A system developing smart strategies for autonomous network resource management in case of disinformation

Abstract

The present invention relates to a system (1) which enables network resource planning by estimating the potential mobility of network users, the persons with whom they communicate or establish relationships and the risky areas by performing location risk analysis, relationship forecasting, and user / subscriber mobility analysis in order to ensure the continuity and efficiency of the communication infrastructure in cases such as disinformation, disaster.

Description

[0001] A SYSTEM DEVELOPING SMART STRATEGIES FOR AUTONOMOUS NETWORK RESOURCE MANAGEMENT IN CASE OF DISINFORMATION

[0002] Technical Field

[0003] The present invention relates to a system which enables network resource planning by estimating the potential mobility of network users, the persons with whom they communicate or establish relationships and the risky areas by performing location risk analysis, relationship forecasting, and user / subscriber mobility analysis in order to ensure the continuity and efficiency of the communication infrastructure in cases such as disinformation, disaster.

[0004] Background of the Invention

[0005] Today, mobile network infrastructures are primarily planned to maximize the communication efficiency of persons and also planned by analyzing the redundant requirements to be used in case of need. In the current technique, there is no smart support and planning platform developed to improve the efficiency of the planning process. The said lack of network planning results in the inability to analyze complex situations within the network due to manual planning, the inability to utilize big data, and the limited use of forecasting metrics. In the state of the art, there are several solutions which enable the autonomization of network planning, allowing the system operator to perform analyses in accordance with financial criteria in the planning of communication networks, and detecting faults in base stations in the mobile communication system by emulating network protocols. However, in the current technique, there is no solution which enables even rare and unexpected events such as disasters and disinformation to be analyzed and taken into account in the network planning phase by using artificial intelligence-based algorithms and complex data fed as well as providing intelligent planning infrastructure to ensure that the grid is designed to meet changing demand in complex and difficult-to-model situations such as disasters and pandemics; including plans to ensure service continuity of the network in the event of unexpected situations such as disasters, earthquakes, pandemics, disinformation, damage to existing sites or unexpected network traffic caused by external factors and connecting the network with next-generation network functions; and designing the planning infrastructure in a cloud-based manner in accordance with nextgeneration mobile communication architectures.

[0006] The Chinese patent document no. CN116662010, an application included in the state of the art, discloses an invention which provides a method of dynamic resource allocation based on a distributed system that enables intelligent resource allocation, improves stability and efficiency and reduces resource consumption and management cost and relates to a distributed system. The method included in the invention comprises the steps of acquiring a plurality of first distributed systems and system resource data of each first distributed system; performing modular integration on the plurality of first distributed systems according to the system resource data to obtain a second distributed system; receiving and responding to a plurality of historical resource allocation requirements through a second distributed system to carry out resource use data monitoring; constructing a plurality of system module network structure diagrams respectively, and performing node relation analysis to obtain a node attribute data set; constructing resource allocation training data according to the node attribute data set and establishing a resource prediction model; and obtaining a target resource distribution by inputting a resource prediction model for target resource allocation demand. In the invention, a plurality of system module network structure diagrams corresponding to a plurality of historical resource utilization data are respectively generated and the system module network structure diagram is provided to perform relationship analysis to obtain the node attribute data set of each system module network structure diagram.

[0007] Summary of the Invention

[0008] An objective of the present invention is to realize a system developing smart strategies for autonomous network resource management in case of disinformation which enables to analyse even rare and unexpected events such as disaster, disinformation by means of artificial intelligence-based algorithms and complex data fed and then to consider them in the network planning phase; to provide a smart planning infrastructure so as to ensure that the network can be designed to meet changing demand in complex and difficult-to-model situations such as disasters and pandemics; to included plans so as to ensure service continuity of the network in the event of unexpected situations such as disasters, earthquakes, pandemics, disinformation, damage to existing sites or unexpected network traffic caused by external factors; to connect the network via next-generation network functions and to design the said planning infrastructure in a cloud-based manner in accordance with next-generation mobile communication architectures.

