Data transmission optimization method of wireless communication module

Abstract

The invention provides a data transmission optimization method of a wireless communication module, belongs to the field of wireless communication, and aims to solve the problems of reducing energy consumption and improving performance while meeting the data transmission quality. The method comprises the following steps: firstly, setting an energy consumption model of the wireless communication module; and then a data transmission evaluation model is established, and evaluation is carried out from multiple aspects of continuity, reliability, real-time performance and the like of data transmission. And then constructing an optimization solution algorithm, and performing optimization solution by using an improved firefly algorithm to obtain an optimal wireless communication module data transmission method. The algorithm comprises the steps of coding system improvement, population initialization method improvement, flight strategy improvement and the like. According to the wireless communication module, the data transmission performance of the wireless communication module can be effectively improved, the service life is prolonged, energy is saved, the wireless communication module can adapt to a dynamically changing wireless communication environment, and real-time optimization is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communications, and more specifically relates to a data transmission optimization method for a wireless communication module. Background Art

[0002] Traditional wireless communication modules mostly use fixed data transmission strategies, such as fixed transmission power, fixed data packet size, etc. However, in complex wireless communication environments, such strategies often fail to meet performance requirements, such as communication quality, latency, reliability, etc. Therefore, it is necessary to optimize data transmission strategies based on specific application backgrounds and communication environments to improve wireless communication performance.

[0003] However, there are many challenges in optimizing data transmission for wireless communication modules. First, the wireless communication environment is highly uncertain and dynamic, such as changes in propagation paths, the presence of interference sources, etc., which make it difficult to optimize data transmission strategies. Secondly, the data transmission optimization problem itself is a multi-objective optimization problem that takes into account multiple factors, such as communication quality, energy consumption, latency, etc., and requires searching a large amount of solution space to find the optimal solution. In addition, how to obtain and establish an accurate energy consumption model and data transmission evaluation model for wireless communication modules is also a major problem. Summary of the invention

[0004] The main goal of the present invention is to solve the energy consumption and performance optimization problems of wireless communication modules during data transmission. Specifically, it is to reduce energy consumption as much as possible while ensuring the quality of data transmission (including reliability, real-time and stability of transmission), thereby extending the device life and improving energy efficiency. At the same time, the present invention also needs to consider solving the problem of complex uncertainty in the wireless communication environment. It is necessary to find a method that can effectively adapt to the dynamic wireless communication environment and perform real-time optimization according to actual conditions.

[0005] In order to achieve the above object, the present invention is implemented by adopting the following technical scheme: the method comprises:

[0006] Establish energy consumption model of wireless communication module, including data sensing, processing and transmission;

[0007] Establish a wireless communication module data transmission evaluation model to evaluate the energy consumption model;

[0008] Construct an optimization solution algorithm, optimize and solve the energy consumption model, and obtain the optimal wireless communication module data transmission method.

[0009] In one embodiment, the energy consumption model of the wireless communication module takes into account that the data transmission distances of different nodes of the sensor are different, the data transmission paths are not fixed, and the loads of different nodes are different.

[0010] In one embodiment, the energy consumption model is:

[0011]

[0012] Where, E is the total energy consumption of the entire wireless sensor network, N is the set of nodes in the network, i is a node in the network, Eelec is the fixed energy consumption per bit of data, εamp is the amplifier energy consumption, k is the size of the data packet transmitted each time, and d i is the transmission distance of node i, T is the number of packets sent by node i, and E DA is the energy consumption of data aggregation, R i is the number of packets received by node i.

[0013] In one embodiment, the data transmission evaluation model evaluates the following aspects: (1) the continuity of data transmission;

[0014] (2) Reliability of data transmission;

[0015] (3) Real-time data transmission.

