A Method, Device, Equipment and Medium for Modeling the Network Load of a Wireless Ad Hoc Network

Abstract

The present application relates to a method, apparatus, device and medium for modeling network load in a wireless ad hoc network. The method includes: considering a distributed mobile ad hoc network scenario, partitioning and extracting multi-dimensional features of network load, including network node scale, node activity level, and node service characteristics; using a multi-dimensional Gaussian random process with the same dimensions to model the service characteristics of each node in the network, and performing weighted fusion on the service characteristics of all nodes in the network to obtain global network service characteristics; modeling based on the network node scale, the globally averaged network activity level, and the global network service characteristics to construct a network load model for the distributed mobile ad hoc network. Using this method can accurately describe the multi-dimensional features of network load, meet the description of the characteristics of a dynamic wireless ad hoc network, reduce the computational complexity, 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 technologies, and particularly to a method, device, equipment and medium for modeling wireless ad-hoc network load. Background Art

[0002] A distributed mobile ad-hoc network is a dynamic wireless self-organizing network composed of mobile nodes. Without relying on fixed infrastructure (such as base stations or routers), the nodes can move freely and transmit data through multi-hop communication and cooperation, which is suitable for dynamic and non-centralized facility scenarios.

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

[0004] Traditional queuing theory models can quickly evaluate network performance and provide analytical solutions. The arrival model of queuing theory is described based on the Poisson process, and the level of network load is characterized by the arrival rate parameter, which provides guidance for the adaptive MAC protocol to a certain extent. However, its limitations include: 1. The model is difficult to reflect the dynamic characteristics of the network environment; 2. The load cannot fully reflect network features; 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 upper-layer protocol mechanisms is ignored. Therefore, network traffic models improved for the above problems have been proposed, including:

[0005] The fractional Brownian motion (FBM) model is often applied to backbone network traffic analysis. It can describe the self-similarity of services but ignores short-term bursty services. The ON / OFF model has the advantage of modeling with a Pareto distribution. It can describe bursty traffic and is more in line 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 the parameters when the state space is large. Some hybrid models based on the above models have also been proposed. Analyzing the above models comprehensively, although the improved models can model service traffic more accurately, the models are too complex and the parameters are too numerous, resulting in high requirements for computing resources and insufficient real-time feedback on network loads. There are certain limitations in applying them to the load modeling of mobile ad hoc networks.

[0006] Based on the current research progress, the problems to be solved in the network load model are summarized as follows: 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

[0007] Based on this, it is necessary to provide a wireless ad hoc network load modeling method, device, equipment and medium for the above technical problems. By re-modeling the network load parameters based on the Poisson process, it can meet the description of the characteristics of dynamic wireless ad hoc networks, while taking into account the computational complexity of model parameters and the feasibility of fusing heterogeneous traffic in distributed mobile ad hoc networks. The constructed network load model can provide more favorable support for the adaptive reconstruction of the MAC protocol.

[0008] A wireless ad hoc network load modeling method, the method comprising:

[0009] Considering the distributed mobile ad hoc network scenario, extracting multi-dimensional features of network load, including network node scale, node activity level, and node service characteristics; wherein, the network node scale represents the number of nodes in the network, the node activity level represents the probability of generating services by nodes per unit time, and the node service characteristics describe the random process of the length of services generated by nodes changing with time;

[0010] Modeling the service characteristics of each node in the network using a multi-dimensional Gaussian random process with the same dimension, and using the weighted fusion algorithm of multi-dimensional Gaussian components to weight and fuse the service characteristics of all nodes in the network to obtain the global network service characteristics;

[0011] A network load model of a distributed mobile ad hoc network is constructed by modeling according to the scale of network nodes, the global network activity level obtained by averaging based on the activity levels of all nodes, and the global network service characteristics.