[0009] Detailed Description of the Invention

[0010] “A System Developing Smart Strategies for Autonomous Network Resource Management in Case of Disinformation” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0011] Figure 1 is a schematic view of the inventive system developing smart strategies for autonomous network resource management in case of disinformation. The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0012] 1. System

[0013] 2. Electronic device

[0014] 3. Database

[0015] 4. Virtual network function server

[0016] 41. Traffic forecasting virtual network function

[0017] 42. Key performance indicator monitoring virtual network function

[0018] 5. Cloud server

[0019] 51. Location risk analysis server

[0020] 511. Infrastructure analysis service

[0021] 512. Geographic data analysis service

[0022] 513. Historical data analysis service

[0023] 514. Location risk assignment service

[0024] 52. Relationship analysis server

[0025] 521. Social media analysis service

[0026] 522. Mobile network interaction service

[0027] 523. Mobile network-supported geographic analysis service

[0028] 524. Relationship assignment service

[0029] 53. Mobility analysis server

[0030] 531. Current user location provider service

[0031] 532. Historical user location analysis service

[0032] 533. Predictive mobility service

[0033] 54. Resource planning server

[0034] 541. KPI monitoring service

[0035] 542. Traffic forecasting service

[0036] 543. Dynamic resource planning service The inventive system (1) developing smart strategies for autonomous network resource management in case of disinformation which enables to perform location risk analysis, relationship estimation, user / subscriber mobility analysis in order to ensure the continuity and efficiency of the communication infrastructure during disinformation and disaster situations; enables network resource planning by predicting the potential mobility of network users, the persons they communicate or associate with, the risky areas comprises a plurality of electronic devices (2) which are configured to enable the user to communicate with other persons and other persons to communicate with the user by connecting to the network in cases of disaster, disinformation; at least one database (3) which establishes connection with the network and is configured to store information on the base station to which users who connect to the network and receive signals from the network with their electronic devices (2) are connected; to store information on neighboring base stations to which electronic devices (2) connect, and to store records of interactions in the form of calls, messages, images between the user having the electronic device (2) and other persons having the electronic device (2); at least one virtual network function server (4) which establishes connection with the database (3); is located on the the core network included in the network; and is configured to run at least one traffic forecasting virtual network function (41) for ensuring that the data generated in the network in times of disaster, disinformation are processable in a scalable and distributed way; to run at least one key performance indicator monitoring virtual network function (42) configured to collect the base station key performance indicators within the network, the user-based key performance indicators of the electronic device (2) users from the network; to ensure that the data collected from the network by the key performance indicator monitoring virtual network function is transmitted to the traffic forecasting virtual network function (41); to ensure that the key performance indicators of the network are predicted within certain time periods with the traffic forecasting virtual network function (41); and at least one cloud server (5) which establishes connection with the database (3), the virtual network function server (4); is configured to run at least one location risk analysis server (51) and the infrastructure monitoring service (511), the geographical data analysis service (512), the historical data analysis service (513) and the location risk assignment service (514) included in the said location risk server (51); to run the relationship analysis server (52) and the social media analysis service (521), the mobile network interaction service (522), the mobile network supported geographical analysis service (523) and the relationship assignment service (524) included in the relationship analysis server (52); to run at least one mobility analysis server (53) and the current user location provider service (531), the historical user location analysis service (532), the predictive mobility service (533) included in the mobility analysis server (53); to run at least one resource planning server (54) and the KPI monitoring service (541), the traffic forecasting service (542) and the dynamic resource planning service (543) included in in the resource planning server (54); to carry out an assignment transaction of regional risk probabilities by performing location risk analyses in the network in case of a disaster by means of at least one location risk analysis server (51) included theron; to carry out the impact analysis of roads, industrial zones in case of a disaster; to estimate the recurrence frequency of risk factors in the region in past times; to determine the size of the area that may be affected in case of a disaster and the number of potential users of electronic devices (2) that may be affected by the said disaster, i.e. the amount of disinformation; to enable the relationship analysis server (52) included thereon to predict other people with whom the users of the electronic device (52) have a relationship; to analyse the call detail records of the users of the electronic device (52) and then to calculate the probability of the user to meet with the other persons; to predict which of the persons with whom the user is associated the user is most likely to go to, by taking into account their past location information based on the location of the electronic device (2) user in cases of disaster or disinformation by means of the mobility analysis server (53) included thereon; to create the most likely mobility scenarios by analysing the mobility patterns and relationships of the electronic device (2) user by enabling the mobility analysis server (53) get support from machine learning algorithms; to monitor the mobility patterns of the electronic device (54) users by at least one resource planning server (2) located thereon; to plan dynamic resource management, by taking into account the areas where people associated with the user live; to perform resource planning in the network according to the amount of potential increase; and to accelerate the normalization process after a disaster or disinformation by ensuring rapid transfer of resources by performing the backup of resources and the postdisaster resource transfer.

