Ad hoc network data transmission optimization method, device, equipment and medium

Abstract

The invention discloses an ad hoc network data transmission optimization method, device, equipment and medium, and the method comprises the steps: calculating the signal-to-noise ratio of a legal node and the signal-to-noise ratio of an eavesdropper of a target network according to the network topology data and channel parameters of the target network, and carrying out the optimization of the data transmission of the target network according to the signal-to-noise ratio of the legal node and the signal-to-noise ratio of the eavesdropper; calculating a confidential connection probability; calculating the end-to-end average delay of each path in the target network according to the real-time path information and the traffic data; and calculating an optimal bandwidth allocation scheme of the target network by taking the end-to-end average delay as a delay constraint through a preset double-layer ant colony optimization algorithm, and obtaining an optimal path set by taking the confidential connection probability as a confidentiality constraint and combining with the optimal bandwidth allocation scheme. And performing data transmission according to the optimal bandwidth allocation scheme and the optimal path set. According to the invention, the high efficiency and security of ad hoc network data transmission can be improved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a method, apparatus, device, and medium for optimizing data transmission in self-organizing networks. Background Technology

[0002] In today's complex and ever-changing wireless communication environment, the application of wireless ad hoc networks faces numerous severe challenges. Especially in critical fields such as military and emergency rescue, the secure and reliable transmission of information is paramount. Traditional wireless ad hoc network transmission technologies often prove inadequate in the face of malicious attacks and interference.

[0003] Existing single-path transmission schemes are highly susceptible to communication disruptions and information security breaches if the transmission path is attacked or interfered with, making it relatively easy for eavesdroppers to intercept information. While multipath transmission technology improves transmission reliability to some extent, it does not comprehensively optimize for dynamic network changes and complex environmental factors.

[0004] With the increasing diversification and complexity of network attack methods, higher demands are being placed on the security and stability of wireless ad hoc networks. Therefore, it is urgent to develop a wireless ad hoc network system and method that can ensure information security and efficient transmission. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for optimizing data transmission in ad hoc networks, which can improve the efficiency and security of data transmission in ad hoc networks.

[0006] In a first aspect, embodiments of the present invention provide a method for optimizing data transmission in an ad hoc network, comprising:

[0007] Based on the network topology data and channel parameters of the target network, calculate the signal-to-noise ratio (SNR) of the legitimate nodes and the SNR of the eavesdropper in the target network, and calculate the probability of a secure connection based on the SNR of the legitimate nodes and the SNR of the eavesdropper.

[0008] Based on real-time path information and traffic data, the end-to-end average latency of each path in the target network is calculated.

[0009] Using a pre-defined two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, the optimal bandwidth allocation scheme for the target network is calculated. With the confidential connection probability as the confidentiality constraint, and combined with the optimal bandwidth allocation scheme, an optimal path set is obtained. Data transmission is then performed based on the optimal bandwidth allocation scheme and the optimal path set.

[0010] This invention assesses path anti-eavesdropping capabilities based on network topology and channel parameters, calculates the probability of secure connections, and provides constraint data for subsequent optimal path set solving, thereby ensuring data transmission confidentiality. It calculates end-to-end average latency based on real-time path information and traffic data, quantifies path transmission efficiency, and provides latency constraints for optimizing bandwidth allocation. Using a two-layer ant colony optimization algorithm with the end-to-end average latency as a latency constraint, it calculates the optimal bandwidth allocation scheme for the target network, resolving the coupling problem between bandwidth allocation and latency. By using the secure connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, it obtains the optimal path set, maximizing the confidentiality of multi-path combinations. Thus, through the dual constraints of SCP (secure connection probability) and latency, it dynamically coordinates bandwidth and path selection to achieve secure and efficient transmission. Compared with existing technologies, this invention can improve the efficiency and security of data transmission in ad hoc networks.

[0011] Furthermore, the step of calculating the signal-to-noise ratio (SNR) of legitimate nodes and the SNR of eavesdroppers in the target network based on the network topology data and channel parameters of the target network specifically involves:

[0012] Based on the network topology data of the target network, the path loss of the legitimate node link is calculated, and based on the channel parameters of the target network, the signal received power of the legitimate node link is calculated, so as to calculate the signal-to-noise ratio of the legitimate node in the target network based on the path loss and the signal received power of the legitimate node link.

[0013] Based on the network topology data of the target network, the path loss of the eavesdropping link is calculated, and based on the channel parameters of the target network, the signal received power of the eavesdropping link is calculated, so as to calculate the eavesdropper signal-to-noise ratio of the target network based on the path loss and the signal received power of the eavesdropping link.

[0014] This invention provides data support for subsequent SCP (Secret Connection Probability) calculations by calculating the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers in the target network, and accurately quantifies the channel state to ensure the reliability of SCP calculations.

[0015] Furthermore, the step of calculating the secure connection probability based on the signal-to-noise ratio of the legitimate node and the signal-to-noise ratio of the eavesdropper specifically involves:

[0016] Calculate the legitimate link channel capacity based on the signal-to-noise ratio of the legitimate node, and calculate the eavesdropping link channel capacity based on the signal-to-noise ratio of the eavesdropper.

[0017] Calculate the single-hop link security rate based on the legitimate link channel capacity and the eavesdropping link channel capacity;

[0018] Obtain the distance between each adjacent node in the target network and the eavesdropper, and calculate the probability that each path will be cracked by the eavesdropper based on the distance;

[0019] The probability of a secure connection is calculated based on the single-hop security rate and the probability of being breached by an eavesdropper.

[0020] This invention calculates channel capacity based on signal-to-noise ratio (SNR), converting SNR into transmission capacity; evaluates single-link anti-eavesdropping capability by calculating single-hop link security rate; and calculates the probability of secure connection by calculating the probability of each path being cracked by an eavesdropper. Thus, it achieves multi-path global security assessment through a progressive process, providing a basis for security constraints for subsequent optimization algorithms.