[0016] In one embodiment, the data transmission evaluation model is:

[0017] P=α*C+β*R+γ*T-δ*E

[0018] Where: P is the overall performance evaluation index, α, β, γ and δ are weight coefficients, which are used to indicate the relative importance of continuity, reliability, real-time and energy consumption in the overall performance evaluation, C is the continuity of data transmission, R is the reliability of data transmission, T is the real-time of data transmission, and E is energy consumption.

[0019] In one solution, the optimization solution algorithm adopts an improved firefly algorithm, and the improvements include:

[0020] (1) Improve the coding system;

[0021] (2) Improve the population initialization method;

[0022] (3) Improve flight strategy.

[0023] In one embodiment, in the improved coding system, each firefly represents a feasible data transmission path, which is encoded as the numbers of all nodes on the data transmission path from the starting node to the data center, also called the ending node.

[0024] In one embodiment, the steps of the improved population initialization method are as follows:

[0025] 1) Use the adjacency matrix to characterize whether different nodes are adjacent;

[0026] 2) Calculate the search probability of adjacent nodes;

[0027] 3) The current node searches for a node in the adjacent node set as the next node for data transmission based on the search probability using the roulette method;

[0028] 4) Repeat step 3) continuously. When the data reaches the termination node, it indicates that a complete data transmission path has been formed and the next node is no longer searched. Insert a virtual node between the last intermediate node and the termination node, and the initialization of a firefly is completed.

[0029] In one scheme, the improved flight strategy adopts cooperative flight and uses the Hamming distance to define the distance between fireflies; the cooperative flight strategy includes three flight modes: free flight, neighborhood flight and cross-domain flight.

[0030] Beneficial effects of the present invention:

[0031] The data transmission optimization method of the present invention adopts an energy consumption model and an evaluation model, which can simultaneously ensure the reliability, real-time performance and stability of wireless transmission, while reducing energy consumption as much as possible. This means that the service life of the wireless communication module will be significantly improved, which is especially important for some devices that work in harsh environments and are difficult to replace batteries. In addition, the optimization algorithm can achieve real-time optimization, can respond to real-time changes in the wireless communication environment, optimize the data transmission strategy, and ensure the quality of data transmission. The improved firefly algorithm improves the efficiency and practicality of the optimization, greatly reduces the complexity and time of the calculation, and enables the method of the present invention to be effective in practical applications. In general, the present invention can improve the performance of the wireless communication module, extend its service life, and improve the efficiency and quality of data transmission, which has a positive driving effect on promoting the development of wireless communication technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method of the present invention;

[0033] Figure 2 This is a flow chart of the method for initializing the firefly population of the present invention;

[0034] Figure 3 This is a flow chart of the free flight of firefly populations of the present invention;

[0035] Figure 4 This is a flow chart of the firefly population neighborhood flight of the present invention. DETAILED DESCRIPTION

[0036] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0037] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0038] like Figure 1 As shown, a data transmission optimization method of a wireless communication module includes:

[0039] S1. Establish an energy consumption model for wireless communication modules, including data perception, processing and transmission.

[0040] When considering the optimization of data transmission, energy consumption is an important factor that must be considered, especially in wireless sensor networks. Therefore, it is very necessary to first establish an energy consumption model of wireless sensors.

[0041] The energy consumption model of the wireless communication module should take into account that the data transmission distances of different nodes of the sensor are different, the data transmission path is not fixed, the loads of different nodes are different, and the loads of different nodes are different.

[0042] The energy consumption model is:

[0043]

[0044] Where, E is the total energy consumption of the entire wireless sensor network, N is the set of nodes in the network, i is a node in the network, Eelec is the fixed energy consumption per bit of data, εamp is the amplifier energy consumption, k is the size of the data packet transmitted each time, and d i is the transmission distance of node i, T i is the number of packets sent by node i, E DA is the energy consumption of data aggregation, R i is the number of packets received by node i.

[0045] The model calculates the energy consumption of each node independently, so it can reflect the difference in load between different nodes. At the same time, by considering the transmission distance of each node and the number of packets sent and received, it also takes into account the impact of data transmission path and transmission distance.

