.

[0042] - Resource optimization: Efficient bandwidth allocation and management Proactive prioritizing users reduces the risk of saturation.

[0043] - Adaptability to different environments: Whether for infrastructures terrestrial (4G, 5G) or satellite, in mobile transport or large-scale events, the algorithm adapts to many environments.

[0044] Possible variants of the invention: 1. Diversity of communication types:

[0045] Although the invention is primarily described in relation to WebRTC communications, the network quality prediction and optimization system can be applied to a wide variety of communication types. Here are some additional application examples:

[0046] - Online games: In online gaming environments, where low latency is crucial for performance; the algorithm could predict areas where latency increases are to be expected and adjust connection parameters to minimize latency.

[0047] - Video streaming: The invention could be used to adapt in real time the quality of the video stream in streaming services (Netflix, YouTube, etc.) based on predictions of available bandwidth, thus guaranteeing an optimal user experience even in the event of degraded network conditions.

[0048] - Teleworking and videoconferencing: In the context of remote work, where solutions As Zoom or Microsoft Teams are used, the algorithm could adapt video and audio streams to ensure smooth communications and avoid interruptions during online conferences, particularly in uncertain network environments. 2. Different network infrastructures:

[0049] The invention can also be deployed in different types of network infrastructures, whether for mobile networks (4G, 5G) or satellite networks. The predictive system could be adapted to operate with local area networks (LANs) or virtual private networks (VPNs), adjusting its predictions according to the specific characteristics of each infrastructure. 3. Large-scale communication systems:

[0050] In addition to transportation scenarios, the invention could also be applied in large-scale environments such as:

[0051] - Live events (stadiums, concerts): Where network saturation is a This is a common problem due to the massive influx of people connected.

[0052] - Campuses or companies: Where the system could anticipate peak usage of the network, for example during peak hours in classrooms or offices.

[0053] The examples of embodiments of the invention that have just been presented are only some of the possible embodiments.

[0054] The technique of the invention can be carried out indifferently on a reprogrammable computing machine (such as a laptop, a DSP processor or a microcontroller) executing a program with a sequence of instructions, or on a dedicated computing machine (such as an FPGA, an ASIC, or any other specialized hardware module).

[0055] In the case where the invention is implemented on a reprogrammable computing machine, the corresponding program (i.e., the sequence of instructions) may be stored on a removable storage medium (such as an SD card, an external hard drive or a flash drive) or not, this medium being readable partially or totally by a computer or a processor.

Claims

Demands

1. A computer-implemented system for predicting and optimizing connectivity quality in mobile conditions, comprising: • a server configured to collect and process connectivity data provided by a plurality of network distribution APIs for different transport environments (airplanes, trains, land vehicles), • a predictive analytics module integrated into a communication application, intended to evaluate in real time the quality of connectivity based on space and time parameters, and • an adaptive management algorithm aimed at dynamically adjusting the flow of data exchanged based on the available bandwidth.

2. System according to claim 1, wherein the server is configured to collect connectivity quality data based on user geolocation, provided by network distribution APIs.

3. System according to claim 1, wherein the adaptive management algorithm includes a network saturation analysis module based on the number of connected users and the bandwidth capacity for each transport environment.

4. A method for optimizing connectivity quality under mobile conditions, said method comprising the steps of: • real-time monitoring of connectivity quality and network saturation to assess bandwidth availability, • identification of users who have subscribed to a premium connectivity option, • dynamic prioritization of premium users by adjusting allocated network resources according to saturation, and • modulation of quality of service for standard users according to defined priorities.

5. A method according to claim 4, comprising a priority management module configured to automatically adapt the flow rate of

6.

7. data transmitted for standard users in order to prioritize premium users in case of network congestion. A method according to claim 4, wherein the priority allocation includes a step of pre-allocating network resources to premium users in anticipation of periods of saturation. WEB-RTC communication system, integrating: • a user interface configured to display connectivity forecasts and alerts in case of network congestion, and • a communication planning module that allows for the recommendation of call slots based on connectivity quality forecasts.