[0021] Furthermore, the step of calculating the end-to-end average latency of each path in the target network based on real-time path information and traffic data specifically involves:

[0022] Based on the signal-to-noise ratio of the legitimate nodes, the weakest link signal-to-noise ratio of each path is calculated, and based on the weakest link signal-to-noise ratio, the Shannon capacity of each path is calculated, so as to obtain the data packet arrival rate of each path based on the Shannon capacity.

[0023] Using binomial distribution, the probability of correct packet transmission for each path is calculated based on real-time path information and traffic data.

[0024] For each node in the target network, calculate the packet arrival probability distribution of the current node based on the queue status of the predecessor node and the link transmission characteristics.

[0025] Based on the correct transmission probability of the packets and the arrival probability distribution of the data packets, a queue state transition probability matrix is ​​constructed, and the steady-state probability distribution of the queue is obtained by solving the queue state transition probability matrix.

[0026] Calculate the overflow probability of the current node based on the steady-state probability distribution and the data packet arrival probability distribution;

[0027] Using Little's law, the average queuing delay of the current node is calculated based on the overflow probability and the packet arrival probability;

[0028] The average queuing delay of all nodes on each path is summed to obtain the end-to-end average delay of each path.

[0029] This invention calculates the arrival rate of data packets and correlates the signal-to-noise ratio of the weakest link with the path capacity; it quantifies link reliability by calculating the probability of correct packet transmission; it provides data support for the subsequent steady-state probability distribution of queues by calculating the arrival probability distribution of data packets; it quantifies queue congestion risk by calculating the queue state transition matrix and steady-state probability distribution; and it calculates the average queuing delay of the current node using Little's law to reflect the impact of packet loss on delay. Finally, it sums up the results to obtain the end-to-end average delay of each path, providing delay constraints for subsequent data transmission optimization.

[0030] Furthermore, before calculating the optimal bandwidth allocation scheme for the target network using a preset two-layer ant colony optimization algorithm with the end-to-end average latency as the latency constraint, the method further includes:

[0031] Obtain the source and destination nodes of the data to be transmitted, and use a preset depth-first search algorithm to obtain all paths from the source node to the destination node, thus obtaining a set of candidate paths.

[0032] This invention generates a set of candidate paths to provide a search space for subsequent ant colony optimization algorithms, and reduces computational load and improves optimization efficiency by pre-narrowing the search range.

[0033] Furthermore, the optimal bandwidth allocation scheme for the target network is calculated using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint. Specifically:

[0034] Initialize a number of ants and the pheromones at each ant's position, and randomly place the ants on candidate paths in the candidate path set; wherein, each ant corresponds to a randomly selected preset bandwidth allocation scheme; the bandwidth allocation scheme is constrained by the end-to-end average latency.

[0035] Based on the pheromone of the current ant's position and the preset heuristic information, calculate the transfer probability of the current ant moving to another ant's position, and update the current ant's position according to the preset transfer rules and the transfer probability.

[0036] Continue calculating the transition probability of the next ant and update the position of the next ant. When all ants have completed a position update, perform a round of updates to the global pheromone.

[0037] The global pheromone is updated iteratively until a preset iteration termination condition is met, at which point the update of the global pheromone is stopped, and the final pheromone update result is obtained. Based on the pheromone update result, the optimal bandwidth allocation scheme of the target network is obtained.

[0038] This invention employs a two-layer ant colony optimization algorithm, using the end-to-end average latency as a latency constraint, to adjust bandwidth allocation in real time based on the target network's state and calculate the optimal bandwidth allocation scheme for the target network. This reduces end-to-end latency and improves data transmission efficiency in complex multi-path ad hoc network scenarios.

[0039] Furthermore, the optimal path set is obtained by using the secure connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, specifically as follows:

[0040] Initialize a number of ants and the pheromone at each ant's position, and randomly place the ants on candidate paths in the candidate path set; wherein, the candidate paths are all constrained by the confidential connection probability; the candidate paths are allocated bandwidth according to the optimal bandwidth allocation scheme;

[0041] Based on the pheromone of the current ant's position and the preset heuristic information, calculate the transfer probability of the current ant moving to another ant's position, and update the current ant's position according to the preset transfer rules and the transfer probability.

[0042] Continue calculating the transition probability of the next ant and update the position of the next ant. When all ants have completed a position update, perform a round of updates to the global pheromone.

[0043] The global pheromone is updated iteratively until a preset iteration termination condition is met, at which point the update of the global pheromone is stopped, and the final pheromone update result is obtained. Based on the pheromone update result, the optimal path set of the target network is obtained.

[0044] This invention utilizes a two-layer ant colony optimization algorithm, with the confidential connection probability as a confidentiality constraint, combined with the optimal bandwidth allocation scheme, to obtain an optimal path set. This ensures both transmission rate and data confidentiality, thereby achieving secure and efficient data transmission.

[0045] Secondly, embodiments of the present invention provide a self-organizing network data transmission optimization device, including a confidentiality connection module, an average latency module, and a data transmission optimization module, wherein...

[0046] The confidentiality connection module is used to calculate the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers in the target network based on the network topology data and channel parameters of the target network, and to calculate the confidentiality connection probability based on the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers.

[0047] The average latency module is used to calculate the end-to-end average latency of each path in the target network based on real-time path information and traffic data.

[0048] The data transmission optimization module is used to calculate the optimal bandwidth allocation scheme of the target network using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, and with the confidential connection probability as the confidentiality constraint, and in combination with the optimal bandwidth allocation scheme, obtain the optimal path set, and then perform data transmission according to the optimal bandwidth allocation scheme and the optimal path set.

[0049] This invention employs a confidentiality connection module to assess path anti-eavesdropping capabilities based on network topology and channel parameters, calculates the confidentiality connection probability, and provides constraint data for subsequent optimal path set solving, thereby ensuring data transmission confidentiality. An average delay module calculates end-to-end average delay based on real-time path information and traffic data, quantifies path transmission efficiency, and provides delay constraints for optimizing bandwidth allocation. A data transmission optimization module, based on a two-layer ant colony optimization algorithm, calculates the optimal bandwidth allocation scheme for the target network using the end-to-end average delay as a delay constraint, resolving the coupling problem between bandwidth allocation and delay. By using the confidentiality connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, an optimal path set is obtained, maximizing the confidentiality of multi-path combinations. Thus, through the dual constraints of SCP (confidential connection probability) and delay, bandwidth and path selection are dynamically coordinated to achieve secure and efficient transmission.

