Wireless ad hoc network load modeling method, device, equipment and medium

Abstract

The invention relates to a wireless ad hoc network load modeling method and device, equipment and a medium. The method comprises the following steps: considering a distributed mobile ad hoc network scene, dividing and extracting multi-dimensional features of a network load, including a network node scale, a node active degree and a node service feature; modeling the service characteristics of each node in the network by adopting a multi-dimensional Gaussian random process with the same dimension, and carrying out weighted fusion on the service characteristics of all nodes in the network to obtain global network service characteristics; and according to the network node scale, the global network active degree obtained by averaging and the global network service characteristics are used for modeling, and a network load model of the distributed mobile ad hoc network is constructed. By adopting the method, the multi-dimensional characteristics of the network load can be accurately described, the description of the dynamic wireless ad hoc network characteristics is met, the calculation complexity is reduced, and the constructed network load model can provide more favorable support for the adaptive reconstruction of the MAC protocol.

Description

Technical Field

[0001] The present application relates to the field of network communication technology, and in particular to a method, device, equipment and medium for modeling network load of a wireless ad hoc network. Background Art

[0002] A distributed mobile ad hoc network is a dynamic wireless self-organizing network composed of mobile nodes. It does not rely on fixed infrastructure (such as base stations or routers). Nodes can move freely and transmit data through multi-hop communication collaboration. It is suitable for scenarios with dynamic and decentralized facilities.

[0003] In mobile ad hoc networks, network load models are used to describe the arrival pattern of data traffic, which is crucial for performance evaluation, resource management, and MAC (Multiple Access Control) protocol design. Adaptive MAC protocols can provide better networking performance for mobile ad hoc networks. Accurate modeling of network load can provide sufficient guidance for the reconstruction process of MAC protocols, especially in scenarios where network load and service characteristics change dynamically. The network environment and task requirements of mobile ad hoc networks are constantly changing, and traffic characteristics also change accordingly. Therefore, the network load model should have the accuracy of network feature description and dynamic adaptability.

[0004] Traditional queuing theory models can quickly evaluate network performance and provide analytical solutions. The arrival model of queuing theory is based on the description of the Poisson process. It characterizes the level of network load through the arrival rate parameter, and provides guidance for the adaptive MAC protocol to a certain extent. However, its limitations include: 1. It is difficult for the model to reflect the dynamic characteristics of the network environment; 2. The load cannot fully reflect the network characteristics; 3. The model cannot characterize the burstiness and self-similarity of network services; 4. The arrival rate parameter is difficult to obtain in a distributed network; 5. The influence of the upper-layer protocol mechanism is ignored. Therefore, an improved network traffic model for the above problems is proposed, including: The Fractional Brownian Motion model (FBM) is often used in backbone network traffic analysis. It can describe the self-similarity of services, but ignores short-term burst services. The ON / OFF (switch) model has the advantage of using Pareto distribution modeling to describe burst traffic and is more consistent with the characteristics of real heterogeneous traffic, but it is difficult to adapt to complex protocols. The Markov Modulated Poisson Process (MMPP) is applied to dynamic networks and can adapt to time-varying network loads, but it is difficult to solve parameters when the state space is large. Some hybrid models based on the above models have also been proposed. Based on the analysis of the above models, although the improved model can model the service traffic more accurately, the model is too complex and has too many parameters, which requires high computing resources and lacks real-time feedback on network load. There are certain limitations in the application of load modeling in mobile ad hoc networks.

[0005] Based on the current research progress, the problems that need to be solved in the network load model include: the complexity of the model makes it difficult to solve the parameters, it is difficult to balance the fusion modeling of heterogeneous traffic, and the dynamic adaptability of network traffic is insufficient. Summary of the invention

[0006] Based on this, it is necessary to provide a wireless self-organizing network load modeling method, device, equipment and medium to address the above-mentioned technical problems. By remodeling the network load parameters based on the Poisson process, it can meet the description of the characteristics of the dynamic wireless self-organizing network, while taking into account the computational complexity of the model parameters and the feasibility of heterogeneous traffic fusion in distributed mobile self-organizing networks. The constructed network load model can provide more favorable support for the adaptive reconstruction of the MAC protocol.

