Self-adaptive dynamic routing method and system based on Mesh networking

Abstract

The invention relates to a self-adaptive dynamic routing method and system based on Mesh networking, and the method comprises the following steps: counting the number of times of successful message transmission of a route in unit time, calculating the total hop count of the route in a message transmission process, and recording the average end-to-end delay time of the message; carrying out normalization processing on the number of times of successfully transmitting messages by the route, the total hop count of the route and the delay time to obtain normalized parameters; constructing an evaluation matrix by using the normalized parameters, and calculating an entropy value by using an entropy weight method; substituting the entropy value into a weight algorithm, and calculating to obtain a routing weight; and the route weights and the normalized parameters are imported into a scoring algorithm for calculation, comprehensive weighting scores are obtained, and the first several routes with the highest comprehensive weighting scores are selected as the optimal routes. Compared with the prior art, reliability and adaptability can be improved, energy consumption can be reduced, and deployment is more convenient.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and more particularly, to an adaptive dynamic routing method based on Mesh networking. Background Art

[0002] With the rapid development of Internet of Things technologies, Mesh networking technology has become the preferred solution for commercial and industrial lighting control systems due to its advantages such as decentralization, self-healing, and low power consumption. Taking Bluetooth Mesh as an example, it achieves wide coverage through multi-node relaying, supports dynamic routing and self-organizing network functions, and is particularly suitable for complex scenarios such as tunnels, roads, and large buildings.

[0003] The Bluetooth Mesh network realizes multi-path parallel transmission through a controllable flooding relay mechanism to avoid single-point failures, supports the publish / subscribe mode to reduce invalid communication traffic, and can achieve seamless interoperability between lamps, sensors, and control systems after being combined with the DALI standard.

[0004] Currently, Mesh networks mainly adopt two types of algorithms: static routing and dynamic routing. The representative protocol of the static routing algorithm is manual configuration, which is simple and reliable without additional computational overhead, but it cannot adapt to network topology changes and has a high maintenance cost. The representative protocols of the dynamic routing algorithm are RIP and OSPF, which can adapt to network changes with high real-time performance, but have high algorithm complexity, are prone to routing oscillations, and have a slow convergence speed.

[0005] When the existing static routing and dynamic routing algorithms are used in lighting control systems, the following deficiencies exist: insufficient stability, where frequent oscillations of dynamic routing lead to an increase in the message loss rate; low energy efficiency, where redundant forwarding nodes increase ineffective energy consumption; and response delay, where the average delay of emergency control instructions exceeds 500 ms. Therefore, it is necessary to solve these problems. Summary of the Invention

[0006] The present invention aims to solve at least one of the above technical problems in the prior art to some extent. To this end, an object of the present invention is to provide an adaptive dynamic routing method and system based on Mesh networking that can improve reliability and adaptability, reduce energy consumption, and is more convenient for deployment.

[0007] The technical solution for the present invention to solve the above technical problems is as follows: An adaptive dynamic routing method based on Mesh networking includes: Count the number of times of successfully transmitted routing messages per unit time, calculate the total number of hops in the message transmission process, and record the average end-to-end delay time of the messages; Normalize the number of times of successfully transmitted routing messages, the total number of hops, and the delay time to obtain normalized parameters; Construct an evaluation matrix using the normalized parameters, and calculate the entropy value using the entropy weight method; substitute the entropy value into the weight algorithm to calculate and obtain the routing weight. Import the routing weight and the normalized parameters into the scoring algorithm for calculation to obtain the comprehensive weighted score, and select the top several routes with the highest comprehensive weighted score as the optimal routes.

[0008] The beneficial effects of the present invention are as follows: it can improve reliability. The main route and backup route mechanism shortens the network fault recovery time to within 50 ms, avoiding large-area interruption of tunnel lighting; it can also reduce energy consumption. By optimizing the hop count, redundant forwarding is reduced, and the node lifespan is extended by 40%; it can also enhance adaptability. The parameter weights are dynamically adjusted, and the routing stability remains above 98% in complex environments such as heavy rain and electromagnetic interference; it is more convenient for deployment. There is no need for manual configuration of routing strategies, and it supports plug-and-play network expansion.

[0009] Based on the above technical solutions, the present invention can also be improved as follows.

[0010] Further, the normalization processing algorithm is: , where is the original parameter value, is the normalization result.

[0011] Further, the evaluation matrix is matrix , is the number of routes, is the number of parameter types.

[0012] Further, the algorithm for calculating the entropy value using the entropy weight method: ; where .

[0013] Further, the weight algorithm: .

[0014] Further, the scoring algorithm: .

[0015] Further, it also includes the following steps: Select one route with the highest comprehensive weighted score as the main route, and select two routes with the second highest comprehensive weighted score as the backup routes; When the number of failures of the main route exceeds the threshold or times out, immediately enable the backup route and trigger route reconstruction, and recalculate and select the optimal route.

