CN113037358B - Low-orbit satellite Internet of things service model establishing method based on attractor selection algorithm and multi-satellite load balancing method - Google Patents

Low-orbit satellite Internet of things service model establishing method based on attractor selection algorithm and multi-satellite load balancing method Download PDF

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CN113037358B
CN113037358B CN202110231123.6A CN202110231123A CN113037358B CN 113037358 B CN113037358 B CN 113037358B CN 202110231123 A CN202110231123 A CN 202110231123A CN 113037358 B CN113037358 B CN 113037358B
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洪涛
程一凡
张更新
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Nanjing Microstar Communication Technology Co ltd
Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a low-orbit satellite Internet of things service model based on an attractor selection algorithm and a multi-satellite load balancing algorithm; the service model adopts a method of grid division to determine the deployment density of the terminal in the spatial dimension, adopts a method of random position generation, determines the specific coordinates of the terminal by constructing a deployment density matrix of the terminal, utilizing the characteristics of edge cumulative probability distribution function pairs and using mathematical methods such as one-dimensional inversion sampling, affine transformation and the like, and researches event-driven services in the time dimension to accurately capture the burst flow characteristics in a short time range; the multi-satellite load balancing algorithm guides the terminal of the Internet of things to carry out dynamic self-adaptive satellite decision by calculating the network activity and the network state of each satellite in real time, is more suitable for a high-dynamic scene of the satellite Internet of things, and can make self-adaptive decision adjustment on the terminal when the external access scale changes, so that the random access performance is improved.

Description

Low-orbit satellite Internet of things service model establishing method based on attractor selection algorithm and multi-satellite load balancing method
Technical Field
The invention relates to the technical field of satellite communication, in particular to a low-orbit satellite Internet of things service model establishing method and a multi-satellite load balancing method based on an attractor selection algorithm.
Background
The internet of things is a basic network capable of interconnection, intercommunication and interoperation, which is used for connecting all articles in the world with various networks through information sensing equipment and various network access technologies according to a standard communication protocol and a consistent network architecture and carrying out information communication, data exchange, interoperation and management, so that the articles are intelligently identified, positioned, tracked, monitored and managed. With the large-area coverage of various wireless networks, the application of the internet of things gradually permeates into various fields of human activities, and the sensor equipment can be connected in an all-weather and all-around manner through network cables, mobile phone networks, remote wireless networks and the like. However, the terrestrial internet of things cannot effectively solve all the problems due to the limitation of space environment and geographic factors. In remote areas where people cannot reach, it is very difficult to lay base stations and establish communication networks, and because the low-earth satellite communication system has the advantages of low transmission loss and time delay and can realize seamless coverage on the earth in a constellation manner, the satellite internet of things becomes one of reliable choices for assisting a ground network to realize interconnection of everything.
