Ad-hoc network dynamic networking method and system based on MAC layer

Abstract

The invention relates to the technical field of networking methods, in particular to an ad hoc network dynamic networking method and system based on an MAC layer, and the method comprises the steps: obtaining the topological relation information of each node in a network; obtaining distance information between the nodes; generating a topology sensing matrix based on the topological relation information; calculating a priority matrix according to the topology sensing matrix; evaluating the service capability of each node based on the priority matrix and the distance information; calculating the reliability of each node according to the service capability; based on the reliability, time slots are distributed for all the nodes; generating a routing optimization matrix according to the priority matrix, the reliability and the distance information; the priority, the service capability, the reliability, the time slot allocation result and the routing optimization information of each node are output, and by introducing an innovative priority matrix calculation method, the direct connection relationship of the nodes is considered, and the positions and the influence of the nodes in the whole network are captured, so that the network resource allocation is more reasonable, and the routing optimization efficiency is improved. And the overall throughput of the network is obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of networking methods, and more specifically, to a self-organizing dynamic networking method and system based on a MAC layer. Background Art

[0002] With the rapid development of wireless communication technology, self-organizing networks play an increasingly important role in emerging application fields such as the Internet of Things and the Internet of Vehicles. As the key to achieving efficient and flexible network communication, the MAC layer-based self-organizing network dynamic networking technology has always been the focus of academic and industrial circles.

[0003] Traditional ad hoc networking methods mainly rely on routing layer protocols, such as the classic AODV (Ad hoc On-Dem and Distance Vector) and DSR (Dynamic Source Routing). These methods perform well in static or low-dynamic networks, but often have difficulty coping with frequent topology changes in highly dynamic network environments. Especially in scenarios with high node density and strong mobility, these methods are prone to problems such as excessive routing overhead and poor network adaptability.

[0004] In recent years, researchers have tried to sink the self-organizing network mechanism to the MAC layer in order to obtain faster network response speed and lower protocol overhead. However, the existing self-organizing network methods based on the MAC layer still have some significant technical problems. First, most methods only consider simple topological information and ignore important characteristics such as the service capability and reliability of the nodes, resulting in unreasonable allocation of network resources and difficulty in fully realizing the potential of the network. Secondly, the existing methods are still insufficient in dealing with network dynamics, especially when nodes frequently join, leave or move, it is difficult to adjust the network structure in time, affecting the stability and efficiency of communication. In addition, many methods use simple heuristic algorithms in time slot allocation and routing optimization, which is difficult to achieve satisfactory performance in complex network environments. Summary of the invention

[0005] The present invention aims to solve the above technical problems and proposes an innovative MAC layer-based self-organizing network dynamic networking method and system. The method realizes a more intelligent and efficient network organization by comprehensively considering multiple factors such as network topology, node characteristics, and communication requirements. Specifically, the present invention introduces an innovative priority evaluation mechanism, a service capability calculation method, a reliability evaluation model, and a routing optimization algorithm based on multiple factors, which effectively solves the problems of unreasonable resource allocation, poor network adaptability, and low routing efficiency in the prior art.

[0006] The present invention provides a self-organizing dynamic networking method based on MAC layer. The acquisition steps include: Obtain the topological relationship information of each node in the network; Get the distance information between nodes; Processing steps include: Based on the topological relationship information, generating a topological perception matrix; Calculating a priority matrix according to the topology perception matrix; Based on the priority matrix and the distance information, evaluating the service capability of each node; Calculate the reliability of each node based on the service capability; Allocating a time slot to each node based on the reliability; generating a routing optimization matrix according to the priority matrix, the reliability and the distance information; Output steps include: Output the priority, service capability, reliability, time slot allocation results and routing optimization information of each node.

[0007] Preferably, generating a topology perception matrix specifically includes: Constructing the Matrix ,in Representation Node and nodes The topological relationship between nodes and When directly connected, ,otherwise .

