A sensor data receiving method and system based on information network

Abstract

The present application relates to the technical field of sensor data reception, and specifically to a sensor data reception method and system based on an information network, the method comprising: obtaining relevant transmission data of each sensor node; obtaining the candidate probability of each sensor node according to the difference between the transmission data of each sensor node and the sensor nodes within its communication distance; obtaining candidate cluster head nodes; arbitrarily selecting a preset threshold number of candidate cluster head nodes to form a candidate cluster head node sequence; obtaining the coincident nodes of each element of the candidate cluster head sequence; optimizing the candidate probability of each element in the candidate cluster head node sequence according to the distribution characteristics and transmission characteristics of the coincident nodes and the distribution of elements in the candidate cluster head node sequence; obtaining the cluster head node of the information network for receiving data. The present application aims to optimize the selection of cluster head nodes in the information network and improve the efficiency of sensor data reception.

Description

Technical Field

[0001] The present application relates to the technical field of sensor data reception, and in particular to a sensor data reception method and system based on an information network. Background Art

[0002] With the advancement of communication technology, computing technology and perception technology, the information network technology of wireless sensors is also developing continuously. Wireless sensor network is a multi-hop distributed sensor network formed by a large number of low-energy and low-cost sensor nodes deployed in the detection area through wireless communication technology in a self-organizing manner. It is used to collaboratively perceive, sample and process the information of the monitoring targets in the geographical area covered by the network and publish it to users.

[0003] Existing technologies solve the energy limitation problem in wireless sensor networks by optimizing network energy consumption and extending network life. This method ensures that sensor nodes with sufficient energy have a higher chance of being selected as cluster heads. However, it ignores the problem of different transmission distances and performance of sensor nodes, which leads to uneven distribution of selected cluster head nodes. It may also cause some nodes to consume energy too quickly, affecting reliability and data reception capabilities. Summary of the invention

[0004] In view of the above, it is necessary to provide a sensor data receiving method and system based on an information network to solve the above problems.

[0005] The first aspect of the present application provides a sensor data receiving method based on an information network, the method comprising:

[0006] Obtain the transmission data sequence, transmission time, communication distance and remaining energy of each sensor node;

[0007] According to the position relationship between each sensor node and the sensor nodes within its communication distance, the difference between the transmission time and the transmission data sequence, combined with the change of the residual energy before and after the transmission of each sensor node, the candidate probability of each sensor node is obtained;

[0008] According to the candidate probability, a candidate cluster head node is obtained; a preset threshold number of candidate cluster head nodes are randomly selected to form a candidate cluster head node sequence; sensor nodes other than the elements of the candidate cluster head node sequence are allocated to obtain the coincident nodes of each element of the candidate cluster head sequence;

[0009] According to the distribution characteristics and transmission characteristics of all coincident nodes of each element in the candidate cluster head node sequence, combined with the distribution of elements in the candidate cluster head node sequence, the candidate probability of each element in the candidate cluster head node sequence is optimized;

[0010] The candidate cluster head node sequence with the largest sum of the optimized candidate probabilities is used as the cluster head node of the information network to receive the data.

[0011] The process of obtaining the candidate probability of each sensor node is as follows:

[0012] All sensor nodes within the communication distance of each sensor node are regarded as associated nodes of each sensor node;

[0013] According to the distance between each sensor node and its associated node, combined with the similarity between the transmitted data sequences and the difference in transmission time, the transmission performance of each sensor node is obtained;

[0014] According to the difference of residual energy before and after each sensor node transmits data, combined with the transmission performance, the energy efficiency ratio of each sensor node is obtained;

[0015] Obtaining the total transmission distance between each sensor node and all associated nodes, and taking the ratio of the number of all associated nodes of each sensor node to the total transmission distance as the local density of each sensor node;

[0016] The result of fusing the energy efficiency ratio, the residual energy and the local density of each sensor node is used as the candidate probability of each sensor node.