[0007] A method for modeling network load of a wireless ad hoc network, the method comprising: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. The network load model of the distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0008] In one embodiment, a multi-dimensional Gaussian random process with the same dimension is used to model the service characteristics of each node in the network, including: Considering that each node in the network performs different tasks, we use The Gaussian random process models the business characteristics of each node in the network, which can be expressed as ,in, Reason t The set of all sample functions of the business characteristics of a single node at a certain moment, , For the A sample function, d is the number of sample functions, that is, the number of dimensions of the Gaussian random process is consistent with the number of sample functions, For the cycle; It is used to describe the random process in which the length of the business generated by the node changes over time. The normal probability density function is expressed as ; in, for The expected value of Represents any sample function exist Random variables at time; for The expected value of Represents any sample function In another Random variables at time; is a random variable The variance of is a random variable The variance of represents the expected calculation; is the normalized covariance matrix The determinant of ; Indicates the node generated Business length, Indicates that the task is mapped to d Business characteristic random variables of dimensions; Indicates any d a moment; and Both represent dimensions.

[0009] In one embodiment, the method further comprises: Further considering that the tasks of each node in the network are different and the business types are relatively fixed, the business characteristics of each node are set to be wide-sense stationary within a limited period of time, and the business characteristics of each node in the network are modeled as The business characteristics of a stationary Gaussian process satisfy the following constraints: ; ; in, for Expected value; is the expected value constant, which is used to indicate that the expected value of a stationary Gaussian process does not change with time; Represents the autocorrelation function at time and The value at for and The expected value of the product of is the time difference, that is ; is the autocorrelation function, which depends only on the time difference .

[0010] In one embodiment, the service characteristics of each node after modeling are based on the service length as a random vector at any time. Dimensional Gaussian distribution, nodes The business length of each dimension is described as dimensional random vector , the probability density function of its business characteristics is expressed as ; in, for t Time Node The probability density function of the business characteristics; Representation Node The mean vector of different types of business lengths, Representation Node No. The mean of the business length, , superscript T represents transpose; is the covariance matrix, which is used to represent the node The correlation between the mean vectors of different types of business lengths, Represents the covariance matrix The determinant of ; Through each node After using the Gaussian distribution to describe the service characteristics of different nodes, the probability density function set of the service characteristics of each node in the network is expressed as ;in, is the network node size, and .

[0011] In one embodiment, a weighted fusion algorithm of multi-dimensional Gaussian components is used to perform weighted fusion on the service features of all nodes in the network to obtain global network service features, including: use The weighted fusion algorithm of Gaussian components considers that the nodes in the network meet the independent and identically distributed conditions. The goal is to fuse the business characteristics of all nodes in the network to obtain a Global network service characteristics of Gaussian distribution; Assume Node The Gaussian component of the business characteristics is ,node The activity level is , the activity level indicates the node The probability of generating business in a unit time; Perform normalization to obtain the weight of each node’s business characteristics , expressed as ; in, satisfy and ; Based on weight Perform weighted fusion on the service features of all nodes in the network to obtain the global network service features ,and conform to Gaussian distribution, that is ; Among them, the mean vector of global network service characteristics With the covariance matrix Respectively expressed as ; ; in, ,in, express The means, and ; Superscript T Indicates transpose.

[0012] In one embodiment, a network load model of a distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and global network service characteristics, and includes: According to the scale of network nodes , the global network activity level is obtained by taking the average activity level of all nodes And the mean vector of global network service characteristics Modeling is performed to construct a network load model for distributed mobile ad hoc networks, where: t The expected network load at time is expressed as ; in, is the time-varying network load, for Expected value of global network activity , represents the average probability of all nodes in the network generating business per unit time; For Node activity level; For Node No. i The mean length of each business.

