An energy-balanced routing algorithm based on transmission cost

Abstract

The present invention discloses an energy consumption balanced routing algorithm based on transmission cost. The method of selecting cluster heads is optimized through the energy gradient concept, a node evaluation function is constructed to establish a reasonable cluster group, the node evaluation function is updated after each round of data transmission, the importance of user data is classified and a decision index is quantitatively assigned using a hierarchical analysis method to construct a weight matrix, and finally a decision matrix is ​​constructed to search for the optimal node next-hop path to solve the technical problems of high network energy consumption, short overall network life time, and unbalanced network energy consumption in the prior art. Since the cluster heads screened out by the energy gradient are more reasonable, the problem of early death of low-energy nodes is avoided. The number of rounds of death of the first node in the network is delayed by nearly 300 rounds. The network system is complete before the death of the first node, and the life of the complete network system is extended by 33%. The decision matrix for calculating the cost calculates the path with the lowest transmission path energy consumption, ensures that the transmission energy consumption of each node is minimized, and the life cycle of the network system is extended by nearly 40%.

Description

Technical Field

[0001] The invention relates to an energy consumption balancing routing algorithm based on transmission cost, and belongs to the technical field of wireless sensor networks. Background Art

[0002] Wireless sensor technology is a rapidly developing information sensing technology. Wireless sensor networks, characterized by self-organization, ease of deployment, and high fault tolerance, are characterized by their self-organizing nature, making topology control crucial for maximizing their lifespan. These networks are widely used in military, agriculture, environmental monitoring, healthcare, industry, intelligent transportation, building monitoring, space exploration, and other fields. Sensor nodes are randomly distributed across the network in a self-organizing manner, collaboratively sensing, receiving, and transmitting information within their monitoring area. However, sensor nodes are typically battery-powered, resulting in rapid energy consumption. Therefore, balancing energy consumption and extending the lifespan of sensor nodes have become a research hotspot. Cluster routing algorithms optimize and aggregate transmitted data through clustering, reducing the number of nodes communicating directly with the base station. This reduces energy consumption while improving network scalability. Consequently, numerous cluster-based routing algorithms have been proposed. These algorithms include cluster head election, communication between cluster heads and regular nodes, and communication between cluster heads and base stations. The traditional cluster routing algorithm uses a random method to select cluster heads. As a result, nodes with low residual energy may serve as cluster heads multiple times in the network, causing the nodes to die quickly. In addition, the single-hop method and the direct communication between the aggregation node and the base station will cause high energy consumption, resulting in uneven network energy consumption. Summary of the Invention

[0003] The purpose of the present invention is to provide an energy-balanced routing algorithm based on transmission cost, optimize the selection method of cluster heads through the energy gradient concept, construct a node evaluation function to establish reasonable clusters, update the node evaluation function after each round of data transmission, classify the importance of user data and quantify and assign decision indicators using the hierarchical analysis method to construct a weight matrix, and finally construct a decision matrix to search for the optimal node next hop path, so as to solve the technical problems of high network energy consumption, short overall network life time and uneven network energy consumption in the prior art.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] An energy-balanced routing algorithm based on transmission cost includes the following steps:

[0006] (1) In the initial stage of the network, a number of required sensor nodes are randomly distributed in a fixed monitoring area, the properties of the sensor nodes are set, and the network data transmission method adopts the first-order wireless transmission energy consumption model;

[0007] (2) In the cluster head election phase, the energy gradient algorithm is used to establish a cluster head set that meets the conditions;

[0008] (3) In the node clustering phase, a node evaluation function is constructed, the set of node evaluation function values ​​is calculated, and the node clustering is completed. The cluster head election and node clustering process are periodically executed. After the cluster is determined, the TDMA time division multiple access time slot is established;

[0009] (4) After each round of data transmission is completed, update the node evaluation function;

[0010] (5) In the data preprocessing stage, data types are prioritized and classified for transmission, and quantified values ​​are assigned to data types using the hierarchical analysis method to establish a weighting matrix;

[0011] (6) In the data transmission stage, inter-cluster multi-hop routing transmission is adopted. A decision matrix is ​​established based on the decision indicators. The decision matrix is ​​standardized using the range change method to obtain a standardized decision matrix. The weighted matrix is ​​multiplied by the standardized decision matrix to obtain a standardized weighted decision matrix. The optimal next-hop path is searched based on the standardized weighted decision matrix to complete the data transmission of the entire network.