[0008] Preferably, the calculation priority matrix specifically includes: Based on the topology-aware matrix T, a priority matrix P is calculated, where: , in, is the total number of nodes, for The unit matrix, is the topological impact factor Feedback regulator is the topology-aware matrix, is the priority matrix.

[0009] Preferably, the evaluating the service capability of each node specifically includes: For each node i, calculate its service capacity : , in, For Node service capabilities, The priority matrix Middle Line The elements of the column, For nodes and nodes The topological distance between is the distance attenuation adjustment factor, is the total number of nodes.

[0010] Preferably, the calculating the reliability of each node specifically includes: For each node i, calculate its reliability : , Where λ is the scaling factor, For Node Reliability, is the service capability influencing factor, For Node service capabilities, is the topology-aware matrix Middle Line The elements of the column, The priority matrix Middle Line Elements of a column.

[0011] Preferably, allocating a time slot to each node specifically includes: For each node i, calculate its time slot allocation : T , in, For Node The number of time slots allocated, is the total number of available time slots, is the error function, For Node Reliability, is the total number of nodes, represents the floor function, It is the sum of the reliability values ​​of all nodes and is used for normalization.

[0012] Preferably, generating the routing optimization matrix specifically includes: Calculate the routing optimization matrix : , in, is the routing optimization matrix, is the priority matrix, The reliability of each node The diagonal matrix composed of is the physical distance matrix between nodes, is the distance weight factor, Represents element-wise multiplication.

[0013] As a preference, it also includes: Based on the routing optimization matrix Q, selecting an optimal routing path; A network connection is established according to the optimal routing path.

[0014] As a preference, it also includes: The acquisition step, the processing step and the output step are periodically and repeatedly executed to adapt to changes in network topology.

[0015] The self-organizing dynamic networking system based on the MAC layer includes: Get modules for: Obtain the topological relationship information of each node in the network; Get the distance information between nodes; Processing modules for: Based on the topological relationship information, generating a topological perception matrix; Calculating a priority matrix according to the topology perception matrix; Based on the priority matrix and the distance information, evaluating the service capability of each node; Calculate the reliability of each node based on the service capability; Allocating a time slot to each node based on the reliability; generating a routing optimization matrix according to the priority matrix, the reliability and the distance information; Output modules for: Output the priority, service capability, reliability, time slot allocation results and routing optimization information of each node.

[0016] Specifically, the present invention has the following significant beneficial effects: First, by introducing an innovative priority matrix calculation method, the present invention not only considers the direct connection relationship of nodes, but also captures the position and influence of nodes in the entire network. This comprehensive evaluation mechanism makes network resource allocation more reasonable and significantly improves the overall throughput of the network. Experimental results show that compared with traditional methods, the present invention can increase network throughput by more than 25%.

[0017] Secondly, the service capability evaluation and reliability calculation method proposed in this invention cleverly combines factors such as node priority, topological distance, and historical performance. This multi-dimensional evaluation mechanism enables the network to more accurately identify and utilize high-performance nodes while preventing weak nodes from becoming network bottlenecks. This not only improves the overall reliability of the network, but also significantly reduces end-to-end communication delays. Tests show that the delay reduction can reach 37.5%.

[0018] Furthermore, the dynamic time slot allocation mechanism of the present invention realizes more flexible and efficient resource scheduling based on the reliability of nodes and network requirements. This method not only improves spectrum utilization, but also effectively reduces interference between nodes, playing a key role in improving network capacity and communication quality.

[0019] In addition, the routing optimization algorithm proposed by the present invention comprehensively considers multiple factors such as node priority, reliability and physical distance, and can quickly find the optimal data transmission path in a complex and changeable network environment. This not only improves the efficiency of data transmission, but also significantly reduces routing overhead. Test results show that routing overhead can be reduced by 46.7%.

[0020] Finally, the periodic update mechanism of the present invention enables the network to adapt to topology changes in a timely manner and maintain the optimal networking state. This greatly improves the dynamic adaptability of the network, especially in scenarios where nodes frequently move or join / leave the network. Experiments have shown that the network adaptability of the method of the present invention is nearly 50% higher than that of traditional methods.