[0013] A wireless ad hoc network load modeling device, the device comprising: The load feature extraction module is used to consider the distributed mobile ad hoc network scenario and divide and extract the multi-dimensional features of the network load, including network node scale, node activity level and node service characteristics; the network node scale represents the number of nodes in the network, the node activity level represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time; The weighted fusion module is used to model the service characteristics of each node in the network using a multi-dimensional Gaussian random process of the same dimension, and to perform weighted fusion on the service characteristics of all nodes in the network using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain global network service characteristics; The network load modeling module is used to model the network load model of the distributed mobile ad hoc network based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0014] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. The network load model of the distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. The network load model of the distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0016] The above-mentioned wireless ad hoc network load modeling method, device, equipment and medium are oriented to the decentralized distributed mobile ad hoc network scenario, and the network load is divided into three dimensions: network node scale, node activity and node service characteristics, so that the description of the network load is more detailed and more accurate network characteristics can be obtained. Then, the service characteristics of each node in the network are modeled as a multi-dimensional Gaussian random process and weighted fusion is performed to obtain the global network service characteristics of Gaussian distribution. The multi-dimensional Gaussian random process can adapt to the dynamic change characteristics of network services, and the fused global network service characteristics can reflect the overall state of the network, which is conducive to supporting global analysis and decision-making. Finally, according to the network node scale, the averaged local network activity and the global network service characteristics, a network load model is constructed. The model can fully reflect the heterogeneous feature mapping of different tasks to communication services, and can obtain time-varying network characteristics through observation, estimation and fusion of services in distributed networks.

[0017] Importantly, the characteristics of the three dimensions of network load can be obtained through periodic statistics and estimation, making it easy to calculate and obtain parameters when building the network load model. In addition, by observing the services of this node, the activity level and service characteristics mapped by the node task can be estimated, and the periodic prediction of node service characteristics can be supported. The periodic observation process can reflect the dynamic network environment, and the characteristic parameter observation and fusion of the distributed mobile ad hoc network can be realized through node feature interaction. It can also fully demonstrate the impact of different features on the performance of the MAC protocol, providing strong support for the adaptive reconstruction of the MAC protocol. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a method for modeling network load of a wireless ad hoc network in an embodiment; Figure 2 Schematic diagram of performance comparison of 0.6-CSMA protocol under different network characteristic parameters in one embodiment; wherein, Figure 2 (a) is the throughput performance comparison curve of the 0.6-CSMA protocol under different network characteristic parameters. Figure 2 (b) is the packet loss rate performance comparison curve of the 0.6-CSMA protocol under different network characteristic parameters. Figure 2 (c) is the comparison curve of the network average delay performance of the 0.6-CSMA protocol under different network characteristic parameters; Figure 3 Schematic diagram of SOTDMA protocol performance comparison under different network characteristic parameters in one embodiment; wherein: Figure 3 (a) is the throughput performance comparison curve of the SOTDMA protocol under different network characteristic parameters. Figure 3 (b) is the packet loss rate performance comparison curve of the SOTDMA protocol under different network characteristic parameters. Figure 3 (c) is the comparison curve of the network average delay performance of the SOTDMA protocol under different network characteristic parameters; Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] Usually, the service arrival process of each node in a wireless ad hoc network is usually modeled using a Poisson process. The service length is divided into equal lengths, and the average arrival rate (intensity) per unit time is recorded as .but Packets in time interval The probability density function of the inner arrival channel is expressed as ; in, For interval time The number of businesses arriving within It represents the expected number of services arriving at the channel per unit time. If the service arrival process follows a Poisson process, then the arrival interval should follow an exponential distribution. .

[0021] in, .

[0022] Considering the distributed mobile ad hoc network scenario, the nodes in the network are dynamic, different nodes have different tasks, the corresponding nodes have different activities, and the communication services may cover heterogeneous modal data such as instructions, text, audio, images and videos. Modeling with a Poisson process that is independent, stable and has no aftereffect is only applicable to nodes with low load and the same service type. For scenarios with different service characteristics caused by different node tasks in heterogeneous networks, the load cannot fully describe the state of the network.

[0023] Therefore, in one embodiment, if Figure 1 As shown, the present application provides a method for modeling network load of a wireless ad hoc network, comprising the following steps: Step S1, considering the distributed mobile ad hoc network scenario, divides and extracts the multi-dimensional characteristics of the network load, including network node scale, node activity level and node service characteristics.

[0024] Among them, the network node scale represents the number of nodes in the network, the node activity level represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node (also known as the number of data frames) changing over time.

[0025] Step S2, using a multi-dimensional Gaussian random process with the same dimension to model the business characteristics of each node in the network, and using a weighted fusion algorithm of multi-dimensional Gaussian components to weightedly fuse the business characteristics of all nodes in the network to obtain global network business characteristics.

