A wireless mesh network reliability assurance method based on adaptive network coding

Abstract

The present invention provides a method for ensuring the reliability of a wireless mesh network based on adaptive network coding. The method includes initializing routing, dynamic weight adjustment, link selection, and network coding and data transmission steps. First, in the initializing routing step, each node discovers neighboring nodes and establishes a mesh network structure. In the dynamic weight adjustment step, the weight matrix is ​​adjusted according to different service types (such as video streaming, audio streaming, etc.) to optimize transmission reliability and resource utilization. In the link selection step, a state matrix is ​​constructed and the E-D algorithm is used to select the optimal link path to improve transmission reliability and efficiency. In the network coding and data transmission step, the Gaussian process (GP) model is used to calculate the optimal coding degree and redundancy to adapt to dynamic changes in the network environment and improve transmission robustness. The present invention utilizes a software-defined network (SDN) controller to uniformly manage routing, enabling flexible adjustment of link selection strategies based on service needs. By collecting status information of each node in the network, the system adaptively adjusts the node's coding parameters and weight configuration, and selects the optimal route based on node performance. This method effectively reduces packet loss and delay during data packet transmission, improving the transmission efficiency and reliability of multi-hop wireless mesh networks.

Description

Technical Field

[0001] The present invention belongs to the field of wireless network communication technology, and in particular relates to a wireless mesh network reliability assurance method and system based on adaptive network coding. The method realizes efficient and reliable data transmission in a multi-hop wireless mesh network through cross-layer information perception, dynamic weight adjustment and network coding. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and wireless communication technologies, application scenarios such as smart homes, smart transportation, and telemedicine are constantly emerging, driving growing user demand for efficient, stable, and low-latency data transmission over wireless networks. Wireless mesh networks, as multi-hop self-organizing network structures, have become a crucial network architecture for meeting these demands thanks to their high bandwidth, flexibility, and self-healing capabilities. Widely used in smart cities, emergency communications, industrial monitoring, and other fields, wireless mesh networks provide reliable network access for a wide range of devices.

[0003] Although wireless mesh networks offer a degree of flexibility and adaptability, multi-hop transmission often suffers from channel interference, network congestion, and node failure, leading to unstable transmission and failure to meet High Quality of Service (QoS) requirements. Traditional routing protocols struggle to quickly respond to and adapt to diverse service needs in dynamic environments and often fail to provide sufficient transmission reliability.

[0004] The introduction of software-defined networking (SDN) technology can significantly enhance the flexibility and controllability of wireless mesh networks. Through the global view provided by the SDN controller, the network can dynamically adjust routing policies based on information such as service type and link status to accommodate varying QoS requirements. However, achieving fast, accurate path selection and reliable data transmission in mesh networks remains a key technical challenge.

[0005] As an effective technology for mitigating interference and improving data transmission efficiency, network coding can further enhance the reliability of multi-hop mesh networks. Introducing network coding into data transmission can significantly reduce packet loss, improve transmission efficiency, and enhance network robustness in complex environments. Therefore, combining the centralized control advantages of SDN with the efficient transmission characteristics of network coding, developing a wireless mesh network reliability assurance method based on adaptive network coding is crucial for improving the transmission performance of multi-hop wireless networks. Summary of the Invention

[0006] Purpose of the present invention: This invention aims to improve the reliability and transmission efficiency of wireless mesh networks. By combining software-defined networking (SDN) and network coding technologies, introducing network dynamics and service demand dynamics, integrating multi-dimensional cross-layer information perception into the routing process, and performing on-demand routing based on different service types, this invention provides a wireless mesh network reliability assurance method based on adaptive network coding.

[0007] To achieve the above objectives, the present invention provides a wireless multi-hop routing fast decision method based on cross-layer information perception, comprising the following steps:

[0008] Step S1: Initialize routing and node status discovery: Each node discovers neighboring nodes, builds a Mesh network, and records the status information of each node;

[0009] Step S2, dynamic weight adjustment and comprehensive matrix calculation step: according to different business requirements, adjust the weights of each matrix and calculate the comprehensive matrix to provide a basis for link selection;

[0010] Step S3, link selection step: using the ED algorithm to select the optimal transmission path according to the link state matrix;

[0011] Step S4, network coding matrix generation and data coding step: Calculate the optimal coding degree k and redundancy r through the Gaussian process (GP) model, generate the coding matrix and encode the data;

[0012] Step S5, data transmission and reception step: data transmission is performed based on the encoded data packet, and decoding is performed at the receiving end to restore the original data.