[0021] In general, the MAC layer-based self-organizing network dynamic networking method and system proposed in the present invention achieves a comprehensive improvement in network performance through the organic combination of a series of innovative algorithms and mechanisms. It not only solves the key problems in the prior art, but also provides a powerful technical solution for self-organizing networks in complex network environments in the future, which has important theoretical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0023] Please refer to Figure 1 The present invention provides a method and system for dynamic self-organizing network based on MAC layer, which can effectively realize the self-organization and dynamic adaptation of network nodes. The specific implementation of the present invention will be described in detail below.

[0024] First, the method of the present invention includes an acquisition step, a processing step and an output step. In the acquisition step, the system acquires the topological relationship information of each node in the network and the distance information between the nodes. This information is crucial for subsequent network organization and optimization. Preferably, the topological relationship information can be obtained by information exchange between nodes, and the distance information can be calculated based on signal strength or Beidou positioning data.

[0025] In the processing step, the method of the present invention first generates a topology perception matrix based on the acquired topology relationship information. This matrix is ​​the basis for all subsequent calculations. Next, based on the topology perception matrix, the system calculates a priority matrix. The priority matrix reflects the relative importance of each node in the network, which has an important impact on resource allocation and routing selection.

[0026] Based on the priority matrix and distance information, the method further evaluates the service capability of each node. Service capability is an important indicator to measure the role of a node in the network. Subsequently, based on the service capability, the system calculates the reliability of each node. Reliability indicators are crucial to ensure the stability and reliability of the network.

[0027] After obtaining the node reliability, this method allocates time slots to each node. Time slot allocation is a key resource management mechanism at the MAC layer, which directly affects the communication efficiency and fairness of the network. Finally, based on the priority matrix, reliability and distance information, the system generates a routing optimization matrix. This matrix provides an important basis for network routing selection.

[0028] In the output step, the system outputs the priority, service capability, reliability, time slot allocation results and routing optimization information of each node. These outputs provide comprehensive guidance for the actual operation and management of the network.

[0029] Furthermore, in the method of the present invention, the step of generating a topology-aware matrix specifically includes constructing a matrix In the matrix middle, Representation Node and nodes The topological relationship between nodes. and nodes When directly connected, The value of is 1, otherwise it is 0. This representation method intuitively reflects the topological structure of the network and facilitates subsequent calculation and analysis.

[0030] To express this process more clearly, it can be described as: define a The square ,in is the total number of nodes in the network. For the matrix Each element in , if the node and nodes There is a direct connection between them (i.e., they are neighbor nodes), then Otherwise, if the node and nodes If there is no direct connection between This binary representation is not only concise and clear, but also can effectively capture the connectivity information of the network. The matrix constructed in this way , can be directly used in subsequent priority matrix calculation, service capability evaluation, reliability calculation and other steps, providing a solid foundation for the entire self-organizing network dynamic networking method. In addition, the topology perception matrix It can also be used to detect isolated nodes in the network (i.e., nodes that are not directly connected to any other nodes) and to identify clusters or subnets in the network, thus providing important reference for routing optimization and resource allocation.

[0031] Assume there is a network with 4 nodes and the connections between the nodes are as follows: Node 1 and node 2 are directly connected, node 2 and node 3 are directly connected, node 3 and node 4 are directly connected, and node 1 and node 4 are not directly connected.

[0032] Then, the corresponding topological perception matrix It can be expressed as: , In this matrix, all elements on the diagonal are 0, because nodes are not directly connected to themselves. Elements on the off-diagonal are 1 or 0 depending on the connection between nodes. This matrix form makes subsequent calculations and analysis more intuitive and efficient.

[0033] In the step of calculating the priority matrix, the present invention adopts an innovative calculation method. Specifically, the calculation formula of the priority matrix P is as follows: in, is the total number of nodes, is the identity matrix, and is the adjustment parameter, and This formula comprehensively considers the direct connection relationship and indirect influence of nodes, and captures the global characteristics of the network through matrix inversion operations.

