WAPI-oriented low-power-consumption data transmission method and system

Abstract

The invention provides a WAPI-oriented low-power-consumption data transmission method and a WAPI-oriented low-power-consumption data transmission system, which belong to the technical field of wireless communication of a power system, and are used for acquiring the number of neighbor nodes of each node, the distance between each node and a base station calculated by each node according to the strength of a received broadcast signal, and the residual energy of each node; calculating an energy factor, a distance factor and a density factor; and selecting a cluster head node according to a cluster head node selection threshold value calculated by the energy factor, the distance factor and the density factor, and performing clustering by adopting a WAPI-based authentication mechanism. According to the method, factors such as node residual energy, the distance between the node and the base station and the node distribution density are fully considered, so that cluster head election is more reasonable, cluster head distribution in the region is more uniform, and the load balancing capacity of the WSN is improved; and meanwhile, the residual energy of the cluster head node and the distance from the common node to the cluster head node are comprehensively considered, so that the common node can enter the cluster more reasonably.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication of electric power systems, and in particular to a WAPI-oriented low-power data transmission method and system. Background Art

[0002] As the hub of the power system, substations occupy an important position in the operation of the power grid. With the development of smart grids, the number of sensors and smart terminals in substations has increased rapidly. Production management-related services, such as substation video monitoring, environmental intelligent monitoring, and Internet of Things services, have gradually shown requirements for large bandwidth, multiple links, and mobility. Traditional substations mainly transmit services through wired methods (optical fiber, power lines, etc.), but wired communications are limited by construction costs and difficulties, and have gradually become a bottleneck restricting the further development of power grid intelligence. The development of wireless communication technology has provided technical support for the construction and development of power systems. The use of wireless communication technology for remote information collection and monitoring has enhanced the intelligent management of power systems and improved the work efficiency of power systems. However, there are few wireless communication technologies currently used in substations, mainly concentrated on the edge of the distribution network. At the same time, traditional wireless communication technologies such as public network 4G and Wi-Fi have problems such as weak public network wireless signal coverage, service access requires traversing internal and external networks, and poor security, which limits the application of wireless communication technology in power systems. At the same time, there are many sensor detection devices in substations, which are widely distributed and in different environments. In addition, existing wireless devices have difficulty in obtaining power in substation scenarios. Most sensor deployment environments do not have power supply conditions and have extremely strict power consumption requirements. Therefore, power consumption has also become a key issue restricting the application of wireless communication technology in substation scenarios.

[0003] At present, the deployment and application of WAPI wireless network has improved the security of information transmission in power system, but there is little research on low power consumption of WAPI. In substations with many wireless sensors, it is necessary to study how to reduce network power consumption while ensuring the security of information transmission. Sensor nodes in substations are usually deployed in places with complex environments to collect data in places that are difficult for manpower to reach. Usually, the environment where sensor nodes are deployed does not have direct power supply conditions and is difficult to maintain. Their lifespan is usually determined by battery energy.

[0004] In the classic LEACH routing protocol, it is assumed that all nodes can communicate with the base station. However, in practice, the connectivity of wireless communication is limited by distance. When the distance between the cluster head node and the base station is too far, the reliability of this single-hop communication method cannot be guaranteed. In addition, in the wireless communication energy consumption model, the power consumption of node communication is related to the communication distance. When the communication distance is too far, the power consumption will increase significantly. At the same time, the node that becomes the cluster head assumes the function of receiving and sending data, and the energy consumption is much greater than that of ordinary nodes. However, in the classic LEACH protocol, the current remaining energy of the node is not considered, and the node is still selected as the cluster head based on probability, which greatly accelerates the death of the node.

[0005] In the subsequent improvement of the LEACH protocol, some scholars proposed the LEACH-EE protocol, which proposed a short-distance multi-hop data transmission method between the cluster head node and the base station, which can effectively reduce the energy consumption of data transmission. However, the LEACH-EE protocol still does not consider the residual energy of the node. In addition, some scholars further optimized the LEACH-EE protocol and proposed the IMP-LEACH protocol, which fully considered the residual energy of the node and the geographical location information of the node during the cluster head election stage, avoiding low-energy nodes from becoming cluster head nodes and improving the service life of the node. However, the IMP-LEACH protocol does not consider the density of sensor node deployment. In the substation application scenario, the deployment of sensor nodes is not uniform, so the impact of node density on cluster head election should be considered.