A modeling method suitable for a low-orbit satellite Internet of things and an access strategy based on the highest priority are provided in the existing document 'modeling of low-orbit satellite Internet of things traffic'. Analyzing the types of geographic environments in grids by using a grid division mode in a spatial dimension of a service model proposed in the literature, setting corresponding terminal deployment densities for different geographic environments, calculating the terminal deployment densities in the grids by taking area proportions of the different geographic environments as weighting factors through weighting summation, and finally multiplying the terminal deployment densities by the total number of terminals to obtain the number of the terminals in the grids; the method mainly aims at simulating the stable flow generated by the periodic update service mainly existing in the satellite Internet of things in the time dimension, and the Palm-Kinchinte theorem proves that the superposition of the asynchronous and periodic processes can be replaced by a Poisson process. The main ideas of the highest priority based access policy proposed in the literature are: because the distance between the satellite and the ground is far beyond the distance between the ground Internet of things nodes and the base station, the data transmission performance of the physical layer and the power consumption of the ground Internet of things nodes are comprehensively considered, and all Internet of things equipment nodes in the same geographic grid select satellites with the highest priority to access in real time. Wherein the priority is a priority of the geographical grid for the satellite, the value is inversely proportional to the distance from the satellite sub-satellite point to the geographical grid, the closer the distance the higher the priority, and vice versa the lower the priority. The simulation result shows that the peak-to-average ratio of the satellite service is very high, which shows that the busy and idle times of different satellites are uneven due to the difference of the geographic environments of the coverage areas.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a service model in a load balancing method based on an attractor selection algorithm, and a method for determining terminal deployment density by grid division is adopted in spatial dimension, compared with the prior document, the difference is that a method for generating random positions is adopted, a terminal deployment density matrix is constructed, the specific coordinates of a terminal are determined by mathematical methods such as one-dimensional inversion sampling, affine transformation and the like by utilizing the characteristics of edge cumulative probability distribution function pairs, so that the number of terminals in a certain grid can be obtained, the positions of different terminals in the grid can be clearly and intuitively seen, the model of the spatial dimension is more visualized, and the later research is facilitated; in the time dimension, the invention mainly aims at the burst flow caused by the event-driven service and adopts the Beta distribution with limited time;
the multi-satellite load balancing method provided by the invention is mainly inspired by attractor selection algorithm in biology. In a multi-satellite coverage scene, the change of the cell living environment is regarded as the change of the system access scale, the change of the whole access scale is actually reflected in the change of the access scale of each satellite, so that the network activity of each satellite is directly influenced, the network activity can be understood as the superiority and inferiority of the current satellite selection scheme, the network state of each satellite is determined by the network activity of each satellite, the network state can be understood as performance indexes such as the throughput, the access delay and the like of the satellite, the probability of selecting the satellite is represented, finally, a terminal node makes a decision of accessing the satellite according to the real-time network state of each satellite, the satellite is selected to initiate random access, and the network activity of each satellite in the system can be kept in a dynamic balance due to the change of the number of nodes initiating access requests to each satellite, namely the real-time resource utilization rate of the system is higher, and the conditions that a high-priority satellite is busy and a low-priority satellite is idle in the existing scheme are improved.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a low orbit satellite Internet of things service model building method based on an attractor selection algorithm is characterized in that building the model comprises the following steps:
step S1, carrying out grid division on the earth surface and carrying out service analysis to determine the terminal deployment density in a grid; representing the terminal deployment density of the target area by Z (x, y), wherein x and y respectively correspond to an actual geographic latitude coordinate and a longitude coordinate through affine transformation;
step S1.1, calculating normalized two-dimensional node probability density function p X,Y (x, y) is as follows:
Figure SMS_1
the edge cumulative probability distribution function for X is calculated as follows:
Figure SMS_2
and generating random numbers on an X axis according to the distribution function by adopting a one-dimensional inverse transformation sampling method:
Figure SMS_3
wherein, U 1 Represents a random number subject to uniform distribution,
Figure SMS_4
an inverse function representing the cumulative probability distribution of the edge;
step S1.2, extract the x 'th line in Z (x, y), record as Z (y | x'), normalize, record as Z (y | xp Y|X' (y | x'). Calculating the cumulative conditional probability distribution function F of Y for a given X Y|X' (y | x') is as follows:
Figure SMS_5
generating a random number on a Y axis according to the distribution function by adopting a one-dimensional inversion sampling method:
Figure SMS_6
wherein, U 2 Represents a random number subject to uniform distribution,
Figure SMS_7
an inverse function representing an edge condition cumulative probability distribution;
step S1.3, converting x 'and y' into actual latitude and longitude coordinates λ and Φ by affine transformation:
λ＝x'·λ step +λ start
φ＝y'·φ step +φ start
wherein λ is start And phi start Respectively, the reference coordinate, λ, at which the latitude and longitude are minimal step And phi step Division intervals respectively representing latitude and longitude;
s2, calculating the number of new services generated in each time slot;
step S2.1, using a time-limited Beta distribution, triggering the total number N of internet-of-things terminals at a certain time node between T =0 and T = T, where the probability of random access is p (T), and then the number of triggered terminals in the ith time slot is obtained by the following formula:
Figure SMS_8
wherein t is i Probability distribution function representing the ith random access slot, p (t) representing the Beta distribution:
Figure SMS_9
beta (α, β) denotes the Beta function;
and step S2.2, randomly selecting a corresponding number of terminals to generate new services in each time slot according to the number of the new services calculated in the step S2.1.