[0034] Priority Matrix Used to quantify the relative importance of each node in the network. In an ad hoc network, the priorities of different nodes affect the path selection and resource allocation of data transmission. By calculating the priority matrix, it can ensure that high-priority nodes can obtain more resources and better service quality.

[0035] By introducing and Parameters can flexibly adjust the impact of topology and feedback mechanism on priority. For example, when the network topology changes, the priority matrix can be quickly updated through adjustment to ensure the adaptability and robustness of the network.

[0036] Priority Matrix It can help the system allocate resources according to the importance of nodes. High-priority nodes can obtain more bandwidth, time slots and other resources, thereby improving the performance and service quality of the entire network. Through the priority matrix, it can ensure that key nodes have a higher priority in the network, thereby reducing data transmission delays and packet loss rates, and improving network reliability and stability.

[0037] In practical applications, and The choice of has a significant impact on the calculation results. Usually, you can choose As the initial value. These parameters can be adjusted according to the specific situation of the network. For example, in a network with a high node density, to strengthen the impact of direct connection; in the case of a more complex network structure, you can increase to strengthen the role of indirect connection.

[0038] An important advantage of this calculation method is that it can fully reflect the importance of nodes in the network. It not only takes into account the number of direct connections of the node, but also captures the position and influence of the node in the entire network through matrix operations. This is of great significance for subsequent routing selection and resource allocation.

[0039] Through the above steps, the method of the present invention can effectively analyze the network topology, calculate the node priority, and lay the foundation for subsequent network organization and optimization. This MAC layer-based self-organizing network dynamic networking method has strong adaptability and flexibility, and can achieve efficient network organization and management in a complex and changeable network environment. In a preferred embodiment of the present invention, based on the priority matrix calculated above, the method further evaluates the service capability of each node. Specifically, for each node i, its service capability The calculation formula is as follows: , in, The service capability of the node. is the priority matrix P Line The elements of the column, is the topological distance between nodes, is the distance attenuation adjustment factor, is the total number of nodes.

[0040] Service capability evaluation is used to measure the processing and transmission capabilities of each node in the network. In an ad hoc network, the service capabilities of different nodes vary greatly. Reasonable evaluation of the service capability of each node can help the system better allocate tasks and resources and improve overall efficiency.

[0041] By introducing the distance attenuation term , which can effectively reduce the impact of distant nodes on service capabilities. This design makes the service capability of a node depend not only on its priority, but also on the distance to its neighboring nodes, which is more in line with the actual network environment. The service capability evaluation formula can be updated in real time according to changes in the network topology, ensuring that the service capability of each node always matches the current network status. This is especially important for ad hoc networks because the network topology may change frequently. By evaluating the service capability of each node, the system can assign tasks to the most suitable nodes and avoid overloading or idling some nodes, thereby improving the resource utilization and service quality of the entire network.

[0042] This formula cleverly combines the node priority and topological distance, and simulates the impact of distance on service capacity through an exponential decay function. The choice of has an important influence on the calculation results. Usually, you can choose As the initial value. In the case of a large network or a dispersed distribution of nodes, it can be appropriately increased value to mitigate the attenuation effect of distance on service capacity.

[0043] Reliability calculation is used to evaluate the stability and reliability of each node in the network. In an ad hoc network, the reliability of the node directly affects the success rate of data transmission and the overall performance of the network. By calculating the reliability of the node, it can ensure that key tasks are assigned to reliable nodes and reduce the risk of data transmission failure.

[0044] After obtaining the service capacity of each node, the method of the present invention further calculates the reliability of the node. For each node i, its reliability The calculation formula is as follows: , in, is the reliability of node i, is the service capability influencing factor, is the service capability of node i, is the element in the i-th row and j-th column of the topology-aware matrix T, is the element in the jth row and ith column of the priority matrix P.

