A data transmission optimization method for a wireless communication module

Abstract

The application 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 data transmission quality. The method first sets the energy consumption model of the wireless communication module. Then, a data transmission evaluation model is established to evaluate the continuity, reliability and real-time performance of data transmission. Next, an optimization solving algorithm is constructed, and an improved firefly algorithm is used for optimization solving to obtain the optimal data transmission method of the wireless communication module. The algorithm includes steps such as coding system improvement, population initialization method improvement, and flight strategy improvement. Through the application, the data transmission performance of the wireless communication module can be effectively improved, the service life is prolonged, energy is saved, and the wireless communication environment can be adapted to dynamic changes, and real-time optimization is realized.

Description

Technical Field

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

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

[0003] However, optimizing data transmission for wireless communication modules presents numerous challenges. First, the wireless communication environment is highly uncertain and dynamic, with factors such as changes in propagation paths and the presence of interference sources, all of which complicate data transmission strategy optimization. Second, data transmission optimization is inherently a multi-objective optimization problem considering multiple factors, such as communication quality, energy consumption, and latency, requiring the search of a large solution space to find the optimal solution. Furthermore, obtaining and establishing accurate energy consumption models and data transmission evaluation models for wireless communication modules is also a significant challenge. Summary of the Invention

[0004] The main objective of this invention is to address the energy consumption and performance optimization issues of wireless communication modules during data transmission. Specifically, it aims to minimize energy consumption while ensuring data transmission quality (including reliability, real-time performance, and stability), thereby extending device lifespan and improving energy efficiency. Simultaneously, this invention also considers addressing the complex and uncertain nature of wireless communication environments, requiring a method that effectively adapts to dynamic wireless communication environments and can perform real-time optimization based on actual conditions.

[0005] To achieve the above objectives, the present invention employs the following technical solution: the method comprises:

[0006] Establish a power consumption model for wireless communication modules, including data sensing, processing, and transmission;

[0007] Establish a data transmission evaluation model for wireless communication modules to evaluate energy consumption models;

[0008] An optimization algorithm is constructed to optimize the energy consumption model and obtain the optimal data transmission method for the wireless communication module.

[0009] In one scheme, the energy consumption model of the wireless communication module takes into account the different data transmission distances of different nodes of the sensor, the non-fixed data transmission path, and the different loads of different nodes.

[0010] In one scheme, the energy consumption model is as follows:

[0011]

[0012] Where E is the total power 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 power consumption per bit of data, εamp is the amplifier power consumption, k is the size of the data packet transmitted each time, and d i Where E is the transmission distance of node i, T is the number of data packets sent by node i, and E is the transmission distance of node i. DA It's the energy consumption of data aggregation, R i It represents the number of data packets received by node i.

[0013] In one scheme, 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 approach, the data transmission evaluation model is as follows:

[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 weighting coefficients used to represent the relative importance of continuity, reliability, real-time performance 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 performance of data transmission and E is energy consumption.

[0019] In one approach, the optimization algorithm employs an improved firefly algorithm, with improvements including:

[0020] (1) Improve the coding system;

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

[0022] (3) Improve flight strategies.

[0023] In one scheme, the improved coding system, where each firefly represents a feasible data transmission path, is coded as the number of all nodes on the data transmission path from the starting node to the data center, also known as the ending node.

[0024] In one approach, the improved population initialization method comprises the following steps:

[0025] 1) Use an adjacency matrix to represent whether different nodes are adjacent;

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

[0027] 3) Based on the search probability, the current node uses the roulette wheel method to search for a node in the set of adjacent nodes as the next node for data transmission;

[0028] 4) Repeat step 3) continuously. When the data reaches the terminal 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 terminal node, which completes the initialization of a firefly.