[0012] 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:

[0013] Considering that the tasks executed by each node in the network are different, a multi-dimensional Gaussian random process with the same dimension is used to model the service characteristics of each node in the network, expressed as , where is a set composed of all sample functions of the service characteristics of a single node at t time, , is the th sample function, d is the number of sample functions, that is, the number of dimensions of the Gaussian random process is the same as the number of sample functions, is the period;

[0014] is used to describe the random process of the service length generated by the node changing with time. The -dimensional normal probability density function of this process is expressed as

[0015] ;

[0016] Among them, is the expectation value of, represents the random variable of any sample function at time; is the expectation value of, represents the random variable of any sample function at another time; is the variance of the random variable ; is the variance of the random variable ; represents expectation calculation; is the determinant of the normalized covariance matrix ; represents the th service length generated by the node, represents the service characteristic random variable of the task mapped to d dimensions; represents any d times; and both represent dimensions.

[0017] In one embodiment, the above method further includes:

[0018] Further considering that the tasks of each node in the network are different and the service types are relatively fixed, assuming that the service characteristics of each node are generally stationary within a finite period of time, the service characteristics of each node in the network are modeled as a D-dimensional stationary Gaussian process, and the service characteristics of the stationary Gaussian process satisfy the following constraints:

[0019] ;

[0020] ;

[0021] where is the expected value of is an expected value constant, used to indicate that the expected value of the stationary Gaussian process does not change with time; represents the value of the autocorrelation function at times and ; is the expected value of the product of and is the time difference, i.e., ; is the autocorrelation function, which only depends on the time difference .

[0022] In one embodiment, the service characteristics of each node after modeling are a D-dimensional Gaussian distribution with the service length as a random vector at any moment. The service lengths of each dimension of node are described as a D-dimensional random vector , and the probability density function of its service characteristics is expressed as

[0023]

[0024] ; t where is t the probability density function of the service characteristics of node at time represents the mean vector of the service lengths of different types of node , represents the mean of the th service length of node , , the superscript T represents the transpose; is the covariance matrix, which is used to represent nodes the correlation between the mean vectors of the lengths of different types of services, represents the covariance matrix of the determinant;

[0025] After describing the service characteristics of different nodes through the dimensional Gaussian distribution of each node, the set of probability density functions of the service characteristics of each node in the network is expressed as ; where, is the scale of the network nodes, and .

[0026] In one embodiment, using the weighted fusion algorithm of multi-dimensional Gaussian components, the service characteristics of all nodes in the network are weighted and fused to obtain the global network service characteristics, including:

[0027] Using the weighted fusion algorithm of dimensional Gaussian components, considering that each node in the network satisfies the independent and identically distributed condition, the goal is to fuse the service characteristics of all nodes in the network to obtain a dimensional Gaussian distribution of the global network service characteristics;

[0028] Assume that the Gaussian component of the service characteristic of node is , and the activity level of node is , and the activity level represents the probability that node generates services within a unit time; by performing normalization processing on , the weights of the service characteristics of each node are obtained , which is expressed as

[0029] ;

[0030] Among them, satisfies and ;

[0031] Based on the weights the service characteristics of all nodes in the network are weighted and fused to obtain the global network service characteristics , and conforms to dimensional Gaussian distribution, that is ; where, the mean vector of the global network service characteristics and the covariance matrix are respectively expressed as

[0032] ;

[0033] ;

[0034] Among them, , among which, represents the th mean value in ; the superscript T represents transpose.