[0045] The reliability calculation formula takes into account the service capability of a node and the priority of its neighboring nodes. In this way, not only the reliability of a single node can be evaluated, but also the connectivity and stability of the entire network can be taken into account.

[0046] Non-linear mapping: By using This nonlinear mapping function can smoothly map service capabilities to reliability values, avoiding the impact of extreme values ​​on the results. This design makes reliability assessment more robust and accurate. By calculating the reliability of nodes, the system can select the most reliable nodes as relay nodes for data transmission, reducing the risk of data transmission failure and improving the robustness and fault tolerance of the network.

[0047] This formula converts service capacity into a component of reliability through the Sigmoid function, taking into account the connectivity of the node with other nodes. The choice of has a significant impact on the reliability calculation results. Usually, In a network with high node reliability requirements, the value can be appropriately increased. to enhance the impact of service capability on reliability.

[0048] Time slot allocation is used to allocate communication time slots to each node to ensure that multiple nodes do not conflict at the same time. In an ad hoc network, reasonable time slot allocation can improve channel utilization, reduce interference, and increase network throughput and response speed.

[0049] After obtaining the node reliability, the method of the present invention further allocates time slots to each node. For each node i, its time slot allocation The calculation formula is as follows: , in, The number of time slots allocated to node i, is the total number of available time slots, erf is the error function, is the reliability of node i, is the total number of nodes, represents the floor function, It is the sum of the reliability values ​​of all nodes and is used for normalization.

[0050] By introducing the error function , the reliability value can be smoothly mapped to the time slot allocation, ensuring that high-reliability nodes get more time slots, while low-reliability nodes get fewer time slots. This design not only guarantees the priority of high-reliability nodes, but also takes into account the needs of other nodes, achieving fair allocation. Through reasonable time slot allocation, it is possible to avoid multiple nodes competing for the same channel at the same time, reduce the number of collisions and retransmissions, and thus improve channel utilization and network throughput. The time slot allocation formula can be updated in real time according to the reliability of the node, ensuring that when the network topology changes, the time slot allocation can quickly adapt to the new network status and maintain efficient communication performance.

[0051] This formula converts node reliability into the proportion of time slot allocation through an error function, ensuring smoothness and fairness of allocation. In practical applications, the selection of the total number of available time slots M needs to be determined based on the scale of the network and communication requirements. For example, for a medium-sized network with 50 nodes, M=1000 can be selected as the initial value.

[0052] Route Optimization Matrix It is used to select the best transmission path to ensure that data can be transmitted efficiently in the network. In ad hoc networks, route optimization is a key link to improve network performance. By generating a route optimization matrix, the best next-hop node can be selected for each node to reduce transmission delay and energy consumption.

[0053] Finally, the method of the present invention generates a routing optimization matrix. The calculation formula of the routing optimization matrix Q is as follows: , in, is the routing optimization matrix, is the priority matrix, The reliability of each node The diagonal matrix composed of is the physical distance matrix between nodes, is the distance weight factor, Represents the Hadamard product (element-wise multiplication) operation.

[0054] Route Optimization Matrix The node priority, reliability and physical distance are taken into account at the same time to ensure that the selected path is not only the shortest but also the most reliable. This comprehensive optimization method can significantly improve the success rate and efficiency of data transmission. , which can effectively reduce the delay caused by long-distance transmission and ensure that data can reach the destination in a short time. It can also be used for energy-saving optimization. By selecting a node that is closer to the node as the next hop, energy consumption can be reduced and the battery life of the node can be extended, which is particularly suitable for energy-constrained ad hoc network environments.

[0055] This formula takes into account node priority, reliability, and physical distance, providing comprehensive guidance for route selection. The choice of has an important impact on the routing optimization results. Usually, you can choose As the initial value. In networks with high requirements for transmission delay, it can be appropriately increased to strengthen the influence of the distance factor.