[0012] The purpose of the present invention can be further achieved by the following technical measures:

[0013] Furthermore, in the above-mentioned energy consumption balanced routing algorithm based on transmission cost, the first-order wireless transmission energy consumption model in step (1) is as follows:

[0014]

[0015] Where, k: amount of data transmitted, d: transmission distance, d0: transmission distance threshold, E elec : RF energy consumption coefficient, ξ fs : Free model space magnification, ξ mp : Multipath attenuation model amplification factor.

[0016] Furthermore, in the above energy consumption balanced routing algorithm based on transmission cost, the energy gradient algorithm in step (2) divides the node energy into three gradients, defines the energy coefficient ratio according to the network node density, calculates the node energy lower limit, compares the node energy, and completes the cluster head election.

[0017] The energy gradient algorithm steps are as follows:

[0018] Step 1: Define the energy proportional coefficient λ, the calculation formula is:

[0019]

[0020] Among them, ρ i : Node density within the node competition radius, Average density of network nodes;

[0021] Step 2: Calculate the node energy lower limit E th , the calculation formula is:

[0022]

[0023] in, Network average energy;

[0024] Step 3: All nodes compare node energy E at the beginning i and the lower limit energy E th , E i Greater than E th The nodes enter the valid cluster head set and complete the cluster head election.

[0025] Furthermore, in the above energy consumption balanced routing algorithm based on transmission cost, the node evaluation function in step (3) is as follows:

[0026]

[0027] Among them, E cur-i : Remaining energy of node i, d i : distance from node to cluster head, The average distance from all nodes to the cluster head, the influence factor is α+β=1.

[0028] Furthermore, in the above energy consumption balanced routing algorithm based on transmission cost, the influence factors in step (4) change accordingly. After each round of data transmission is completed, due to the changes in the number of surviving nodes, node energy, and node communication distance, the influence factors α and β will change according to the node residual energy E. cur-i , the distance from the node to the cluster head d i As for dynamic changes, when the residual energy of the node decreases, α increases; when the distance from the node to the cluster head d i When increases, β decreases.

[0029] Furthermore, in the above-mentioned energy consumption balanced routing algorithm based on transmission cost, the data preprocessing in step (5) is as follows:

[0030] Step 1: Prioritize the data types based on the importance of the data required by the user. Urgent data is defined as first-level priority data, and periodic data is defined as second-level priority data. First-level priority data includes: node remaining energy (E), number of hops to the base station (n), transmission distance (d), transmission efficiency (η), available bandwidth (B), etc.

[0031] Step 2: Use the analytic hierarchy process to quantify the importance of the first-level priority data into 1, 1 / a, 1 / b, 1 / c, 1 / d, a+b+c+d=9, and then determine the quantitative value of each parameter based on the relative importance of any two parameters;

[0032] Step 3: Combine the weights of each indicator to establish a weighted matrix WM. The calculation formula is:

[0033] WM=[W1 W2...W m ] T

[0034] Among them, W i Quantize the value of the i-th parameter.

