Low-delay satellite edge computing service selection method oriented to security sensitive services

Abstract

The invention relates to the technical field of satellite mobile communication, and discloses a low-delay satellite edge computing service selection method for security sensitive services, which comprises the following steps of: determining eavesdropping capacity of each service satellite according to spatial distribution information of an eavesdropping satellite and each service satellite; calculating the utility of each service satellite according to the current connection information, the secrecy capacity and the constructed utility function; determining the average effectiveness of the system according to the effectiveness of the target service satellite and the proportion of the number of the Internet of Things devices connected with the target service satellite to the total number of the Internet of Things devices; and reselecting the service satellites according to the actual utility and the average utility. By quantifying the eavesdropping risk and constructing the secrecy capacity as the security index, the utility of the service satellite connected with the current Internet of Things equipment is calculated, and the service satellite accessed by the Internet of Things equipment is dynamically selected in combination with the utility, so that each Internet of Things equipment in the system obtains the optimal service satellite selection. And the risk of data eavesdropping and the queuing waiting time delay of the computing service are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of satellite mobile communication technology, and in particular to a low-latency satellite edge computing service selection method for security-sensitive services. Background Art

[0002] Satellite Mobile Edge Computing (SatMEC) networks, through the collaboration of Low Earth Orbit (LEO) satellites and ground-based equipment, provide wide-area coverage and low-latency services for scenarios such as the Internet of Things (IoT) and real-time communications. However, their open communication architecture and dynamic topology make security threats, particularly eavesdropping attacks, a core challenge hindering their development. Eavesdropping satellites can intercept communication links between ground-based equipment and satellites to steal sensitive data (such as industrial control instructions and emergency rescue information), posing a serious security risk.

[0003] On the one hand, existing solutions fail to fully consider the varying security requirements of different services. Security-sensitive applications like military communications and financial data transmission require extremely high levels of data confidentiality and integrity. However, current general-purpose security measures cannot be customized to meet the stringent security requirements of specific applications. This results in insufficient protection against complex security threats, potentially creating security risks and even leading to serious consequences such as sensitive information leaks and system failures. On the other hand, traditional service selection solutions lack the flexibility to adapt to dynamic network changes. Service requests in satellite networks are random and volatile, and security threats are constantly changing. Existing static service selection strategies cannot flexibly adjust based on real-time network conditions and the severity of security threats, making it difficult to balance security and resource efficiency. Sudden increases in network traffic or security attacks can easily lead to irrational resource allocation, causing some satellite nodes to be overloaded while others remain idle, reducing overall network performance and adaptability. Furthermore, in complex satellite network environments, existing security measures struggle to integrate multiple security strategies. Different security technologies lack effective coordination mechanisms, preventing them from forming an integrated whole, resulting in a one-sided security approach. For example, while physical layer security technologies can prevent eavesdropping, they are powerless against malware intrusions. Encryption technologies can ensure data confidentiality, but key management presents challenges. The fragmentation of existing security strategies prevents satellite networks from fully leveraging the strengths of various security technologies in the face of sophisticated attacks, reducing overall security. Summary of the Invention

[0004] In view of this, the present invention provides a low-latency satellite edge computing service selection method for security-sensitive services to solve the problems of high latency and low overall security in data transmission between IoT devices and service satellites.

[0005] In a first aspect, the present invention provides a low-latency satellite edge computing service selection method for security-sensitive services, the method comprising:

[0006] Obtain the current connection information between the service satellite and each IoT device, and use the preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite;

[0007] Determine the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite;

[0008] Construct a utility function based on current connection information and confidentiality capacity, and use the utility function to calculate the utility of each service satellite;

[0009] Determine the average utility of the system according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices;

[0010] The utility of the service satellite connected to the target IoT device is compared with the system average utility, and the service satellite to be connected is selected for the target IoT device based on the comparison result.

[0011] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention establishes spatial distribution information of eavesdropping satellites and each service satellite and calculates the eavesdropping capacity. The confidentiality capacity is constructed as a utility component based on the eavesdropping capacity, and the distribution and potential threats of eavesdropping satellites are quantified in real time to provide a comprehensive risk assessment and improve overall security. The utility of each service satellite is calculated using the collected information, and the access selection of the service satellite is dynamically performed by comparing the individual utility with the system average utility, so as to achieve the satellite selection for each IoT device in the system to obtain the best service, thereby effectively reducing the risk of data eavesdropping and queuing delay.

[0012] In an optional embodiment, the spatial distribution information of the eavesdropping satellite and each service satellite is established using a preset process model, including:

[0013] Obtain the orbital height of the eavesdropping satellite and use it in combination with the radius of the Earth to derive the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite.

[0014] The spatial distribution information of the eavesdropping satellite and each service satellite is determined according to the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite.

[0015] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention determines the spatial distribution information of eavesdropping satellites and each service satellite by introducing an eavesdropping risk assessment model based on a binomial point process, and quantifies the spatial distribution of eavesdropping satellites in real time, which facilitates the analysis of the potential threat of eavesdropping satellites to each service satellite and provides a more comprehensive risk assessment.