[0050] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0051] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the self-organizing network data transmission optimization method as described in any of the above.

[0052] Fourthly, embodiments of the present invention provide a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device / apparatus containing the computer-readable storage medium to perform the self-organizing network data transmission optimization method as described in any of the preceding claims.

[0053] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0054] Figure 1This is a schematic diagram of a self-organizing network data transmission optimization method provided in an embodiment of the present invention;

[0055] Figure 2 This invention provides a robust, hot-standby redundant self-organizing network MIMO multipath secure transmission system.

[0056] Figure 3 This is a structural diagram of a self-organizing network data transmission optimization device provided in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1:

[0059] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing data transmission in a self-organizing network, comprising the following steps:

[0060] S11, Based on the network topology data and channel parameters of the target network, calculate the signal-to-noise ratio of the legitimate nodes and the signal-to-noise ratio of the eavesdropper in the target network, and calculate the probability of secure connection based on the signal-to-noise ratio of the legitimate nodes and the signal-to-noise ratio of the eavesdropper.

[0061] In this embodiment, calculating the legitimate node signal-to-noise ratio (SNR) and eavesdropper SNR of the target network based on the network topology data and channel parameters of the target network specifically involves: calculating the path loss of the legitimate node link based on the network topology data of the target network, and calculating the signal received power of the legitimate node link based on the channel parameters of the target network, so as to calculate the legitimate node SNR of the target network based on the path loss and the signal received power of the legitimate node link; and calculating the path loss of the eavesdropping link based on the network topology data of the target network, and calculating the signal received power of the eavesdropping link based on the channel parameters of the target network, so as to calculate the eavesdropper SNR of the target network based on the path loss and the signal received power of the eavesdropping link.

[0062] In one specific embodiment, a self-organizing network MIMO multipath secure transmission model is constructed. This model includes a system model, a queue model, and a security model. The system model defines the network physical foundation and channel characteristics; the queue model obtains the transmission delay; and the security model calculates the probability of secure connections.

[0063] In one specific embodiment, the system model first determines the network architecture (distribution of the set of legitimate nodes and the set of eavesdroppers) and the channel model (distance between nodes, channel coefficients, and SNR calculation method); wherein the system model sets the network architecture and nodes as follows: the set of legitimate nodes is denoted as Φ L ={a i i = 1, 2, ..., N L The set of eavesdroppers is denoted as Φ. E ={E j Let the network be a collection of nodes, where j = 1, 2, ..., and the distribution of eavesdroppers follows a homogeneous Poisson point process (PPP) with density . Assume that each legitimate node can obtain the state information of other legitimate nodes through a route discovery process and can globally adopt a transmission strategy. Furthermore, legitimate nodes possess perfect instantaneous channel state information (CSI), and eavesdroppers can obtain the perfect CSI of the eavesdropping channel. There are M single paths from the source node to the destination node in the network, forming a path set Π. M ={Π M,1 ,…,Π M,M}, where the m-th path K M,m This represents the number of nodes along the path.

[0064] Furthermore, by calculating the signal-to-noise ratio (SNR) of the links between legitimate nodes, the signal quality is reflected, providing basic data for the subsequent analysis of the transmission efficiency of the queue model and the confidentiality of the security model. For adjacent nodes in the m-th path... and Its channel coefficient and distance are respectively represented by |h M,m,i,i+1 | and d M,m,i,i+1 The formula for calculating the received signal-to-noise ratio of a legitimate node is as follows:

[0065]

[0066] Furthermore, the signal-to-noise ratio (SNR) received by the eavesdropper is calculated to assess the eavesdropping risk and provides a key parameter for calculating the confidentiality rate of the security model. The SNR received by the eavesdropper is:

[0067]

[0068] Among them, nodes With the eavesdropper E j The channel coefficient and distance are respectively and p A For all legal nodes to have the same transmit power, σ 2 α represents the average noise power, and α is the path loss exponent.

[0069] Preferably, the relay nodes employ a decode-forward (DF) scheme. Simultaneously, hot standby redundancy devices are deployed. These devices are in standby mode and can perform environmental monitoring and information collection during normal network operation. Their location distribution must ensure rapid response to unexpected situations in the network.

[0070] In this embodiment, calculating the secure connection probability based on the signal-to-noise ratio (SNR) of the legitimate node and the SNR of the eavesdropper specifically involves: calculating the legitimate link channel capacity based on the legitimate node SNR, and calculating the eavesdropping link channel capacity based on the eavesdropper SNR; calculating the single-hop secure rate based on the legitimate link channel capacity and the eavesdropping link channel capacity; obtaining the distance between each adjacent node in the target network and the eavesdropper, and calculating the probability that each path will be compromised by the eavesdropper based on the distance; and calculating the secure connection probability based on the single-hop secure rate and the probability of being compromised by the eavesdropper.

[0071] In one specific embodiment, an (M,M) secret sharing scheme is adopted, in which the secret information is divided into M parts. The receiver can only recover the information when all parts are correctly received. Assuming that eavesdroppers collude, they can intercept information from single-path multi-hop through maximum ratio combining, but for paths in different frequency bands, the eavesdroppers need to intercept them separately.

[0072] In one specific embodiment, for a single-hop link Its security rate The calculation is as follows:

[0073]

[0074] Where, β M,m,i,i+1 It is the channel gain (SNR) between legitimate nodes. It is the channel gain (SNR) of the eavesdropper.

[0075] It should be noted that if the signal-to-noise ratio β of a legitimate node... M,m,i,i+1 The corresponding amount of information that can be transmitted is greater than the eavesdropper's signal-to-noise ratio. The information can be kept confidential when the corresponding amount of information is available. Therefore, the confidentiality rate... Positive values ​​are needed to ensure confidentiality.