[0013] A further improvement of the present invention is that step S1 further includes the following steps:

[0014] Step S1.1: Each node periodically uploads its own status information to the network controller, including link quality (TQ value), signal strength, and adjacency relationship;

[0015] Step S1.2: The network controller maintains the adjacency matrix, signal strength matrix, and TQ value matrix based on the information uploaded by the nodes;

[0016] Step S1.3: The controller maintains the above matrix in different frequency bands respectively to support link selection requirements of different frequency bands.

[0017] A further improvement of the present invention is that step S2 further includes the following steps:

[0018] Step S2.1: The network controller assigns different weights to the adjacency matrix, signal strength matrix, and TQ value matrix based on the service type to reflect the priority of the service demand;

[0019] Step S2.2: For different service types (e.g., video streaming, audio streaming, file transfer), dynamically adjust the weight matrix to meet the different service requirements for reliability and latency;

[0020] Step S2.3: Calculate a comprehensive matrix based on the adjusted weights to serve as basic data for link selection.

[0021] A further improvement of the present invention is that step S3 further includes the following steps:

[0022] Step S3.1, observe the state matrix of each frequency band and select the frequency band;

[0023] Step S3.2: Use the ED algorithm to evaluate the link state matrix and select the optimal link path that meets the transmission requirements.

[0024] A further improvement of the present invention is that step S4 further includes the following steps:

[0025] Step S4.1, calculating the coding degree k and redundancy r by using a Gaussian process (GP) model to optimize the objective function, thereby determining the key parameters of the network coding;

[0026] Step S4.2: data partitioning, generating a k×k identity matrix and an r×k random matrix to construct the coding matrix required for network coding;

[0027] Step S4.3: synthesize a coding matrix for encoding the data packet, wherein the coding matrix is ​​obtained by a linear combination of an identity matrix and a random matrix or a matrix operation;

[0028] Step S4.4: Encode the data packet to be transmitted according to the encoding matrix and then send it to the destination node.

[0029] A further improvement of the present invention is that step S5 further includes the following steps:

[0030] Step S5.1: After receiving the encoded data packet, the receiving node extracts the packet header information and records the transmission path and link quality of the data packet;

[0031] Step S5.2: If the number of data packets is less than k, resend the packet to ensure the reliability of data transmission;

[0032] Step S5.3: The receiving node decodes the data packet using the encoding matrix to restore the original data.

[0033] A further improvement of the present invention is that in step S4.1, the objective function is: f(x)=max(λ1P success -λ2B-λ3t delay), which includes the following input features: coding degree k: refers to the number of coding blocks after the data packet is divided, which affects the reliability of transmission; redundancy r: refers to the number of redundant data packets generated by network coding, which is used to enhance the reliability of transmission; packet loss rate P loss :Used to calculate the reception success rate: Bandwidth B: used to evaluate the transmission capacity of the link; delay t delay : Used to measure the delay performance of the link; weight parameters λ1, λ2, λ3: correspond to the importance of coding degree, redundancy, reception success rate, bandwidth and delay in the objective function, respectively, and are used to dynamically adjust the link selection strategy in different business scenarios to meet the needs of specific businesses.

[0034] A further improvement of the present invention is that, in step S4, the GP model is used to dynamically calculate the coding degree k and redundancy r, and these two parameters are optimized by the RBF kernel function and the expected improvement (EI) criterion to ensure optimal coding efficiency and reliability under different network conditions and business requirements.

[0035] A further improvement of the present invention is that the calculation of the GP model includes the following steps:

[0036] Step S4.1.1, define the kernel function of the GP model as Where α0 and t are the amplitude and length parameters of the kernel function, which are used to measure the similarity between input features;

[0037] Step S4.1.2, assuming the mean function m(x) = 0 to simplify the model calculation;

[0038] Step S4.1.3: Calculate the optimal values ​​of the coding degree k and redundancy r based on the maximization of the acquisition function, maximize the expected improvement (EI) and expected reduction (ED), and improve the coding redundancy and transmission reliability of the network.