[0035] Furthermore, in the above-mentioned energy consumption balanced routing algorithm based on transmission cost, the decision matrix is ​​established in step (6), and the steps are as follows:

[0036] Step 1: Establish the decision matrix DM based on the decision indicators. The calculation formula is:

[0037]

[0038] Among them, k is the number of effective next-hop cluster heads, m is the number of decision indicators, α k,m is the effective next hop cluster head CH k The mth decision indicator;

[0039] Step 2: Use the range change method to standardize the decision matrix. The calculation formula is:

[0040]

[0041]

[0042] Among them, A tj : Matrix element quantization value, X tj : Decision indicator, n i,j : Quantitative value of each row and column of decision indicators;

[0043] The standardized decision matrix SM is obtained, and the calculation formula is:

[0044]

[0045] Step 3: Weight the standardized decision matrix SM to obtain the standardized weighted decision matrix FM. The calculation formula is:

[0046]

[0047] Each element of FM represents the next hop cluster head CH j The cost of cj , the calculation formula is:

[0048]

[0049] Step 4: Each cluster head searches for the optimal next-hop path according to the decision matrix to complete the data transmission of the entire network.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) The cluster heads selected by energy gradient are more reasonable, which avoids the problem of early death of low-energy nodes. The first node of the traditional LEACH algorithm dies in about 500 rounds, while the first node of the PEGASIS algorithm dies in about 900 rounds. The number of rounds of death of the first node in the network is delayed by nearly 300 rounds.

[0052] (2) The complete network system is before the first node dies. The complete network system of the LEACH algorithm is 500 rounds ago, the complete network system of the PEGASIS algorithm is 900 rounds ago, and the complete network system of the present algorithm is 1200 rounds ago. The life of the complete network system is extended by 33%;

[0053] (3) The decision matrix of the calculated cost calculates the transmission path with the lowest energy consumption, ensuring that the transmission energy consumption of each node is minimized and the total energy consumption of the network is also minimized. The last node of the LEACH algorithm dies around 1250 rounds, the last node of the PEGASIS algorithm dies around 1100 rounds, and the last node of this algorithm dies around 1250 and 1750 rounds, and the life cycle of the network system is extended by nearly 40%. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a first-order wireless transmission energy consumption model diagram of the present invention;

[0055] Figure 2 This is a flow chart of an energy consumption balanced routing algorithm based on transmission cost of the present invention;

[0056] Figure 3 To compare the node survival status under different definitions using the method of the present invention with other methods under the same network conditions;

[0057] Figure 4 The network energy consumption under different definitions is compared using the method of the present invention with other methods under the same network conditions. DETAILED DESCRIPTION

[0058] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0059] refer to Figure 1This is a first-order wireless transmission energy consumption model diagram, which includes the transmitter, amplifier and output end. The data transmission energy consumption of the sensor node is calculated by the distance.

[0060] refer to Figure 2 This is a flowchart of an energy-balanced routing algorithm based on transmission cost. In the initial stage, the network is initialized and a cluster head set is established by calculating and comparing the node energy and the node energy lower limit. It is determined whether the node is within the communication range of the cluster head. The nodes within the communication range of the cluster head calculate the node evaluation function value and select the cluster head to join the cluster. After the node joins the cluster, a TDMA time division multiple access time slot is established. The inter-cluster multi-hop algorithm is used to select the next hop route searched through the decision matrix or directly transmit data to the base station. The cycle process ends when the network energy is exhausted.

[0061] The specific implementation is as follows:

[0062] a. In the initial stage of the network, set the sensor node properties and select the first-order wireless transmission model:

[0063] A number of required sensor nodes are randomly distributed in a fixed monitoring area. At the beginning, the base station sends a network access message, as shown in the attached Figure 1 As shown in Figure 2, the sensor nodes have simple information processing capabilities and can use data fusion technology to calculate the distance between them and the source node based on the strength of the received signal. They can control their own transmit and receive power based on the transmission distance. The base station has strong computing and information processing capabilities and sufficient energy. The transmission method adopts the first-order wireless transmission energy consumption model, as shown in the attached figure. Figure 1 As shown,

[0064]

[0065] b. In the cluster head election phase, the energy gradient algorithm is used. The energy gradient algorithm steps are as follows:

[0066] 1) Define the energy proportional coefficient λ, and the calculation formula is:

[0067]

[0068] 2) Calculate the node energy lower limit E th , the calculation formula is:

[0069]

[0070] 3) All nodes compare node energy E at the beginning i and the lower limit energy E th , E i Greater than E th The nodes enter the valid cluster head set and complete the cluster head election.