[0017] The transmission performance of each sensor node is obtained as follows:

[0018] The location distance between each sensor node and a single associated node is taken as the transmission distance;

[0019] Obtaining a distance measurement between data transmitted by each sensor node and data transmitted by a single associated node, recorded as a first distance; obtaining a time interval between a transmission moment of each sensor node and a reception moment of a single associated node; calculating a negative correlation mapping result of fusing the first distance and the time interval;

[0020] The negative correlation mapping result is integrated with the transmission distance to obtain a transmission performance factor between each sensor node and a single associated node;

[0021] The cumulative sum of the transmission performance factors between each sensor node and all associated nodes is taken as the transmission performance of each sensor node.

[0022] The step of obtaining the energy efficiency ratio of each sensor node is as follows:

[0023] Obtain the ratio of the difference in residual energy before and after data transmission from each sensor node to a single associated node and the number of data points in the corresponding transmission data sequence, which is recorded as the average energy loss; calculate the sum of the average energy losses transmitted by each sensor to all associated nodes; and use the ratio of the transmission performance to the sum as the energy efficiency ratio of each sensor node.

[0024] The step of obtaining a candidate cluster head node is as follows:

[0025] All sensor nodes are classified based on the candidate probabilities, and the mean of the candidate probabilities of all sensor nodes in each category is calculated. When the number of sensor nodes in the category with the largest mean is greater than or equal to a preset threshold, the sensor nodes in the category with the largest mean are used as candidate cluster head nodes; otherwise, a preset number of sensor nodes with the largest candidate probabilities are used as candidate cluster head nodes.

[0026] The obtaining of the coincident nodes of each element of the candidate cluster head sequence is specifically as follows:

[0027] Allocate each sensor node to the candidate cluster head node closest to it in the candidate cluster head node sequence as the same cluster sensor node of the candidate cluster head node;

[0028] The transmission paths of all sensor nodes in the same cluster of each candidate cluster head node are obtained, and the sensor nodes that appear on two or more transmission paths are regarded as the coincident nodes on all transmission paths of each candidate cluster head node.

[0029] The optimization of the candidate probability of each element in the candidate cluster head node sequence is specifically as follows:

[0030] According to the number of all coincident nodes of each element of the candidate cluster head node sequence and the number of sensor nodes that transmit information to the coincident nodes, combined with the energy efficiency ratio, the coincidence degree of the same cluster sensor nodes of each element of the candidate cluster head node sequence is obtained;

[0031] According to the overlap degree of each candidate cluster head node and the distribution of all elements in the candidate cluster head node sequence in the information network, the candidate probability optimization factor of each element in the candidate cluster head node sequence is obtained;

[0032] The candidate probability of each element of the candidate cluster head node sequence is merged with the candidate probability optimization factor to obtain the optimized candidate probability of each element of the candidate cluster head node sequence.

[0033] The degree of overlap between each element of the candidate cluster head node sequence and the cluster sensor node is obtained as follows:

[0034] The overlap degree of each candidate cluster head node with the cluster sensor node is recorded as , its formula form is: ;in, represents the number of all coincident nodes of each candidate cluster head node, Indicates The number of sensor nodes that need to receive information from the coincident nodes, Indicates Energy efficiency ratio of the overlapping nodes.

[0035] The candidate probability optimization factor of each element of the candidate cluster head node sequence is obtained as follows:

[0036] Obtain the minimum distance between each element in the candidate cluster head node sequence and the remaining elements; obtain the distance between each element in the candidate cluster head node sequence and the sink node, recorded as the second distance; add the ratio of the minimum distance of all elements in the candidate cluster head node sequence to the second distance, and use the ratio of the addition result to the overlap as the candidate probability optimization factor of each element in the candidate cluster head node sequence.