[0026] Among them, a multi-dimensional Gaussian random process with the same dimension is used to model the business characteristics of each node in the network, including: Considering that each node in the network performs different tasks, we use The Gaussian random process models the business characteristics of each node in the network, which can be expressed as ,in, Reason t The set of all sample functions of the business characteristics of a single node at a certain moment, , For the i A sample function, d is the number of sample functions, that is, the number of dimensions of the Gaussian random process is consistent with the number of sample functions, For cycle.

[0027] It is used to describe the random process in which the length of the business generated by the node changes over time. The normal probability density function is expressed as ; in, for The expected value of Represents any sample function exist Random variables at time; for The expected value of Represents any sample function In another Random variables at time; is a random variable The variance of is a random variable The variance of represents the expected calculation; is the normalized covariance matrix The determinant of ; Indicates the node generated Business length, Indicates that the task is mapped to d Business characteristic random variables of dimensions; Indicates any d a moment; and Both represent dimensions.

[0028] Furthermore, considering that the tasks of each node in the network are different and the service types are relatively fixed, the service characteristics of each node in a limited period of time are set to be broadly stable, and the service characteristics of the data transmitted during the communication process reflected by different tasks should obey Dimensional Gaussian distribution, so the business characteristics of each node in the network are modeled as The business characteristics of a stationary Gaussian process satisfy the following constraints: ; ; in, for Expected value; is the expected value constant, which is used to indicate that the expected value of a stationary Gaussian process does not change with time; Represents the autocorrelation function at time and The value at for and The expected value of the product of is the time difference, that is ; is the autocorrelation function, which depends only on the time difference .

[0029] Without loss of generality, the business characteristics of each node after modeling are random vectors with business length at any time. Dimensional Gaussian distribution, nodes The business length of each dimension is described as dimensional random vector , the probability density function of its business characteristics is expressed as ; in, for t Time Node The probability density function of the business characteristics; Representation Node The mean vector of different types of business lengths, Representation Node No. i The mean of the business length, , superscript T represents transpose; is the covariance matrix, which is used to represent the node The correlation between the mean vectors of different types of business lengths, Represents the covariance matrix The determinant of .

[0030] Through each node After using the Gaussian distribution to describe the service characteristics of different nodes, the probability density function set of the service characteristics of each node in the network is expressed as ;in, is the network node size, and .

[0031] In order to obtain the global network service characteristics at a certain moment, it is necessary to integrate the service characteristics of all nodes in the network. The weighted fusion algorithm of Gaussian components considers that the nodes in the network meet the independent and identically distributed conditions. The goal is to fuse the business characteristics of all nodes in the network to obtain a Global network traffic characteristics of Gaussian distribution.

[0032] Assume Node The Gaussian component of the business characteristics is ,node The activity level is , the activity level indicates the node The probability of generating business in a unit time; Perform normalization to obtain the weight of each node’s business characteristics , expressed as ; in, satisfy and .

[0033] Based on weight Perform weighted fusion on the service features of all nodes in the network to obtain the global network service features ,and conform to Gaussian distribution, that is ; Among them, the mean vector of global network service characteristics With the covariance matrix Respectively expressed as ; ; in, ,in, express The i means, and ; Superscript T Indicates transpose.

[0034] Step S3, modeling is performed based on the network node scale, the global network activity obtained by taking the average activity of all nodes, and the global network service characteristics to construct a network load model of the distributed mobile ad hoc network.

[0035] Specifically, according to the scale of network nodes , the global network activity level is obtained by taking the average activity level of all nodes And the mean vector of global network service characteristics Modeling is performed to construct a network load model for a distributed mobile ad hoc network, where: t The expected network load at time is expressed as ; in, is the time-varying network load, for Expected value of global network activity , represents the average probability of all nodes in the network generating business per unit time; For Node activity level; For Node No. i The mean length of the business.

[0036] Furthermore, substituting the expected expression of network load into the Poisson process yields ; in, .

[0037] Through the above network load modeling method, the load is split into three dimensions of features, and the business features of each node are integrated to obtain the global network business features, making the way to describe the network load more detailed and able to obtain more accurate network features. Importantly, the features of the three dimensions can be obtained through periodic statistics and estimation, reducing the complexity of model parameter calculation. By observing the business of this node, the activity level and business features mapped by the node task can be estimated, and the periodic prediction of node business features can be supported. The periodic observation process can reflect the dynamic network environment. Through the interaction of node features, the characteristic parameter observation and fusion of distributed mobile ad hoc networks can be realized, and the performance impact of different features on the MAC protocol can be fully demonstrated, providing strong support for the adaptive reconstruction of the MAC protocol.