[0046] This model is suitable for situations where the distance is not large and the energy consumption of each node is mainly from sending and receiving data. For more complex networks, more parameters may need to be introduced, such as path loss, mutual interference between nodes, etc.

[0047] S2. Establish a wireless communication module data transmission evaluation model to evaluate the energy consumption model.

[0048] The data transmission evaluation model is evaluated from the following aspects:

[0049] (1) Continuity of data transmission; this can be measured by the duration of successful data transmission or the probability of successful data packet transmission within a period of time. The continuity of data transmission can be calculated using the following formula:

[0050]

[0051] Among them, C represents continuity, S i is the time when node i successfully transmits, d i is the transmission distance of node i.

[0052] (2) Reliability of data transmission; measured by the packet loss rate, error rate, or the ratio of received packets to sent packets. The following formula can be defined to measure reliability:

[0053]

[0054] Among them, R represents reliability, R i is the number of packets received by node i, T i is the number of packets sent by node i.

[0055] (3) The real-time performance of data transmission is measured by the delay of data packets. The following formula can be defined to express the real-time performance:

[0056]

[0057] Among them, T represents real-time performance, t represents the total delay from the sending node to the receiving node, and N represents the total number of nodes in the network.

[0058] These evaluation indicators are combined to form an overall evaluation model. The data transmission evaluation model is:

[0059] P=α*C+β*R+γ*T-δ*E(2)

[0060] Where: P is the overall performance evaluation index, α, β, γ and δ are weight coefficients, which are used to indicate the relative importance of continuity, reliability, real-time and energy consumption in the overall performance evaluation, C is the continuity of data transmission, R is the reliability of data transmission, T is the real-time of data transmission, and E is energy consumption.

[0061] This model unifies continuity, reliability, real-time and energy consumption in one framework. By adjusting the values ​​of α, β, γ and δ, different performance indicators can be weighed according to actual needs. For example, if real-time is the most important requirement, the value of γ can be increased; if energy efficiency is more important, the value of δ can be increased.

[0062] S3. Construct an optimization solution algorithm to optimize and solve the energy consumption model to obtain the optimal wireless communication module data transmission method. The optimization solution algorithm adopts an improved firefly algorithm. In view of the problem that the coordinate coding method of the basic firefly algorithm is difficult to describe the data transmission path, an integer coding system integrating virtual nodes is established; a firefly population initialization method based on search probability is further proposed to ensure the success rate of firefly initialization; based on the neighborhood flight of the basic firefly algorithm, free flight is proposed to enhance the algorithm's computational efficiency, and cross-domain flight is designed to avoid the algorithm from falling into local optimality, forming a firefly flight strategy that coordinates free flight, neighborhood flight, and cross-domain flight.

[0063] Improvements include:

[0064] (1) Improve the coding system; the basic firefly algorithm is encoded according to the Cartesian coordinate system, and each firefly corresponds to a point in the feasible solution space in the Cartesian coordinate system, which makes it difficult to describe the data transmission path of the structure monitoring wireless communication module. In the integer coding system proposed in this application, each firefly represents a feasible data transmission path, which is encoded as the number of all nodes on the data transmission path from the starting node to the data center (also called the ending node).

[0065] Assume that there are N nodes in a monitoring area. All nodes including the data center are numbered without duplication. The code of each firefly is the code of the data transmission path. The codes of firefly ① (path ①), firefly ② (path ②) and firefly ③ (path ③) are: C 1 ={1,4,23,27,30}, C 2 ={1,8,22,30}, C 3 ={1,13,5,7,17,21,30}, nodes other than the starting node and the ending node on the data transmission path are called intermediate nodes.