[0006] In the paper "Improved Cluster Routing Algorithm Based on Node Location and Residual Energy", a LEACH-EPN protocol is proposed, which comprehensively considers the node residual energy, node geographical location and node distribution density. However, in the LEACH-EPN protocol, the analysis of node density only considers the number of nodes, while ignoring the influence of node communication radius. In practice, the communication radius of wireless communication nodes is related to their own energy, so the node density should be considered in combination with the node communication radius. In addition, in the LEACH-EPN protocol, the node clustering condition does not consider the influence of the node communication radius. Summary of the invention

[0007] The object of the present invention is to provide a low-power data transmission method and system for WAPI to solve at least one technical problem existing in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a low-power data transmission method for WAPI, comprising:

[0010] Obtain the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0011] Calculate the energy factor, distance factor and density factor according to the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0012] The cluster head node is elected according to the cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node transmits data stably based on multi-hop relay until the end of the round.

[0013] Furthermore, the residual energy factor considering the node residual energy is calculated as:

[0014]

[0015] Among them, E icurrent is the current remaining energy of node i, E0 is the initial energy of the node, E avg is the average remaining energy of the current surviving nodes.

[0016] Furthermore, the distance factor considering the distance between the node and the base station is calculated as:

[0017]

[0018] Among them, d0 is the WSN energy consumption model division threshold, d i is the distance between the node and the base station, and a is the path loss exponent.

[0019] Furthermore, the density factor is calculated as:

[0020]

[0021] Among them, N iR is the number of all neighbor nodes within the communication radius of node i; N alive is the number of surviving nodes in the entire network, R i is the communication radius of node i.

[0022] Furthermore, the rules for common nodes to join the cluster are:

[0023] Condition 1: Ordinary node j is within the communication radius of cluster head node i, which ensures the connectivity between ordinary nodes and cluster head nodes;

[0024] Condition 2: If ordinary node j is covered by two or more cluster head nodes at the same time, that is, there are multiple cluster head nodes that meet condition 1. In this case, the priority clustering probability is calculated by comprehensively considering the communication radius of the cluster head node, the distance from the ordinary node to the cluster head node, and the remaining energy of the cluster head node. The ordinary node selects the largest cluster head node to enter the cluster based on the calculated series of clustering priority probabilities.

[0025] Furthermore, the probability formula for priority clustering is:

[0026]

[0027] Among them, E icurrent is the remaining energy of the current cluster head node i; E avg is the average remaining energy of all nodes in the current network; R ci is the current communication radius of cluster head node i; dij represents the distance from ordinary node j to cluster head node i.

[0028] Furthermore, the cluster head selection threshold calculation formula is:

[0029]

[0030] Among them, E i is the residual energy factor; D i is the distance factor; N i is the density factor.

[0031] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the low-power data transmission method for WAPI as described in the first aspect is implemented.

[0032] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, the processor and the memory communicating with each other, the memory storing program instructions executable by the processor, and the processor calling the program instructions to execute the low-power data transmission method for WAPI as described in the first aspect.

[0033] In a fifth aspect, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the low-power data transmission method for WAPI as described in the first aspect.

[0034] The invention has the following beneficial effects: by introducing the residual energy factor, the distance factor and the node density factor, the factors such as the residual energy of the node, the distance between the node and the base station and the node distribution density are fully considered, so that the cluster head election is more reasonable, the cluster head distribution in the area is more uniform, and the load balancing ability of the WSN network is improved; at the same time, the concept of cluster entry priority probability is proposed in the stage of ordinary node cluster entry, and the residual energy of the cluster head node and the distance between the ordinary node and the cluster head node are comprehensively considered, so that the ordinary node cluster entry is more reasonable.

[0035] Additional advantages of the present invention will be more clearly given in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0037] Figure 1 Schematic diagram of the WSN energy consumption model described in an embodiment of the present invention.

[0038] Figure 2 This is a flowchart of the EIMP-LEACH algorithm described in an embodiment of the present invention.