[0016] In an optional embodiment, determining the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite includes:

[0017] Obtaining an effective eavesdropping probability that the eavesdropping satellite is located in an effective eavesdropping area of the target service satellite, and determining the number of eavesdropping satellites within a preset distance in the effective eavesdropping area of the target service satellite based on the effective eavesdropping probability;

[0018] Calculate the eavesdropping capacity of each eavesdropping satellite within a preset distance within the effective eavesdropping area of the target service satellite;

[0019] According to the eavesdropping capacity of each eavesdropping satellite and the number of eavesdropping satellites, the total eavesdropping capacity of the target service satellite is determined as the eavesdropping capacity of the target service satellite;

[0020] The confidentiality capacity of the target service satellite is calculated based on the eavesdropping capacity of the target service satellite.

[0021] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention solves the defect of security threat assessment in the existing technology that relies too much on deterministic models by calculating the eavesdropping capacity of the target service satellite and using the eavesdropping capacity as an indicator to quantify security risks. The potential data interception capability of the eavesdropping satellite is calculated by integration. This method improves the accuracy of eavesdropping risk assessment.

[0022] In an optional embodiment, the current connection information includes: the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently connected. Based on the current connection information, the confidentiality capacity, and the utility function, the following is constructed:

[0023] Obtain the current number of connected IoT devices and the total number of IoT devices of the target service satellite, and determine the instantaneous confidentiality capacity allocated to each IoT device connected to the target service satellite in combination with the current confidentiality capacity of the target service satellite;

[0024] The queuing waiting delay of the target service satellite is obtained, and the utility function of the target service satellite is constructed based on the instantaneous confidentiality capacity and the queuing waiting delay.

[0025] In an optional implementation, obtaining the queuing waiting delay of the target service satellite includes:

[0026] Obtain the current service request arrival rate of the current device task issued by the IoT device to the target service satellite, and the current service rate of the target service satellite;

[0027] The queuing waiting delay of the target service satellite is determined according to the current service request arrival rate and the current service rate.

[0028] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention can more accurately evaluate the service efficiency of satellite resources by introducing queuing waiting delay and instantaneous confidentiality capacity into the utility function. The instantaneous confidentiality capacity reflects the security of the satellite communication system, while the queuing waiting delay represents the timeliness of the system. Combining the two can find the optimal balance between performance and security.

[0029] In an optional embodiment, the calculation formula for determining the system average utility is as follows based on the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices:

[0030]

[0031] in, represents the average utility of the system, x i (t) represents the ratio of the number of IoT devices connected to the i-th service satellite to the total number of IoT devices, π i (t) represents the utility of the i-th service satellite, and M represents the total number of service satellites.

[0032] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention utilizes a decentralized dynamic decision-making algorithm. IoT devices can autonomously select the optimal service satellite based solely on their own utility and the system's average utility, effectively balancing the system load, avoiding overload of the service satellite, and effectively reducing the queuing delay of IoT devices.

[0033] In an optional embodiment, comparing the utility of the serving satellite connected to the target IoT device with the system average utility, and selecting a serving satellite for the target IoT device to connect to based on the comparison result, includes:

[0034] If the utility of the serving satellite is not less than the average utility, there is no need to adjust the serving satellite that the target IoT device selects to connect to;

[0035] If the utility of the target service satellite is less than the average utility, the service satellite that the IoT device chooses to connect to at the next moment is adjusted;

[0036] Return to the step of calculating the utility of each service satellite using the utility function until the utility of the target service satellite is no less than the average utility.

[0037] The low-latency satellite edge computing service selection method for security-sensitive services provided by the present invention is beneficial in the satellite mobile edge network. It adjusts the satellite service selection strategy of the device terminal by comparing the utility of the device accessing the service satellite and the average utility, realizes real-time switching of satellite service selection and acquisition of the optimal service selection, balances the load of the satellite mobile edge computing network, avoids the problem of overload of the service satellite, and significantly improves the overall performance of the system.

[0038] In a second aspect, the present invention provides a low-latency satellite edge computing service selection device for security-sensitive services, the device comprising:

[0039] The information acquisition module is used to obtain the current connection information between the service satellite and the IoT device, and use the preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite;

[0040] An eavesdropping capacity calculation module is used to determine the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite;

[0041] A utility calculation module is used to construct a utility function based on current connection information and confidentiality capacity, and calculate the utility of each service satellite using the utility function;

[0042] an average utility calculation module, configured to determine the average utility of the system according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices;

[0043] The service selection module is used to compare the utility of the service satellite connected to the target IoT device with the system average utility, and select the service satellite to be connected for the target IoT device based on the comparison result.