[0066] Since the number of intermediate nodes on each data transmission path is not fixed, the code lengths of different fireflies are different. In order to facilitate the subsequent firefly movement operation, a virtual node numbered 0 is inserted between the last intermediate node and the terminal node to maintain the length of each code N. It is worth noting that the virtual node is only used to maintain the length of the code, and does not exist in the actual wireless communication module. The corrected code of Firefly ①, Firefly ② and Firefly ③ is: C 1 ={1,4,23,27,0…30}, C 2 ={1,8,22,0,…,0,30} and C 3 ={1,13,5,7,17,21,0,…,0,30}.

[0067] (2) Improve the population initialization method; the distribution state of the initialization population in the feasible solution space directly affects the quality and solution rate of the optimal solution. The basic firefly algorithm evenly or randomly distributes fireflies in the feasible solution space, which is not suitable for use in the data transmission path optimization of the structure monitoring wireless sensor network. Figure 2 As shown, this application proposes a method for initializing a firefly population based on search probability, the steps are:

[0068] S301, using an adjacency matrix to characterize whether different nodes are adjacent to each other; based on the assumption that a node can communicate with any node within the maximum distance range of its data transmission, the N×N dimensional adjacency matrix is ​​constructed as follows:

[0069]

[0070] Where: A ij represents the element in the i-th (i=1,2,…,N) row and j-th (j=1,2,…,N) column of the adjacency matrix, d e,ij represents the Euclidean distance between node i (i = 1, 2, ..., N) and node j (j = 1, 2, ..., N), d e,max A represents the maximum data transmission distance of a node. Because a node is not allowed to transmit data to itself, all diagonal elements of the adjacency matrix are 0. ij =1 indicates that node j is an adjacent node of node i.

[0071] S302: Calculate the search probability of adjacent nodes.

[0072]

[0073] Where: P i,k represents the search probability of the kth neighboring node of node i, d e,ik and d e,iu Respectively represent the Euclidean distance between node i and the kth and uth adjacent nodes; W represents the total number of adjacent nodes of node i; EL,k and E L,u Respectively represent the residual energy of the kth and uth adjacent nodes; I n,k and I n,u They represent the idleness of the kth and uth adjacent nodes respectively.

[0074] S303: The current node searches for a node in the adjacent node set as the next node for data transmission based on the search probability using the roulette method. To prevent data from being transmitted back to a node that is already on the data transmission path, the search probability is set to 0.

[0075] S304, repeat step S303 continuously. When the data reaches the end node, it indicates that a complete data transmission path has been formed, and the next node is no longer searched. A virtual node is inserted between the last intermediate node and the end node, and the initialization of a firefly is completed. If the search cannot reach the end node, it indicates that this path cannot complete data transmission, and the search starts again from the starting node.

[0076] (2) Improve flight strategy.

[0077] Due to the use of integer coding, the Euclidean distance in the basic firefly algorithm cannot represent the distance between fireflies, and the distance between fireflies is defined using the Hamming distance;

[0078]

[0079] Where: d k,rs represents the Hamming distance between firefly r and firefly s; c r and c s Respectively represent the codes of firefly r and firefly s, It represents logical XOR operation; ∨ represents logical OR operation; ∧ represents logical AND operation; Represents the logical NOT operation.

[0080] The brightness of fireflies is defined as the optimization objective function value, which is the evaluation index RPI of wireless sensor network data transmission shown in formula (2). The brightness of fireflies in the population (i.e., the objective function value) is calculated using formula (2), and the firefly with the highest brightness is called the cluster head.

[0081] The cooperative flight strategy includes three flight modes: free flight, neighborhood flight, and cross-domain flight. The mode selection probability δ(δ∈[0,1]) generated randomly is used for selection. When 0≤δ<1 / 3, free flight is performed; when 1 / 3≤δ<2 / 3, neighborhood flight is performed; when 2 / 3≤δ≤1, cross-domain flight is performed.