Further, in the step S2.1, α =3 and β =4 are selected; p (t) is rewritten as:
Figure SMS_10
a multi-satellite load balancing method of a low-orbit satellite Internet of things service model based on an attractor selection algorithm is characterized by comprising the following steps of:
l1, determining the total number of terminals initiating random access in real time by a satellite side through a load estimation method, and calculating the success rate of the random access in real time;
note that the available preamble sequence for satellite n is R j,n The number of uplink access requests in the jth random access time slot is M j,n If the terminal node selects a preamble sequence, the probability of the terminal node being in the idle state is:
Figure SMS_11
the number of idle preamble sequences in the jth random access slot is I j,n The probability that the preamble sequence is in the idle state is:
p idle ＝I j,n /R j,n 。
the total number of terminals initiating random access to the satellite n in the jth random access time slot is:
Figure SMS_12
the probability that a terminal initiating a random access request to a satellite n in the jth random access time slot can complete successful access is as follows:
Figure SMS_13
the number of loads successfully accessing satellite n in jth random access slot:
Figure SMS_14
/>
under the condition of no idle leader sequence, the estimated value of the total terminal number for initiating random access to the satellite n in the jth random access time slot:
Figure SMS_15
where lambertiw (·) represents a lambertian w function.
According to the estimated total load number of the successfully accessed satellite n in the jth random access time slot, calculating the access success probability p of the satellite side in the jth random access time slot s ：
Figure SMS_16
Step L2, according to the node side comprehensive access success rate p s Calculating the equipment satisfaction degree h of the satellite side by using the satellite real-time communication elevation angle theta, the satellite real-time coverage node number q and the available lead code number r of the satellite:
h＝w 1 ·p s +w 2 ·r+w 3 ·θ+w 4 ·q
wherein w 1 、w 2 、w 3 、w 4 For the weight coefficient, the weight is calculated by a dispersion maximization method in the multi-attribute decision as follows:
Figure SMS_17
wherein v represents an element value of the normalization matrix;
and L3, calculating the instantaneous network activity factor of the satellite at the time t by the node side according to the equipment satisfaction as follows:
Figure SMS_18
and calculating a network state factor of the satellite according to the network activity factor, and further calculating access probability for the node to select satellite access:
Figure SMS_19
wherein
Figure SMS_20
k is the number of the satellite with the maximum instantaneous network activity;
the access probability is calculated as follows:
Figure SMS_21
wherein N represents the total number of satellites with network activity other than 0;
step L4, recording
Figure SMS_22
Representing a cumulative probability density vector, where p i Representing the probability of the terminal selecting satellite i to initiate random access. The terminal selects the satellite initiating random access and determines the satellite initiating random access through the following steps:
step L4.1, terminals in the same grid generate random numbers q which are subject to uniform distribution i ～U(0,1)；
Step L4.2, each terminal generates a random number q by itself i Comparing with elements in S until finding the first ratio q in S i Small element S 1j ；
Step L4.3, take out S 1j The column label j is the number of the satellite initiating the random access for the terminal.
Has the beneficial effects that:
1. the business model provided by the invention adopts a random position generation method in the space dimension, the number of terminals in a certain grid can be obtained through a series of mathematical methods, and the positions of different terminals in the grid can be clearly and visually seen, so that the model of the space dimension is more visualized and is beneficial to the later research; in the time dimension, the event-driven service is researched, and the burst flow characteristics in a short time range are accurately captured. According to the two dimensions of space and time, the service model provided by the invention clearly shows the time-space uneven characteristics of the low-orbit satellite internet of things service.