[0016] The beneficial effects of adopting the above further solution are as follows.

[0017] Another technical solution for the present invention to solve the above technical problems is as follows: An adaptive dynamic routing system based on Mesh networking, comprising: The collection module counts the number of messages successfully transmitted by the route within a unit time, calculates the total number of hops of the route during the message transmission process, and records the average end-to-end delay time of the messages; The processing module normalizes the number of messages successfully transmitted by the route, the total number of hops of the route, and the delay time to obtain the normalized parameters; The weight module constructs an evaluation matrix using the normalized parameters and calculates the entropy value using the entropy weight method; substitutes the entropy value into the weight algorithm to calculate and obtain the route weight; The scoring module imports the route weight and the normalized parameters into the scoring algorithm for calculation to obtain the comprehensive weighted score, and selects the top several routes with the highest comprehensive weighted score as the optimal routes.

[0018] The beneficial effects of the present invention are as follows: it can improve reliability, and the main route and standby route mechanism shorten the network fault recovery time to within 50 ms, avoiding large-area interruption of tunnel lighting; it can also reduce energy consumption, reduce redundant forwarding through hop optimization, and extend the node life by 40%; it can also enhance adaptability, dynamically adjust the parameter weights, and still maintain a route stability of more than 98% in complex environments such as heavy rain and electromagnetic interference; it is more convenient for deployment, does not require manual configuration of routing strategies, and supports plug-and-play network expansion. Description of the Drawings

[0019] Figure 1 It is a flowchart of an adaptive dynamic routing method based on Mesh networking of the present invention; Figure 2 It is a module block diagram of an adaptive dynamic routing system based on Mesh networking of the present invention.

[0020] In the drawings, the list of components represented by each reference numeral is as follows: 1. Collection module, 2. Processing module, 3. Weight module, 4. Scoring module. Detailed Embodiment

[0021] The principles and features of the present invention are described below with reference to the drawings. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0022] As Figure 1 shown, an adaptive dynamic routing method based on Mesh networking includes the following steps: Count the number of messages successfully transmitted by the route within a unit time, calculate the total number of hops of the route during the message transmission process, and record the average end-to-end delay time of the messages; Normalize the number of messages successfully transmitted by the route, the total number of hops of the route, and the delay time to obtain the normalized parameters; Construct an evaluation matrix using the normalized parameters, and calculate the entropy value using the entropy weight method; substitute the entropy value into the weight algorithm to calculate and obtain the routing weight. Import the routing weight and the normalized parameters into the scoring algorithm for calculation to obtain the comprehensive weighted score, and select the top several routes with the highest comprehensive weighted scores as the optimal routes.

[0023] In the above embodiments, the normalization processing algorithm is: ; where is the original parameter value, is the normalization result.

[0024] In the above embodiments, the evaluation matrix is matrix , is the number of routes, is the number of parameter types.

[0025] In the above embodiments, the algorithm for calculating the entropy value using the entropy weight method: ; where .

[0026] In the above embodiments, the weight algorithm: .

[0027] When the total number of parameters is 3: ; where is the th parameter's entropy value.

[0028] In the above embodiments, the scoring algorithm: .

[0029] Stability parameter (S), count the number of times the route successfully transmits messages within a unit time, the higher the frequency, the more stable the link quality; energy efficiency parameter (E), calculate the total number of hops of the route, the fewer the hops, the lower the transmission energy consumption; real-time parameter (T), record the average end-to-end delay of the message, the shorter the time-consuming, the faster the response; Scoring algorithm: .

[0030] In the above embodiments, the following steps are further included: Select one route with the highest comprehensive weighted score as the primary route, and select two routes with the second highest comprehensive weighted scores as the backup routes; When the number of failures of the primary route exceeds the threshold or times out , immediately enable the backup route and trigger route reconstruction, and recalculate and select the optimal route.

[0031] The switching conditions for the primary route and the backup route are: .

[0032] In the specific application of this embodiment, 100 lighting nodes are deployed inside a 3-kilometer-long tunnel to achieve dynamic dimming control under vehicle flow perception, with the requirement that the command response time is less than 200 ms and the reliability is greater than 99.9%. Using the existing OSPF algorithm to adjust the routing, its message transmission success rate is 89.2%, the average end-to-end delay is 480 ms, the network power consumption is 150 mW / node, and the routing reconstruction time is 850 ms. Using the adaptive dynamic routing method based on Mesh networking, its message transmission success rate is 99.5%. Compared with the existing technology, the transmission success rate is increased by 10.3%; the average end-to-end delay is 120 ms, and the average end-to-end delay is reduced by 75%; the network power consumption is 110 mW / node, and the network power consumption is reduced by 26.7%; the routing reconstruction time is less than 50 ms, and the routing reconstruction time is reduced by 94%. During the vehicle peak period, this embodiment dynamically adjusts the routing to keep the delay of the lighting control command in the critical area stable between 80 - 150 ms, far lower than 400 - 600 ms of the traditional algorithm.