[0044] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flow chart of a method for selecting a low-latency satellite edge computing service for security-sensitive services according to an embodiment of the present invention;

[0048] Figure 2 1 is a flowchart of another method for selecting a low-latency satellite edge computing service for security-sensitive services according to an embodiment of the present invention;

[0049] Figure 3 1. A schematic diagram of a process for dynamically adjusting a low-latency satellite edge computing service selection method for security-sensitive services using evolutionary game theory according to an embodiment of the present invention;

[0050] Figure 4 3. This is a schematic diagram comparing the relationship between the number of IoT devices and the average system utility under different service selection strategies in another low-latency satellite edge computing service selection method for security-sensitive services according to an embodiment of the present invention;

[0051] Figure 5 3. This is a schematic diagram comparing the mean values of risk probabilities under different service selection strategies in another low-latency satellite edge computing service selection method for security-sensitive services according to an embodiment of the present invention;

[0052] Figure 6 3. This is a schematic diagram comparing service satellite load balancing under different service selection strategies in another low-latency satellite edge computing service selection method for security-sensitive services according to an embodiment of the present invention;

[0053] Figure 7 1 is a structural block diagram of a low-latency satellite edge computing service selection device for security-sensitive services according to an embodiment of the present invention;

[0054] Figure 8 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0056] An embodiment of the present invention provides a low-latency satellite edge computing service selection method for security-sensitive services. It quantifies the distribution and potential threats of eavesdropping satellites in real time, and dynamically selects service satellites for IoT devices by comparing individual utility with the overall average utility of the system, effectively reducing the risk of data eavesdropping and queuing delays.

[0057] The low-latency satellite edge computing service selection method for security-sensitive services provided by an embodiment of the present invention is applicable to satellite mobile edge computing systems to reduce the security risks and delays of data transmission. The embodiment of the present invention constructs a system model including multiple in-orbit satellites providing edge computing services and security-sensitive ground Internet of Things devices, and proposes a dynamic service selection mechanism, in which the Internet of Things devices dynamically select a service satellite among multiple service satellites based on the confidentiality capacity and queuing delay of the current service satellite.

[0058] To adapt to the highly dynamic environment of satellite networks, this method, based on evolutionary game theory, uses dynamic replication equations to model the iterative evolution of device selection strategies. Specifically, the method involves initializing the device selection ratio, broadcasting the number of currently connected devices to the satellite, and then calculating the utility of the currently selected satellite. This calculation compares the utility with the system average to determine the next serving satellite. If the current utility is lower than the average, a different serving satellite is randomly selected with a certain probability. If the current utility is higher than or equal to the average, the current selection remains unchanged. Through multiple rounds of iteration, this method ultimately ensures that each device in the system receives the optimal serving satellite, effectively reducing the risk of data eavesdropping and queuing delays.

[0059] Current solutions to SatMEC network eavesdropping threats mainly focus on the following technologies, but there are still significant shortcomings.

[0060] (1) Physical layer security enhancement technology: Existing solutions improve the physical layer confidentiality capacity of a single link through technologies such as beamforming, artificial noise injection, and intelligent reflecting surfaces (RIS). For example, eavesdropping signals can be suppressed by optimizing antenna directivity, or anti-interference strategies can be designed by utilizing channel randomness. Although such methods can improve the security of a single link, they have the following problems: lack of quantification of random threats, failure to consider the random spatial distribution characteristics of eavesdropping satellites, and inability to assess system-level risks; insufficient dynamic adaptability, reliance on static channel models, and difficulty in coping with real-time topology changes caused by high-speed satellite movement.

[0061] (2) Static encryption and authentication technology: Traditional methods rely on encryption algorithms such as elliptic curve cryptography (ECC) and quantum key distribution (QKD) to ensure communication confidentiality. However, there are the following problems: key management is complex, key distribution and update efficiency is low in highly dynamic satellite networks, and it is difficult to match the transient nature of satellite coverage; security and efficiency are separated, focusing only on data encryption and ignoring efficiency indicators such as task queuing delay and computing resource allocation.

[0062] (3) Static service selection strategies based on distance: Some strategies use physical distance or signal strength as optimization targets, such as selecting the nearest satellite node to reduce transmission latency. The drawbacks of this approach include: security risk blind spots, where the eavesdropping threat of satellite nodes is not quantified, which may cause devices to connect to high-risk nodes; and load imbalance, where fixed strategies cannot be dynamically adjusted, which can easily lead to some satellites being overloaded while other nodes are idle.

[0063] (4) Traditional game theory optimization method: Existing studies have attempted to model resource allocation problems through game theory, but most of them are based on the following assumptions: a deterministic network environment, assuming that the eavesdropping satellite position is fixed or the channel state is predictable, which is inconsistent with the randomness of the real scene; a single optimization objective, which only optimizes one of the latency, energy consumption or confidentiality capacity, and lacks a joint trade-off between security and efficiency.

[0064] Currently, research on satellite network security continues to advance in multiple directions, aiming to ensure the reliability of satellite communications and the security of data.

[0065] Integration of Emerging Technologies: With the continuous advancement of technology, emerging technologies such as artificial intelligence, blockchain, and edge computing are gradually being integrated into the field of satellite network security. Artificial intelligence and machine learning algorithms excel in detecting anomalies in satellite networks. By learning and analyzing large amounts of network data, they can quickly identify security threats such as distributed denial of service (DDoS) attacks and malware intrusions. Blockchain technology provides a decentralized, tamper-proof solution for satellite network data management, ensuring data security and credibility. The introduction of edge computing technology enables data processing at edge nodes close to the data source or user, reducing data transmission latency, improving network responsiveness, and also contributing to enhanced network security.