2. The multi-satellite load balancing method provided by the invention guides the terminal of the Internet of things to carry out dynamic self-adaptive satellite decision by calculating the network activity and the network state of each satellite in real time, is more suitable for a high-dynamic scene of the satellite Internet of things, and can make self-adaptive decision adjustment for the terminal when the external access scale changes, thereby improving the resource utilization rate of a system, reducing the peak-to-average ratio of services and improving the random access performance.
Drawings
FIG. 1 is a diagram of a multi-star coverage scenario provided by the present invention;
FIG. 2 is a block diagram of a business model of the Internet of things of a low orbit satellite provided by the invention;
FIG. 3 is a flow chart of a business model of the Internet of things of low orbit satellites provided by the invention;
FIG. 4 is a block diagram of a multi-satellite load balancing method provided by the present invention;
FIG. 5 is a flow chart of a multi-satellite load balancing method provided by the present invention;
FIG. 6 is a graph of satellite traffic based on the highest priority access policy;
FIG. 7 is a graph of satellite traffic for an attractor selection algorithm based access policy;
FIG. 8 is a histogram of the peak-to-average ratio of traffic for a portion of satellites using a highest priority based access policy and using an attractor selection algorithm based access policy;
FIG. 9 is a graph of average access delay versus access size using a highest priority based access policy and using an attractor selection algorithm based access policy;
FIG. 10 is a graph of system throughput versus access size using a highest priority based access policy and using an attractor selection algorithm based access policy;
fig. 11 is a graph of average retransmission times versus access size using an access policy based on highest priority and using an access policy based on an attractor selection algorithm.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
The invention discloses a low earth orbit satellite Internet of things service model establishing method and a multi-satellite load balancing method based on an attractor selection algorithm, which are mainly used for improving the satellite service peak-to-average ratio, the on-satellite resource utilization rate and the random access performance of a terminal node in a multi-satellite coverage low earth orbit satellite Internet of things scene shown in figure 1. For the scenario of the internet of things of a multi-satellite coverage low-orbit satellite shown in fig. 1, due to the fact that the low-orbit satellite is in a high dynamic state and the change of the geographic environment of a beam coverage area, the following problems exist in the traditional distance-based access strategy for partial event-driven burst traffic internet of things service, wherein the traditional distance-based access strategy has heterogeneity in two dimensions of space-time:
(1) Massive access requests are converged to the satellite with the highest priority, so that serious lead code resource conflict is caused, and system performance is reduced, such as access success rate, time delay, throughput performance and the like;
(2) The busy degree of the highest priority satellite is far higher than that of the low priority satellite, so that the busy and idle of satellite services are uneven, the peak-to-average ratio of the services is larger, and the utilization rate of the whole resources of a low-orbit satellite system is low.