[0035] In one embodiment, a network load model of a distributed mobile ad hoc network is constructed by modeling according to the scale of network nodes, the global network activity level obtained by averaging based on the activity levels of all nodes, and the global network service characteristics, including:

[0036] According to the scale of network nodes , the global network activity level obtained by averaging based on the activity levels of all nodes , and the mean vector of the global network service characteristics for modeling to construct a network load model of a distributed mobile ad hoc network, where t the expectation of the network load at time

[0037] ;

[0038] Among them, is the time-varying network load, is 's expected value; the global network activity level represents the average probability of generating services by all nodes in the network per unit time; is the activity level of node ; is the th i mean value of the service length of node

[0039] A wireless ad hoc network load modeling device, the device includes:

[0040] A load feature extraction module, which is used to consider the distributed mobile ad hoc network scenario and divide and extract multi-dimensional features of the network load, including the scale of network nodes, the node activity level, and the node service characteristics; among them, the scale of network nodes represents the number of nodes in the network, the node activity level represents the probability of a node generating services per unit time, and the node service characteristics describe the random process of the service length generated by the node changing over time;

[0041] A weighted fusion module, which is used to model the service characteristics of each node in the network by using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics;

[0042] A network load modeling module, which is used to perform modeling according to the scale of network nodes, the global network activity level obtained by averaging the activity levels of all nodes, and the global network service characteristics, and construct a network load model for a distributed mobile ad hoc network.

[0043] A computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0044] Considering the distributed mobile ad hoc network scenario, multi-dimensional features of network load are partitioned and extracted, including the scale of network nodes, the node activity level, and the node service characteristics. Among them, the scale of network nodes represents the number of nodes in the network, the node activity level represents the probability of generating services by a node within a unit time, and the node service characteristics describe the random process of the length of services generated by a node changing with time.

[0045] Model the service characteristics of each node in the network using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics.

[0046] Perform modeling according to the scale of network nodes, the global network activity level obtained by averaging the activity levels of all nodes, and the global network service characteristics, and construct a network load model for a distributed mobile ad hoc network.

[0047] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0048] Considering the distributed mobile ad hoc network scenario, multi-dimensional features of network load are partitioned and extracted, including the scale of network nodes, the node activity level, and the node service characteristics. Among them, the scale of network nodes represents the number of nodes in the network, the node activity level represents the probability of generating services by a node within a unit time, and the node service characteristics describe the random process of the length of services generated by a node changing with time.

[0049] Model the service characteristics of each node in the network using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics.

[0050] Perform modeling according to the scale of network nodes, the global network activity level obtained by averaging the activity levels of all nodes, and the global network service characteristics, and construct a network load model for a distributed mobile ad hoc network.

[0051] The above-mentioned method, device, equipment and medium for modeling the network load of a wireless ad hoc network are oriented to the decentralized distributed mobile ad hoc network scenario. The network load is divided into three dimensions: the scale of network nodes, the activity degree of nodes, and the service characteristics of nodes, making the description of the network load more refined and enabling more accurate network characteristics to be obtained. Then, the service characteristics of each node in the network are modeled as a multi-dimensional Gaussian random process and weighted and fused to obtain the global network service characteristics with a 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 beneficial to supporting global analysis and decision-making. Finally, according to the scale of network nodes, the average local network activity degree, and the global network service characteristics, a network load model is constructed. This model can not only fully reflect the heterogeneous characteristic mapping from different tasks to communication services, but also obtain time-varying network characteristics through the observation, estimation, and fusion of services in a distributed network.

[0052] Importantly, the characteristics of the three dimensions of network load can all be obtained through periodic statistics and estimation, making the parameters easy to calculate and obtain when constructing the network load model. Moreover, through the observation of the service of this node, the activity degree and service characteristics mapped by the tasks of this node can be estimated, and the periodic prediction of the service characteristics of the node can be supported. The periodic observation process can reflect the dynamic network environment. Through node feature interaction, the observation and fusion of the characteristic parameters of the distributed mobile ad hoc network can be realized, and the performance impact of different characteristics on the MAC protocol can be fully demonstrated, providing strong support for the adaptive reconstruction of the MAC protocol. Description of the Drawings

[0053] Figure 1 It is a schematic flowchart of a method for modeling the network load of a wireless ad hoc network in an embodiment;

[0054] Figure 2 It is a schematic diagram for comparing the performance differences of the 0.6-CSMA protocol under different network characteristic parameters in an embodiment; among them, 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 network average delay performance comparison curve of the 0.6-CSMA protocol under different network characteristic parameters;

[0055] Figure 3 It is a schematic diagram for comparing the performance differences of the SOTDMA protocol under different network characteristic parameters in an embodiment; among them, Figure 3 (a) is the throughput performance comparison curve of the SOTDMA protocol under different network characteristic parameters, Figure 3(b) is the comparison curve of the packet loss rate performance 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;

[0056] Figure 4 It is the internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0057] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to 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.