[0066] According to an embodiment of the present invention, an embodiment of a low-latency satellite edge computing service selection method for security-sensitive services is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0067] In this embodiment, a low-latency satellite edge computing service selection method for security-sensitive services is provided, which can be used in the above-mentioned computer system. Figure 1 is a flow chart of a method for selecting a low-latency satellite edge computing service for security-sensitive services according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0068] Step S101: obtain the current connection information between the service satellite and each IoT device, and use a preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite.

[0069] Specifically, a satellite service system includes at least one service satellite, which provides edge computing services to IoT devices on the ground. Eavesdropping satellites intercept or monitor the service satellite's communication signals through technical means. In a satellite mobile edge computing network, IoT devices on the ground can be considered a group. The proportion of IoT devices connected to each service satellite may vary. For example, a group includes N IoT devices, and the satellite service system includes M service satellites. The number of IoT devices connected to each service satellite may vary, but M service satellites can meet the communication needs of all N IoT devices. This is for example only and is not intended to be limiting.

[0070] If there are eavesdropping satellites in the satellite system, it is necessary to determine the positional relationship of the eavesdropping satellites relative to the service satellites, that is, the spatial distribution information of the eavesdropping satellites and the service satellites. Random process models such as the binomial point process and the Poisson point process can be used to estimate the spatial distribution of the eavesdropping satellites and perform risk assessment. This is only an example and is not limited to this.

[0071] Step S102: determining the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite.

[0072] Specifically, a probabilistic model can be used to quantify the eavesdropping risk based on the spatial distribution information of eavesdropping satellites and each service satellite, providing a basis for the subsequent dynamic adjustment of service selection. The quantification of eavesdropping risk is based on the potential distribution of eavesdropping satellites and their impact on data transmission security. The eavesdropping capacity is defined as a measurement standard, and the confidentiality capacity can be directly calculated using the eavesdropping capacity.

[0073] Step S103: constructing a utility function based on the current connection information and confidentiality capacity, and calculating the utility of each service satellite using the utility function.

[0074] Specifically, each service satellite constructs a utility function based on its currently connected IoT devices and eavesdropping capacity, and uses the utility function to calculate the utility of each service satellite. The transmission rate reflects the data transmission efficiency of the service satellite, and the eavesdropping capacity measures the security of the service satellite. The current connection information and calculated utility of each service satellite can be broadcast to the IoT devices connected to the service satellite, so as to comprehensively evaluate the information of each service satellite and facilitate each IoT device to adjust its selection of the next service satellite based on the comprehensive evaluation results. It should be noted that the current connection information and calculated utility of each service satellite can be broadcast to the IoT devices connected to the service satellite, including: the current connection information (number of connected devices) of each service satellite can be directly broadcast to the IoT devices to be connected. At the same time, a separate controller can be set up on the ground to collect the utility of each service satellite at the current moment and broadcast it to each IoT device on the ground.

[0075] Step S104 , determining the system average utility according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices.

[0076] Specifically, each IoT device on the ground can receive device access information and utility from each service satellite, and use this to calculate the average utility of the system. The average utility will reflect the overall performance of all IoT devices and serve as a measure for evaluating the efficiency of the overall system.

[0077] Step S105 : comparing the utility of the service satellite to which the target IoT device is connected with the system average utility, and selecting a service satellite to be connected for the target IoT device based on the comparison result.

[0078] Specifically, the utility of each IoT device accessing a serving satellite is compared with the average utility. If the utility is lower than the average, the target IoT device's currently selected serving satellite may not be optimal in terms of performance or security. The IoT device's selected serving satellite needs to be adjusted and a new serving satellite needs to be selected for the IoT device. For example, switching IoT device A from the first serving satellite to the second serving satellite is an example and not intended to be limiting. Conversely, if the utility of the IoT device's selected serving satellite is higher than or equal to the average utility, the current selection remains unchanged, and each IoT device continues to use the services provided by the current serving satellite.

[0079] Through evolutionary games, IoT devices in satellite mobile edge networks can switch service satellites in real time and make optimal strategy choices, while achieving higher transmission rates while ensuring reliability and security.

[0080] The low-latency satellite edge computing service selection method for security-sensitive services provided in this embodiment establishes spatial distribution information of eavesdropping satellites and each service satellite and calculates the eavesdropping capacity. The confidentiality capacity is constructed as a utility component based on the eavesdropping capacity, and the distribution and potential threats of eavesdropping satellites are quantified in real time. A comprehensive risk assessment is provided to improve overall security. The utility of each service satellite is calculated using the collected information, and access selection of service satellites is dynamically performed by comparing individual utility with the system average utility. This enables satellite selection to provide the best service for each IoT device in the system, effectively reducing the risk of data eavesdropping and queuing delays.

[0081] In this embodiment, a low-latency satellite edge computing service selection method for security-sensitive services is provided, which can be used in the above-mentioned computer system. Figure 2 is a flow chart of a method for selecting a low-latency satellite edge computing service for security-sensitive services according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0082] Step S201: obtain the current connection information between the service satellite and each IoT device, and use a preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite.