[0076] Furthermore, the probability P of the confidential link of the path sc (Π M This is equivalent to the probability that at least one path remains unbroken by the eavesdropper. The probability of secure connection (SCP) for such a path is calculated using the following formula:

[0077]

[0078] in, The set of eavesdroppers ΦE U's expectations m The probability that the m-th path is cracked by the eavesdropper is defined as:

[0079]

[0080] in, α is the distance between adjacent nodes in the path and the eavesdropper, and α is the path loss exponent.

[0081] In one specific embodiment, for the case of multiple paths (i.e., M ≥ 2 paths), the calculation of SCP can be derived by analyzing the minimum SNR of multiple paths:

[0082]

[0083] The minimum signal-to-noise ratio of the path determines whether communication confidentiality can be maintained. Specifically, the path's confidentiality rate... The signal-to-noise ratio needs to be greater than that of the eavesdropper. Therefore, when calculating the probability of a secure connection, it is necessary to ensure that at least one path has a positive secure rate.

[0084] For the case where M=2, the SCP formula is:

[0085]

[0086] Further derivation reveals that, by using the probabilistic generating functional (PGFL) to simplify the calculation, we obtain:

[0087]

[0088] Where, λ E ρ is the density of eavesdroppers, and ρ is the distance variable. It is the distance between nodes.

[0089] S12, Calculate the end-to-end average latency of each path in the target network based on real-time path information and traffic data;

[0090] In this embodiment, calculating the end-to-end average latency of each path in the target network based on real-time path information and traffic data specifically involves: calculating the weakest link signal-to-noise ratio of each path based on the signal-to-noise ratio of the legitimate nodes, and calculating the Shannon capacity of each path based on the weakest link signal-to-noise ratio, so as to obtain the packet arrival rate of each path based on the Shannon capacity; calculating the packet correct transmission probability of each path based on real-time path information and traffic data using a binomial distribution; calculating the packet arrival probability distribution of the current node for each node in the target network based on the queue state and link transmission characteristics of the predecessor node; constructing a queue state transition probability matrix based on the packet correct transmission probability and the packet arrival probability distribution, so as to obtain the steady-state probability distribution of the queue by solving the queue state transition probability matrix; calculating the overflow probability of the current node based on the steady-state probability distribution and the packet arrival probability distribution; calculating the average queuing latency of the current node based on the overflow probability and the packet arrival probability using Little's law; and summing the average queuing latency of all nodes on each path to obtain the end-to-end average latency of each path.

[0091] In one specific embodiment, data stream arrival rate is measured by constructing a queue model. The calculation is as follows:

[0092]

[0093] Among them, B m The bandwidth allocated to the m-th path, N frame `frames per second` represents the number of frames per second, and `packetlength` represents the length of the data packets. This formula uniformly describes the data arrival rate of the (m)th path, and, combined with Bernoulli processes (batch size (s), probability (p)) and network resources (bandwidth (B_m), frame rate (F), packet length (L)), provides a foundation for queue state analysis. Output Input into subsequent queue state analysis.

[0094] Preferably, the network uses Frequency Division Multiple Access (FDMA) to achieve simultaneous multipath transmission, and Time Division Multiple Access (TDMA) is used within a single path, with nodes transmitting data in different time slots according to a fixed transmission capacity.

[0095] In one specific embodiment, let x i,t Let be the queue length of the i-th node at time t. This forms a Markov chain. For queue i, define γ. i (n,r) represents the probability of correctly receiving r data packets when n data packets are transmitted on the i-th link. The calculation formula is:

[0096]

[0097] Where, ∈ i This represents the probability of packet errors in the link.

[0098] In one specific embodiment, the formula for calculating the queue state transition probability is:

[0099]

[0100] In one specific embodiment, by solving Steady-state probability can be obtained Where P i Let Q be the transition probability matrix. i This represents the buffer length of the queue. By calculating the queue state transition probability, the transmission dynamics of data packets from the previous node to the current node are reflected, providing a basis for steady-state probability and delay analysis.

[0101] In one specific embodiment, when calculating each node i When i = 1, Bernoulli's arrival probability can be directly applied. However, when i ≥ 2, the probability of S data packets successfully being transmitted from queue i-1 to queue i is... for:

[0102]

[0103] in, It is the steady-state probability of queue i-1, which is obtained by solving... We obtain, where P i-1 It is the transition probability matrix of queue i-1, and its elements The probability of transitioning from queue length n1 to n2 is given by... The calculation yielded the result. This represents the probability of correctly receiving r data packets when n data packets are transmitted on the (i-1)th link, ∈ i-1 Let be the packet error probability of link i-1. This calculation method considers the steady-state condition of the previous queue and the impact of link transmission characteristics on the arrival probability of the current queue, reflecting the dynamic relationship between queues.

[0104] In one specific embodiment, for any queue i, the average number of packets lost due to transmission buffer overflow is:

[0105]

[0106] in, and By calculating different combinations of the number of arriving data packets s and the queue length j, the overflow and packet loss situation was accurately measured.

[0107] Furthermore, the overflow probability is calculated, and delay analysis provides a key indicator: the overflow probability P of queue i. l i for:

[0108]

[0109] In one specific embodiment, the queuing delay of a single node is calculated according to Little's law, taking into account both queue length and overflow probability to reflect node transmission efficiency, thereby allowing the calculation of the average queuing delay of the queue. The formula is:

[0110]

[0111] in, This represents the probability of queue overflow. It represents the arrival rate of each node, reflecting the impact of packet loss and queue overflow on latency. It organically combines queue length, arrival rate, and overflow probability to reflect the latency characteristics of the queue.

[0112] In one specific embodiment, the end-to-end average delay D of the m-th path is finally obtained. m for:

[0113]

[0114] The end-to-end average delay reflects the impact of queue delays at all nodes along the path on the end-to-end transmission delay.