[0037] In a second aspect, an embodiment of the present application further provides a sensor data receiving system based on an information network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0038] In the above scheme, various parameters of each sensor node during transmission are first obtained, and the candidate probability of the sensor node is obtained by analyzing the position between the sensor nodes, the time of data transmission and the transmission energy loss, and the candidate cluster head node is obtained. The beneficial effect is that by selecting nodes with low energy consumption and short distance, network resources can be effectively allocated and used, energy waste can be reduced, and the overall efficiency of the network can be improved; further, if a sensor node needs to process data from multiple sensor nodes, the load will increase. Based on this, the overlapped nodes of the candidate cluster head nodes are obtained, which is helpful for the subsequent analysis of the overlapped status of the transmission paths of the sensor nodes in the cluster of the candidate cluster head nodes; then, according to the distribution of the overlapped nodes and the transmission with the candidate probability, the selection of the cluster head node is completed, and the beneficial effect is to ensure the uniform distribution of the cluster head nodes, while taking into account the data transmission energy loss between the nodes. By optimizing the selection of the cluster head nodes, the balance of energy consumption can be achieved, thereby improving the receiving efficiency of sensor data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a method for receiving sensor data based on an information network provided in one embodiment of the present application;

[0040] Figure 2 A flowchart for obtaining candidate probabilities provided for one embodiment of the present application;

[0041] Figure 3 A specific flow chart for optimizing candidate probabilities provided for one embodiment of the present application. DETAILED DESCRIPTION

[0042] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0044] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flow chart includes one or more steps for implementing the method. Without departing from the scope of protection of the present application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] The following is a detailed description of a sensor data receiving method and system based on an information network provided by the present application in conjunction with the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for receiving sensor data based on an information network provided by an embodiment of the present application, the method comprising the following steps:

[0048] The first step is to obtain the transmission data sequence, transmission time, communication distance and remaining energy of each sensor node.

[0049] The sensor node consists of a sensing component, a processing component, a communication component, an energy supply component and software. The sensing unit is used to collect physical information around the sensor and information about the monitoring target, such as light intensity, humidity, pressure, etc., and convert the collected information into signals. The communication component is used to control the communication between sensors, and the parameters of this component determine the communication range of the sensor. The energy supply component provides energy for the operation of the sensor node.

[0050] In the present application, the transmission data sequence of each sensor node is obtained through the sensing component, the communication distance of the sensor node is obtained through the communication component, and the residual energy of the sensor node is obtained through the energy supply component.

[0051] The second step: According to the position relationship between each sensor node and the sensor nodes within its communication distance, the difference between the transmission time and the transmission data sequence, combined with the change of the residual energy before and after the transmission of each sensor node, the candidate probability of each sensor node is obtained.

[0052] The nodes in the wireless sensor information network are usually divided into two types: ordinary sensor nodes and aggregation nodes, also called sink nodes. Data transmission is usually divided into three steps, namely data collection by sensor nodes, sending the collected data to sink nodes wirelessly, and data aggregation and analysis by sink nodes. In the data transmission process, the most common algorithm is cluster-based information collection. In this method, some sensor nodes are called cluster head nodes. The cluster head node is responsible for collecting data transmitted by nodes in the cluster, and can perform basic data compression and other processing, and then transmit the received information to the sink node.

[0053] The fixed cluster radius clustering protocol HEED (A Hybrid, Energy-Efficient Distributed clustering approach) algorithm is an energy efficiency-oriented cluster head node selection algorithm, which mainly considers the residual energy of the node and the network density to determine the selection of the cluster head node. This application optimizes the process of selecting the cluster head node in the HEED algorithm, and then obtains the optimal cluster head node to complete the sensor data reception of the information network.

[0054] In the sensor nodes of several information networks, the communication distance of each sensor node is obtained through the communication component, and all sensor nodes within the communication distance of each sensor node are used as associated nodes of each sensor node. It should be noted that each sensor node has multiple associated nodes.

[0055] In this embodiment, there are 50 sensor nodes in the information network, and the implementer can adjust the number of sensor nodes by himself.

[0056] According to the position of each sensor node and its associated node, the similarity between the transmitted data, and the difference in transmission time, the transmission performance of each sensor node is obtained:

[0057] The position distance between each sensor node and a single associated node is used as the transmission distance; the distance measurement between the transmission data of each sensor node and the transmission data of the single associated node is obtained, recorded as the first distance; the time interval between the transmission moment of each sensor node and the reception moment of the single associated node is obtained; the negative correlation mapping result of fusing the first distance and the time interval is calculated; the negative correlation mapping result is fused with the transmission distance to obtain the transmission performance factor of each sensor node and the single associated node; the cumulative sum of the transmission performance factors between each sensor node and all associated nodes is used as the transmission performance of each sensor node.