[0039] In order to achieve the purpose of the invention, the present invention also provides a system for implementing the above-mentioned wireless mesh network reliability assurance method based on adaptive network coding.

[0040] The present invention has the following beneficial effects: It utilizes software-defined networking technology, enabling the controller to centrally manage routing. It also introduces network coding technology, dynamically adjusting coding parameters using a Gaussian process model to improve data transmission stability. Based on cross-layer information perception and dynamic weight adjustment, the system implements on-demand routing tailored to different service requirements, reducing latency and improving the reliability and efficiency of data packet transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a diagram showing the implementation of the wireless Mesh network reliability algorithm based on adaptive network coding provided by the present invention.

[0042] Figure 2 This is a diagram of the specific process of network coding.

[0043] Figure 3 This is the algorithm flow chart provided by the present invention, which shows the overall process of routing initialization, maintenance, adaptive coding and data transmission in the network. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] It should be emphasized that, in the process of describing the present invention, various formulas and constraints are distinguished by using consistent labels, but it is not excluded that different labels are used to mark the same formulas and / or constraints. The purpose of this setting is to more clearly illustrate the characteristics of the present invention.

[0046] like Figure 1 As shown in the figure, the present invention provides a wireless mesh network reliability algorithm based on adaptive network coding. A network controller is added to the traditional mesh network, and the network global information is collected by the network controller. Then, network coding technology is introduced to improve the transmission reliability and efficiency of the network. The addition of network coding allows the data packet to carry not only its own information but also some redundant information during the transmission process. Figure 2 As shown, when data packet loss or network congestion occurs, the receiving node can use redundant information to recover data, reducing the number of retransmissions. This approach greatly reduces the possibility of data loss and enhances the robustness of the network in complex and dynamic environments. Specifically, the network controller dynamically calculates the optimal coding degree and redundancy for each path based on the collection of node status information across the entire network to meet the needs of different service types. For example, for IoT and voice services with high reliability requirements, the controller will increase redundancy to ensure data integrity; while for video services that require high throughput, it will improve transmission efficiency while maintaining a certain degree of reliability. In this way, through the combination of network coding and on-demand routing, the present invention effectively solves the common problems of packet loss, delay and network congestion in multi-hop wireless mesh networks, and comprehensively guarantees the quality of service (QoS) of various services.

[0047] The adaptive network coding algorithm process of the present invention is as follows Figure 2 As shown, the following steps are included:

[0048] The first part is the initialization routing, maintenance and link selection part:

[0049] Step S1: Initialize routing and node status discovery: Each node discovers neighboring nodes, builds a Mesh network, and records the status information of each node; specifically:

[0050] Step S1.1: Each node periodically uploads its own status information to the network controller, including link quality (TQ value), signal strength, and adjacency relationship;

[0051] Step S1.2: The network controller maintains the adjacency matrix, signal strength matrix, and TQ value matrix based on the information uploaded by the nodes;

[0052] Step S1.3: The controller maintains the above matrix in different frequency bands respectively to support link selection requirements of different frequency bands.

[0053] Step S2, dynamic weight adjustment and comprehensive matrix calculation step: According to the different requirements of the service type, the weights of each matrix are adjusted and the comprehensive matrix is ​​calculated to provide a basis for link selection; specifically:

[0054] Step S2.1: The network controller assigns different weights to the adjacency matrix, signal strength matrix, and TQ value matrix based on the service type to reflect the priority of the service demand;

[0055] Step S2.2: For different service types (e.g., video streaming, audio streaming, file transfer), dynamically adjust the weight matrix to meet the different service requirements for reliability and latency;

[0056] Step S2.3: Calculate a comprehensive matrix based on the adjusted weights to serve as basic data for link selection.

[0057] Step S3, link selection step: using the ED algorithm to select the optimal transmission path according to the link state matrix; specifically:

[0058] Step S3.1, observe the state matrix of each frequency band and select the frequency band;

[0059] Step S3.2: Use the ED algorithm to evaluate the link state matrix and select the optimal link path that meets the transmission requirements.