[0115] Preferably, a hot standby redundancy device can be introduced to improve the resilience of the present invention. The improvement of the data transmission method in the present invention by the hot standby redundancy device can be specifically reflected in the following aspects:

[0116] (1) Signal Enhancement: The device can select the optimal path and improve the signal quality along that path. The device enhances signal strength by selecting the best location, especially in environments with high interference or high attenuation;

[0117] (2) Reduce attack surface: The device can avoid ground obstacles, thereby reducing signal loss at the physical layer, avoiding signal attenuation, and can be quickly deployed to the critical path to restore communication interrupted by attacks or node failures.

[0118] (3) Dynamic path takeover: When the network is attacked, the hot standby redundancy device will quickly take over the attacked path and connect to the new multipath, thereby ensuring the continuity and security of communication.

[0119] In one specific embodiment, the signal gain β of the path M,m,i,i+1 After being connected via the hot standby redundancy device, it becomes:

[0120]

[0121] Where, ρ u This indicates the additional signal gain (dB) provided by the hot standby redundancy device.

[0122] Furthermore, the confidentiality rate of the path. This will also correspondingly increase the security rate of the path. The calculation formula is:

[0123] R′ m =max[C m (β′ M,m )-C e (β′ M,m ),0]

[0124] Among them, C m With C e The calculations represent the changes in bandwidth allocation and signal-to-noise ratio after introducing hot standby redundancy. Furthermore, the probability of secure connection (SCP) along the path will also be improved.

[0125]

[0126] By connecting the device, the network's confidentiality is enhanced, and the system can provide stronger security when facing eavesdropper attacks and node failures.

[0127] In one specific embodiment, an optimization objective function is constructed under the hot standby redundancy device. Specifically, the optimization objective of this invention is to maximize the system's security and transmission performance while considering hot standby redundancy end-to-end delay constraints, secure connection probability (SCP) constraints, bandwidth constraints, path mutual exclusion constraints, and path legality constraints, i.e.:

[0128]

[0129] P sc (Π M )>ζ,

[0130]

[0131]

[0132] in, Indicates from A S To A D The complete set of feasible M-paths. B represents the average arrival rate of the path. m For bandwidth allocation for each path, B u For bandwidth allocation of hot standby redundant devices, B tol Indicates the total available bandwidth budget of the system, Π MFor multipath selection variables, appropriate selection of multipaths can optimize system confidentiality and throughput. This indicates the time delay of the recovery path after introducing a hot standby redundancy device, where τ represents the upper limit of end-to-end delay (system delay threshold), and P represents the upper limit of end-to-end delay (system delay threshold). sc The system-level secure connection probability is represented by ζ, which represents the minimum secure connection probability threshold required by the system, and the path mutual exclusion constraint. and path legality constraints B m As one of the optimization variables, adjusting its value can affect system performance. M For multipath selection variables, reasonable selection can optimize system security and transmission performance.

[0133] S13. Using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, calculate the optimal bandwidth allocation scheme for the target network, and with the confidential connection probability as the confidentiality constraint, combine the optimal bandwidth allocation scheme to obtain the optimal path set, and perform data transmission according to the optimal bandwidth allocation scheme and the optimal path set.

[0134] In one specific embodiment, after constructing the optimization objective function under the hot standby redundancy device, since there are two tightly coupled optimization variables—multipath selection and bandwidth allocation—a two-layer ant colony optimization algorithm is used to transform the original optimization problem. Transform into a bilevel optimization problem and Therefore, the optimal model is solved.

[0135] First, upper-level optimization After obtaining the optimal bandwidth through lower-level optimization, select the path set Π. M To maximize system throughput while satisfying confidentiality constraints:

[0136]

[0137] Among them, T(Π) M This represents the local maximum throughput of multipath propagation after achieving optimal bandwidth through lower-level optimization. It needs to satisfy the Confidentiality Constraint (SCP) (ensuring the probability of a secure connection is not less than a threshold). Π M,i ∩Π M,j =

[0138] {A S A D}, This indicates a path mutual exclusion / node non-overlapping constraint.

[0139] Secondly, lower-level optimization Given path selection Π M Optimize bandwidth allocation B mThis maximizes system throughput while satisfying latency constraints.

[0140]

[0141] Among them, T(Π) M B m This represents the local maximum throughput after obtaining the optimal path set through upper-level optimization. It needs to satisfy bandwidth allocation constraints and latency constraints (ensuring that the end-to-end latency does not exceed a threshold). B i B represents the bandwidth allocation for the i-th path. j This represents the bandwidth allocation to the j-th hot standby redundant device.

[0142] This indicates the time delay of the recovery path after introducing a hot standby redundancy device, where τ represents the upper limit of the end-to-end delay (system delay threshold).

[0143] In this embodiment, before calculating the optimal bandwidth allocation scheme of the target network using a preset two-layer ant colony optimization algorithm with the end-to-end average latency as the latency constraint, the method further includes: obtaining the source node and destination node of the data to be transmitted, and obtaining all paths from the source node to the destination node using a preset depth-first search algorithm to obtain a candidate path set.

[0144] In one specific embodiment, depth-first search (DFS) is used to list all paths from the source node to the destination node in a given topology, and paths that meet the conditions are selected to form a feasible multipath set, providing a search space for the ant colony algorithm.

[0145] In this embodiment, obtaining the optimal path set by using the confidential connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme specifically involves: initializing a number of ants and the pheromone for each ant's position, and randomly placing the ants on candidate paths in the candidate path set; wherein, the candidate paths are all constrained by the confidential connection probability; the candidate paths are allocated bandwidth according to the optimal bandwidth allocation scheme; based on the pheromone of the current ant's position and preset heuristic information, the transfer probability of the current ant moving to another ant's position is calculated, and the position of the current ant is updated according to the preset transfer rules and the transfer probability; the transfer probability of the next ant is calculated, and the position of the next ant is updated; when all ants have completed one position update, the global pheromone is updated once; the global pheromone update is iteratively performed until a preset iteration termination condition is reached, at which point the global pheromone update is stopped, and the final pheromone update result is obtained, so as to obtain the optimal path set of the target network based on the pheromone update result.