[0029] In one scheme, the improved flight strategy adopts cooperative flight, using 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 this invention:

[0031] The data transmission optimization method of this invention employs an energy consumption model and an evaluation model, simultaneously ensuring the reliability, real-time performance, and stability of wireless transmission while minimizing energy consumption. This means that the lifespan of the wireless communication module will be significantly extended, which is especially important for devices operating in harsh environments where battery replacement is difficult. Furthermore, the optimization algorithm enables real-time optimization, responding to real-time changes in the wireless communication environment to optimize data transmission strategies and ensure data transmission quality. The improved firefly algorithm further enhances the efficiency and practicality of optimization, greatly reducing computational complexity and time, enabling the method of this invention to be effective in practical applications. In summary, this invention improves the performance and extends the lifespan of wireless communication modules, while also improving data transmission efficiency and quality, thus playing a positive role in promoting the development of wireless communication technology. Attached Figure Description

[0032] Figure 1 This is a flowchart of the method of the present invention;

[0033] Figure 2 This is a flowchart of the firefly population initialization method of the present invention;

[0034] Figure 3 This is a flowchart illustrating the free flight process of a firefly population according to the present invention.

[0035] Figure 4 This is a flowchart illustrating the neighboring flight process of firefly populations in this invention. Detailed Implementation

[0036] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0037] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

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

[0039] S1. Establish a power consumption model for the wireless communication module, including data sensing, processing, and transmission.

[0040] Energy consumption is a crucial factor to consider when optimizing data transmission, especially in wireless sensor networks. Therefore, establishing an energy consumption model for wireless sensors is essential.

[0041] The energy consumption model of the wireless communication module needs to take into account the different data transmission distances of different nodes of the sensor, the non-fixed data transmission path, and the different loads of different nodes.

[0042] The energy consumption model is as follows:

[0043]

[0044] Where E is the total power 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 power consumption per bit of data, εamp is the amplifier power consumption, k is the size of the data packet transmitted each time, and d i T is the transmission distance of node i. i E is the number of data packets sent by node i. DA It's the energy consumption of data aggregation, R i It represents the number of data packets received by node i.

[0045] This model performs independent energy consumption calculations for each node, thus reflecting the load differences between different nodes. Furthermore, by considering the transmission distance of each node and the number of data packets sent and received, it also takes into account the impact of data transmission paths and distances.

[0046] This model is suitable for short distances, assuming that each node's energy consumption mainly comes from sending and receiving data. For more complex networks, it may be necessary to introduce more parameters, such as path loss and inter-node interference.

[0047] S2. Establish a data transmission evaluation model for wireless communication modules to evaluate energy consumption models.

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

[0049] (1) Data transmission continuity; this can be measured by the duration of successful data transmission, or the probability of data packets being successfully transmitted within a certain period. Data transmission continuity can be calculated using the following formula:

[0050]

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

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

[0053]

[0054] Where R represents reliability, R i T is the number of data packets received by node i. i It represents the number of data packets sent by node i.

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

[0056]

[0057] Where 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 a holistic evaluation model. The data transmission evaluation model is as follows:

[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 weighting coefficients used to represent the relative importance of continuity, reliability, real-time performance 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 performance of data transmission and E is energy consumption.

[0061] This model unifies continuity, reliability, real-time performance, and energy consumption within a single framework. By adjusting the values ​​of α, β, γ, and δ, different performance metrics can be traded off according to actual needs. For example, if real-time performance is the most important requirement, the value of γ can be increased; if energy efficiency is a greater concern, the value of δ can be increased.

[0062] S3. Construct an optimization algorithm to optimize the energy consumption model and obtain the optimal data transmission method for the wireless communication module. The optimization algorithm adopts an improved firefly algorithm. Addressing the problem that the coordinate encoding method of the basic firefly algorithm is difficult to describe the data transmission path, an integer encoding system integrating virtual nodes is established. Furthermore, a firefly population initialization method based on search probability is proposed to ensure the success rate of firefly initialization. Based on neighborhood flight in the basic firefly algorithm, free flight is proposed to enhance the computational efficiency of the algorithm, and cross-domain flight is designed to avoid the algorithm getting trapped in local optima, forming a firefly flight strategy that coordinates free flight, neighborhood flight, and cross-domain flight.

[0063] Improvements include:

[0064] (1) Improved coding system: The basic firefly algorithm is coded according to the Cartesian coordinate system. Each firefly corresponds to a point in the feasible solution space of the Cartesian coordinate system, which is difficult to describe the data transmission path of the structure monitoring wireless communication module. The integer coding system proposed in this application encodes each firefly as a feasible data transmission path, and its coding is the number of all nodes on the data transmission path from the starting node to the data center (also known as the ending node).