[0058] Usually, the service arrival process of each node in a wireless ad hoc network is usually modeled by a Poisson process. The service length is divided into equal lengths, and the average arrival rate (intensity) per unit time is denoted as . Then The probability density function of packets arriving at the channel within the time interval

[0059] ;

[0060] where is the number of services arriving within the interval time , represents the expectation of the number of services arriving at the channel per unit time, . If the service arrival process follows a Poisson process, the arrival interval time should follow an exponential distribution

[0061] .

[0062] where .

[0063] Considering the distributed mobile ad hoc network scenario, the nodes in the network are dynamic, the tasks of different nodes are different, the corresponding node activities are different, and the communication services may cover heterogeneous modal data such as instructions, texts, audios, images, and videos. Simply modeling with a Poisson process with independence, stationarity, and lack of aftereffect is only applicable to nodes with low load and the same service type. For the scenario of different service characteristics caused by different node tasks in a heterogeneous network, the load cannot fully describe the state of the network.

[0064] Therefore, in an embodiment, as Figure 1 shown, the present application provides a method for modeling the network load of a wireless ad hoc network, including the following steps:

[0065] Step S1, considering the distributed mobile ad hoc network scenario, extract the multi-dimensional features of the network load, including the network node scale, node activity level, and node service characteristics.

[0066] 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 services per unit time, and the node service characteristics describe the random process of the service length (also known as the number of data frames) generated by the node changing over time.

[0067] Step S2, use a multi-dimensional Gaussian random process with the same dimension to model the service characteristics of each node in the network, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics.

[0068] Among them, using a multi-dimensional Gaussian random process with the same dimension to model the service characteristics of each node in the network includes:

[0069] Considering that the tasks executed by each node in the network are different, use a multi-dimensional Gaussian random process to model the service characteristics of each node in the network, expressed as , where is the set composed of all sample functions of the service characteristics of a single node at t time, , is the i th sample function, d is the number of sample functions, that is, the dimension number of the Gaussian random process is the same as the number of sample functions, is the period.

[0070] Used to describe the random process of the service length generated by the node changing over time, and the dimensional normal probability density function of this process is expressed as

[0071] ;

[0072] Among them, is the expectation value of, represents any sample function at time; is the expectation value of, represents any sample function at another time; is the variance of the random variable ; is the random variable Variance; Denote the expectation calculation; Is the normalized covariance matrix Determinant of; Denote the th service length generated by the node, Denote the task mapped to d Service characteristic random variables of dimensions; Denote any d Moments; And Both denote the dimension.

[0073] Furthermore, considering that the tasks of each node in the network are different and the service types are relatively fixed, it is assumed that the service characteristics of each node are generally stationary within a finite period of time. At the same time, the service characteristics of the data transmitted in the communication process reflected by different tasks should follow Dimensional Gaussian distribution. Therefore, the service characteristics of each node in the network are modeled as Dimensional stationary Gaussian process. The service characteristics of the stationary Gaussian process satisfy the following constraints:

[0074] ;

[0075] ;

[0076] Where, Is Expected value of; Is the expected value constant, used to indicate that the expected value of the stationary Gaussian process does not change with time; Denote the value of the autocorrelation function at time And ; Is And Expected value of the product of; Is the time difference, that is ; Is the autocorrelation function, which only depends on the time difference .