[0146] In one specific embodiment, the upper-level solution is obtained using a two-layer ant colony optimization algorithm, including:

[0147] (1) Ant colony initialization: In S p Randomly place N inside A The set S consists of only a few ants. A And ensure that the multipath corresponding to each ant satisfies the confidentiality constraint P. sc (Π M )>ζ, initial pheromone τ j (0) = τ0, to ensure that the initial search space covers a multipath combination that satisfies safety.

[0148] (2) Ant position update: Calculate the probability of position transfer Where τ j For the pheromone at position j of ant (reflecting the historical optimal path), η ij =O j -O i For heuristic information, O i Let be the cost function value corresponding to the local maximum throughput obtained by ant i from the lower layer optimization, and α and β be adjustment parameters. Based on the transition probability and rules... If q≤q0 (development mode, select the optimal option), otherwise l next =L(Exploration mode, based on the probability selection of roulette), where q is a uniformly distributed random variable in [0,1], q0 is a custom threshold, and L is the next position selected by roulette.

[0149] (3) Local search: After updating the position, through l next =l next +R*r gen Perform a local search, where R is [-R max ,R max A uniformly distributed random variable, where r is the range reduction factor, and gen is the current iteration number that is less than the maximum iteration number gen. max .

[0150] (4) Global pheromone update: After each iteration, press τ i (t+1)=(1-ρ)τ i (t)+Δτ i Update global pheromones, where Δτ i This represents the pheromone increment after a local search. ρ is the pheromone decay coefficient.

[0151] In this embodiment, the step of calculating the optimal bandwidth allocation scheme for the target network using a preset two-layer ant colony optimization algorithm with the end-to-end average latency as the latency constraint specifically involves: initializing a number of ants and the pheromone for each ant's position, and randomly placing the ants on candidate paths in the candidate path set; wherein each ant corresponds to a randomly selected preset bandwidth allocation scheme; all bandwidth allocation schemes are constrained by the end-to-end average latency; calculating the transfer probability of the current ant moving to another ant position based on the pheromone at the current ant position and preset heuristic information, and updating the current ant's position according to preset transfer rules and the transfer probability; continuing to calculate the transfer probability of the next ant and updating the position of the next ant; when all ants have completed one position update, performing a round of global pheromone updates; iteratively updating the global pheromone until a preset iteration termination condition is reached, stopping the global pheromone update to obtain the final pheromone update result, and obtaining the optimal bandwidth allocation scheme for the target network based on the pheromone update result.

[0152] In one specific embodiment, the lower-level solution is obtained using a two-layer ant colony optimization algorithm, including:

[0153] (1) Ant colony initialization: Given the multipath Π selected by the upper-level optimization M Random initialization Only ants Its bandwidth allocation Satisfying bandwidth constraints And calculate whether the maximum delay check is satisfied. If the conditions are not met, reinitialize. Initial pheromones. Ensure that the allocation scheme covers the feasible domain.

[0154] (2) Ant position update: Similar to the previous layer, calculate the transition probability. in and These are the lower-level pheromones and heuristic information, respectively. o i and o j The cost function value for the ant at the current position is used to update the position according to a similar rule.

[0155] For a better explanation of the working principle and process of this invention, see [link to relevant documentation]. Figure 2 One example is a robust hot-standby redundant self-organizing network MIMO multipath secure transmission system provided by an embodiment of the present invention.

[0156] This invention assesses path anti-eavesdropping capabilities based on network topology and channel parameters, calculates the probability of secure connections, and provides constraint data for subsequent optimal path set solving, thereby ensuring data transmission confidentiality. It calculates end-to-end average latency based on real-time path information and traffic data, quantifies path transmission efficiency, and provides latency constraints for optimizing bandwidth allocation. Using a two-layer ant colony optimization algorithm with the end-to-end average latency as a latency constraint, it calculates the optimal bandwidth allocation scheme for the target network, resolving the coupling problem between bandwidth allocation and latency. By using the secure connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, it obtains the optimal path set, maximizing the confidentiality of multi-path combinations. Thus, through the dual constraints of SCP (secure connection probability) and latency, it dynamically coordinates bandwidth and path selection to achieve secure and efficient transmission. Compared with existing technologies, this invention can improve the efficiency and security of data transmission in ad hoc networks.

[0157] Example 2:

[0158] like Figure 3 As shown, this embodiment provides a self-organizing network data transmission optimization device, including a confidentiality connection module 001, an average latency module 002, and a data transmission optimization module 003, wherein...

[0159] The confidentiality connection module 001 is used to calculate the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers in the target network based on the network topology data and channel parameters of the target network, and to calculate the confidentiality connection probability based on the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers.

[0160] In this embodiment, the confidentiality connection module 001 calculates the signal-to-noise ratio (SNR) of legitimate nodes and the SNR of eavesdroppers in the target network based on the network topology data and channel parameters of the target network. Specifically, the confidentiality connection module 001 calculates the path loss of the legitimate node link based on the network topology data of the target network, and calculates the signal received power of the legitimate node link based on the channel parameters of the target network, so as to calculate the legitimate node SNR of the target network based on the path loss and the signal received power of the legitimate node link; and calculates the path loss of the eavesdropping link based on the network topology data of the target network, and calculates the signal received power of the eavesdropping link based on the channel parameters of the target network, so as to calculate the eavesdropper SNR of the target network based on the path loss and the signal received power of the eavesdropping link.

[0161] In this embodiment, the confidentiality connection module 001 calculates the confidentiality connection probability based on the signal-to-noise ratio of the legitimate node and the signal-to-noise ratio of the eavesdropper. Specifically, the confidentiality connection module 001 calculates the legitimate link channel capacity based on the legitimate node signal-to-noise ratio and the eavesdropping link channel capacity based on the eavesdropper signal-to-noise ratio; calculates the single-hop link confidentiality rate based on the legitimate link channel capacity and the eavesdropping link channel capacity; obtains the distance between each adjacent node in the target network and the eavesdropper, and calculates the probability that each path will be cracked by the eavesdropper based on the distance; and calculates the confidentiality connection probability based on the single-hop confidentiality rate and the probability of being cracked by the eavesdropper.