The service model in the load balancing method based on the attractor selection algorithm provided by the invention adopts a method for determining the terminal deployment density by grid division in the spatial dimension, but is different from the prior document, adopts a method for generating random positions, determines the specific coordinates of the terminal by using mathematical methods such as one-dimensional inversion sampling, affine transformation and the like by constructing a terminal deployment density matrix and utilizing the characteristics of edge cumulative probability distribution function pairs, so that not only can the number of the terminals in a certain grid be obtained, but also the positions of different terminals in the grid can be clearly and intuitively seen, so that the model of the spatial dimension is more visualized and is beneficial to the later research; the present invention is mainly directed to bursty traffic caused by event-driven services in the time dimension, and therefore mainly analyzes such bursty traffic, which has a surge and a surge characteristic, which conforms to the second service model proposed by 3GPP, that is, a large number of nodes in a network access the network in a highly synchronized manner. In order to model such bursty traffic, a time-limited Beta distribution is adopted, and assuming that nodes of the internet of things with a total number of N are triggered at a certain time node between T =0 and T = T, and the probability of random access is p (T), the number of triggered nodes in the ith time slot may be calculated by the following formula:
Figure SMS_23
wherein t is i Denotes the ith random access slot, p (t) denotes the probability distribution function of the Beta distribution,
Figure SMS_24
beta (α, β) represents the Beta function, which distribution best fits the real case when α =3, β = 4. Thus p (t) can be rewritten to ^ er>
Figure SMS_25
As shown in fig. 2-3, the method for generating the service model of the internet of things of the satellite specifically comprises the following steps:
step S1, carrying out grid division on the earth surface and carrying out service analysis to determine the terminal deployment density in a grid; representing the terminal deployment density of the target area by Z (x, y), wherein x and y correspond to actual geographic latitude and longitude coordinates respectively through affine transformation;
step S1.1, calculating normalizationNormalized two-dimensional node probability density function p X,Y (x, y) are as follows:
Figure SMS_26
the edge cumulative probability distribution function for X is calculated as follows:
Figure SMS_27
and generating random numbers on an X axis according to the distribution function by adopting a one-dimensional inverse transformation sampling method:
Figure SMS_28
wherein, U 1 Represents a random number subject to uniform distribution,
Figure SMS_29
an inverse function representing an edge cumulative probability distribution;
step S1.2, extracting the x 'th line in Z (x, y), recording as Z (y | x'), normalizing, and recording as p Y|X' (y | x'). Calculating the cumulative conditional probability distribution function F of Y for a given X Y|X' (y | x') as follows:
Figure SMS_30
generating a random number on a Y axis according to the distribution function by adopting a one-dimensional inverse transformation sampling method:
Figure SMS_31
wherein, U 2 Represents a random number subject to uniform distribution,
Figure SMS_32
representing edge conditional cumulative probability distributionsAn inverse function;
step S1.3, converting x 'and y' into actual latitude and longitude coordinates λ and Φ by affine transformation:
λ＝x'·λ step +λ start
φ＝y'·φ step +φ start
wherein λ is start And phi start Denotes the reference coordinate, λ, at which the latitude and longitude are minimum, respectively step And phi step Division intervals respectively representing latitude and longitude;
s2, calculating the number of new services generated in each time slot;
step S2.1, using a time-limited Beta distribution, triggering the total number N of internet-of-things terminals at a certain time node between T =0 and T = T, where the probability of random access is p (T), and then the number of triggered terminals in the ith time slot is obtained by the following formula:
Figure SMS_33
wherein t is i Represents the ith random access slot, p (t) represents the probability distribution function of the Beta distribution:
Figure SMS_34
beta (α, β) denotes a Beta function; in the embodiment of the present invention, when α =3 and β =4, the distribution is most suitable for the actual situation. Thus p (t) can be rewritten as
Figure SMS_35
And step S2.2, randomly selecting a corresponding number of terminals to generate new services in each time slot according to the number of the new services calculated in the step S2.1.
The invention also provides a low-orbit satellite multi-satellite load balancing method based on the attractor selection algorithm, which is mainly inspired by the attractor selection algorithm in biology: the change of the cell living environment is equivalent to inputting two signals (actually, a plurality of signals, for example, two signals) to the cell, the input of the two signals directly influences the synthesis amount of the nutrient components required by the cell, so that the concentration of the two nutrient components in the cell is determined, finally, the cell controls the self life activity at the moment by adjusting the gene expression of the cell, and the change of the life activity leads the nutrient components in the cell to approach a dynamic balance; in a multi-satellite coverage scene, the change of the cell living environment is regarded as the change of the system access scale, the change of the whole access scale is actually reflected in the change of the access scale of each satellite, so that the network activity of each satellite is directly influenced, the network activity can be understood as the superiority and inferiority of the current satellite selection scheme, the network state of each satellite is determined by the network activity of each satellite, the network state can be understood as performance indexes such as the throughput, the access delay and the like of the satellite, the probability of selecting the satellite is represented, finally, a terminal node makes a decision of accessing the satellite according to the real-time network state of each satellite, the satellite is selected to initiate random access, and the network activity of each satellite in the system can be kept in a dynamic balance due to the change of the number of nodes initiating access requests to each satellite, namely the real-time resource utilization rate of the system is higher, and the conditions that a high-priority satellite is busy and a low-priority satellite is idle in the existing scheme are improved.