[0077] Without loss of generality, the service characteristics of each node after modeling are Dimensional Gaussian distribution with the service length as the random vector at any moment. The service lengths of each dimension of node Are described as Dimensional random vector , and the probability density function of its service characteristics is expressed as

[0078] ;

[0079] Where, is t the probability density function of the service characteristics at the time node ; represents the node the mean vector of the lengths of different types of services, represents the node the i th mean of the service length, , the superscript T represents the transpose; is the covariance matrix, used to represent the correlation between the mean vectors of the lengths of different types of services of the node ; represents the covariance matrix determinant.

[0080] After describing the service characteristics of different nodes through the -dimensional Gaussian distribution of each node, the set of probability density functions of the service characteristics of each node in the network is expressed as ; where, is the scale of the network nodes, and .

[0081] To obtain the global network service characteristics at a certain moment, it is necessary to fuse the service characteristics of all nodes in the network. Specifically, using the -dimensional Gaussian component weighted fusion algorithm, considering that each node in the network satisfies the independent and identically distributed condition, the goal is to fuse the service characteristics of all nodes in the network to obtain a -dimensional Gaussian distribution of the global network service characteristics.

[0082] Assume that the Gaussian component of the service characteristics of node is , and the activity level of node is , and the activity level represents the probability that node generates services within a unit time; by normalizing , the weights of the service characteristics of each node are obtained, expressed as

[0083] ;

[0084] where, satisfies and .

[0085] Based on the weight to weight and fuse the service characteristics of all nodes in the network, the global network service characteristics are obtained, and conforms to -dimensional Gaussian distribution, that is ; Among them, the mean vector of the global network service characteristics and the covariance matrix are respectively expressed as

[0086] ;

[0087] ;

[0088] Among them, , among which, represents the i th mean in , and T ; The superscript

[0089] represents transpose. Step S3, based on the network node scale, the global network activity level obtained by averaging the activity levels of all nodes, and the global network service characteristics, a model is built to construct the network load model of the distributed mobile ad hoc network.

[0090] Specifically, according to the network node scale , the global network activity level obtained by averaging the activity levels of all nodes , and the mean vector of the global network service characteristics , a model is built to construct the network load model of the distributed mobile ad hoc network, where t the expectation of the network load at time

[0091] ;

[0092] Among them, is the time-varying network load, is 's expected value; The global network activity level represents the average probability of generating services by all nodes in the network per unit time; is the activity level of node ; is the mean of the th service length of node i .

[0093] Furthermore, substituting the expected value expression of the network load into the Poisson process gives

[0094] ;

[0095] Among them, .

[0096] Through the above network load modeling method, the load is split into features in three dimensions, and the service features of each node are fused to obtain the global network service features, making the way of describing network load more refined and enabling more accurate network features to be obtained. Importantly, the features in the three dimensions can all be obtained through periodic statistics and estimation, reducing the computational complexity of model parameters. By observing the service of this node, the activity level and service features mapped by the node tasks can be estimated, and periodic prediction of the node service features is supported. The periodic observation process can reflect the dynamic network environment. Through node feature interaction, the observation and fusion of the characteristic parameters of the distributed mobile ad hoc network 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.

[0097] In specific implementation, to verify the guiding significance of the network load model constructed in this application for the MAC protocol reconstruction, further simulation experiments were carried out for verification. The focus was on verifying the sufficiency of the model in depicting network load, and the characteristic fusion process and parameter estimation process of heterogeneous services were not emphasized. By simulating the performance curves of the MAC protocol under the same load conditions, the performance differences of the MAC protocol under different features were obtained, which could reflect the limitations of the traditional Poisson distribution modeling arrival process and prove that the network load model constructed in this application could accurately depict network features. The specific simulation experiment process was as follows: Set performance parameters. First, set an observation period time slots, and statistically estimate the network load characteristics within the period , assuming that the network characteristics and load in each period are stable; and taking 5000 time slots as the step length, statistically calculate the throughput, packet loss rate, and network average delay performance of the network for 10 steps, reflecting the advantages and disadvantages of the MAC protocol in terms of effectiveness, reliability, and timeliness.