[0039] Figure 3 This is a flow chart of the clustering method described in an embodiment of the present invention.

[0040] Figure 4 This is a node deployment distribution diagram described in an embodiment of the present invention.

[0041] Figure 5 This is a comparison diagram of the number of cluster head nodes described in an embodiment of the present invention.

[0042] Figure 6 This is a relationship diagram between the number of surviving nodes and the number of cycles described in an embodiment of the present invention.

[0043] Figure 7 This is a relationship diagram between the death node ratio and the number of cycles described in an embodiment of the present invention.

[0044] Figure 8 This is a comparison diagram of the system residual energy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0046] It should be understood by those skilled in the art that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0047] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0048] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0050] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0051] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0052] At present, wireless communication is less used in substations. The main reason is the security and stability of wireless communication. The wireless public network is easily attacked and monitored. Therefore, the present invention improves on the basis of the existing low-power LEACH protocol to meet the low-power WAPI network, thereby meeting the needs of substations for wireless networks. And further analyze the basic principles of the LEACH protocol and the shortcomings of the existing improved LEACH protocol, and propose the EIMP-LEACH protocol (enhance-improvement-LEACH enhanced improved LEACH protocol). By introducing the node residual energy factor, node distance factor, and node density factor to improve the threshold formula of cluster head election, comprehensively consider the factors such as node residual energy, node distance from the base station, node communication radius, and node distribution density, and add the constraint condition of the optimal number of clusters, so that the cluster head election is more reasonable, the cluster head distribution in the area is more balanced, so that the energy consumption distribution is more uniform, and the network life is further improved. In addition, the present invention proposes a more reasonable node clustering mechanism, which comprehensively considers the two key influencing factors of the distance between the ordinary node and the cluster head node and the residual energy of the cluster head node, and jointly determines the ordinary node clustering rules. In the data transmission stage, the cluster head network is used to select relays for data forwarding to transmit data to the base station, which overcomes the problem of high energy consumption when the traditional LEACH protocol cluster head node directly transmits data to the base station.

[0053] Example 1

[0054] In this embodiment 1, a low-power data transmission method for WAPI is provided, and the method includes the following steps: obtaining the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node; calculating the energy factor, distance factor and density factor according to the obtained number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node; electing a cluster head node according to a cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the unelected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node performs stable data transmission according to the multi-hop relay until the end of the round.

[0055] Specifically, in this embodiment, the base station broadcasts in the deployment area, and each node calculates the distance between itself and the base station through the received signal strength; the node calculates the number of neighboring nodes, and reports it to the base station together with its own remaining energy and the calculated distance between the node and the base station, and calculates the remaining energy factor, distance factor, and node density factor; each node generates a random number in the range of 0 to 1, and compares it with the improved cluster head election threshold formula, and elects the cluster head node under the condition of meeting the optimal number of cluster heads, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the improved cluster entry conditions and at the same time, adopt the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node performs stable data transmission based on multi-hop relay until the end of the round.

[0056] In this embodiment, the residual energy factor considering the node residual energy is calculated as:

[0057]

[0058] Among them, E icurrent is the current remaining energy of node i, E0 is the initial energy of the node, E avg is the average remaining energy of the current surviving nodes.

[0059] The distance factor considering the distance between the node and the base station is calculated as:

[0060]

[0061] Among them, d0 is the WSN energy consumption model division threshold, d i is the distance between the node and the base station, and a is the path loss exponent.

[0062] The density factor is calculated as:

[0063]

[0064] Among them, N iR is the number of all neighbor nodes within the communication radius of node i; N alive is the number of surviving nodes in the entire network, R i is the communication radius of node i.

[0065] The rules for common nodes to join the cluster are:

[0066] Condition 1: Ordinary node j is within the communication radius of cluster head node i, which ensures the connectivity between ordinary nodes and cluster head nodes;

[0067] Condition 2: If ordinary node j is covered by two or more cluster head nodes at the same time, that is, there are multiple cluster head nodes that meet condition 1. In this case, the priority clustering probability is calculated by comprehensively considering the communication radius of the cluster head node, the distance from the ordinary node to the cluster head node, and the remaining energy of the cluster head node. The ordinary node selects the largest cluster head node to enter the cluster based on the calculated series of clustering priority probabilities.