[0162] The average latency module 002 is used to calculate the end-to-end average latency of each path in the target network based on real-time path information and traffic data.

[0163] In this embodiment, the average latency module 002 calculates the end-to-end average latency of each path in the target network based on real-time path information and traffic data. Specifically, the average latency module 002 calculates the weakest link signal-to-noise ratio of each path based on the signal-to-noise ratio of the legitimate nodes, and calculates the Shannon capacity of each path based on the weakest link signal-to-noise ratio, so as to obtain the packet arrival rate of each path based on the Shannon capacity; it calculates the packet correct transmission probability of each path based on real-time path information and traffic data through binomial distribution; for each node in the target network, it calculates the packet correct transmission probability based on the queue status and link of the predecessor node. Based on transmission characteristics, calculate the data packet arrival probability distribution of the current node; construct a queue state transition probability matrix based on the correct packet transmission probability and data packet arrival probability distribution, and obtain the steady-state probability distribution of the queue by solving the queue state transition probability matrix; calculate the overflow probability of the current node based on the steady-state probability distribution and the data packet arrival probability distribution; calculate the average queuing delay of the current node based on Little's law, the overflow probability and the data packet arrival probability; sum the average queuing delays of all nodes on each path to obtain the end-to-end average delay of each path.

[0164] The data transmission optimization 003 module is used to calculate the optimal bandwidth allocation scheme of the target network using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, and with the confidential connection probability as the confidentiality constraint, and in combination with the optimal bandwidth allocation scheme, obtain the optimal path set, and perform data transmission according to the optimal bandwidth allocation scheme and the optimal path set.

[0165] In this embodiment, data transmission optimization 003 uses a preset two-layer ant colony optimization algorithm with the end-to-end average latency as the latency constraint to calculate the optimal bandwidth allocation scheme for the target network. Specifically, data transmission optimization 003 initializes several ants and the pheromone for each ant's position, and randomly places the ants on candidate paths in the candidate path set; wherein each ant corresponds to a preset bandwidth allocation scheme; the bandwidth allocation schemes all have the end-to-end average latency as the latency constraint; based on the pheromone of the current ant's position and preset heuristic information, the transfer probability of the current ant moving to other ant positions is calculated, and the position of the current ant is updated according to the preset transfer rules and the transfer probability; the transfer probability of the next ant is calculated, and the position of the next ant is updated; when all ants have completed one position update, the global pheromone is updated once; the global pheromone update is iteratively performed until a preset iteration termination condition is reached, at which point the global pheromone update is stopped, and the final pheromone update result is obtained, so as to obtain the optimal bandwidth allocation scheme for the target network based on the pheromone update result.

[0166] In this embodiment, data transmission optimization 003 uses the secure connection probability as a confidentiality constraint and combines it with the optimal bandwidth allocation scheme to obtain the optimal path set. Specifically, data transmission optimization 003 initializes several ants and the pheromone of each ant's position, and randomly places the ants on candidate paths in the candidate path set; wherein, the candidate paths are all constrained by the secure connection probability; the candidate paths are allocated bandwidth according to the optimal bandwidth allocation scheme; based on the pheromone of the current ant's position and preset heuristic information, the transfer probability of the current ant moving to other ant positions is calculated, and the position of the current ant is updated according to the preset transfer rules and the transfer probability; the transfer probability of the next ant is calculated, and the position of the next ant is updated; when all ants have completed one position update, the global pheromone is updated once; the global pheromone update is iteratively performed until a preset iteration termination condition is reached, at which point the global pheromone update is stopped, and the final pheromone update result is obtained, so as to obtain the optimal path set of the target network based on the pheromone update result.

[0167] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0168] This invention employs a confidentiality connection module 001 to evaluate the anti-eavesdropping capability of a path based on network topology and channel parameters, and calculates the confidentiality connection probability to provide constraint data for subsequent optimal path set solving, thereby ensuring data transmission confidentiality. An average delay module 002 calculates the end-to-end average delay based on real-time path information and traffic data, quantifying path transmission efficiency and providing delay constraints for optimizing bandwidth allocation. A data transmission optimization module 003, based on a two-layer ant colony optimization algorithm, calculates the optimal bandwidth allocation scheme for the target network using the end-to-end average delay as a delay constraint, resolving the coupling problem between bandwidth allocation and delay. By using the confidentiality connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, an optimal path set is obtained, maximizing the confidentiality of multi-path combinations. Thus, through the dual constraints of SCP (confidential connection probability) and delay, bandwidth and path selection are dynamically coordinated to achieve secure and efficient transmission.

[0169] Example 3:

[0170] This embodiment provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0171] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the self-organizing network data transmission optimization method as described in any of the above.

[0172] Example 4:

[0173] This invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when running, controls the device or apparatus containing the computer-readable storage medium to execute the self-organizing network data transmission optimization training method as described in any of the preceding embodiments.

[0174] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0175] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for optimizing data transmission in an ad hoc network, characterized in that, include: Based on the network topology data and channel parameters of the target network, calculate the signal-to-noise ratio (SNR) of the legitimate nodes and the SNR of the eavesdropper in the target network, and calculate the probability of a secure connection based on the SNR of the legitimate nodes and the SNR of the eavesdropper. Based on real-time path information and traffic data, the end-to-end average latency of each path in the target network is calculated. Using a pre-defined two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, the optimal bandwidth allocation scheme for the target network is calculated. With the confidential connection probability as the confidentiality constraint, and combined with the optimal bandwidth allocation scheme, an optimal path set is obtained. Data transmission is then performed based on the optimal bandwidth allocation scheme and the optimal path set.