[0056] Through the above steps, the method of the present invention realizes the whole process from network topology analysis to route optimization. This MAC layer-based self-organizing network dynamic networking method has strong adaptability and flexibility, and can realize efficient network organization and management in a complex and changeable network environment. In particular, the present method provides a comprehensive and effective solution for the dynamic organization of the network by comprehensively considering multiple factors, such as node priority, service capability, reliability and physical distance. In another preferred embodiment of the present invention, based on the aforementioned generated route optimization matrix Q, the present method further realizes the selection of the optimal routing path and the establishment of network connection. Specifically, for the communication between any two nodes i and j, the system will select the optimal routing path based on the corresponding element values ​​in the matrix Q. Preferably, the Dijkstra algorithm or the A* algorithm can be used to realize the search for the shortest path, wherein the weight of the path is determined by the corresponding elements of the matrix Q.

[0057] After the routing path selection is completed, the method will establish a network connection according to the selected path. This process involves frame exchange and handshake mechanism of the MAC layer. Preferably, the RTS / CTS (Request to Send / Clear to Send) mechanism can be used to avoid the hidden terminal problem and improve the communication efficiency of the network. In addition, the method can also dynamically adjust the transmission power of the RTS / CTS frame according to the value of the routing optimization matrix Q to balance the communication quality and energy consumption.

[0058] Taking into account the dynamic change characteristics of the network topology, the method of the present invention will periodically repeat the aforementioned acquisition step, processing step and output step in practical applications. This periodic update mechanism enables the network to adapt to topology changes in a timely manner and maintain the optimal networking state. Preferably, the update cycle can be adjusted according to the dynamic degree of the network. For example, in a scenario where nodes move frequently, a shorter update cycle, such as 10 seconds, can be selected; while in a relatively stable network, the update cycle can be appropriately extended, such as 30 seconds or longer.

[0059] In addition, this method can also dynamically adjust the update frequency according to the network load. When the network load is detected to be high, the update frequency can be appropriately reduced to reduce additional computing and communication overhead; when the network load is low, the update frequency can be increased to improve the network's response speed to topology changes.

[0060] In the system embodiment of the present invention, the MAC layer-based self-organizing dynamic networking system is designed to implement the above method. The system mainly includes an acquisition module 1, a processing module 2 and an output module 3.

[0061] The acquisition module 1 is responsible for acquiring the topological relationship information of each node in the network and the distance information between the nodes. Preferably, the module can collect network topology information by regularly broadcasting detection frames and estimate the distance between nodes by measuring the received signal strength (RSSI). In some embodiments, if the node is equipped with a Beidou module, the acquisition module can also directly use Beidou data to obtain more accurate location information.

[0062] Processing module 2 is the core of the system and is responsible for performing a series of complex computing tasks. First, it generates a topology perception matrix based on the acquired topological relationship information. Then, the priority matrix is ​​calculated based on the topology perception matrix. Next, the processing module will evaluate the service capability of each node, calculate the node reliability, allocate time slots to the nodes, and finally generate a routing optimization matrix. These calculation processes involve complex operations such as matrix operations and nonlinear function calculations. Therefore, in actual implementation, high-performance embedded processors or FPGAs can be considered to improve computing efficiency.

[0063] The output module 3 is responsible for outputting the processing results, including the priority, service capability, reliability, time slot allocation results and routing optimization information of each node. These output information can be intuitively displayed to the network administrator through the system's user interface, or can be passed to other network management tools or upper-layer applications through standard interfaces.

[0064] It is worth noting that the modules of this system do not operate in isolation, but work closely together and cooperate with each other. For example, the network information collected by the acquisition module will directly affect the calculation results of the processing module, and the output of the processing module will guide the acquisition module to adjust its information collection strategy. This kind of collaborative work between modules ensures that the system can efficiently and flexibly cope with complex and changing network environments.

[0065] In general, the MAC layer-based self-organizing network dynamic networking method and system provided by the present invention have significant innovation and practicality. By comprehensively considering factors such as network topology, node characteristics, and communication requirements, the present invention provides a comprehensive and effective solution for self-organizing networks in complex network environments. In particular, the method and system of the present invention have shown obvious advantages in improving network communication efficiency, enhancing network adaptability, and optimizing resource allocation, providing important technical support for the development of emerging application fields such as the Internet of Things and the Internet of Vehicles in the future.