[0083] Specifically, the above step S201 includes:

[0084] Step S2011: Obtain the orbital altitude of the eavesdropping satellite, and deduce the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite in combination with the radius of the earth.

[0085] Specifically, the binomial point process (BPP) is used to model the spatial distribution of eavesdropping satellites. Assume that the eavesdropping satellites are located at a radius of R+H. e (where R is the radius of the Earth, H e The IoT devices on the ground are located at a distance R from the center of the earth. By deducing the geometric relationship, we can derive the distance d between the IoT devices on the ground and the eavesdropping satellite. e The cumulative distribution function (CDF) is:

[0086]

[0087] in, For ground terminals to be located at the horizon The maximum distance at which a satellite can be eavesdropped.

[0088] Step S2012: determining the spatial distribution information of the eavesdropping satellite and each service satellite based on the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite.

[0089] Specifically, the location information of the IoT device and the orbital height of the eavesdropping satellite are substituted into the above cumulative distribution function to obtain the spatial distribution information of the eavesdropping satellite and each service satellite.

[0090] The low-latency satellite edge computing service selection method for security-sensitive services provided in this embodiment determines the spatial distribution information of eavesdropping satellites and each service satellite by introducing an eavesdropping risk assessment model based on a binomial point process, and quantifies the spatial distribution of eavesdropping satellites in real time, thereby facilitating the analysis of the potential threat of eavesdropping satellites to each service satellite and providing a more comprehensive risk assessment.

[0091] Step S202: determining the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite.

[0092] Specifically, the above step S202 includes:

[0093] Step S2021: obtaining an effective eavesdropping probability that the eavesdropping satellite is located in an effective eavesdropping area of the target service satellite, and determining the number of eavesdropping satellites within a preset distance in the effective eavesdropping area of the target service satellite according to the effective eavesdropping probability.

[0094] Specifically, the probability density function (PDF) is the cumulative distribution function in formula (1) for the distance d between the IoT device and the eavesdropping satellite. eThe derivative of , that is:

[0095]

[0096] Regarding the quantification of eavesdropping risk, the eavesdropping capacity is defined to quantify the eavesdropping risk. Assume that there are K eavesdropping satellites that are evenly and independently distributed, and the effective eavesdropping area of a service satellite is within a distance of d. e The number of eavesdropping satellites at e ) follows a binomial distributionΞ(d e )~Binomial(K,ξ i ),in is the probability that a single eavesdropping satellite is located in the effective eavesdropping area of the i-th serving satellite.

[0097] The effective eavesdropping area of the service satellite is defined as a spherical cap area centered on the service satellite with an angular radius ψ eff,i It can be calculated by the following formula: Among them, H i is the orbital altitude of the service satellite and R is the radius of the Earth.

[0098] The surface area of the effective eavesdropping area is:

[0099]

[0100] The total surface area of the sphere at the orbital altitude of the service satellite is: S S =4π(R+H i ) 2 ,

[0101] The probability that a single eavesdropping satellite is located within the effective eavesdropping area of i service satellites is:

[0102]

[0103] Step S2022: Calculate the eavesdropping capacity of each eavesdropping satellite at a preset distance within the effective eavesdropping area of the target serving satellite.

[0104] Specifically, the eavesdropping capacity of a single service satellite can be calculated by the following formula:

[0105] C(d e )=Wlog2(1+γ(d e )) (3)

[0106] Among them, C(d e ) indicates that the distance of a single service satellite is d e The eavesdropping capacity is in bit / s, W is the channel bandwidth, is the signal-to-noise ratio (α is the path loss index, and η is a parameter related to transmit power, antenna gain, etc.).

[0107] Step S2023: Determine the total eavesdropping capacity of the target service satellite as the eavesdropping capacity of the target service satellite according to the eavesdropping capacity of each eavesdropping satellite and the number of eavesdropping satellites.

[0108] Specifically, the total eavesdropping capacity of the i-th service satellite is the integral of the eavesdropping capacity of all eavesdropping satellites at all distances:

[0109]

[0110] in, Indicates the total eavesdropping capacity of the target service satellite.

[0111] Step S2024: Calculate the confidentiality capacity of the target service satellite based on the eavesdropping capacity of the target service satellite.

[0112] Specifically, this embodiment comprehensively considers the confidentiality capacity of each service satellite and the queuing delay when there is an eavesdropping satellite. The total instantaneous confidentiality capacity of the target service satellite when the IoT device is connected to the target service satellite is:

[0113]

[0114] in, represents the current confidentiality capacity, i.e., the total instantaneous confidentiality capacity of the target service satellite, γ i (t) represents the signal-to-noise ratio at the serving satellite i, Indicates the current eavesdropping capacity.

[0115] The low-latency satellite edge computing service selection method for security-sensitive services provided in this embodiment calculates the eavesdropping capacity of the target service satellite and uses the eavesdropping capacity as an indicator to quantify security risks. The potential data interception capability of the eavesdropping satellite is calculated by integration. This solves the defect in the existing technology that security threat assessment relies too much on deterministic models, and improves the accuracy of eavesdropping risk assessment.

[0116] Step S203: construct a utility function based on the current connection information and confidentiality capacity, and use the utility function to calculate the utility of each service satellite.