[0037] The electronic devices (2) included in the inventive system (1) are smart device such as smartphone, tablet, computer configured to enable the user and other persons to establish a mobile connection with each other, by being used by the user and the other persons associated with the user.

[0038] The database (3) included in the inventive system (1) is configured to store the call detail records and location records of the electronic devices (2) receiving signal from the network.

[0039] The virtual network function server (4) included in the inventive system (1) is configured to provide hourly, daily, weekly, monthly, monthly, yearly estimation of KPI values such as utilization rate, data transmission rate, downstream transmission volume, upstream transmission volume, average signal level, user measurement reports, signal strength, connected cells and signal received as location based on the electronic device (2) users and base stations in the network, recorded by the traffic forecasting virtual network function (41) and the switch performance indicator monitoring virtual network function (42) located thereon; and to enable forecast the network traffic by forecasting the big data stored in the database (3). The key performance indicator monitoring virtual network function (42) included on the virtual network function server (4) is configured to monitor the activities within the network by being positioned within the core network; to ensure that key performance indicators of the base stations such as utilization rate, data transmission rate, downward transmission volume, upward transmission volume, average signal levels are retrieved from the network and stored in the database (3) and to receive the key performance indicators of the users, such as user measurement reports, signal strength, cells connected, cells that can be connected, from the network and to record them in the database (3). The virtual network function server (4) is configured to process the migration of services and functions in mobile networks thanks to the multiple access boundary computing (MEC) principle in 5G and 6G networks with a scalable structure; and locally process the large data in a divide-and-conquer manner by moving the indicator monitoring virtual network function (42) on it to the end segments in the network with the MEC principle.

[0040] The location risk analysis server (51) on the cloud server (5) included in the inventive system (1) is configured to perform spatial analysis and assign the risk probabilities of the regions in the network; to analyse the impact of buildings, roads, industrial zones, hotels, shopping centers, hotels, shopping centers and infrastructure / superstructure in case of a disaster with the infrastructure analysis service (511) on the location risk analysis server (51); and assess the results stored in databases (3) and the resilience of each structure in the network areas. The geographical analysis service (512) included on the location risk analysis server (51) on the cloud server (5) is configured to analyse the earth by evaluating factors such as fault lines, soil structure, rock structure, elevation, and to evaluate the living areas by creating geographical risk maps within the network. The location risk analysis server (51) on the cloud server (5) is configured to analyse the history of the spatial structure based on the output information of the historical data analysis service (513) and the infrastructure analysis service (511) and the information of the geographical analysis service (512); to examine when the risk factors in the region occurred in the past and to evaluate the frequency of recurrence of risk factors, causes and structural damage analysis. The cloud server (5) is configured to feed the location risk assignment module (514) with the outputs of the infrastructure analysis service (511), geographical analysis service (512) and historical data analysis service (513) in the location risk analysis server (51); to determine location-based risk factors in the network with the location risk assignment service (514); to combine location-based risk factors with structural analysis results and to assign the amount of potential disinformation in structures based on probability. The cloud server (5) is configured to enable the relationship assignment server (52) on the cloud server (5) to predict the people with whom the users of electronic devices (2) have a relationship in post-disaster or disinformation moments and to perform social media analyses with the social media analysis service (521) in the relationship assignment server (52), which enables the estimation of the people and relationship affinities of the electronic device (2) users through their social media accounts. The cloud server (5) is configured to enable the mobile network interaction service (522) in the relationship assignment server (52) on the cloud server (5) to examine the call detail records of electronic device (2) users on mobile networks and to enable relationship estimation based on records such as calls and messages; and to use information such as the periods, frequencies and last recording times of call detail records (CDR) to estimate the proximity relationship. The cloud server (5) is configured to perform spatial analysis by using the mobile network supported geographical analysis service (523) in the relationship assignment server (52) on the cloud server (5) and the station information to which the electronic device (2) users are connected; to analyze the frequency of the person and the user of the electronic device (2) to be estimated as a result of these analyses and to assess the likelihood of people coming together, taking into account the type of communication and the location of the communication in the CDR records. The cloud server (5) is configured to combine the relationship assignment service (524) in the relationship assignment server (52) on the cloud server (5) with the results of the social media analysis service (521), mobile network interaction service (522), mobile network supported geographical analysis service (523) and thus to prepare the relationship assignment results, which are the output of the relationship assignment server (52); and to predict the relationships and mobility of electronic device (2) users in the post-disaster or postdisinformation period.