The multi-satellite load balancing method shown in fig. 4-5 includes the following steps:
l1, determining the total number of terminals initiating random access in real time by a satellite side through a load estimation method, and calculating the success rate of the random access in real time;
note that the available preamble sequence for satellite n is R j,n The number of uplink access requests in the jth random access time slot is M j,n If the terminal node selects a preamble sequence, the probability of the terminal node being in the idle state is:
Figure SMS_36
the number of idle preamble sequences in the jth random access slot is I j,n The probability that the preamble sequence is in the idle state is:
p idle ＝I j,n /R j,n 。
the total number of terminals initiating random access to the satellite n in the jth random access time slot is:
Figure SMS_37
the probability that a terminal initiating a random access request to a satellite n in the jth random access time slot can complete successful access is as follows:
Figure SMS_38
the number of loads successfully accessing satellite n in jth random access slot:
Figure SMS_39
under the condition of no idle leader sequence, the estimated value of the total terminal number for initiating random access to the satellite n in the jth random access time slot:
Figure SMS_40
wherein lambertiw (·) represents a lambertian w function;
according to the estimated total load number of the successfully accessed satellite n in the jth random access time slot, calculating the access success probability p of the satellite side in the jth random access time slot s ：
Figure SMS_41
Step L2, according to the node side comprehensive access success rate p s Calculating the equipment satisfaction degree h of the satellite side by using the satellite real-time communication elevation angle theta, the satellite real-time coverage node number q and the available lead code number r of the satellite:
h＝w 1 ·p s +w 2 ·r+w 3 ·θ+w 4 ·q
wherein w 1 、w 2 、w 3 、w 4 For the weight coefficient, the weight is calculated by a dispersion maximization method in the multi-attribute decision as follows:
Figure SMS_42
wherein v represents the element value of the normalization matrix;
and L3, calculating the instantaneous network activity factor of the satellite at the time t by the node side according to the equipment satisfaction as follows:
Figure SMS_43
and calculating a network state factor of the satellite according to the network activity factor, and further calculating access probability for the node to select satellite access:
Figure SMS_44
wherein
Figure SMS_45
k is the number of the satellite with the maximum instantaneous network activity;
the access probability is calculated as follows:
Figure SMS_46
wherein N represents the total number of satellites with network activity different from 0;
step L4, recording
Figure SMS_47
Representing a cumulative probability density vector, where p i Representing the probability of the terminal selecting satellite i to initiate random access. Terminal selects satellite channel for initiating random accessThe following steps are determined:
l4.1, terminals in the same grid generate random numbers q which are subject to uniform distribution i ～U(0,1)；
Step L4.2, random number q to be generated by each terminal i Comparing with elements in S until finding the first ratio q in S i Small element S 1j ；
Step L4.3, take out S 1j The column label j is the number of the satellite which initiates the random access for the terminal.
An embodiment is provided below to further evaluate the superiority of the technical solution of the present invention by comparing the access policy based on the highest priority in the prior art with the access policy based on the attractor selection provided by the present invention.