[0066] In order to verify the superiority of the MAC layer-based self-organizing network dynamic networking method proposed in the present invention, a series of theoretical analysis and experimental verification are carried out. The following will introduce in detail the comparative experiment of the embodiment of the present invention and the traditional method.

[0067] Embodiment 1: In this embodiment, the aforementioned MAC layer-based self-organizing dynamic networking method is adopted. The specific parameters are set as follows: topology influence factor α=0.3, feedback adjustment factor β=0.2, distance attenuation adjustment factor σ=2, service capacity influence factor λ=0.5, distance weight factor γ=0.1. The network scale is set to 50 nodes, and the total number of available time slots M=1000.

[0068] Comparative Example 1: For comparison, the widely used Ad hoc On-Demand Distance Vector (AODV) routing protocol is selected as a benchmark. AODV is a typical on-demand routing protocol with good adaptability, but does not have the characteristics of priority evaluation and service capacity calculation in the method of the present invention.

[0069] Experimental setup: A simulation environment was built to simulate a dynamically changing wireless network. Nodes were randomly distributed in an area of ​​100m x100m and moved randomly at a speed of 0-2m / s. The experiment lasted for 1 hour and data was recorded every 5 minutes. The main test indicators included: network throughput, end-to-end delay, routing overhead, and network adaptability.

[0070] The experimental method is as follows: 1. Network throughput: It measures the amount of data successfully transmitted per unit time by generating random data streams in the network.

[0071] 2. End-to-end delay: measures the average transmission time of a data packet from the source node to the destination node.

[0072] 3. Routing overhead: Calculate the proportion of control messages generated by routing protocols to total network traffic.

[0073] 4. Network resilience: measuring the time it takes for the network to return to normal operation by suddenly removing and adding nodes.

[0074] The experimental results are as follows: Test indicators Example 1 Comparative Example 1 Performance Improvements Network throughput 7.8 Mbps 6.2 Mbps 25.8% End-to-end latency 45 ms 72 ms 37.5% Routing cost 8% 15% 46.7% Network adaptability 2.5 s 4.8 s 47.9% The experimental results are analyzed as follows: 1. Network throughput: The method of the present invention improves the network throughput by 25.8% compared with the traditional AODV method. This is mainly due to the priority matrix and service capability evaluation mechanism in the method of the present invention, which enables more efficient use of network resources.

[0075] 2. End-to-end delay: The method of the present invention reduces the end-to-end delay by 37.5%. This is because the method comprehensively considers node priority, reliability and physical distance through the routing optimization matrix and can select a better transmission path.

[0076] 3. Routing overhead: The routing overhead of the method of the present invention is reduced by 46.7% compared with the traditional method. This shows that the method is more efficient in maintaining network topology and routing information and reduces unnecessary control message transmission.

[0077] 4. Network adaptability: When faced with changes in network topology, the method of the present invention can restore normal network operation faster than traditional methods, and its adaptability is improved by 47.9%. This is due to the periodic update mechanism of the method and the comprehensive consideration of the dynamic characteristics of nodes.

[0078] These experimental results fully demonstrate the superiority of the method of the present invention. In particular, the method shows significant advantages in terms of network adaptability, which is particularly important for dynamically changing wireless network environments. The method of the present invention successfully improves the overall performance of the network by comprehensively considering multiple factors and adopting innovative algorithms.

[0079] It is worth noting that the method of the present invention has achieved significant improvements in all indicators, which shows that the method not only performs well in a specific aspect, but also improves the overall network performance. This comprehensive performance improvement is of great significance for complex practical application scenarios, such as the Internet of Things or the Internet of Vehicles.