[0117] Specifically, the current connection information includes: the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently connected. The above step S203 includes:

[0118] Step S2031: Obtain the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently connected to the target service satellite, and determine the instantaneous confidentiality capacity allocated to each IoT device connected to the target service satellite in combination with the current confidentiality capacity of the target service satellite.

[0119] Specifically, the ratio of the number of IoT devices currently connected to the target service satellite to the total number of IoT devices currently is calculated based on the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently.

[0120] The calculation formula for the instantaneous confidentiality capacity allocated to each IoT device when connected to the target service satellite is:

[0121]

[0122] Among them, x i (t) represents the ratio of the number of IoT devices that choose service satellite i to the total number of IoT devices at time t, and N represents the total number of IoT devices in the system.

[0123] Step S2032: Obtain the queuing waiting delay of the target service satellite, and construct a utility function of the target service satellite based on the instantaneous confidentiality capacity and the queuing waiting delay.

[0124] Specifically, the utility function of the target service satellite is calculated as:

[0125]

[0126] in, represents the instantaneous confidentiality capacity of the IoT device when it is connected to the service satellite i at time t, π i (t) represents the utility function (i.e., the IoT device calculates the utility of the target service satellite selected at time t based on the information obtained), D i (t) represents the queuing waiting delay of service satellite i at time t, α and β are weight factors, which represent the relative importance of confidentiality capacity and queuing waiting delay, respectively.

[0127] In some optional implementations, in step S2032, obtaining the queuing waiting delay of the target service satellite includes:

[0128] Step a1: Obtain the current service request arrival rate of the current device task sent by the IoT device to the target service satellite and the current service rate of the target service satellite.

[0129] Step a2: determining the queuing waiting delay of the target service satellite according to the current service request arrival rate and the current service rate.

[0130] Specifically, in formula (7), D i (t) represents the queuing delay of the service satellite i at time t, based on the M / M / 1 queuing system, (where λ i (t) is the arrival rate of device service requests arriving at the service queue of service satellite i at time t, μi is the service rate of serving satellite i).

[0131] The low-latency satellite edge computing service selection method for security-sensitive services provided in this embodiment can more accurately evaluate the service efficiency of satellite resources by introducing queuing waiting delay and instantaneous confidentiality capacity into the utility function. The instantaneous confidentiality capacity reflects the security of the satellite communication system, while the queuing waiting delay represents the timeliness of the system. Combining the two can find the optimal balance between performance and security.

[0132] Step S204 , determining the system average utility according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices.

[0133] Specifically, the average utility is calculated based on the utility of each service satellite. Alternatively, an IoT device may obtain the utility of each service satellite, calculate the average utility based on the utility of each service satellite, and transmit the average utility to each IoT device. The calculation formula for the average utility is:

[0134]

[0135] in, represents the average utility of the system, x i (t) represents the ratio of the number of IoT devices connected to the i-th service satellite to the total number of IoT devices, π i (t) represents the utility of the i-th service satellite, and M represents the total number of service satellites.

[0136] The low-latency satellite edge computing service selection method for security-sensitive services provided in this embodiment utilizes a decentralized dynamic decision-making algorithm. IoT devices can autonomously select the optimal service satellite based solely on the utility of their own selected service satellite and the system average utility, effectively balancing the system load, avoiding service satellite overload, and improving resource utilization and user satisfaction.

[0137] Step S205 : comparing the utility of the service satellite to which the IoT device is connected with the system average utility, and selecting a service satellite to be connected for the target IoT device based on the comparison result.

[0138] Specifically, the above step S205 includes:

[0139] In step S2051, if the utility of the serving satellite is not less than the average utility, there is no need to adjust the serving satellite selected by the target IoT device for connection.

[0140] Specifically, if Figure 3As shown in , after calculating the average utility of each service satellite, it is necessary to judge whether the IoT device connected to the target service satellite needs to reselect another service satellite based on the relationship between the utility of the target service satellite and the average utility. If the utility of the target service satellite is not less than the average utility, that is, This indicates that the IoT devices that select this service satellite can obtain better services. If all IoT devices can obtain better services, the current IoT devices will maintain their connections with the service satellite.

[0141] Step S2052: If the utility of the target service satellite is less than the average utility, the service satellite selected by the target IoT device for connection at the next moment is adjusted.

[0142] Specifically, if Figure 3 As shown in , the utility of each service satellite needs to be compared with the average utility separately. If the utility of the target service satellite is less than the average utility, that is, The IoT devices connected to the target service satellite will select other service satellites with a fixed probability to explore better service options.

[0143] Step S2053: Return to the step of calculating the utility of each service satellite using the utility function until the utility of each service satellite is no less than the average utility.

[0144] Specifically, if Figure 3 As shown in the figure, when the service satellites to which each IoT device connects at the next moment are reselected, the proportion of connected IoT devices on each service satellite changes. The process of calculating the utility of each service satellite must be repeated again and again until the utility of the target service satellite is no less than the average utility and the IoT device's strategy selection remains unchanged. This strategy is the optimal service selection strategy. Ultimately, through the stable state achieved through the dynamic adjustment process based on evolutionary game theory, each IoT device in the system can receive optimal service, achieving the goal of optimizing network efficiency and user security.