[0068] The probability formula for priority clustering is:

[0069]

[0070] Among them, E icurrent is the remaining energy of the current cluster head node i; E avg is the average remaining energy of all nodes in the current network; R ci is the current communication radius of cluster head node i; dij represents the distance from ordinary node j to cluster head node i.

[0071] The cluster head selection threshold calculation formula is:

[0072]

[0073] Among them, E i is the residual energy factor; D i is the distance factor; N i is the density factor.

[0074] Example 2

[0075] In this embodiment 2, based on the analysis of the classic LEACH algorithm, the cluster head election threshold formula in the LEACH algorithm is reasonably improved, and the conditions for ordinary nodes to enter the cluster are readjusted. The specific improvement scheme described in this embodiment and the parameters and model definitions involved are detailed as follows.

[0076] In this embodiment, for the network model, the proposed improved LEACH algorithm (EIMP-LEACH) has the same network model as the LEACH algorithm. At the same time, in order to ensure the stability of the WSN network and the convenience of simulation and avoid the influence of other factors, the WSN network model is stipulated as follows in this embodiment: (1) The wireless sensor nodes have the same communication capability, the same initial energy, and a unique identity code; (2) Each node in the deployment area has the ability to communicate with the base station. When communicating with the base station, it is not necessary to consider its communication radius. (3) The nodes are randomly deployed in the area and will not move after the deployment is completed; (4) The base station (sink node) is fixed in position and has a stable power supply, and the computing and data processing capabilities are not limited.

[0077] For the energy model, the energy consumption model of the sensor node mainly includes: perception consumption, communication consumption and data processing consumption, among which communication consumption accounts for the main part of the energy consumption. Based on this, in this embodiment, a free space model and a multipath fading model are established according to the communication distance between the sending node and the receiving node, such as Figure 1 shown.

[0078] According to the first-order radio communication energy consumption model, the energy consumed by cluster head node i to send l bits of data to the base station at distance d is calculated by formula (1), and the model division threshold is determined by formula (2).

[0079]

[0080] Where: E elec is the unit energy consumption of the transmitting and receiving circuits; when d<d0, the free space model is selected; when d>d0, the multipath fading model is selected. ε fs and ε mp are the transmission power amplifier energy consumption parameters under the free space channel model (a=2) and the multipath fading channel model (a=4). d0 is the model division threshold.

[0081] In this embodiment, based on the study of the existing improved LEACH algorithm, the shortcomings of the existing improved LEACH algorithm are analyzed, and an improved LEACH algorithm (EIMP-LEACH) that comprehensively considers the node residual energy, the node position from the base station, and the node distribution density is proposed. The improvements are mainly reflected in: ① Considering the factors such as the node's residual energy, the distance from the base station, and the node density, the cluster head node selection threshold formula is redefined; ② The cluster head is selected through the new threshold formula, and the optimal number of cluster heads is specified; ③ The rules for ordinary nodes to enter the cluster are redefined, and two-way selection is adopted between the node and the cluster head node, and the residual energy of the cluster head node is fully considered based on the distance of the ordinary node from the cluster head node. The basic step flow chart of the improved LEACH algorithm (EIMP-LEACH algorithm) is shown below. Figure 2 As shown. First, cluster head election is performed based on the proposed improved cluster head election threshold formula, and then clustering is performed based on the proposed improved common node entry conditions; the base station counts the location information and number of cluster heads in the current round, broadcasts the cluster head networking command, and the cluster head node performs inter-cluster head relay networking after receiving the command; the cluster head collects the information collected by other nodes in the cluster, and transmits the information to the base station according to the path after networking in step three.

[0082] In the cluster routing protocol, the number of cluster heads has a great impact on network performance. The optimal number of cluster heads is:

[0083]

[0084] Where Nalive represents the number of surviving nodes in the region; M represents the side length of the region; Indicates the distance from the cluster head node to the base station.