[0060] The second part is the adaptive network coding part:

[0061] Step S4, network coding matrix generation and data coding step: The coding degree k and redundancy r are calculated by the Gaussian process (GP) model to optimize the objective function, generate the coding matrix and encode the data; specifically:

[0062] Step S4.1: Calculate the coding degree k and redundancy r using a Gaussian process (GP) model to optimize the objective function, thereby determining key parameters of network coding.

[0063] When the controller receives a transmission request from a source node, it first determines the transmission service type and then determines whether the node already has coding degree and redundancy. If not, the initialized coding degree and redundancy are directly sent to the source node. Otherwise, the controller selects the feasible range of coding degree and redundancy and the threshold of the objective function according to the service type. If the objective function value f(x)=max(λ1P success -λ2B-λ3t delay ) reaches the threshold, then the information of maintaining the original coding degree and redundancy is directly sent down; otherwise, the Gaussian process (GP) model is used to calculate the coding degree k and redundancy r. The specific process is to calculate the acquisition function values ​​of all possible points based on the current Gaussian process model (different feasible sets are selected according to different business types), find the largest point as the output (this point has the greatest possibility of improving the objective function), send the found maximum expected coding degree and redundancy to the source node, record its objective function value after the data transmission is completed, and based on the current transmission result (X new ,f new ), update the Gaussian process model.

[0064] Step S4.2: Data partitioning to generate a k×k identity matrix and an r×k random matrix for constructing the coding matrix required for network coding;

[0065] After receiving the data stream from the terminal, the source node divides the data into blocks according to the data threshold T for encoding and transmission. The source node continuously receives data and puts the received data into a buffer. Whenever the data in the buffer reaches the threshold T, it extracts T bytes of data and regards it as a data block. The data block is further divided into multiple original data packets according to the size of the data packet. When the source node receives the message sent by the controller, it generates a k×k identity matrix based on the encoding degree and an r×k random matrix based on the redundancy to encode the data packet. The data range in the random matrix is ​​GF(256).

[0066] Step S4.3: synthesize a coding matrix for encoding the data packet, wherein the coding matrix is ​​obtained by a linear combination of an identity matrix and a random matrix or a matrix operation;

[0067] Step S4.4: Encode the data packet to be transmitted according to the encoding matrix and then send it to the destination node.

[0068] The data packet to be transmitted is encoded according to the coding matrix. At the same time, the header of each coded packet needs to be supplemented with metadata (coding coefficients (row vectors of matrix G), packet sequence number, data block sequence number) before it can be transmitted.

[0069] The third part is the data transmission and reception part:

[0070] Step S5, data transmission and reception step: data transmission is performed based on the encoded data packet, and decoding is performed at the receiving end to restore the original data; specifically:

[0071] Step S5.1: After receiving the encoded data packet, the receiving node extracts the packet header information and records the transmission path and link quality of the data packet;

[0072] Step S5.2: If the number of data packets is less than k, resend the packet to ensure the reliability of data transmission;

[0073] Step S5.3: The receiving node decodes the data packet using the encoding matrix to restore the original data.