2. The method for optimizing data transmission in an ad hoc network as described in claim 1, characterized in that, The step of calculating the signal-to-noise ratio (SNR) of legitimate nodes and the SNR of eavesdroppers in the target network based on the network topology data and channel parameters of the target network specifically involves: Based on the network topology data of the target network, the path loss of the legitimate node link is calculated, and based on the channel parameters of the target network, the signal received power of the legitimate node link is calculated, so as to calculate the signal-to-noise ratio of the legitimate node in the target network based on the path loss and the signal received power of the legitimate node link. Based on the network topology data of the target network, the path loss of the eavesdropping link is calculated, and based on the channel parameters of the target network, the signal received power of the eavesdropping link is calculated, so as to calculate the eavesdropper signal-to-noise ratio of the target network based on the path loss and the signal received power of the eavesdropping link.

3. The method for optimizing data transmission in an ad hoc network as described in claim 1, characterized in that, The calculation of the secure connection probability based on the signal-to-noise ratio of the legitimate node and the signal-to-noise ratio of the eavesdropper is specifically as follows: Calculate the legitimate link channel capacity based on the signal-to-noise ratio of the legitimate node, and calculate the eavesdropping link channel capacity based on the signal-to-noise ratio of the eavesdropper. Calculate the single-hop link security rate based on the legitimate link channel capacity and the eavesdropping link channel capacity; Obtain the distance between each adjacent node in the target network and the eavesdropper, and calculate the probability that each path will be cracked by the eavesdropper based on the distance; The probability of a secure connection is calculated based on the single-hop security rate and the probability of being breached by an eavesdropper.

4. The method for optimizing data transmission in an ad hoc network as described in claim 1, characterized in that, The step of calculating the end-to-end average latency of each path in the target network based on real-time path information and traffic data specifically involves: Based on the signal-to-noise ratio of the legitimate nodes, the weakest link signal-to-noise ratio of each path is calculated, and based on the weakest link signal-to-noise ratio, the Shannon capacity of each path is calculated, so as to obtain the data packet arrival rate of each path based on the Shannon capacity. Using binomial distribution, the probability of correct packet transmission for each path is calculated based on real-time path information and traffic data. For each node in the target network, calculate the packet arrival probability distribution of the current node based on the queue status of the predecessor node and the link transmission characteristics. Based on the correct transmission probability of the packets and the arrival probability distribution of the data packets, a queue state transition probability matrix is ​​constructed, and the steady-state probability distribution of the queue is obtained by solving the queue state transition probability matrix. Calculate the overflow probability of the current node based on the steady-state probability distribution and the data packet arrival probability distribution; Using Little's law, the average queuing delay of the current node is calculated based on the overflow probability and the packet arrival probability; The average queuing delay of all nodes on each path is summed to obtain the end-to-end average delay of each path.

5. The method for optimizing data transmission in an ad hoc network as described in claim 1, characterized in that, Before calculating the optimal bandwidth allocation scheme for the target network using a preset two-layer ant colony optimization algorithm with the end-to-end average latency as the latency constraint, the method further includes: Obtain the source and destination nodes of the data to be transmitted, and use a preset depth-first search algorithm to obtain all paths from the source node to the destination node, thus obtaining a set of candidate paths.

6. The method for optimizing data transmission in an ad hoc network as described in claim 5, characterized in that, The optimal bandwidth allocation scheme for the target network is calculated using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint. Specifically: Initialize a number of ants and the pheromones at each ant's position, and randomly place the ants on candidate paths in the candidate path set; wherein, each ant corresponds to a randomly preset bandwidth allocation scheme; the bandwidth allocation scheme is constrained by the end-to-end average latency. Based on the pheromone of the current ant's position and the preset heuristic information, calculate the transfer probability of the current ant moving to another ant's position, and update the current ant's position according to the preset transfer rules and the transfer probability. Continue calculating the transition probability of the next ant and update the position of the next ant. When all ants have completed a position update, perform a round of updates to the global pheromone. The global pheromone is updated iteratively until a preset iteration termination condition is met, at which point the update of the global pheromone is stopped, and the final pheromone update result is obtained. Based on the pheromone update result, the optimal bandwidth allocation scheme of the target network is obtained.

7. The method for optimizing data transmission in an ad hoc network as described in claim 5, characterized in that, The optimal path set is obtained by using the secure connection probability as a confidentiality constraint and combining it with the optimal bandwidth allocation scheme, specifically as follows: Initialize a number of ants and the pheromone at each ant's position, and randomly place the ants on candidate paths in the candidate path set; wherein, the candidate paths are all constrained by the confidential connection probability; the candidate paths are allocated bandwidth according to the optimal bandwidth allocation scheme; Based on the pheromone of the current ant's position and the preset heuristic information, calculate the transfer probability of the current ant moving to another ant's position, and update the current ant's position according to the preset transfer rules and the transfer probability. Continue calculating the transition probability of the next ant and update the position of the next ant. When all ants have completed a position update, perform a round of updates to the global pheromone. The global pheromone is updated iteratively until a preset iteration termination condition is met, at which point the update of the global pheromone is stopped, and the final pheromone update result is obtained. Based on the pheromone update result, the optimal path set of the target network is obtained.

8. A self-organizing network data transmission optimization device, characterized in that, It includes a secure connection module, an average latency module, and a data transmission optimization module, among which, The confidentiality connection module is used to calculate the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers in the target network based on the network topology data and channel parameters of the target network, and to calculate the confidentiality connection probability based on the signal-to-noise ratio of legitimate nodes and the signal-to-noise ratio of eavesdroppers. The average latency module is used to calculate the end-to-end average latency of each path in the target network based on real-time path information and traffic data. The data transmission optimization module is used to calculate the optimal bandwidth allocation scheme of the target network using a preset two-layer ant colony optimization algorithm, with the end-to-end average latency as the latency constraint, and with the confidential connection probability as the confidentiality constraint, and in combination with the optimal bandwidth allocation scheme, obtain the optimal path set, and then perform data transmission according to the optimal bandwidth allocation scheme and the optimal path set.

9. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the self-organizing network data transmission optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device / apparatus containing the computer-readable storage medium to perform the self-organizing network data transmission optimization method as described in any one of claims 1 to 7.

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