[0041] The cloud server (5) is configured to enable the instant location of users of electronic devices (2) to be recorded in the database (3) with the existing user location provider service (531) in the mobility analysis server (53) located thereon; to enable the use of services / functions in mobile networks in the form of location management function (LMF) and gateway mobile location center (GMLC) to determine electronic device (2) user locations across a wide range of technologies from 2G to 5G and beyond; and to ensure that the station information to which the electronic device (2) users are connected is also used in the location. The cloud server (5) is configured to retrieve historical locations of the users of the electronic device (2) from the database (3) with the historical user location analysis service

[0042] (532) available on the mobility analysis server (53) located thereon; to analyze the location proximity of the two electronic device (2) users, the locations they visit and the places they visit periodically; and to enable the detection of movement patterns of the users of the electronic device (2) by providing analyses at different time intervals, such as seasonal mobility, holiday mobility, daily mobility. The cloud server (5) is configured to predict the location of the electronic device (2) users in times of disaster or disinformation with the predictive mobility service

[0043] (533) in the mobility analysis server (53) on the cloud server (5), based on historical location information; to predict which of the people with whom these users are associated are most likely to go to and to analyze the mobility patterns and relationships of the electronic device (2) user through machine learning algorithms to create the most likely mobility scenarios.

[0044] The cloud server (5) is configured to enable temporal and spatial analysis of network resources by enabling the KPI monitoring service (541) and the traffic forecasting virtual network function (41) on the resource planning server (54) to work together; to enable analysis of the efficiency of the network by considering the neighboring cells by evaluating the relationship of network resources with neighboring resources according to their location in spatial analysis; and to determine the capacity and utilization status of network resources by examining the use of network data at different time periods of the day, weekly, monthly, yearly, as well as on special days and holidays in temporal analysis. The cloud server (5) is configured to enable future network traffic forecasts to be generated by using data collected by the traffic forecasting virtual network function (41) and stored in the database (3) with the traffic forecasting service (542) on the resource planning server (54) located on the cloud server (5); to combine the network traffic forecasts with spatial analysis in the KPI monitoring service (541) to identify efficient and underutilized resources in the network, taking into account neighboring resources in the network and to enable the use of time series machine learning techniques in the form of recurrent neural network (RRN) and autoregressive model (AR) to predict future resource utilization. The cloud server (5) is configured to enable network resource planning in normal times and to identify the regions that may be affected in disaster situations with the dynamic resource planning service (543) on the resource planning server (54) to enable dynamic, effective and predictive network resource management by combining location risk analysis, user activity analysis, instant KPI status and estimated traffic usage to ensure that as many network resources as required are always available when needed; and to enable rapid management of the recovery process of the relevant network in disaster and disinformation situations. Industrial Applicability of the Invention

[0045] In the inventive system (1), the user the electronic device (2) communicates with his / her relatives via mobile communication. In case of disaster or disinformation, using the traffic forecasting and KPI monitoring functions on the virtual network function server (4) located in the core network in the network and the services on the cloud server (5) located in the system (1) enables the establishment of uninterrupted communication in the network by performing important functions such as geographical risk analysis, user relationship analysis, mobility forecasts and resource management in specific regions in the network.