[0074] Example 2: A wireless mesh network reliability assurance system based on adaptive network coding, the system includes an SDN controller and distributed nodes, each node has three wireless communication modules (1.4G, 2.4G, 5G); the controller is used to collect and summarize the status information of each node, select the optimal link path by constructing a state matrix and combining it with the ED algorithm, and generate a network coding matrix through the coding degree and redundancy calculated by the Gaussian process model, thereby realizing service type-driven network coding and link selection strategies.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wireless mesh network reliability assurance method based on adaptive network coding, wherein the assurance system includes an SDN controller and distributed nodes, each node having three wireless communication modules; characterized by: The following steps are involved: Step S1: Initialize routing and node status discovery: Each node discovers neighboring nodes, builds a Mesh network, and records the status information of each node; Step S2, dynamic weight adjustment and comprehensive matrix calculation step: according to different business requirements, adjust the weights of each matrix and calculate the comprehensive matrix to provide a basis for link selection; Step S3, link selection step: using the ED algorithm to select the optimal transmission path according to the link state matrix; Step S4, network coding matrix generation and data coding step: calculate the optimal coding degree k and redundancy r through the Gaussian process GP model, generate the coding matrix and encode the data; Step S5, data transmission and reception step: performing data transmission based on the encoded data packet, and decoding at the receiving end to restore the original data; Wherein, step S4 further includes the following steps: Step S4.1, calculating the coding degree k and redundancy r by using the Gaussian process (GP) model to optimize the objective function, thereby determining the key parameters of the network coding; Step S4.2: data partitioning, generating a k×k identity matrix and an r×k random matrix to construct the coding matrix required for network coding; Step S4.3: synthesize a coding matrix for encoding the data packet, wherein the coding matrix is ​​obtained by a linear combination of an identity matrix and a random matrix or a matrix operation; Step S4.4: Encode the data packet to be transmitted according to the encoding matrix and send it to the destination node; Where: The objective function in step S4.1 is: f(x) = max(λ1P success -λ2B-λ3t delay ), which includes the following input features: coding degree k: refers to the number of coding blocks after the data packet is divided, which affects the reliability of transmission; redundancy r: refers to the number of redundant data packets generated by network coding, which is used to enhance the reliability of transmission; packet loss rate P loss :Used to calculate the reception success rate: Bandwidth B: used to evaluate the transmission capacity of the link; delay t delay : used to measure the delay performance of the link; weight parameters λ1, λ2, and λ3: correspond to the importance of coding degree, redundancy, reception success rate, bandwidth, and delay in the objective function, respectively, and are used to dynamically adjust the link selection strategy in different business scenarios to meet specific business needs; In step S4, the GP model is used to dynamically calculate the coding degree k and redundancy r, and these two parameters are optimized using the RBF kernel function and the expected improvement (EI) criterion to ensure optimal coding efficiency and reliability under different network conditions and service requirements. The calculation of the GP model includes the following steps: Step S4.1.1, define the kernel function of the GP model as Where α0 and t are the amplitude and length parameters of the kernel function, which are used to measure the similarity between input features; Step S4.1.2, assuming the mean function m(x) = 0 to simplify the model calculation; Step S4.1.3: Calculate the optimal values ​​of the coding degree k and redundancy r based on the maximization of the acquisition function, maximize the expected improvement EI and the expected reduction ED, so as to improve the coding redundancy and transmission reliability of the network.

2. The method for ensuring reliability of a wireless mesh network based on adaptive network coding according to claim 1, wherein: Step S1 also includes the following sub-steps: Step S1.1: Each node periodically uploads its own status information to the network controller, including link quality (TQ), signal strength, and adjacency. Step S1.2: The network controller maintains the adjacency matrix, signal strength matrix, and TQ value matrix based on the information uploaded by the nodes; Step S1.3: The controller maintains the above matrix in different frequency bands respectively to support link selection requirements of different frequency bands.

3. The method for ensuring reliability of a wireless mesh network based on adaptive network coding according to claim 2, wherein: Step S2 includes the following steps: Step S2.1: The network controller assigns different weights to the adjacency matrix, signal strength matrix, and TQ value matrix based on the service type to reflect the priority of the service demand; Step S2.2: For different service types, including video streaming, audio streaming, and file transfer, dynamically adjust the weight matrix to meet the different service requirements for reliability and latency; Step S2.3: Calculate a comprehensive matrix based on the adjusted weights to serve as basic data for link selection.

4. The method for ensuring reliability of a wireless mesh network based on adaptive network coding according to claim 3, wherein: Step S3 further includes the following steps: Step S3.1, observe the state matrix of each frequency band and select the frequency band; Step S3.2: Use the ED algorithm to evaluate the link state matrix and select the optimal link path that meets the transmission requirements.

5. The method for ensuring reliability of wireless mesh networks based on adaptive network coding according to claim 4, characterized in that: Step S5 further includes the following steps: Step S5.1: After receiving the encoded data packet, the receiving node extracts the packet header information and records the transmission path and link quality of the data packet; Step S5.2: If the number of data packets is less than k, resend the packet to ensure the reliability of data transmission; Step S5.3: The receiving node decodes the data packet using the encoding matrix to restore the original data.

6. A wireless mesh network reliability assurance system based on adaptive network coding, characterized by: The invention comprises a device and a controller for executing any one of the methods of claims 1 to 5, wherein the controller is used to collect and summarize the status information of each node, select the optimal link path by constructing a state matrix and combining it with an ED algorithm, and generate a network coding matrix by calculating the coding degree and redundancy using a Gaussian process model, thereby realizing a service type-driven network coding and link selection strategy.

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