Performing a joint simulation experiment based on STK and MATLAB, establishing a constellation model from the STK, adopting an OneWeb constellation, leading ephemeris report sampling time to be 1 minute and sampling interval to be 5ms, and leading the ephemeris report into the MATLAB for further simulation. The geographical range of longitude-180 degrees to 180 degrees and latitude-70 degrees to 70 degrees is divided into 10080 geographical grids for analysis according to the interval of longitude 2.5 degrees and latitude 2 degrees. The total number of the distributed nodes of the Internet of things is 5000-10000, the node service arrival model is Beta distribution, the time for generating new services is 100 time slots (5 ms is one time slot), the node backoff retransmission mode is binary retransmission, and the length of a backoff window is 50 time slots.
As shown in fig. 6, which is a graph of satellite traffic volume using the access policy based on the highest priority, it can be seen that the satellites with the satellite numbers 1, 4, 5, 9, and 12 always do not bear any traffic, and the utilization rate of system resources is low.
Fig. 7 is a graph of satellite traffic volume using an attractor selection algorithm-based access policy, and it can be seen that compared with the original policy, 12 satellites respectively bear certain services, and the utilization rate of system resources is improved.
Fig. 8 is a histogram of the peak-to-average ratio of services of a partial satellite under two strategies, and it can be seen visually that the multi-satellite load balancing method provided by the present invention improves the peak-to-average ratio of services to a certain extent.
Fig. 9 is a graph of the change of the average access delay with the access scale under two strategies, and it can be seen that with the increase of the access scale, the average access delay of the multi-satellite load balancing method provided by the present invention has no substantial change, while the average access delay of the method in the existing literature increases sharply.
Fig. 10 is a graph of variation of system throughput with access scale under two strategies, and it can be seen that as the access scale increases, the system throughput of the multi-satellite load balancing method provided by the present invention shows a trend of first increasing and then decreasing, whereas the system throughput of the method in the existing document gradually decreases, and the former is obviously better than the latter on the whole.
Fig. 11 is a graph of the average retransmission times under the two strategies along with the change of the access scale, and it can be seen that along with the increase of the access scale, the average retransmission times of the multi-satellite load balancing method provided by the present invention basically has no significant change, while the average retransmission times of the methods in the existing documents are increased sharply.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (3)

1. A low orbit satellite Internet of things service model building method based on an attractor selection algorithm is characterized in that building the model comprises the following steps:
step S1, carrying out grid division on the earth surface and carrying out service analysis to determine the terminal deployment density in a grid; representing the terminal deployment density of the target area by Z (x, y), wherein x and y correspond to actual geographic latitude and longitude coordinates respectively through affine transformation;
step S1.1, calculating normalized two-dimensional node probability density function p X,Y (x, y) are as follows:
Figure FDA0004092359460000011
the edge cumulative probability distribution function for X is calculated as follows:
Figure FDA0004092359460000012
generating a random number on an X axis according to the edge cumulative probability distribution function of the X by adopting a one-dimensional inverse transformation sampling method:
Figure FDA0004092359460000013
wherein, U 1 Represents a random number subject to uniform distribution,
Figure FDA0004092359460000014
an inverse function representing an edge cumulative probability distribution;
step S1.2, extract the x 'th line in Z (x, y), record as Z (y | x'), normalize, record as p Y|X' (y | x'); calculating the cumulative conditional probability distribution function F of Y for a given X Y|X' (y | x') is as follows:
Figure FDA0004092359460000015
generating a random number on a Y axis according to the cumulative conditional probability distribution function of the Y by adopting a one-dimensional inverse transformation sampling method:
Figure FDA0004092359460000016
wherein, U 2 Represents a random number subject to uniform distribution,
Figure FDA0004092359460000017
indicating edge condition accumulationAn inverse function of the probability distribution;
step S1.3, converting x 'and y' into actual latitude and longitude coordinates λ and Φ by affine transformation:
λ＝x'·λ step +λ start
φ＝y′·φ step +φ start
wherein λ is start And phi start Denotes the reference coordinate, λ, at which the latitude and longitude are minimum, respectively step And phi step Division intervals respectively representing latitude and longitude;
s2, calculating the number of new services generated in each time slot;
step S2.1, using a time-limited Beta distribution, triggering the total number N of internet-of-things terminals at a certain time node between T =0 and T = T, where the probability of random access is p (T), and then the number of triggered terminals in the ith time slot is obtained by the following formula:
Figure FDA0004092359460000021
wherein t is i Represents the ith random access slot, p (t) represents the probability distribution function of the Beta distribution:
Figure FDA0004092359460000022
beta (α, β) denotes the Beta function;
and step S2.2, randomly selecting a corresponding number of terminals to generate new services in each time slot according to the number of the new services calculated in the step S2.1.