[0071] c. During the node clustering phase, a node evaluation function is constructed, and the calculation formula is:

[0072]

[0073] Calculate the set of node evaluation function values, complete the node clustering, periodically execute the cluster head election and node clustering process, and establish TDMA time division multiple access time slots after the clustering is determined.

[0074] d. Update the node evaluation function. After each round of data transmission is completed, due to the changes in the number of surviving nodes, node energy, and node communication distance, the impact factors α and β will be adjusted according to the node's remaining energy E. cur-i , the distance from the node to the cluster head d i As for dynamic changes, when the residual energy of the node decreases, α increases; when the distance from the node to the cluster head d i When increases, β decreases.

[0075] e. In the data preprocessing stage, the data and processing method steps are as follows:

[0076] 1) Prioritize the data types for transmission based on the importance of the data required by the user. Urgent data is defined as first-level priority data, and periodic data is defined as second-level priority data. The first-level priority data includes: node remaining energy (E), number of hops to the base station (n), transmission distance (d), transmission efficiency (η), available bandwidth (B), etc.

[0077] 2) Using the analytic hierarchy process, the importance of the first-level priority data is quantified as 1, 1 / a, 1 / b, 1 / c, 1 / d, a+b+c+d=9, and then the quantitative value of each parameter is determined according to the relative importance of any two parameters;

[0078] 3) Combine the weights of each indicator to establish a weighted matrix WM, and the calculation formula is:

[0079] WM=[W1 W2...W m ] T

[0080] f. In the data transmission phase, the data transmission steps are as follows:

[0081] 1) Establish the decision matrix DM according to the decision indicators. The calculation formula is:

[0082]

[0083] 2) Use the range change method to standardize the decision matrix. The calculation formula is:

[0084]

[0085]

[0086] The standardized decision matrix SM is obtained, and the calculation formula is:

[0087]

[0088] 3) Weight the standardized decision matrix SM to obtain the standardized weighted decision matrix FM, which is calculated as follows:

[0089]

[0090] Each element of FM represents the next hop cluster head CH j The cost of c j , the calculation formula is:

[0091]

[0092] 4), each cluster head searches for the optimal next-hop path according to the decision matrix to complete the data transmission of the entire network.

[0093] g. The present invention optimizes the selection of cluster heads through the energy gradient concept, constructs a node evaluation function to establish a reasonable cluster group, updates the node evaluation function after each round of data transmission, classifies the importance of user data, and uses the hierarchical analysis method to quantify and assign decision indicators to construct a weight matrix. Finally, the decision matrix is ​​constructed to search for the optimal node next hop path, so as to solve the technical problems of high network energy consumption, short overall network life time, and uneven network energy consumption in the existing technology, as shown in the attached figure. Figure 3 As shown in the figure, the death time of nodes in various algorithms is recorded. In the algorithm network of the present invention, the first node death round is delayed by nearly 150 rounds, the complete network system life cycle is extended by nearly 50%, and the total network life cycle is extended by 33%, which solves the problem of short network life cycle in the prior art. Figure 4 As shown in the figure, the average energy consumption of various algorithm networks is compared. The average energy consumption of the algorithm network per round is maintained between 0.00025-0.00035J, which is significantly lower than the traditional algorithm compared, solving the problems of high network energy consumption and unbalanced energy in the prior art.