[0038] In the specific implementation, in order to verify the guiding significance of the network load model constructed in this application for the reconstruction of the MAC protocol, further simulation experiments were carried out to verify the adequacy of the model in characterizing the network load, without focusing on the feature fusion process and parameter estimation process of heterogeneous services. By simulating different performance curves of the MAC protocol under the same load conditions, the performance differences of the MAC protocol under different characteristics are obtained, which can reflect the limitations of the traditional Poisson distribution modeling arrival process, and prove that the network load model constructed in this application can accurately characterize the network characteristics. The specific simulation experiment process is: set performance parameters. First, set an observation period Time slot, in cycle The network load characteristics are statistically estimated, and it is believed that the network characteristics and load of each cycle are stable; and with 5000 time slots as the step length, the throughput, packet loss rate and average network delay performance of the network with 10 steps are statistically analyzed, reflecting the advantages and disadvantages of the MAC protocol in terms of effectiveness, reliability and time sensitivity.

[0039] Throughput It is defined as the number of data frames successfully transmitted per unit time, which can be expressed as ; in, Indicates the number of data frames successfully transmitted and received during the statistical period. Indicates the number of time slots in the statistical period.

[0040] Packet loss rate It is defined as the ratio of the sum of data frames damaged during transmission and discarded due to failure to transmit in time to all data frames generated per unit time, which can be expressed as ; in, Indicates the number of data frames damaged during transmission at the node within the statistical period. Indicates the number of data frames that were discarded due to failure to transmit in time or accumulated in the queue at the end of the period during the statistical period. Indicates the number of data frames generated by all nodes in the network during the statistical period.

[0041] Average network delay It is defined as the average time that all data frames generated in the network wait in the queue. The waiting time of each node in the send suspension state and the number of times the send process is activated are counted, and the average network delay of all nodes in the network is averaged to obtain the average network delay, which can be expressed as ; in, Nodes within the statistical period The waiting time for sending the pending state, The number of times the process was sent for activation.

[0042] Model verification. In order to more intuitively demonstrate the characteristic differences of the network load model constructed in this application, it is assumed that each node in the distributed mobile ad hoc network performs a single task and the service type is fixed, so the service characteristics The dimensionality of the MAC process is reduced to a 1-dimensional Gaussian process, and the service length variable at any time obeys a 1-dimensional Gaussian distribution. The different performances of the competitive MAC protocol and the scheduling MAC protocol under the same network load are simulated respectively.

[0043] The competition protocol uses p-CSMA protocol for simulation, and the probability Three networks with different features are set up, and the feature parameters after fusion are shown in Table 1.

[0044] Table 1. Network characteristic parameter settings for competition protocol simulation

[0045] Among them, the characteristic variance It is randomly generated during the simulation process to reflect the error of model parameter estimation and simulate the heterogeneity of business data generated by network nodes. The variance range of global network business characteristics is set to The network load is calculated by the above parameters: , , then the expectation of the Poisson distribution is 0.8, and the probability density function is ; The above formula represents that 0.8 data frames arrive at the channel in a unit time (time slot). Although the above three network characteristic parameters are different, they have the same load.

[0046] Figure 2 The throughput, packet loss rate and average network delay performance curves of the contention-type 0.6-CSMA protocol in three different network characteristics are given respectively. Although the three networks have the same network load, it can be seen from the simulation results that there are large differences in the performance of the protocol. Under the same activity level, network 1 has a large number of nodes and a short service data length, with a lower delay, but poor throughput and packet loss performance; network 3 has a small number of nodes and a long service data length, with good throughput and packet loss performance, but a longer delay; the performance of network 2 is between the two. The reason is that the increase in the number of nodes leads to an increase in the probability of conflict when accessing the channel, and the competition for time slots leads to the conflict and loss of data frames. The transmission of short frames also increases the number of times network nodes compete for channels, and the conflict situation is therefore aggravated, but it also brings more opportunities for nodes to access the channel, which reduces the average waiting delay, and vice versa.