[0080] Through further parameter optimization, it is found that when the topology influence factor α=0.35, the feedback adjustment factor β=0.18, the distance attenuation adjustment factor σ=2.2, the service capacity influence factor λ=0.55, and the distance weight factor γ=0.08 are set, the method of the present invention can achieve the best performance. Under this parameter configuration, the network throughput is further improved to 8.2 Mbps, the end-to-end delay is reduced to 41 ms, the routing overhead is reduced to 7.5%, and the network adaptability is improved to 2.2 s.

[0081] In general, these experimental results and analysis fully prove that the proposed MAC layer-based self-organizing network dynamic networking method has significant advantages in improving network performance, reducing communication overhead and enhancing network adaptability. This method provides a powerful technical solution for self-organizing networks in complex network environments in the future.

[0082] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-organizing dynamic networking method based on MAC layer, characterized in that: include: The acquisition steps include: Obtain the topological relationship information of each node in the network; Get the distance information between nodes; Processing steps include: Based on the topological relationship information, generating a topological perception matrix; Calculating a priority matrix according to the topology perception matrix; Based on the priority matrix and the distance information, evaluating the service capability of each node; Calculate the reliability of each node based on the service capability; Allocating a time slot to each node based on the reliability; generating a routing optimization matrix according to the priority matrix, the reliability and the distance information; Output steps include: Output the priority, service capability, reliability, time slot allocation results and routing optimization information of each node.

2. The method according to claim 1, characterized in that: The generating of the topology perception matrix specifically includes: Constructing the Matrix ,in Representation Node and nodes The topological relationship between nodes and When directly connected, ,otherwise .

3. The method according to claim 2, characterized in that The calculation priority matrix specifically includes: Based on the topology-aware matrix T, a priority matrix P is calculated, where: , in, is the total number of nodes, for The unit matrix, is the topological impact factor Feedback regulator is the topology-aware matrix, is the priority matrix.

4. The method according to claim 3, characterized in that The evaluation of the service capability of each node specifically includes: For each node i, calculate its service capacity : ， in, For Node service capabilities, The priority matrix Middle Line Elements of the column, For Node and nodes The topological distance between is the distance attenuation adjustment factor, is the total number of nodes.

5. The method according to claim 4, characterized in that The calculation of the reliability of each node specifically includes: For each node i, calculate its reliability : ， Where λ is the scaling factor, For Node Reliability, is the service capability influencing factor, For Node service capabilities, is the topology-aware matrix Middle Line Elements of the column, The priority matrix Middle Line Elements of a column.

6. The method according to claim 5, characterized in that The allocating time slots to each node specifically includes: For each node i, calculate its time slot allocation : T ， in, For Node The number of time slots allocated, is the total number of available time slots, is the error function, For Node Reliability, is the total number of nodes, represents the floor function, It is the sum of the reliability values ​​of all nodes and is used for normalization.

7. The method according to claim 6, characterized in that Generating the routing optimization matrix specifically includes: Calculate the routing optimization matrix : , in, is the routing optimization matrix, is the priority matrix, The reliability of each node The diagonal matrix composed of is the physical distance matrix between nodes, is the distance weight factor, Represents element-wise multiplication.

8. The method according to claim 7, characterized in that Also includes: Based on the routing optimization matrix Q, selecting an optimal routing path; A network connection is established according to the optimal routing path.

9. The method according to claim 8, characterized in that Also includes: The acquisition step, the processing step and the output step are periodically and repeatedly executed to adapt to changes in network topology.

10. A MAC layer-based self-organizing dynamic networking system that executes the method according to any one of claims 1 to 9, characterized in that: include: Get modules for: Obtain the topological relationship information of each node in the network; Get the distance information between nodes; Processing modules for: Based on the topological relationship information, generating a topological perception matrix; Calculating a priority matrix according to the topology perception matrix; Based on the priority matrix and the distance information, evaluating the service capability of each node; Calculate the reliability of each node based on the service capability; Allocating a time slot to each node based on the reliability; generating a routing optimization matrix according to the priority matrix, the reliability and the distance information; Output modules for: Output the priority, service capability, reliability, time slot allocation results and routing optimization information of each node.

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