[0082] like Figure 3As shown, free flight is achieved by bypassing the intermediate node with the worst performance on the data transmission path represented by the firefly itself, thereby improving its own brightness. The steps are:

[0083] (1) Calculate the performance index of all intermediate nodes. The i-th intermediate node S i Performance index H i Defined as:

[0084]

[0085] Where, d e,d represents the i-th intermediate node S i With the previous node S i-1 The Euler distance between .

[0086] (2) Find the node S with the smallest performance index on the data transmission path min The previous node S i-1 . Previous node S i-1 According to the search probability shown in formula (4), the roulette method is used to search for a node in the adjacent node set as the next temporary node S for data transmission. min T.

[0087] (3) If this temporary node S min T can be connected with the node S with the smallest performance index min The next node S i+1 Communication, then use the temporary node S min T replaces node S min , forming a new path, and the firefly's free flight is completed. On the contrary, if this temporary node S i-1 Cannot be compared with the node S with the smallest performance index min The next node S i+1 Communication, then at node S i-1 Re-search the next temporary node S in the adjacent node set min T, until a temporary node that can communicate is found, and this temporary node is also used to replace node S min , forming new paths and enabling the free flight of fireflies.

[0088] like Figure 4 As shown in the figure, neighborhood flight means that fireflies fly to fireflies with higher brightness in the neighborhood to increase their own brightness. The steps are:

[0089] (1) Calculate the objective function value R of fireflies r and s in the population r and R s , if R r <R s , then firefly r moves towards firefly s.

[0090] (2) Randomly select a common node (except the end point) in the firefly r and firefly s codes, and use the d after the common node in the firefly s code m After the intermediate nodes replace the common nodes of Firefly r encoding, m intermediate nodes, d m Indicates the moving distance. d m The value of is determined according to the following formula:

[0091] d m =random(1,N m ) (7)

[0092] N m =min(0.5d h,rs ,N left,r ,N left,s ) (8)

[0093] Where: random(·) means to select (1,N m ), a random number between left,r and N left,s They represent the number of nodes (including intermediate nodes and virtual nodes) between the public node and the data center encoded by Firefly-r and Firefly-s, respectively.

[0094] (3) After replacement, if two adjacent nodes on the data transmission path are not adjacent nodes or there are duplicate nodes, it means that data transmission cannot be carried out or returned, and this path becomes an invalid path. Starting from the common node, the roulette method is used to search for a node in the adjacent node set as the next node for data transmission until a valid path is formed.

[0095] Cross-domain flight means that fireflies fly to the cluster head to improve their brightness and avoid falling into the local optimum. Its implementation steps are the same as those of domain flight, but the flight target is different.

[0096] Embodiment 1:

[0097] First, take a wireless communication network consisting of 50 nodes as an example. The nodes are numbered from 1 to 50, where node 1 is the starting node and node 50 is the data center or the ending node. First, an energy consumption model is established, assuming that the fixed energy consumption Eelec per bit of data is 50nJ / bit and the amplifier energy consumption εamp is 100pJ / bit / m 2 , the energy consumption EDA of data aggregation is 5nJ / bit / signal.

[0098] Secondly, a data transmission evaluation model is established, and the relative importance of continuity, reliability, real-time and energy consumption in the overall performance evaluation is set with weight coefficients α = 0.25, β = 0.25, γ = 0.25 and δ = 0.25 respectively.

[0099] Then, an optimization solution algorithm is constructed, and the improved firefly algorithm is used to optimize the data transmission of the wireless communication module. First, the coding system is improved so that each firefly represents a feasible data transmission path, and its code is the number of all nodes on the path from the starting node to the data center.

[0100] Next, the population is initialized, and the adjacency matrix is ​​used to characterize whether different nodes are adjacent. According to the search probability, the roulette method is used to search for a node in the adjacent node set as the next node for data transmission until the data reaches the termination node position, forming a complete data transmission path.