[0033] This embodiment can improve reliability. The main routing and standby routing mechanisms shorten the network fault recovery time to within 50 ms, avoiding large-area interruption of tunnel lighting; it can also reduce energy consumption. By optimizing the number of hops, redundant forwarding is reduced, and the node life is extended by 40%; it can also enhance adaptability. By dynamically adjusting the parameter weights, the routing stability remains above 98% in complex environments such as heavy rain and electromagnetic interference; it is more convenient for deployment. There is no need for manual configuration of routing strategies, and it supports plug-and-play network expansion. Embodiment

[0034] As Figure 2 shown, an adaptive dynamic routing system based on Mesh networking includes: A collection module (1) that counts the number of messages successfully transmitted by the routing per unit time, calculates the total number of routing hops during the message transmission process, and records the average end-to-end delay time of the message. A processing module (2), where the collection module normalizes the number of messages successfully transmitted by the routing, the total number of routing hops, and the delay time to obtain the normalized parameters. A weight module (3) that uses the normalized parameters to construct an evaluation matrix and calculates the entropy value using the entropy weight method; substitutes the entropy value into the weight algorithm to calculate and obtain the routing weight. A scoring module (4) that imports the routing weight and the normalized parameters into the scoring algorithm for calculation, obtains the comprehensive weighted score, and selects the top several routes with the highest comprehensive weighted score as the optimal routes.

[0035] In the above embodiment, it further includes: A switching module, which selects the routing with the highest comprehensive weighted score as the primary route and selects the two routings with the second highest comprehensive weighted score as the backup routes; When the failure times of the primary route exceed the threshold or time out immediately enable the backup route and trigger route reconstruction to recalculate and select the optimal route.

[0036] This embodiment can improve reliability. The primary route and backup route mechanisms shorten the network fault recovery time to within 50 ms, avoiding large-area interruption of tunnel lighting; it can also reduce energy consumption. By optimizing the hop count, redundant forwarding is reduced, and the node life is extended by 40%; it can also enhance adaptability. By dynamically adjusting the parameter weights, the routing stability of more than 98% can still be maintained in complex environments such as heavy rain and electromagnetic interference; it is more convenient for deployment. There is no need for manual configuration of routing strategies, and it supports plug-and-play network expansion.

[0037] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive dynamic routing method based on Mesh networking, characterized in that, Including the following steps: Count the number of messages successfully transmitted by the route within a unit time, calculate the total number of hops of the route during the message transmission process, and record the average end-to-end delay time of the message; Normalize the number of messages successfully transmitted by the route, the total number of hops of the route, and the delay time to obtain the normalized parameters; Construct an evaluation matrix using the normalized parameters and calculate the entropy value using the entropy weight method; Substitute the entropy value into the weight algorithm to calculate and obtain the route weight; Import the route weight and the normalized parameters into the scoring algorithm for calculation, obtain the comprehensive weighted score, and select the top several routes with the highest comprehensive weighted score as the optimal routes.

2. The adaptive dynamic routing method based on Mesh networking according to claim 1, wherein The normalization processing algorithm is as follows: , where is the original parameter value, is the normalization result.

3. The adaptive dynamic routing method based on Mesh networking according to claim 2, wherein The evaluation matrix is matrix , where is the number of routes, and is the number of parameter types.

4. The adaptive dynamic routing method based on Mesh networking according to claim 3, wherein Algorithm for calculating entropy value using entropy weight method: ; where .

5. The adaptive dynamic routing method based on Mesh networking according to claim 4, characterized in that, Weight algorithm: .

6. The adaptive dynamic routing method based on Mesh networking according to claim 5, wherein Scoring algorithm: .

7. The adaptive dynamic routing method based on Mesh networking according to claim 1, characterized in that It also includes the following steps: Select one route with the highest comprehensive weighted score as the primary route, and select two routes with the second highest comprehensive weighted score as the backup routes; When the number of failures of the primary route exceeds the threshold or times out, immediately enable the backup route and trigger route reconstruction, and recalculate and select the optimal route.

8. An adaptive dynamic routing system based on Mesh networking, characterized in that, Including: A collection module that counts the number of messages successfully transmitted by the route within a unit time, calculates the total number of hops of the route during the message transmission process, and records the average end-to-end delay time of the message; A processing module that normalizes the number of messages successfully transmitted by the route, the total number of hops of the route, and the delay time to obtain the normalized parameters; A weight module that constructs an evaluation matrix using the normalized parameters and calculates the entropy value using the entropy weight method; Substitute the entropy value into the weight algorithm to calculate and obtain the route weight; A scoring module that imports the route weight and the normalized parameters into the scoring algorithm for calculation, obtains the comprehensive weighted score, and selects the top several routes with the highest comprehensive weighted score as the optimal routes.

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