[0145] It should be noted that the average utility of the satellite mobile edge network system is related to the number of connected device terminals. For different access strategies, when IoT devices and service satellites make access choices, the comparison curve of the relationship between the average utility of the system and the total number of device terminals is as follows: Figure 4 As shown by Figure 4 It can be seen that the average utility of resource allocation for service satellites using evolutionary games is closest to the optimal utility. When using different methods to allocate resources for service satellites, the confidentiality capacity of each service satellite in the final allocation result can be used to evaluate the risk probability of each service satellite, such as Figure 5 As shown in the figure, the risk probability comparison of each service satellite in the corresponding allocation results after different methods are used to allocate resources to the service satellite is shown in the figure. Figure 5It can be seen that the allocation result corresponding to the dynamic adjustment based on evolutionary game has the lowest risk. By calculating the standard deviation of the connected IoT devices after the service satellite allocates resources, the standard deviation is used to compare the load balancing of the service satellite after dynamic adjustment using different methods. Figure 6 It can be seen that the standard deviation of the dynamic resource allocation results of the service satellite obtained by dynamic adjustment based on evolutionary game is the smallest, indicating that the load balancing is the best.

[0146] This embodiment provides a low-latency satellite edge computing service selection method for security-sensitive services. In a satellite mobile edge network, the satellite service selection strategy of the device terminal is adjusted by comparing the utility of the device accessing the service satellite and the average utility. This achieves real-time switching of satellite service selection and acquisition of the optimal service selection, balances the load of the satellite mobile edge computing network, avoids the problem of service satellite overload, and significantly improves the overall performance of the system.

[0147] In this embodiment, a low-latency satellite edge computing service selection device for security-sensitive services is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0148] This embodiment provides a low-latency satellite edge computing service selection device for security-sensitive services, such as Figure 7 Shown, including:

[0149] The information acquisition module 701 is used to obtain the current connection information between the service satellite and each IoT device, and to establish the spatial distribution information of the eavesdropping satellite and each service satellite using a preset process model.

[0150] The eavesdropping capacity calculation module 702 is used to determine the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite.

[0151] The utility calculation module 703 is used to construct a utility function based on the current connection information and confidentiality capacity, and calculate the utility of each service satellite using the utility function.

[0152] The average utility calculation module 704 is used to determine the system average utility according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices.

[0153] The service selection module 705 is configured to compare the utility of the service satellite to which the target IoT device is connected with the system average utility, and select a service satellite to be connected for the target IoT device based on the comparison result.

[0154] In some optional implementations, the information acquisition module 701 includes:

[0155] The distribution function determination unit is used to obtain the orbital height of the eavesdropping satellite and deduce the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite in combination with the radius of the earth.

[0156] The spatial distribution information determination unit is used to determine the spatial distribution information of the eavesdropping satellite and each service satellite based on the cumulative distribution function of the distance between the Internet of Things device and the eavesdropping satellite.

[0157] In some optional implementations, the eavesdropping capacity calculation module 702 includes:

[0158] The eavesdropping satellite number determination unit is used to obtain the effective eavesdropping probability that the eavesdropping satellite is located in the effective eavesdropping area of the target service satellite, and determine the number of eavesdropping satellites within a preset distance in the effective eavesdropping area of the target service satellite according to the effective eavesdropping probability.

[0159] A single eavesdropping capacity determination unit is used to calculate the eavesdropping capacity of each eavesdropping satellite at a preset distance within the effective eavesdropping area of the target service satellite.

[0160] The total eavesdropping capacity determining unit is used to determine the total eavesdropping capacity of the target service satellite as the eavesdropping capacity of the target service satellite according to the eavesdropping capacity of each eavesdropping satellite and the number of eavesdropping satellites.

[0161] The confidentiality capacity determination unit is used to determine the current confidentiality capacity of the target service satellite according to the eavesdropping capacity of the target service satellite.

[0162] In some optional implementations, the utility calculation module 703 includes:

[0163] The instantaneous confidentiality capacity determination unit is used to obtain the current number of connected IoT devices and the current total number of IoT devices of the target service satellite, and determine the instantaneous confidentiality capacity allocated to each IoT device connected to the target service satellite in combination with the current confidentiality capacity of the target service satellite.

[0164] The utility function building unit is used to obtain the queuing waiting delay of the target service satellite and build the utility function of the target service satellite based on the instantaneous confidentiality capacity and the queuing waiting delay.

[0165] In some optional implementations, the utility function establishment unit includes:

[0166] The service request arrival rate acquisition subunit is used to obtain the current service request arrival rate of the current device service request sent by the Internet of Things device arriving at the target service satellite, and the current service rate of the target service satellite.

[0167] The queuing waiting delay calculation subunit is used to determine the queuing waiting delay of the target service satellite according to the current service request arrival rate and the current service rate.

[0168] In some optional implementations, the service selection module 705 includes:

[0169] The first stable state judgment unit is configured to determine, if the utility of the serving satellite is not less than the average utility, whether or not the serving satellite to which the target IoT device is connected does not need to be adjusted.