[0085] In practice, the communication range of a wireless sensor is related to the transmission power of the sensor node, and the transmission power is limited by the energy of the sensor node. Generally, the greater the energy of the wireless sensor, the greater its communication radius; the smaller the energy of the wireless sensor, the smaller its communication radius. Therefore, in order to be more in line with the actual situation, this embodiment adjusts the communication radius of the node according to the energy size of the sensor node, and improves the communication node radius. The method is:

[0086]

[0087] In the formula, R i is the communication radius of sensor node i; R max is the maximum communication radius of the sensor node; E icurrent is the current remaining energy of sensor node i; E0 is the initial energy of sensor node; E min To ensure the minimum energy for sensor nodes to survive.

[0088] The cluster head node plays the role of data aggregation within the cluster and data forwarding between clusters. It frequently communicates with ordinary nodes within the cluster and other cluster head nodes, and its energy consumption is much greater than that of ordinary nodes. Therefore, how to select cluster head nodes is the key to balancing network power consumption and extending network life. In this embodiment, the cluster head election method is improved by comprehensively considering three aspects: node residual energy, node location from the base station, and node distribution density.

[0089] In the above assumptions, each sensor node in the WSN network carries the same amount of energy at the beginning of deployment, but as data is aggregated and forwarded, some nodes consume too much energy, such as cluster head nodes and nodes in areas where data transmission is more frequent. When the energy of a node is exhausted, the node becomes a dead node and no longer participates in data transmission in the network. Node death will affect data transmission in some areas, and in severe cases will affect the connectivity of the entire WSN network. When electing cluster head nodes, the remaining energy of the node should be fully considered to ensure the balance of energy consumption in the WSN network. Therefore, the residual energy factor calculation method considering the residual energy of the node in this embodiment is expressed as follows:

[0090]

[0091] Where E icurrent is the current remaining energy of node i, E0 is the initial energy of the node, E avg is the average remaining energy of the current surviving nodes, and its calculation formula is:

[0092] The above expression divides the nodes of the whole network into nodes with residual energy greater than the average residual energy of the whole network and nodes with residual energy less than the average residual energy of the whole network according to the average residual energy of the whole network, so that the nodes with residual energy greater than the average residual energy of the whole network have a greater probability of being elected as cluster heads. However, as the network runs, the residual energy of the nodes and the average residual energy of the whole network gradually decrease, and the differentiation of their ratios is not large. Therefore, this embodiment also comprehensively considers the current residual energy of the nodes and the initial energy of the nodes, so that the nodes with larger current residual energy are more likely to be elected as cluster head nodes, making the balance of energy factors in cluster head election more reasonable.

[0093] It can be seen from the WSN energy consumption model that when the amount of data sent by the nodes is the same, the power consumption of the nodes mainly depends on the communication distance. Therefore, when conducting low-power research on WSN networks, the distance between the sending node and the receiving node should be considered. In the LEACH protocol, the cluster head node plays the role of data aggregation within the cluster and data forwarding outside the cluster. The amount of data forwarded is much larger than the amount of data sent by ordinary nodes to the cluster head node. Therefore, when selecting the cluster head, the location of the node from the base station should be fully considered. Therefore, this embodiment proposes a distance factor that considers the distance between the node and the base station. The calculation method is as follows:

[0094]

[0095] Where d0 is the WSN energy consumption model division threshold, d i is the distance between the node and the base station, a is the path loss exponent, a=2 in the free space channel model, and a=4 in the multipath fading channel model.

[0096] The above formula (6) ensures that the probability of a node far away from the base station being elected as a cluster head is small, and at the same time stipulates that i When d<0, a=2; when d i When >d0, a=4, which ensures that the node whose distance from the base station is greater than d0 has a smaller probability of being elected as the cluster head.

[0097] In the substation application scenario, the distribution of wireless sensor nodes is usually uneven. In the vicinity of important power equipment, such as transformers, cables, busbars, etc., there are a large number of sensors deployed with a large density. The data transmitted is frequent and the data volume is large. If the number of cluster head nodes in this area does not match the node density, the cluster head nodes will die quickly, affecting the network life. Therefore, the node distribution density should be fully considered when performing cluster head election. Based on the above analysis, the concept of cluster head election density factor is proposed in this embodiment. The calculation method of density factor is as follows:

[0098]

[0099] Where NiR is the number of all neighbor nodes within the communication radius of node i; N alive is the number of surviving nodes in the entire network, R i is the communication radius of node i. m Indicates the length of the substation area set for simulation, y m Indicates the width of the substation area in the simulation setting.