[0065] Assuming there are N nodes in a certain monitoring area, all nodes, including the data center, are numbered without repetition. 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 C1 = {1, 4, 23, 27, 30}, C2 = {1, 8, 22, 30}, and C3 = {1, 13, 5, 7, 17, 21, 30}, respectively. On the data transmission path, the nodes other than the starting node and the ending node are called intermediate nodes.

[0066] Because the number of intermediate nodes on each data transmission path is not fixed, the encoding lengths of different fireflies are different. To facilitate subsequent firefly movement operations, a virtual node numbered 0 is inserted between the last intermediate node and the termination node to maintain the length of each encoding as N. It is worth noting that the virtual node is only used to maintain the encoding length. In the actual wireless communication module, there is no firefly ①, firefly ②, and firefly ③. The corrected encodings are: C1={1,4,23,27,0…30}, C2={1,8,22,0,…,0,30}, and C3={1,13,5,7,17,21,0,…,0,30}.

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

[0068] S301. Adjacency matrices are used to characterize whether different nodes are adjacent. Based on the assumption that a node can communicate with any node within its maximum data transmission distance range, the N×N dimensional adjacency matrix is ​​constructed as follows:

[0069]

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

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

[0072]

[0073] In the formula: P i,k d represents the search probability of the k-th neighboring node of node i. e,ik and d e,iu Let E represent the Euclidean distances between node i and its k-th and u-th adjacent nodes, respectively; W represents the total number of adjacent nodes of node i; E represents the total number of adjacent nodes of node i. L,k and E L,uI represents the remaining energy of the k-th and u-th adjacent nodes, respectively; n,k and I n,u These represent the idle degree of the k-th and u-th adjacent nodes, respectively.

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

[0075] S304. Repeat step S303 continuously. When the data reaches the termination node, it indicates that a complete data transmission path has been formed, and the search for the next node is stopped. Insert a virtual node between the last intermediate node and the termination node, thus completing the initialization of a firefly. If the search cannot reach the termination node, it indicates that the data transmission path cannot be completed, and the search starts again from the starting node.

[0076] (2) Improve flight strategies.

[0077] Since integer encoding is used, the Euclidean distance in the basic firefly algorithm cannot represent the distance between fireflies. Hamming distance is used to define the distance between fireflies.

[0078]

[0079] In the formula: d k,rs c represents the Hamming distance between firefly r and firefly s; r and c s These represent the codes for firefly r and firefly s, respectively. ∨ represents the logical XOR operation; ∨ represents the logical OR operation; ∧ represents the logical AND operation; Represents the logical NOT operation.

[0080] The brightness of fireflies is defined as the objective function value, i.e., the evaluation index RPI of wireless sensor network data transmission shown in Equation (2). The brightness (i.e., the objective function value) of fireflies in the population is calculated using Equation (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]) is randomly generated. When 0≤δ<1 / 3, free flight is performed; when 1 / 3≤δ<2 / 3, neighborhood flight is performed; and when 2 / 3≤δ≤1, cross-domain flight is performed.

[0082] like Figure 3 As shown, free flight is achieved by bypassing the worst-performing intermediate node on the data transmission path represented by the firefly itself, thereby increasing its brightness. The steps are as follows:

[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] In the formula, d e,d S represents the i-th intermediate node. i With the previous node S i-1 The Eulerian distance between them.

[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 Based on the search probability shown in equation (4), the roulette wheel method is used to search for a node in the set of adjacent nodes as the next temporary node S for data transmission. min T.

[0087] (3) If this temporary node S min T can be compared with the node S with the smallest performance index. min The next node S i+1 Communication is achieved using a temporary node S. min T replaces node S min This forms a new path, completing the firefly's free flight. Conversely, if this temporary node S... i-1 It cannot be with the node S with the smallest performance index. min The next node S i+1 Communication takes place at node S. i-1 Find the next temporary node S in the set of adjacent nodes. min T continues until a temporary node capable of communication is found, and this temporary node is then used to replace node S. min This creates new paths, enabling fireflies to fly freely.