[0058] In the process of acquiring the transmission performance described in this embodiment, the first distance refers to the dynamic time warping (DTW) distance between the historical transmission data sequence of each sensor node and the historical reception data sequence of a single associated node; multiple variables can be fused by multiplication, and the negative correlation mapping result of the variable refers to the reciprocal of the variable, that is, the transmission performance factor of each sensor and the single associated node is specifically: calculating the reciprocal of the product of the first distance and the time interval; calculating the product of the reciprocal and the transmission distance, and obtaining the transmission performance factor of each sensor node and the single associated node.

[0059] It should be understood that the smaller the first distance is, the smaller the loss of data transmitted between sensor nodes is; the ratio of the transmission distance to the time interval measures the transmission speed between sensor nodes, and the greater the transmission speed, the better the transmission performance of each sensor.

[0060] The transmission performance of each sensor node can only reflect the efficiency of the sensor node in data transmission, but if the energy consumption of the sensor node in the process of data transmission is not considered, the sensor node will have good transmission performance. Because its communication components show strong transmission capabilities under high-power operation, this high-power operation usually leads to a significant increase in the energy consumption of the sensor node. In the long run, although the node can effectively transmit data at a larger communication distance, its battery life may be shortened, thus affecting the energy efficiency and life of the entire network. Therefore, in the process of network design and cluster head selection, it is necessary to balance the transmission performance and energy consumption to ensure that the energy resources of the node are effectively managed.

[0061] Based on this, according to the difference in residual energy before and after each sensor node transmits data, combined with the transmission performance, the energy efficiency ratio of each sensor node is obtained:

[0062] Obtain the ratio of the difference in residual energy before and after data transmission from each sensor node to a single associated node and the number of data points in the corresponding transmission data sequence, which is recorded as the average energy loss; calculate the sum of the average energy losses transmitted by each sensor to all associated nodes; and use the ratio of the transmission performance to the sum as the energy efficiency ratio of each sensor node.

[0063] It should be understood that the average energy loss reflects the energy consumed by the sensor node to transmit unit data, and reflects the energy efficiency of transmitting data; the larger the energy efficiency ratio, the higher the transmission efficiency and the lower the energy consumption of the sensor node during the data transmission process; the smaller the energy efficiency ratio, the greater the energy loss of the sensor node during the data transmission process.

[0064] Sensor nodes with higher energy consumption may reduce their candidate probability. This is because the cluster head node needs to bear more communication burden, including communication with other nodes and data forwarding. In order to extend the overall life of the network, it is more reasonable to select nodes with more energy as cluster heads, which can handle more communication tasks and maintain the stability of the network.

[0065] According to the distance between each sensor node and all sensor nodes within the communication distance, combined with the residual energy and the energy efficiency ratio, the candidate probability of each sensor node is obtained:

[0066] The total transmission distance between each sensor node and all associated nodes is obtained, and the ratio of the number of all associated nodes of each sensor node to the total transmission distance is used as the local density of each sensor node; the energy efficiency ratio, residual energy and local density of each sensor node are fused as the candidate probability of each sensor node.

[0067] In this embodiment, the candidate probability of each sensor node is specifically the product of the energy efficiency ratio, the residual energy and the local density of each sensor node.

[0068] Among them, the candidate probability acquisition flow chart is as follows: Figure 2 shown.

[0069] It should be understood that the greater the local density, the greater the energy efficiency ratio and the greater the residual energy, which means that there are more sensor nodes around it and the communication distance is shorter, which usually reduces the energy consumption of data transmission. Therefore, when selecting candidate nodes, nodes with high local density are usually given a higher candidate probability because of their higher communication efficiency and lower energy consumption. This helps to optimize the energy efficiency and performance of the overall network.

[0070] The third step: according to the candidate probability, obtain the candidate cluster head node; arbitrarily select a preset threshold number of candidate cluster head nodes to form a candidate cluster head node sequence; allocate sensor nodes except the elements of the candidate cluster head node sequence to obtain the overlapping nodes of each element of the candidate cluster head sequence.