[0098] Throughput is defined as the number of successfully transmitted data frames per unit time and can be expressed as

[0099] ;

[0100] where represents the number of successfully transmitted received data frames within the statistical period, and represents the number of time slots in the statistical period.

[0101] Packet loss rate is defined as the proportion of the total number of data frames damaged during transmission and discarded due to failure to transmit in time in the total number of generated data frames per unit time and can be expressed as

[0102] ;

[0103] where Indicates the number of data frames damaged during transmission by the node within the statistical period. Indicates the number of data frames discarded by the node due to failure to transmit in time or backlogged in the queue at the end of the period within the statistical period. Indicates the number of data frames generated by all nodes in the network within the statistical period.

[0104] Network average delay Is defined as the average waiting time of all data frames generated in the network in the queue. The waiting time of each node in the send-pending state and the number of times the send process is activated are counted, and the network average delay of all nodes in the network is averaged to obtain the network average delay, which can be expressed as

[0105] ;

[0106] Among them, Is the waiting time of node in the send-pending state within the statistical period, is the number of times the send process is activated.

[0107] Model verification. To more intuitively show 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. Therefore, the dimensional Gaussian process is reduced to a 1-dimensional Gaussian process, and the service length variable at any time follows a 1-dimensional Gaussian distribution. The different performance performances of the competitive MAC protocol and the scheduling MAC protocol are respectively simulated under the condition of the same network load.

[0108] The p-CSMA protocol is selected for simulation for the competitive protocol, and the probability . Three networks with different characteristics are set, and the fused characteristic parameters are shown in Table 1.

[0109] Table 1 Setting of network characteristic parameters for simulation of competitive protocols

[0110]

[0111] Among them, the characteristic variance is randomly generated during the simulation to reflect the error of model parameter estimation and simulate the heterogeneity of service data generated by network nodes. The variance value range of the global network service characteristics is set to . Through the above parameters, the network load is calculated as , , then the expectation of the Poisson distribution is 0.8, and the probability density function is

[0112] ;

[0113] The above equation represents that 0.8 data frames arrive at the channel per unit time (time slot). Although the above three network characteristic parameters are different, they have the same load.

[0114] Figure 2 The throughput, packet loss rate, and network average delay performance curves of the contention-based 0.6-CSMA protocol are given respectively in three different network characteristics. Although the three networks have the same network load, it can be seen from the simulation results that there are significant differences in the protocol performance. Under the same activity level, Network 1 has a large number of nodes and short service data lengths, with lower delay, but poor throughput and packet loss rate performance; Network 3 has fewer nodes and long service data lengths, with better throughput and packet loss rate performance, but 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 collision when accessing the channel, and the competition for time slots results in the collision and loss of data frames. The transmission of short frames also increases the number of times the network nodes compete for the channel, thus intensifying the collision situation. However, this also brings an increase in the opportunity for nodes to access the channel, reducing the average waiting delay. Vice versa.

[0115] The scheduling protocol selects the SOTDMA protocol for simulation. Similarly, three networks with different characteristics are set, and the fused characteristic parameters are shown in Table 2.

[0116] Table 2 Settings of Simulation Network Characteristic Parameters for Scheduling Protocols

[0117]

[0118] Similarly, the variance value range of the global network service characteristics is set to . Through the above parameter calculation, the network load is approximately obtained as , , then the expectation of the Poisson distribution is 1, and the probability density function is . The three networks with different characteristics still have the same load.