[0101] Finally, the flight strategy was improved and collaborative flight was adopted. The distance between fireflies was defined by Hamming distance, and three flight modes including free flight, neighborhood flight and cross-domain flight were performed for search.

[0102] After a certain number of iterations, the optimal data transmission path is obtained, for example, starting from node 1, passing through nodes 4, 7, 12, 18, 22, 28, 34, 38, and 45, and finally reaching the terminal node 50. Under this path, the energy consumption of data transmission is the lowest, and the corresponding data transmission performance evaluation index P reaches the highest, realizing the data transmission optimization of the wireless communication module.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0104] It should be understood that the detailed description of the technical solutions of the present invention by means of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art may modify the technical solutions described in the embodiments, or replace some of the technical features by equivalents, based on reading the specification of the present invention; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data transmission optimization method for a wireless communication module, characterized in that: The method includes: Establish energy consumption model of wireless communication module, including data sensing, data processing and data transmission; Establish a wireless communication module data transmission evaluation model to evaluate the energy consumption model; Construct an optimization solution algorithm, optimize and solve the energy consumption model, and obtain the optimal wireless communication module data transmission method.

2. The data transmission optimization method of a wireless communication module according to claim 1, characterized in that: The wireless communication module energy consumption model takes into account that the data transmission distances of different nodes of the sensor are different, the data transmission paths are not fixed, and the loads of different nodes are different.

3. The data transmission optimization method of a wireless communication module according to claim 2, characterized in that: The energy consumption model is: Where, E is the total energy consumption of the entire wireless sensor network, N is the set of nodes in the network, i is a node in the network, Eelec is the fixed energy consumption per bit of data, εamp is the amplifier energy consumption, k is the size of the data packet transmitted each time, and d i is the transmission distance of node i, T i is the number of packets sent by node i, E DA is the energy consumption of data aggregation, R i is the number of packets received by node i.

4. The data transmission optimization method of a wireless communication module according to claim 1, characterized in that: The data transmission evaluation model is evaluated from the following aspects: (1) Continuity of data transmission; (2) Reliability of data transmission; (3) Real-time data transmission.

5. The data transmission optimization method of a wireless communication module according to claim 4, characterized in that: The data transmission evaluation model is: P=α*C+β*R+γ*T-δ*E Where: P is the overall performance evaluation index, α, β, γ and δ are weight coefficients, which are used to indicate the relative importance of continuity, reliability, real-time and energy consumption in the overall performance evaluation, C is the continuity of data transmission, R is the reliability of data transmission, T is the real-time of data transmission, and E is energy consumption.

6. The data transmission optimization method of a wireless communication module according to claim 1, characterized in that: The optimization solution algorithm adopts the improved firefly algorithm, and the improvements include: (1) Improve the coding system; (2) Improve the population initialization method; (3) Improve flight strategy.

7. The data transmission optimization method of a wireless communication module according to claim 6, characterized in that: In the improved coding system, each firefly represents a feasible data transmission path, which is encoded as the numbers of all nodes on the data transmission path from the starting node to the data center, also called the ending node.

8. The data transmission optimization method of a wireless communication module according to claim 6, characterized in that: The steps of the improved population initialization method are as follows: 1) Use the adjacency matrix to characterize whether different nodes are adjacent; 2) Calculate the search probability of adjacent nodes; 3) The current node searches for a node in the adjacent node set as the next node for data transmission based on the search probability using the roulette method; 4) Repeat step 3) continuously. When the data reaches the termination node, it indicates that a complete data transmission path has been formed and the next node is no longer searched. Insert a virtual node between the last intermediate node and the termination node, and the initialization of a firefly is completed.

9. The data transmission optimization method of a wireless communication module according to claim 6, characterized in that: The improved flight strategy adopts cooperative flight and uses the Hamming distance to define the distance between fireflies; the cooperative flight strategy includes three flight modes: free flight, neighborhood flight and cross-domain flight.

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