[0071] All sensor nodes are classified based on the candidate probabilities, and the mean of the candidate probabilities of all sensor nodes in each category is calculated. When the number of sensor nodes in the category with the largest mean is greater than or equal to a preset threshold, the sensor nodes in the category with the largest mean are used as candidate cluster head nodes; otherwise, a preset number of sensor nodes with the largest candidate probabilities are used as candidate cluster head nodes.

[0072] In this embodiment, the K-Means clustering algorithm is used to classify the sensor nodes, and the absolute value of the difference in candidate probabilities between the sensor nodes is used as the metric of the clustering algorithm. The number of clustering categories is set to 2, and the implementer can adjust it according to the actual situation; among them, the K-Means clustering algorithm is an existing well-known technology, and this application will not go into details. In addition, the implementer can also choose other classification methods, and this application does not limit this.

[0073] Furthermore, the preset threshold value is 6; the preset number value is 12; the implementer can adjust it according to the actual situation, and this application does not impose any restrictions on this.

[0074] A preset threshold number of candidate cluster head nodes are randomly selected to form a candidate cluster head node sequence, and each sensor node is assigned to the candidate cluster head node closest to it in the candidate cluster head node sequence. It should be noted that when the sensor nodes are assigned, the candidate cluster head nodes need to be within the communication distance of the sensor nodes; based on this, each candidate cluster head node has a corresponding sensor node in the same cluster.

[0075] In the information network, when the sensor nodes transmit data to the cluster head node, they usually transmit the data one level at a time, that is, the data collected by the sensor node is transmitted to the adjacent sensor node, and the adjacent sensor node then transmits the data to the next level until it is transmitted to the corresponding cluster head node. Each sensor node can obtain a transmission path. The transmission paths of all sensor nodes in the same cluster of each candidate cluster head node are obtained, and the sensor nodes that appear on two or more transmission paths are regarded as coincident nodes on all transmission paths of each candidate cluster head node.

[0076] The fourth step: according to the distribution characteristics and transmission characteristics of all overlapping nodes of each element of the candidate cluster head node sequence, combined with the energy efficiency ratio and the distribution of elements in the candidate cluster head node sequence, the candidate probability of each element in the candidate cluster head node sequence is optimized.

[0077] As the number of overlapping nodes increases for each candidate cluster head node, these nodes need to process data from multiple sensor nodes, which increases their load. If these overlapping nodes have high energy consumption, that is, they have already used more energy, the load imbalance of the cluster head node may be exacerbated. This is because nodes with higher energy consumption may exhaust their energy faster when processing the additional data burden, resulting in an uneven load distribution.

[0078] According to the number of all coincident nodes of each element of the candidate cluster head node sequence and the number of sensor nodes that transmit information to the coincident nodes, combined with the energy efficiency ratio, the coincidence degree of the same cluster sensor nodes of each element of the candidate cluster head node sequence is obtained:

[0079] The overlap degree of each candidate cluster head node with the cluster sensor node is recorded as , its formula form is: ;in, represents the number of all coincident nodes of each candidate cluster head node, Indicates The number of sensor nodes that need to receive information from the coincident nodes, Indicates Energy efficiency ratio of the overlapping nodes.

[0080] According to the overlap degree of each candidate cluster head node and the distribution of all elements in the candidate cluster head node sequence in the information network, the candidate probability optimization factor of each element in the candidate cluster head node sequence is obtained: the minimum distance between each element in the candidate cluster head node sequence and the remaining elements is obtained; the distance between each element in the candidate cluster head node sequence and the sink node is obtained, which is recorded as the second distance; the ratio of the minimum distance of all elements in the candidate cluster head node sequence to the second distance is added, and the ratio of the addition result to the overlap degree is used as the candidate probability optimization factor of each element in the candidate cluster head node sequence.

[0081] It should be understood that the larger the ratio of the minimum distance to the second distance, the larger the distance between the candidate cluster head nodes, while the distance from each candidate cluster head node to the sink node is relatively small, which indicates that the candidate cluster head nodes are evenly distributed in the network, which helps to balance the load and communication distance, thereby improving the overall performance and stability of the network, and thus the larger the probability optimization factor.