[0100] Analyzing the above formula, we can get that at the beginning of network deployment, N iR The larger the density factor N i The larger the N iR The smaller the density factor N i The smaller it is, the smaller the node communication radius is, and the number of neighbor nodes of the node decreases with the consumption of energy. However, no node death will occur in a short time, which will lead to the deterioration of the balance of the density factor. Therefore, the above formula fully considers the communication radius of the node when calculating the density node. Even if the communication radius is reduced, the purpose of cluster head election balance can be achieved.

[0101] In the cluster head election process, the energy differences of different cluster head nodes will lead to different communication radius. At the same time, due to the different energy of cluster head nodes, the number of ordinary nodes they accommodate is also different. Considering the above two factors, the following conditions for ordinary nodes to enter the cluster are given:

[0102] Condition 1: Ordinary node j is within the communication radius of cluster head node i, which ensures the connectivity between ordinary nodes and cluster head nodes;

[0103] Condition 2: If common node j is covered by two or more cluster head nodes at the same time, that is, there are multiple cluster head nodes that meet condition 1, in this case, the present invention comprehensively considers the three factors of the communication radius of the cluster head node, the distance from the common node to the cluster head node, and the remaining energy of the cluster head node, and proposes a priority clustering probability. In addition, the common node selects the largest cluster head node to enter the cluster based on a series of calculated priority clustering probabilities CHO(i,j). The formula for priority clustering probability is:

[0104]

[0105] Where E icurrent is the remaining energy of the current cluster head node i; E avg is the average remaining energy of all nodes in the current network; R ci is the current communication radius of cluster head node i; dij represents the distance from ordinary node j to cluster head node i.

[0106] Based on the above analysis, the improvement of the cluster head selection threshold formula can be expressed as follows:

[0107]

[0108] Where E i is the residual energy factor; D i is the distance factor; N i is the density factor, the percentage of p nodes becoming cluster head nodes, r is the current round number, and G is the set of nodes that have not been elected as cluster heads in the most recent 1 / p rounds.

[0109] In this embodiment, the overall clustering flow chart of the improved EIMP-LEACH algorithm is as follows: Figure 3 As shown, the method comprises the following steps: Step 1: the base station broadcasts in the deployment area, and each node calculates the distance between itself and the base station by the received signal strength; Step 2: the node calculates the number of neighboring nodes, and reports the number of neighboring nodes, together with its own remaining energy and the distance between the node and the base station calculated in Step 1, to the base station; Step 3: the base station calculates the remaining energy factor, the distance factor, and the node density factor according to the data obtained in Step 1 and Step 2; Step 4: each node generates a random number in the range of 0 to 1, and compares it with the improved cluster head election threshold formula, and elects the cluster head node under the condition of meeting the optimal number of cluster heads, and the un-elected nodes become ordinary nodes; Step 5: after the cluster head election is completed, the ordinary nodes are clustered according to the improved cluster entry conditions and the authentication mechanism based on WAPI; Step 6: after the clustering is completed, the cluster head node performs stable data transmission according to the multi-hop relay until the end of the round.

[0110] Based on the above theoretical analysis, in this embodiment, the improved algorithm is simulated using the Matlab simulation platform, and the simulation results are compared with the classic LEACH algorithm and the IMP-LEACH algorithm. The initial parameters used in the simulation are shown in Table 1 below:

[0111] Table 1 Simulation initialization parameters

[0112]

[0113]

[0114] Using the Matlab simulation platform, 400 nodes are randomly generated in an area of ​​100m×100m, and the base station is deployed at the position of x=50, y=120. The node distribution is as follows: Figure 4 shown.

[0115] The number of cluster head nodes in each round of LEACH, IMP-LEACH algorithm and EIMP-LEACH algorithm proposed in this embodiment is compared and analyzed. Figure 5 Analysis Figure 5It can be seen that the algorithm proposed in this embodiment introduces the optimal number of cluster heads to limit the number of cluster head elections, so the number of cluster head nodes in each round is relatively balanced. Compared with the LEACH and IMP-LEACH algorithms, the situation where all nodes are elected as cluster heads in some rounds will not occur, making the cluster head election more reasonable.