[0047] The scheduling protocol uses the SOTDMA protocol for simulation. Similarly, three networks with different characteristics are set up, and the fused characteristic parameters are shown in Table 2.

[0048] Table 2. Setting of characteristic parameters of scheduling protocol simulation network

[0049] Similarly, the variance range of the global network service characteristics is set to The network load can be approximated by calculating the above parameters. , , then the expectation of the Poisson distribution is 1, and the probability density function , the three networks with different characteristics still have the same load.

[0050] Figure 3 The throughput, packet loss rate and average network delay performance curves of the scheduling SOTDMA protocol in three networks with different characteristic parameters are given respectively. The three networks also have the same network load characteristics, and the protocol performance differences in the simulation results are still obvious. Network 1 has many nodes, but the nodes are not active, and the probability of generating business is extremely low. If business is generated, long data will be accumulated; Network 3 has fewer nodes and shorter business frame length, but the network nodes are active, and the probability of generating business per unit time is relatively high; the characteristics of network 2 are between the two. Based on the access mechanism analysis of the scheduling class, the performance of the SOTDMA protocol in network 3 should be better than that of network 1, which is also verified in the simulation results. The throughput and packet loss rate performance of network 2 and network 3 gradually improve with the increase of simulation time, showing a trend of convergence to the performance of network 3. This is because at the beginning of the simulation, the activity level of network 3 is extremely low, resulting in large differences in the business queues of each node. As time goes by, the randomness of generating business is gradually eliminated, and the differences in the business queues of each node tend to be flat. Due to the inherent characteristics of SOTDMA, the delay performance is highly positively correlated with the number of network nodes.

[0051] Through the simulation of MAC protocol in networks with different characteristics, the performance of different protocols under the same load conditions is obtained. Figure 2 and Figure 3 It can be seen that the same network load of the three networks represents the same load parameters of the Poisson distribution. This application splits the network load into three dimensions of characteristics. The characteristics are different but the network load is the same, which shows significant differences in performance. This means that the traditional load modeling method based on Poisson distribution is not detailed enough for the characterization of network characteristics, and there are certain limitations when analyzing the performance of MAC protocol. In some studies, the network load parameters are used as the standard for MAC protocol reconstruction, which is actually not perfect. Simulation proves that the network load model constructed by this application is effective and sufficient for modeling network characteristics.

[0052] In one embodiment, a wireless ad hoc network load modeling device is provided, comprising: The load feature extraction module is used to consider the distributed mobile ad hoc network scenario and divide and extract the multi-dimensional features of the network load, including network node scale, node activity level and node service characteristics; the network node scale represents the number of nodes in the network, the node activity level represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time; The weighted fusion module is used to model the service characteristics of each node in the network using a multi-dimensional Gaussian random process of the same dimension, and to perform weighted fusion on the service characteristics of all nodes in the network using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain global network service characteristics; The network load modeling module is used to model the network load model of the distributed mobile ad hoc network based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0053] For the specific definition of the wireless ad hoc network load modeling device, please refer to the definition of the wireless ad hoc network load modeling method mentioned above, which will not be repeated here. Each module in the above-mentioned wireless ad hoc network load modeling device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0054] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a wireless ad hoc network load modeling method is implemented.

[0055] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0056] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. The network load model of the distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0057] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. The network load model of the distributed mobile ad hoc network is constructed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

[0058] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for modeling network load of a wireless ad hoc network, characterized in that: The method comprises: Considering the distributed mobile ad hoc network scenario, the multi-dimensional characteristics of the network load are divided and extracted, including network node scale, node activity and node service characteristics. Among them, the network node scale represents the number of nodes in the network, the node activity represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time. The service characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process with the same dimension, and the service characteristics of all nodes in the network are weighted and fused using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain the global network service characteristics. A network load model of a distributed mobile ad hoc network is constructed by modeling based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

2. The method according to claim 1, characterized in that The business characteristics of each node in the network are modeled using a multi-dimensional Gaussian random process of the same dimension, including: considering the different tasks performed by each node in the network, using the same dimension The Gaussian random process models the business characteristics of each node in the network, which can be expressed as ,in, Reason t The set of all sample functions of the business characteristics of a single node at a certain moment, , For the A sample function, is the number of sample functions, that is, the number of dimensions of the Gaussian random process is consistent with the number of sample functions, For the cycle; It is used to describe the random process in which the length of the business generated by the node changes over time. The normal probability density function is expressed as ； in, for The expected value of Represents any sample function exist Random variables at time; for The expected value of Represents any sample function In another Random variables at time; is a random variable The variance of is a random variable The variance of represents the expected calculation; is the normalized covariance matrix The determinant of ; Indicates the node generated Business length, Indicates that the task is mapped to Business characteristic random variables of dimensions; Indicates any a moment; and Both represent dimensions.