[0082] Based on the candidate probability optimization factor, the candidate probability of each element of the candidate cluster head node sequence is optimized to obtain the optimized cluster head candidate probability , the formula is: ;in, represents the candidate probability of each element in the candidate cluster head sequence, represents the candidate probability optimization factor of each element of the candidate cluster head sequence, Represents the cluster head candidate probability of each element in the candidate cluster head sequence.

[0083] Among them, the specific flow chart for optimizing the candidate probability is as follows: Figure 3 shown.

[0084] The fifth step: taking the candidate cluster head node sequence with the largest sum of the optimized candidate probabilities as the cluster head node of the information network to receive the data.

[0085] The candidate cluster head node in the candidate cluster head node sequence with the largest sum of optimized cluster head candidate probabilities is used as the cluster head node of the information network to complete the sensor data reception of the information network. That is, the data collected by the sensor node is first sent to the cluster head node of the cluster to which it belongs, and the cluster head node compresses the received data and then sends it to the sink node after pre-processing.

[0086] Based on the same inventive concept as the above method, an embodiment of the present application also provides a sensor data receiving system based on an information network, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned sensor data receiving methods based on an information network are implemented.

[0087] In summary, the embodiment of the present application obtains various parameters of each sensor node during transmission, and obtains the candidate probability of the sensor node and the candidate cluster head node by analyzing the position between the sensor nodes, the time of data transmission and the transmission energy loss. The beneficial effect is that by selecting nodes with low energy consumption and short distance, network resources can be effectively allocated and used, energy waste can be reduced, and the overall efficiency of the network can be improved. Furthermore, if a sensor node needs to process data from multiple sensor nodes, the load will increase. Based on this, obtaining the overlapping nodes of the candidate cluster head nodes will help to analyze the overlapping status of the transmission paths of the sensor nodes in the cluster of the candidate cluster head nodes in the subsequent analysis. Then, according to the distribution of the overlapping nodes and the transmission with the candidate probability, the selection of the cluster head node is completed. The beneficial effect is to ensure the uniform distribution of the cluster head nodes, while taking into account the data transmission energy loss between the nodes. By optimizing the selection of the cluster head nodes, the balance of energy consumption can be achieved, thereby improving the receiving efficiency of the sensor data.