[0046] The inventive system (1) enables the analysis of even rare and unexpected events such as disasters, disinformation, etc., thanks to artificial intelligence-based algorithms and complex data fed to it, and to take them into consideration during the network planning phase as well as providing a smart planning infrastructure to ensure that the grid is designed to meet the changing demand in complex and difficult-to-model situations such as disasters and pandemics, including plans to ensure service continuity of the network in the event of unexpected situations such as disasters, earthquakes, pandemics, disinformation, damage to existing sites or unexpected network traffic caused by external factors, and connecting the network with next-generation network functions, and designing the planning infrastructure in a cloud-based manner in line with next-generation mobile communication architectures.

[0047] The inventive system (1) submit information and approval comprising the principles of data privacy to the user and it operates within the scope of the Personal Data Protection Law (KVKK). Before accessing the user data, location data, call detail records data used in the system (1), the owner of the electronic device (2) shall obtain permission from the users before the disaster. It is possible to develop various embodiments of the inventive “System (1) Developing Smart Strategies for Autonomous Network Resource Management in Case of Disinformation”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) developing smart strategies for autonomous network resource management in case of disinformation which enables to perform location risk analysis, relationship estimation, user / subscriber mobility analysis in order to ensure the continuity and efficiency of the communication infrastructure during disinformation and disaster situations; enables network resource planning by predicting the potential mobility of network users, the persons they communicate or associate with, the risky areas; comprising a plurality of electronic devices (2) which are configured to enable the user to communicate with other persons and other persons to communicate with the user by connecting to the network in cases of disaster, disinformation; and characterized by at least one database (3) which establishes connection with the network and is configured to store information on the base station to which users who connect to the network and receive signals from the network with their electronic devices (2) are connected; to store information on neighboring base stations to which electronic devices (2) connect, and to store records of interactions in the form of calls, messages, images between the user having the electronic device (2) and other persons having the electronic device (2); at least one virtual network function server (4) which establishes connection with the database (3); is located on the the core network included in the network; and is configured to run at least one traffic forecasting virtual network function (41) for ensuring that the data generated in the network in times of disaster, disinformation are processable in a scalable and distributed way; to run at least one key performance indicator monitoring virtual network function (42) configured to collect the base station key performance indicators within the network, the user-based key performance indicators of the electronic device (2) users from the network; to ensure thatthe data collected from the network by the key performance indicator monitoring virtual network function is transmitted to the traffic forecasting virtual network function (41); to ensure that the key performance indicators of the network are predicted within certain time periods with the traffic forecasting virtual network function (41); and at least one cloud server (5) which establishes connection with the database (3), the virtual network function server (4); is configured to run at least one location risk analysis server (51) and the infrastructure monitoring service (511), the geographical data analysis service (512), the historical data analysis service (513) and the location risk assignment service (514) included in the said location risk server (51); to run the relationship analysis server (52) and the social media analysis service (521), the mobile network interaction service (522), the mobile network supported geographical analysis service (523) and the relationship assignment service (524) included in the relationship analysis server (52); to run at least one mobility analysis server (53) and the current user location provider service (531), the historical user location analysis service (532), the predictive mobility service (533) included in the mobility analysis server (53); to run at least one resource planning server (54) and the KPI monitoring service (541), the traffic forecasting service (542) and the dynamic resource planning service (543) included in in the resource planning server (54); to carry out an assignment transaction of regional risk probabilities by performing location risk analyses in the network in case of a disaster by means of at least one location risk analysis server (51) included theron; to carry out the impact analysis of roads, industrial zones in case of a disaster; to estimate the recurrence frequency of risk factors in the region in past times; to determine the size of the area that may be affected in case of a disaster and the number of potential users of electronic devices (2) that may be affected by the said disaster, i.e. the amount of disinformation; to enable the relationship analysis server (52) included thereon to predict other people with whom theusers of the electronic device (52) have a relationship; to analyse the call detail records of the users of the electronic device (52) and then to calculate the probability of the user to meet with the other persons; to predict which of the persons with whom the user is associated the user is most likely to go to, by taking into account their past location information based on the location of the electronic device (2) user in cases of disaster or disinformation by means of the mobility analysis server (53) included thereon; to create the most likely mobility scenarios by analysing the mobility patterns and relationships of the electronic device (2) user by enabling the mobility analysis server (53) get support from machine learning algorithms; to monitor the mobility patterns of the electronic device (54) users by at least one resource planning server (2) located thereon; to plan dynamic resource management, by taking into account the areas where people associated with the user live; to perform resource planning in the network according to the amount of potential increase; and to accelerate the normalization process after a disaster or disinformation by ensuring rapid transfer of resources by performing the backup of resources and the postdisaster resource transfer.

2. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to Claim 1; characterized by the electronic devices (2) which are smart device such as smartphone, tablet, computer configured to enable the user and other persons to establish a mobile connection with each other, by being used by the user and the other persons associated with the user.

3. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to Claim 1 or 2; characterized by the database (3) which is configured to store the call detail records and location records of the electronic devices (2) receiving signal from the network.

4. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the virtual network function server (4) which is configured to provide hourly, daily, weekly, monthly, monthly, yearly estimation of KPI values such as utilization rate, data transmission rate, downstream transmission volume, upstream transmission volume, average signal level, user measurement reports, signal strength, connected cells and signal received as location based on the electronic device (2) users and base stations in the network, recorded by the traffic forecasting virtual network function (41) and the switch performance indicator monitoring virtual network function (42) located thereon; and to enable forecast the network traffic by forecasting the big data stored in the database (3).

5. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the key performance indicator monitoring virtual network function (42) which is configured to monitor the activities within the network by being positioned within the core network; to ensure that key performance indicators of the base stations such as utilization rate, data transmission rate, downward transmission volume, upward transmission volume, average signal levels are retrieved from the network and stored in the database (3) and to receive the key performance indicators of the users, such as user measurement reports, signal strength, cells connected, cells that can be connected, from the network and to record them in the database (3).

6. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the virtual network function server (4) which is configured to process the migration of services and functions in mobile networks thanks to the multiple access boundary computing (MEC) principle in 5G and 6G networks witha scalable structure; and locally process the large data in a divide-and-conquer manner by moving the indicator monitoring virtual network function (42) on it to the end segments in the network with the MEC principle.

7. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is is configured to perform spatial analysis and assign the risk probabilities of the regions in the network; to analyse the impact of buildings, roads, industrial zones, hotels, shopping centers, hotels, shopping centers and infrastructure / superstructure in case of a disaster with the infrastructure analysis service (511) on the location risk analysis server (51); and assess the results stored in databases (3) and the resilience of each structure in the network areas, by the location risk analysis server (51) located thereon.

8. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to analyse the earth by evaluating factors such as fault lines, soil structure, rock structure, elevation, and to evaluate the living areas by creating geographical risk maps within the network, by the geographical analysis service (512) included on the location risk analysis server (51).

9. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to analyse the history of the spatial structure based on the output information of the historical data analysis service (513) and the infrastructure analysis service (511) and the information of the geographical analysis service (512); to examine when the risk factors in the region occurred in the past and to evaluate the frequency of recurrence of risk factors, causes and structural damage analysis,10. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to feed the location risk assignment module (514) with the outputs of the infrastructure analysis service (511), geographical analysis service (512) and historical data analysis service (513) in the location risk analysis server (51); to determine location-based risk factors in the network with the location risk assignment service (514); to combine locationbased risk factors with structural analysis results and to assign the amount of potential disinformation in structures based on probability.

11. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable the relationship assignment server (52) on the cloud server (5) to predict the people with whom the users of electronic devices (2) have a relationship in post-disaster or disinformation moments and to perform social media analyses with the social media analysis service (521) in the relationship assignment server (52), which enables the estimation of the people and relationship affinities of the electronic device (2) users through their social media accounts.

12. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable the mobile network interaction service (522) in the relationship assignment server (52) on the cloud server (5) to examine the call detail records of electronic device (2) users on mobile networks and to enable relationship estimation based on records such as calls and messages; and to use information such as the periods, frequencies and last recording times of call detail records (CDR) to estimate the proximity relationship.

13. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to perform spatial analysis by using the mobile network supported geographical analysis service (523) in the relationship assignment server (52) on the cloud server (5) and the station information to which the electronic device (2) users are connected; to analyze the frequency of the person and the user of the electronic device (2) to be estimated as a result of these analyses and to assess the likelihood of people coming together, taking into account the type of communication and the location of the communication in the CDR records.

14. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to combine the relationship assignment service (524) in the relationship assignment server (52) on the cloud server (5) with the results of the social media analysis service (521), mobile network interaction service (522), mobile network supported geographical analysis service (523) and thus to prepare the relationship assignment results, which are the output of the relationship assignment server (52); and to predict the relationships and mobility of electronic device (2) users in the post-disaster or postdisinformation period.

15. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable the instant location of users of electronic devices (2) to be recorded in the database (3) with the existing user location provider service (531) in the mobility analysis server (53) located thereon; to enable the use of services / functions in mobile networks in the form of location management function (LMF) and gateway mobile location center (GMLC) to determine electronic device (2) user locations across a wide range oftechnologies from 2G to 5G and beyond; and to ensure that the station information to which the electronic device (2) users are connected is also used in the location.

16. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to retrieve historical locations of the users of the electronic device (2) from the database (3) with the historical user location analysis service (532) available on the mobility analysis server (53) located thereon; to analyze the location proximity of the two electronic device (2) users, the locations they visit and the places they visit periodically; and to enable the detection of movement patterns of the users of the electronic device (2) by providing analyses at different time intervals, such as seasonal mobility, holiday mobility, daily mobility.

17. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to predict the location of the electronic device (2) users in times of disaster or disinformation with the predictive mobility service (533) in the mobility analysis server (53) on the cloud server (5), based on historical location information; to predict which of the people with whom these users are associated are most likely to go to and to analyze the mobility patterns and relationships of the electronic device (2) user through machine learning algorithms to create the most likely mobility scenarios.

18. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable temporal and spatial analysis of network resources by enabling the KPI monitoring service (541) and the traffic forecasting virtual network function (41) on the resource planning server (54) to work together; to enable analysis of the efficiency of the network byconsidering the neighboring cells by evaluating the relationship of network resources with neighboring resources according to their location in spatial analysis; and to determine the capacity and utilization status of network resources by examining the use of network data at different time periods of the day, weekly, monthly, yearly, as well as on special days and holidays in temporal analysis.

19. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable future network traffic forecasts to be generated by using data collected by the traffic forecasting virtual network function (41) and stored in the database (3) with the traffic forecasting service (542) on the resource planning server (54) located on the cloud server (5); to combine the network traffic forecasts with spatial analysis in the KPI monitoring service (541) to identify efficient and underutilized resources in the network, taking into account neighboring resources in the network and to enable the use of time series machine learning techniques in the form of recurrent neural network (RRN) and autoregressive model (AR) to predict future resource utilization.

20. A system (1) developing smart strategies for autonomous network resource management in case of disinformation according to any of the preceding claims; characterized by the cloud server (5) which is configured to enable network resource planning in normal times and to identify the regions that may be affected in disaster situations with the dynamic resource planning service (543) on the resource planning server (54) to enable dynamic, effective and predictive network resource management by combining location risk analysis, user activity analysis, instant KPI status and estimated traffic usage to ensure that as many network resources as required are always available when needed; and to enable rapid management of the recovery process of the relevant network in disaster and disinformation situations.

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