[0170] The utility adjustment unit is used to adjust the service satellite that the target IoT device selects to connect to at the next moment if the utility of the service satellite is less than the average utility.

[0171] The second stable state determining unit is configured to return to the step of calculating the utility of each service satellite using the utility function until the utility of each service satellite is not less than the average utility.

[0172] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0173] The low-latency satellite edge computing service selection device for security-sensitive services in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0174] The embodiment of the present invention also provides a computer device having the above Figure 7 The low-latency satellite edge computing service selection device for security-sensitive services is shown.

[0175] See also Figure 8 , Figure 8 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 8As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 A processor 10 is taken as an example.

[0176] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0177] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0178] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0179] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0180] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0181] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0182] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A low-latency satellite edge computing service selection method for security-sensitive services, characterized in that: The method comprises: Obtain the current connection information between the service satellite and each IoT device, and use the preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite; Determining the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite; Constructing a utility function according to the current connection information and the confidentiality capacity, and calculating the utility of each service satellite using the utility function; Determine the average utility of the system according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices; The utility of the service satellite connected to the target Internet of Things device is compared with the average utility of the system, and a service satellite to be connected is selected for the target Internet of Things device based on the comparison result.

2. The method according to claim 1, characterized in that The preset process model is used to establish the spatial distribution information of the eavesdropping satellite and each service satellite, including: Obtain the orbital height of the eavesdropping satellite and use it in combination with the radius of the Earth to derive the cumulative distribution function of the distance between the IoT device and the eavesdropping satellite. The spatial distribution information of the eavesdropping satellite and each service satellite is determined according to the cumulative distribution function of the distance between the Internet of Things device and the eavesdropping satellite.

3. The method according to claim 1, characterized in that Determining the confidentiality capacity of each service satellite according to the spatial distribution information of the eavesdropping satellite and each service satellite includes: Obtaining an effective eavesdropping probability that the eavesdropping satellite is located in an effective eavesdropping area of the target service satellite, and determining the number of eavesdropping satellites within a preset distance in the effective eavesdropping area of the target service satellite based on the effective eavesdropping probability; Calculate the eavesdropping capacity of each eavesdropping satellite within a preset distance within the effective eavesdropping area of the target service satellite; According to the eavesdropping capacity of each eavesdropping satellite and the number of eavesdropping satellites, the total eavesdropping capacity of the target service satellite is determined as the eavesdropping capacity of the target service satellite; The confidentiality capacity of the target service satellite is calculated based on the eavesdropping capacity of the target service satellite.

4. The method according to claim 1 or 3, characterized in that The current connection information includes: the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently connected. A utility function is constructed based on the current connection information and the confidentiality capacity, including: Obtain the number of IoT devices currently connected to the target service satellite and the total number of IoT devices currently connected to the target service satellite, and determine the instantaneous confidentiality capacity allocated to each IoT device connected to the target service satellite in combination with the current confidentiality capacity of the target service satellite; The queuing waiting delay of the target service satellite is obtained, and a utility function of the target service satellite is constructed based on the instantaneous confidentiality capacity and the queuing waiting delay.

5. The method according to claim 4, characterized in that Get the queuing delay of the target service satellite, including: Obtain the current service request arrival rate of the current device task issued by the IoT device to the target service satellite, and the current service rate of the target service satellite; The queuing waiting delay of the target service satellite is determined according to the current service request arrival rate and the current service rate.

6. The method according to claim 1, characterized in that The calculation formula for determining the system average utility is as follows: in, represents the average utility of the system, x i (t) represents the ratio of the number of IoT devices connected to the i-th service satellite to the total number of IoT devices, π i (t) represents the utility of the i-th service satellite, and M represents the total number of service satellites.

7. The method according to claim 6, characterized in that Comparing the utility of the service satellite connected to the target IoT device with the average utility of the system, and selecting a service satellite for the target IoT device to connect to based on the comparison result, including: If the utility of the serving satellite is not less than the average utility, there is no need to adjust the serving satellite that the target IoT device selects to connect to; If the utility of the target service satellite is less than the average utility, the service satellite that the target IoT device chooses to connect to at the next moment is adjusted; Return to the step of calculating the utility of each service satellite using the utility function until the utility of the target service satellite is no less than the average utility.

8. A low-latency satellite edge computing service selection device for security-sensitive services, characterized in that: The device comprises: The information acquisition module is used to obtain the current connection information between the service satellite and the IoT device, and use the preset process model to establish the spatial distribution information of the eavesdropping satellite and each service satellite; an eavesdropping capacity calculation module, configured to determine the confidentiality capacity of each service satellite based on the spatial distribution information of the eavesdropping satellite and each service satellite; a utility calculation module, configured to construct a utility function based on the current connection information and the confidentiality capacity, and calculate the utility of each service satellite using the utility function; an average utility calculation module, configured to determine the average utility of the system according to the utility of the target service satellite to which the target IoT device is connected and the ratio of the number of IoT devices connected to the target service satellite to the total number of IoT devices; The service selection module is used to compare the utility of the service satellite connected to the target Internet of Things device with the system average utility, and select a service satellite to be connected for the target Internet of Things device based on the comparison result.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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