[0088] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two continuous operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0089] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; the technical solutions recorded in the above embodiments are modified, or some of the technical features are replaced by equivalents, which does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A sensor data receiving method based on an information network, characterized in that: The method comprises the following steps: Obtain the transmission data sequence, transmission time, communication distance and remaining energy of each sensor node; According to the position relationship between each sensor node and the sensor nodes within its communication distance, the difference between the transmission time and the transmission data sequence, combined with the change of the residual energy before and after the transmission of each sensor node, the candidate probability of each sensor node is obtained; According to the candidate probability, a candidate cluster head node is obtained; a preset threshold number of candidate cluster head nodes are randomly selected to form a candidate cluster head node sequence; sensor nodes other than the elements of the candidate cluster head node sequence are allocated to obtain the coincident nodes of each element of the candidate cluster head node sequence; According to the distribution characteristics and transmission characteristics of all coincident nodes of each element in the candidate cluster head node sequence, combined with the distribution of elements in the candidate cluster head node sequence, the candidate probability of each element in the candidate cluster head node sequence is optimized; The candidate cluster head node sequence with the largest sum of the optimized candidate probabilities is used as the cluster head node of the information network to receive the data; The process of obtaining the candidate probability of each sensor node is as follows: All sensor nodes within the communication distance of each sensor node are regarded as associated nodes of each sensor node; According to the distance between each sensor node and its associated node, combined with the similarity between the transmitted data sequences and the difference in transmission time, the transmission performance of each sensor node is obtained; According to the difference of residual energy before and after each sensor node transmits data, combined with the transmission performance, the energy efficiency ratio of each sensor node is obtained; Obtaining the total transmission distance between each sensor node and all associated nodes, and taking the ratio of the number of all associated nodes of each sensor node to the total transmission distance as the local density of each sensor node; The result of fusing the energy efficiency ratio, the residual energy and the local density of each sensor node is used as the candidate probability of each sensor node; The candidate probability of each sensor node is specifically the product of the energy efficiency ratio, the residual energy and the local density of each sensor node; The transmission performance of each sensor node is obtained as follows: The location distance between each sensor node and a single associated node is taken as the transmission distance; Obtaining a distance measurement between data transmitted by each sensor node and data transmitted by a single associated node, recorded as a first distance; obtaining a time interval between a transmission moment of each sensor node and a reception moment of a single associated node; Calculating the reciprocal of the product of the first distance and the time interval; calculating the product of the reciprocal and the transmission distance to obtain a transmission performance factor between each sensor node and a single associated node; The cumulative sum of the transmission performance factors between each sensor node and all associated nodes is taken as the transmission performance of each sensor node; The steps of obtaining the energy efficiency ratio of each sensor node are: Obtain the ratio of the difference between the residual energy of each sensor node before and after data transmission to a single associated node and the number of data points in the corresponding transmission data sequence, and record it as the average energy loss; calculate the sum of the average energy losses transmitted by each sensor to all associated nodes; and use the ratio of the transmission performance to the sum as the energy efficiency ratio of each sensor node; The step of obtaining a candidate cluster head node is as follows: Based on the candidate probability, all sensor nodes are divided into two categories using the K-Means clustering algorithm, and the mean of the candidate probabilities of all sensor nodes in each category is calculated. When the number of sensor nodes in the category with the largest mean is greater than or equal to a preset threshold, the sensor nodes in the category with the largest mean are used as candidate cluster head nodes; otherwise, a preset number of sensor nodes with the largest candidate probabilities are used as candidate cluster head nodes; The obtaining of the coincident nodes of each element of the candidate cluster head node sequence is specifically as follows: Allocate each sensor node to the candidate cluster head node closest to it in the candidate cluster head node sequence as a sensor node in the same cluster as the candidate cluster head node; Obtain the transmission paths of all sensor nodes in the same cluster of each candidate cluster head node, and regard the sensor nodes that appear on two or more transmission paths as the coincident nodes on all transmission paths of each candidate cluster head node; The optimization of the candidate probability of each element in the candidate cluster head node sequence is specifically as follows: According to the number of all coincident nodes of each element of the candidate cluster head node sequence and the number of sensor nodes that transmit information to the coincident nodes, combined with the energy efficiency ratio, the coincidence degree of each element of the candidate cluster head node sequence is obtained; According to the overlap degree of each candidate cluster head node and the distribution of all elements in the candidate cluster head node sequence in the information network, the candidate probability optimization factor of each element in the candidate cluster head node sequence is obtained; The candidate probability of each element of the candidate cluster head node sequence is merged with the candidate probability optimization factor to obtain the optimized candidate probability of each element of the candidate cluster head node sequence; The expression of candidate probability is: ;in, represents the candidate probability of each element in the candidate cluster head node sequence, represents the candidate probability optimization factor of each element in the candidate cluster head node sequence, represents the cluster head candidate probability of each element in the candidate cluster head node sequence; The overlap degree of each element of the candidate cluster head node sequence is obtained as follows: The coincidence degree of each candidate cluster head node is recorded as , its formula form is: ;in, represents the number of all coincident nodes of each candidate cluster head node, Indicates The number of sensor nodes that need to receive information from the coincident nodes, Indicates Energy efficiency ratio of coincident nodes; The candidate probability optimization factor of each element of the candidate cluster head node sequence is obtained as follows: Obtain the minimum distance between each element in the candidate cluster head node sequence and the remaining elements; obtain the distance between each element in the candidate cluster head node sequence and the sink node, recorded as the second distance; add the ratio of the minimum distance of all elements in the candidate cluster head node sequence to the second distance, and use the ratio of the addition result to the overlap as the candidate probability optimization factor of each element in the candidate cluster head node sequence.

2. A sensor data receiving system based on an information network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to claim 1 are implemented.

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