[0094] The above implementation cases are only for illustrating the technical ideas of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solutions in accordance with the technical ideas proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. An energy consumption balanced routing method based on transmission cost, characterized in that: The method includes the following steps: (1) In the initial stage of the network, a number of required sensor nodes are randomly distributed in a fixed monitoring area, the properties of the sensor nodes are set, and the network data transmission method adopts the first-order wireless transmission energy consumption model; (2) In the cluster head election phase, the energy gradient algorithm is used to establish a cluster head set that meets the conditions; The energy gradient algorithm divides node energy into three gradients, defines the energy coefficient ratio according to the network node density, calculates the node energy lower limit, compares the node energy, and completes the cluster head election. The energy gradient algorithm steps are as follows: Step 1: Define the energy proportional coefficient λ, the calculation formula is: Among them, ρ i : Node density within the node competition radius, Average density of network nodes; Step 2: Calculate the node energy lower limit E th , the calculation formula is: in, Network average energy; Step 3: All nodes compare node energy E at the beginning i and the lower limit energy E th , E i Greater than E th The nodes enter the valid cluster head set and complete the cluster head election; (3) In the node clustering phase, a node evaluation function is constructed, the set of node evaluation function values ​​is calculated, and the node clustering is completed. The cluster head election and node clustering process are periodically executed. After the cluster is determined, the TDMA time division multiple access time slot is established; The node evaluation function is as follows: Among them, E cur-i : Remaining energy of node i, d i : distance from node to cluster head, The average distance from all nodes to the cluster head has an impact factor of α+β=1; (4) After each round of data transmission is completed, update the node evaluation function; The corresponding changes of the influencing factors are as follows: After each round of data transmission is completed, due to the changes in the number of surviving nodes, node energy, and node communication distance, the influencing factors α and β will change according to the node remaining energy E. cur-i , the distance from the node to the cluster head d i As for dynamic changes, when the residual energy of the node decreases, α increases; when the distance from the node to the cluster head d i When increases, β decreases; (5) In the data preprocessing stage, data types are prioritized and classified for transmission, and quantified values ​​are assigned to data types using the hierarchical analysis method to establish a weighting matrix; The data preprocessing steps are as follows: Step 1: Prioritize the data types based on the importance of the data required by the user. Urgent data is defined as first-level priority data, and periodic data is defined as second-level priority data. The first-level priority data includes: node remaining energy (E), number of hops to the base station (n), transmission distance (d), transmission efficiency (η), and available bandwidth (B); Step 2: Use the analytic hierarchy process to quantify the importance of the first-level priority data into 1, 1 / a, 1 / b, 1 / c, 1 / d, a+b+c+d=9, and then determine the quantitative value of each parameter based on the relative importance of any two parameters; Step 3: Combine the weights of each indicator to establish a weighted matrix WM. The calculation formula is: WM=[W1 W2...W m ] T Among them, W i Quantize the value of the i-th parameter; (6) In the data transmission phase, inter-cluster multi-hop routing transmission is adopted. A decision matrix is ​​established based on the decision index. The decision matrix is ​​standardized using the range change method to obtain a standardized decision matrix. The weighted matrix is ​​multiplied by the standardized decision matrix to obtain a standardized weighted decision matrix. The optimal next-hop path is searched based on the standardized weighted decision matrix to complete the data transmission of the entire network. The steps to build a decision matrix are as follows: Step 1: Establish the decision matrix DM based on the decision indicators. The calculation formula is: Among them, k is the number of effective next-hop cluster heads, m is the number of decision indicators, α k,m is the effective next hop cluster head CH k The mth decision indicator; Step 2: Standardize the decision matrix using the range change method. The calculation formula is: Among them, A tj : Matrix element quantization value, X tj : Decision indicator, n i,j : Quantitative value of each row and column of decision indicators; The standardized decision matrix SM is obtained, and the calculation formula is: Step 3: Weight the standardized decision matrix SM to obtain the standardized weighted decision matrix FM. The calculation formula is: Each element of FM represents the next hop cluster head CH j The cost of c j , the calculation formula is: Step 4: Each cluster head searches for the optimal next-hop path according to the decision matrix to complete the data transmission of the entire network.

2. The energy consumption balanced routing method based on transmission cost according to claim 1, characterized in that: The first-order wireless transmission energy consumption model in step (1) is as follows: Where, k: amount of data transmitted, d: transmission distance, d0: transmission distance threshold, E elec : RF energy consumption coefficient, ξ fs : Free model space magnification, ξ mp : Multipath attenuation model amplification factor.

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