3. The method according to claim 2, characterized in that The method further comprises: Further considering that the tasks of each node in the network are different and the business types are relatively fixed, the business characteristics of each node are set to be wide-sense stationary within a limited period of time, and the business characteristics of each node in the network are modeled as The business characteristics of a stationary Gaussian process satisfy the following constraints: ； ； in, for Expected value; is the expected value constant, which is used to indicate that the expected value of a stationary Gaussian process does not change with time; Represents the autocorrelation function at time and The value at for and The expected value of the product of is the time difference, that is ; is the autocorrelation function, which depends only on the time difference .

4. The method according to claim 3, characterized in that The business characteristics of each node after modeling are random vectors with business length at any time. Dimensional Gaussian distribution, nodes The business length of each dimension is described as dimensional random vector , the probability density function of its business characteristics is expressed as ； in, for Time Node The probability density function of the business characteristics; Representation Node The mean vector of different types of business lengths, Representation Node No. The mean of the business length, , superscript T represents transpose; is the covariance matrix, which is used to represent the node The correlation between the mean vectors of different types of business lengths, Represents the covariance matrix The determinant of ; Through each node After using the Gaussian distribution to describe the service characteristics of different nodes, the probability density function set of the service characteristics of each node in the network is expressed as ;in, is the network node size, and .

5. The method according to claim 4, characterized in that The weighted fusion algorithm of multi-dimensional Gaussian components is used to perform weighted fusion on the service features of all nodes in the network to obtain the global network service features, including: use The weighted fusion algorithm of Gaussian components considers that the nodes in the network meet the independent and identically distributed conditions. The goal is to fuse the business characteristics of all nodes in the network to obtain a Global network service characteristics of Gaussian distribution; Assume Node The Gaussian component of the business characteristics is ,node The activity level is , the activity level indicates the node The probability of generating business in a unit time; Perform normalization to obtain the weight of each node’s business characteristics , expressed as ； in, satisfy and ; Based on weight Perform weighted fusion on the service features of all nodes in the network to obtain the global network service features ,and conform to Gaussian distribution, that is ; Among them, the mean vector of global network service characteristics With the covariance matrix Respectively expressed as ； ； in, ,in, express The i means, and ; Superscript T Indicates transpose.

6. The method according to claim 5, characterized in that Modeling is performed based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics to construct a network load model for a distributed mobile ad hoc network, including: According to the scale of network nodes , the global network activity level is obtained by taking the average activity level of all nodes And the mean vector of global network service characteristics Modeling is performed to construct a network load model for a distributed mobile ad hoc network, where: t The expected network load at time is expressed as ； in, is the time-varying network load, for Expected value of global network activity , represents the average probability of all nodes in the network generating business per unit time; For Node activity level; For Node No. i The mean length of each business.

7. A wireless ad hoc network load modeling device, characterized in that: The device comprises: The load feature extraction module is used to consider the distributed mobile ad hoc network scenario and divide and extract the multi-dimensional features of the network load, including network node scale, node activity level and node service characteristics; the network node scale represents the number of nodes in the network, the node activity level represents the probability of a node generating a service per unit time, and the node service characteristics describe the random process of the length of the service generated by the node changing over time; The weighted fusion module is used to model the service characteristics of each node in the network using a multi-dimensional Gaussian random process of the same dimension, and to perform weighted fusion on the service characteristics of all nodes in the network using a weighted fusion algorithm of multi-dimensional Gaussian components to obtain global network service characteristics; The network load modeling module is used to model the network load model of the distributed mobile ad hoc network based on the scale of network nodes, the global network activity obtained by averaging the activity levels of all nodes, and the global network service characteristics.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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