[0116] The number of surviving nodes in each round of each algorithm is compared and analyzed, such as Figure 6 As shown, in addition, this embodiment further statistics the relationship between the number of cycles and the node death ratio, as shown in Figure 7 As shown. Figure 6 It can be seen that the number of surviving nodes of the improved LEACH algorithm (EIMP-LEACH) in this embodiment is significantly higher than that of the LEACH and IMP-LEACH algorithms under the same number of cycles. No node survives after about 1576 rounds of the LEACH algorithm; all nodes of the IMP-LEACH algorithm run out of energy after 1790 rounds, while all nodes of the improved EIMP-LEACH algorithm proposed in this embodiment die after 2046 rounds. This shows that the algorithm improved in this embodiment can greatly delay the death time of nodes and significantly increase the network life. Analysis Figure 7 It can be seen that the EIMP-LEACH algorithm proposed in this embodiment can greatly improve the network life, especially in the early stage of sensor network operation, the difference is obvious, which shows that the EIMP-LEACH algorithm proposed in this embodiment has good load balancing ability at the beginning of network deployment. Figure 8 It can be seen that in the same round, the residual energy of the LEACH algorithm and the IMP-LEACH algorithm is lower than that of the EIMP-LEACH algorithm proposed in this embodiment. This shows that the EIMP-LEACH algorithm proposed in this embodiment can effectively reduce network energy consumption, has better load balancing capability, and the overall performance of the network is more superior.

[0117] The full name of LEACH protocol is "Low Power Adaptive Clustering Hierarchical Protocol", which is a wireless sensor network routing protocol. The algorithm based on the LEACH protocol is called the LEACH algorithm. The basic idea of ​​the algorithm is to randomly select cluster head nodes in a cyclic manner and evenly distribute the energy load of the entire network to each sensor node, so as to reduce network energy consumption and improve the overall survival time of the network. The LEACH routing protocol is mainly divided into two stages: cluster establishment stage and stable operation stage. The total time of the cluster establishment stage and the stable operation stage is a round. At the same time, in order to reduce the overhead of the protocol, the duration of the stable operation stage is greater than that of the cluster establishment stage. In the cluster establishment stage, the sensor node randomly generates a random number between 0 and 1, and compares it with the threshold T(n). If it is less than the threshold, the node will be elected as the cluster head. The calculation formula of T(n) is as follows:

[0118]

[0119] Where: P is the percentage of nodes that become cluster head nodes, r is the current round number, and G is the set of nodes that have not been elected as cluster heads in the most recent 1 / P rounds. After the cluster head node is selected, it broadcasts the message of becoming a cluster head. The remaining sensor nodes decide which cluster to join based on the received signal strength and inform the cluster head to complete the cluster establishment process.

[0120] In the stable operation phase, the sensor nodes transmit the collected data to the cluster head node, which then fuses the collected data and transmits the information to the base station. After the stable operation phase lasts for a period of time, the network re-enters the cluster establishment phase for the next round of cluster reconstruction.

[0121] WAPI (WLAN Authentication and Privacy Infrastructure) is a wireless LAN security protocol that uses the advanced ternary peer-to-peer (TePA) network security technology architecture to make up for the defects and loopholes in WiFi network architecture and protocol design, and has high security.

[0122] In summary, the low-power clustering routing improved algorithm EIMP-LEACH proposed in this embodiment solves the problems of unbalanced cluster head election and poor network load balancing ability in the traditional LEACH algorithm, which leads to large network energy consumption and short network life. The proposed clustering routing algorithm EIMP-LEACH is an improvement of the classic clustering routing algorithm LEACH. The most important improvement is to improve the threshold formula in the cluster head election stage. By introducing the residual energy factor, distance factor, and node density factor, it fully considers the factors such as the node residual energy, the distance between the node and the base station, and the node distribution density, making the cluster head election more reasonable, the cluster head distribution in the area more uniform, and improving the load balancing ability of the WSN network; at the same time, the concept of clustering priority probability is proposed in the stage of ordinary node clustering, which comprehensively considers the residual energy of the cluster head node and the distance between the ordinary node and the cluster head node, making the ordinary node clustering more reasonable. In addition, the proposed EIMP-LEACH algorithm, the classic LEACH algorithm and the IMP-LEACH algorithm were simulated and analyzed using the Matlab simulation platform. The results show that compared with the traditional LEACH algorithm and the IMP-LEACH algorithm, the proposed algorithm has better load balancing capability and better overall performance. Under the same experimental conditions, it effectively reduces network power consumption and greatly improves network life.