2. The method for establishing the low earth orbit satellite internet of things business model based on the attractor selection algorithm as claimed in claim 1, wherein in the step S2.1, α =3 and β =4 are selected; rewrite p (t) as:
Figure FDA0004092359460000023
3. a multi-satellite load balancing method for a low earth orbit satellite internet of things service model based on an attractor selection algorithm, which is characterized by comprising the following steps:
l1, determining the total number of terminals initiating random access in real time by a satellite side through a load estimation method, and calculating the success rate of the random access in real time;
note that the leader sequence available for satellite n is R j,n The number of uplink access requests in the jth random access time slot is M j,n If the terminal node selects a preamble sequence, the probability of the terminal node being in the idle state is:
Figure FDA0004092359460000024
the number of idle preamble sequences in the jth random access slot is I j,n The probability that the preamble sequence is in the idle state is:
p idle ＝I j,n /R j,n ；
the total number of terminals initiating random access to the satellite n in the jth random access time slot is:
Figure FDA0004092359460000031
the probability that a terminal initiating a random access request to a satellite n in the jth random access time slot can complete successful access is as follows:
Figure FDA0004092359460000032
the number of loads successfully accessing satellite n in jth random access slot:
Figure FDA0004092359460000033
under the condition of no idle leader sequence, the estimated value of the total terminal number for initiating random access to the satellite n in the jth random access time slot:
Figure FDA0004092359460000034
wherein lambert (·) represents a lambertian w function;
according to the estimated total load number of the successfully accessed satellite n in the jth random access time slot, calculating the access success probability p of the satellite side in the jth random access time slot s ：
Figure FDA0004092359460000035
/>
L2, according to the node side comprehensive access success rate p s Calculating the equipment satisfaction degree h of the satellite side according to the satellite real-time communication elevation angle theta, the satellite real-time coverage node number q and the satellite available lead code number r t ：
h t ＝w 1 ·p s +w 2 ·r+w 3 ·θ+w 4 ·q
Wherein w 1 、w 2 、w 3 、w 4 For the weight coefficient, the weight is calculated by a dispersion maximization method in the multi-attribute decision as follows:
Figure FDA0004092359460000041
wherein v represents an element value of the normalization matrix;
and L3, calculating the instantaneous network activity factor of the satellite at the time t by the node side according to the equipment satisfaction as follows:
Figure FDA0004092359460000042
and calculating a network state factor of the satellite according to the network activity factor, and further calculating access probability for the node to select satellite access:
Figure FDA0004092359460000043
wherein
Figure FDA0004092359460000044
k is the number of the satellite with the maximum instantaneous network activity;
the access probability is calculated as follows:
Figure FDA0004092359460000045
wherein N represents the total number of satellites with network activity different from 0;
step L4, recording
Figure FDA0004092359460000046
Representing a cumulative probability density vector, where p i Representing the probability that the terminal selects a satellite i to initiate random access; the terminal selects the satellite initiating random access and determines the satellite initiating random access through the following steps:
step L4.1, terminals in the same grid generate random numbers q which are subject to uniform distribution i ～U(0,1)；
Step L4.2, random number q to be generated by each terminal i Comparing with elements in S until finding the first ratio q in S i Small element S 1j ；
Step L4.3, take out S 1j The column label j is the number of the satellite which initiates the random access for the terminal.
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