[0088] like Figure 4 As shown, neighborhood flight refers to a firefly flying towards a brighter firefly in its neighborhood to increase its own brightness. The steps are as follows:

[0089] (1) Calculate the objective function value R of firefly r and firefly s in the population. r and R s If R r <R s Then firefly r moves toward firefly s.

[0090] (2) Randomly select a common node (excluding the endpoint) from the firefly r and firefly s codes, and use the d after the common node encoded by firefly s. m After replacing the common node of the firefly r-encoded intermediate node, d m There are intermediate nodes, d m Indicates the distance traveled. 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] In the formula: random(·) represents selecting (1, N) m A random number between N and 1. left,r and N left,s These represent the number of nodes (including intermediate nodes and virtual nodes) between the public node and the data center, respectively, encoded by firefly r and firefly s.

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

[0095] Cross-domain flight refers to fireflies flying towards the head of the cluster to increase their brightness and avoid getting trapped in local optima. The implementation steps are the same as those for territorial flight, only the target of the flight is different.

[0096] Example 1:

[0097] First, let's take a wireless communication network consisting of 50 nodes as an example, numbered from 1 to 50, where node 1 is the starting node and node 50 is the data center or termination node. We'll first establish an energy consumption model, assuming the fixed energy consumption per bit (Eelec) is 50 nJ / bit and the amplifier energy consumption (εamp) is 100 pJ / bit / m. 2 The energy consumption of data aggregation EDA is 5nJ / bit / signal.

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

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

[0100] Next, population initialization is performed. The adjacency matrix is ​​used to represent whether different nodes are adjacent. Based on the search probability, the roulette wheel method is used to search for a node in the set of adjacent nodes as the next node for data transmission until the data reaches the terminal node position, forming a complete data transmission path.

[0101] Finally, the flight strategy was improved by adopting cooperative flight, defining the distance between fireflies using Hamming distance, and performing three flight modes for searching: free flight, neighborhood flight, and cross-domain flight.

[0102] After a certain number of iterations, the optimal data transmission path was 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 for data transmission is the lowest, and the corresponding data transmission performance evaluation index P reaches the highest level, thus achieving data transmission optimization of the wireless communication module.

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

[0104] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing data transmission of a wireless communication module, characterized in that: The method comprises: A wireless communication module energy consumption model is established, including data sensing, data processing and data transmission; A wireless communication module data transmission evaluation model is established for evaluating the energy consumption model; An optimization solving algorithm is constructed to optimize and solve the energy consumption model, and the optimal wireless communication module data transmission method is obtained; The wireless communication module energy consumption model considers 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; The energy consumption model is: Where E is the total power 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 power consumption per bit of data, εamp is the amplifier power consumption, k is the size of the data packet transmitted each time, and d i T is the transmission distance of node i. i E is the number of data packets sent by node i. DA It's the energy consumption of data aggregation, R i It is the number of data packets received by node i; The data transmission evaluation model evaluates from the following aspects: (1) continuity of data transmission; (2) reliability of data transmission; (3) real-time performance of data transmission; The data transmission evaluation model is: P = α * C + β * R + γ * T - δ * E Wherein: P is the overall performance evaluation index, α, β, γ and δ are weight coefficients, indicating the relative importance of continuity, reliability, real-time performance 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 performance of data transmission, and E is the energy consumption; The optimization solving algorithm uses an improved firefly algorithm, and the improvement points include: (1) improving the coding system; (2) improving the population initialization method; (3) improving the flight strategy; The improved coding system, each firefly represents a feasible data transmission path, and its coding is the number of all nodes on the data transmission path from the starting node to the data center, also known as the termination node; The improved population initialization method comprises the following steps: 1) Use the adjacency matrix to represent whether different nodes are adjacent; 2) Calculate the search probability of adjacent nodes; 3) The current node searches a node in the adjacent node set as the next node of data transmission according to the search probability by using the roulette method; 4) Repeat step 3) constantly, 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; a virtual node is inserted between the last intermediate node and the termination node, that is, the initialization of one firefly is completed; The improved flight strategy uses cooperative flight, and the distance between fireflies is defined by using Hamming distance; the cooperative flight strategy includes three flight modes of free flight, neighborhood flight and cross-domain flight.

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