[0119] Figure 3The throughput, packet loss rate, and network average delay performance curves of the scheduling-based SOTDMA protocol are respectively given in three different characteristic parameter networks. The three networks also have the same network load characteristics, and the performance differences of the simulation results are still obvious. Network 1 has many nodes, but the nodes are inactive, and the probability of generating traffic is extremely low. Once traffic is generated, it will accumulate a long amount of data. Network 3 has fewer nodes and shorter service frame lengths, but the network nodes are active, and the probability of generating traffic per unit time is relatively large. The characteristics of Network 2 are between the two. Based on the analysis of the access mechanism of the scheduling class, the performance of the SOTDMA protocol in Network 3 should be better than that in 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 the simulation time, showing a trend of converging 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 a large difference in the service queues of each node. As time goes by, the randomness of generating traffic is gradually eliminated, and the difference in the service queues of each node tends to be flat. Due to the inherent characteristics of SOTDMA, the delay performance is highly positively correlated with the number of network nodes.

[0120] Through the simulation of the MAC protocol in different characteristic networks, the different protocol performances under the same load conditions are obtained. From Figure 2 and Figure 3 it can be seen that the same network load of the three networks means the same load parameters of the Poisson distribution. In this application, the network load is split into multi-dimensional characteristics in three dimensions. Although the characteristics are different but the network loads are the same, there are significant differences in performance, which means that the traditional load modeling method based on the Poisson distribution does not depict the network characteristics finely enough and has certain limitations in analyzing the performance of the MAC protocol. In some studies, using the network load parameters as the standard for MAC protocol reconstruction is actually not perfect. The simulation proves that the network load model constructed in this application is effective and sufficient for modeling network characteristics.

[0121] In one embodiment, a wireless ad hoc network load modeling device is provided, including:

[0122] A load characteristic extraction module, which is used to consider the distributed mobile ad hoc network scenario and divide and extract multi-dimensional characteristics of the network load, including the network node scale, node activity level, and node service characteristics. 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 traffic per unit time, and the node service characteristics describe the random process of the length of traffic generated by the node changing over time.

[0123] A weighted fusion module, which is used to model the service characteristics of each node in the network by using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics.

[0124] A network load modeling module, which is used to perform modeling according to the scale of network nodes, the global network activity level obtained by averaging the activity levels of all nodes, and the global network service characteristics, and construct a network load model for a distributed mobile ad hoc network.

[0125] For the specific limitations of the wireless ad hoc network load modeling device, reference can be made to the limitations on the wireless ad hoc network load modeling method in the above text, which will not be elaborated here. Each module in the above wireless ad hoc network load modeling device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0126] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 4 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, it implements a wireless ad hoc network load modeling method.

[0127] Those skilled in the art can understand that Figure 4 the structure shown in

[0128] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0129] Considering the distributed mobile ad hoc network scenario, multi-dimensional features of network load are divided and extracted, including the scale of network nodes, the node activity level, and the node service characteristics; among them, the scale of network nodes represents the number of nodes in the network, the node activity level represents the probability of generating services by nodes within a unit time, and the node service characteristics describe the random process of the change of the service length generated by nodes over time;

[0130] Model the service characteristics of each node in the network using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics;

[0131] Model according to the network node scale, the global network activity level obtained by averaging based on the activity levels of all nodes, and the global network service characteristics, and construct a network load model for the distributed mobile ad hoc network.

[0132] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0133] Considering the distributed mobile ad hoc network scenario, divide and extract multi-dimensional characteristics of the network load, including the network node scale, node activity level, and node service characteristics; 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 services within a unit time, and the node service characteristics describe the random process of the service length generated by the node changing over time;

[0134] Model the service characteristics of each node in the network using a multi-dimensional Gaussian random process with the same dimension, and use the weighted fusion algorithm of multi-dimensional Gaussian components to perform weighted fusion on the service characteristics of all nodes in the network to obtain the global network service characteristics;

[0135] Model according to the network node scale, the global network activity level obtained by averaging based on the activity levels of all nodes, and the global network service characteristics, and construct a network load model for the distributed mobile ad hoc network.

[0136] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0137] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

[0138] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended 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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