[0123] Example 3

[0124] This embodiment 3 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the WAPI-oriented low-power data transmission method as described above is implemented. The method includes:

[0125] Obtain the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0126] Calculate the energy factor, distance factor and density factor according to the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0127] The cluster head node is elected according to the cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node transmits data stably based on multi-hop relay until the end of the round.

[0128] Example 4

[0129] This embodiment 4 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, the processor calls the program instructions to execute the WAPI-oriented low-power data transmission method as described above, the method comprising:

[0130] Obtain the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0131] Calculate the energy factor, distance factor and density factor according to the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0132] The cluster head node is elected according to the cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node transmits data stably based on multi-hop relay until the end of the round.

[0133] Example 5

[0134] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the WAPI-oriented low-power data transmission method as described above, and the method includes:

[0135] Obtain the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0136] Calculate the energy factor, distance factor and density factor according to the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node;

[0137] The cluster head node is elected according to the cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node transmits data stably based on multi-hop relay until the end of the round.

[0138] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A low-power data transmission method for WAPI, characterized in that: include: Obtain the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node; Calculate the energy factor, distance factor and density factor according to the number of neighbor nodes of each node, the distance between each node and the base station calculated by the received broadcast signal strength, and the remaining energy of each node; The cluster head node is elected according to the cluster head node selection threshold calculated by the energy factor, distance factor and density factor, and the un-elected nodes become ordinary nodes; after the cluster head election is completed, the ordinary nodes are clustered according to the cluster entry rules and the WAPI-based authentication mechanism; after the clustering is completed, the cluster head node transmits data stably based on multi-hop relay until the end of the round.

2. The WAPI-oriented low-power data transmission method according to claim 1, characterized in that: The residual energy factor considering the node residual energy is calculated as: Among them, E icurrent is the current remaining energy of node i, E0 is the initial energy of the node, E avg is the average remaining energy of the current surviving nodes.

3. The WAPI-oriented low-power data transmission method according to claim 1, characterized in that: The distance factor considering the distance between the node and the base station is calculated as: Among them, d0 is the WSN energy consumption model division threshold, d i is the distance between the node and the base station, and a is the path loss exponent.

4. The WAPI-oriented low-power data transmission method according to claim 1, characterized in that: The density factor is calculated as: Among them, N iR is the number of all neighbor nodes within the communication radius of node i; N alive is the number of surviving nodes in the entire network, R i is the communication radius of node i.

5. The WAPI-oriented low-power data transmission method according to claim 1, characterized in that: The rules for common nodes to join the cluster are: Condition 1: Ordinary node j is within the communication radius of cluster head node i, which ensures the connectivity between ordinary nodes and cluster head nodes; Condition 2: If ordinary node j is covered by two or more cluster head nodes at the same time, that is, there are multiple cluster head nodes that meet condition 1. In this case, the priority clustering probability is calculated by comprehensively considering the communication radius of the cluster head node, the distance from the ordinary node to the cluster head node, and the remaining energy of the cluster head node. The ordinary node selects the largest cluster head node to enter the cluster based on the calculated series of clustering priority probabilities.

6. The WAPI-oriented low-power data transmission method according to claim 5, characterized in that: The probability formula for priority clustering is: Among them, E icurrent is the remaining energy of the current cluster head node i; E avg is the average remaining energy of all nodes in the current network; R ci is the current communication radius of cluster head node i; dij represents the distance from ordinary node j to cluster head node i.

7. The WAPI-oriented low-power data transmission method according to claim 1, characterized in that: The cluster head selection threshold calculation formula is: Among them, E i is the residual energy factor; D i is the distance factor; N i is the density factor.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, the low-power data transmission method for WAPI according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the low-power data transmission method for WAPI as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the low-power data transmission method for WAPI as described in any one of claims 1 to 6.

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