Sensor network clustering routing method based on coverage area division and energy consumption balancing

Abstract

The application provides a sensor network clustering routing method based on coverage area division and energy consumption balance, and the method comprises the following steps: obtaining a network structure of sensor nodes; performing area division on the network structure according to a pre-set sink node, so as to obtain at least two node areas; calculating a distance factor and a residual energy factor of nodes in each node area according to the node areas obtained by division; calculating a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node area where each node is located; determining a cluster head in a current period according to a pre-set selection rule and the cluster head selection threshold of each node, and the remaining nodes are ordinary nodes, and clustering the ordinary nodes according to the cluster head; and performing a fusion task for the nodes after clustering until the next period, so that the network structure is processed in a hierarchical manner, the sensor nodes can be effectively managed, the network energy consumption is reduced, and the network resources are optimized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of sensor network optimization, and particularly relates to a sensor network clustering routing method based on coverage area division and energy consumption balance. BACKGROUND

[0002] The number of sensor network nodes is large and the distribution range is wide, and the coverage optimization of the nodes is the primary consideration. The coverage optimization effect directly affects the perception service quality provided by the sensor network. Adopting certain strategies to optimize the network coverage performance is of great significance for reasonably allocating network resources, better completing the perception of target areas, data collection, and improving the network operation efficiency. The network clustering routing protocol needs to design a special cluster head selection and maintenance mechanism, which has high requirements for the cluster head. When facing uncertain scenes of tasks and environment, the flexibility and reliability of the clustering structure are insufficient. Therefore, how to comprehensively consider the node mobility, wireless link quality, node energy consumption and specific application tasks and other factors when designing the clustering routing protocol is a key problem.

[0003] For the coverage problem of large-scale sensor networks, the centralized processing structure has high requirements for network communication bandwidth and computing and storage resources, and is difficult to be applied on a large scale. The distributed processing structure needs to consider the problem of mutual communication between nodes. Therefore, how to solve the coverage problem of large-scale sensor networks has become a technical problem to be solved at present. SUMMARY

[0004] In view of the above problems of the prior art, the purpose of the present application is to provide a sensor network clustering routing method based on coverage area division and energy consumption balance, which can effectively realize the management of large-scale sensor networks and reduce network energy consumption.

[0005] In order to solve the above technical problems, the specific technical solutions of the present application are as follows:

[0006] On the one hand, the present application provides a sensor network clustering routing method based on coverage area division and energy consumption balance, which comprises:

[0007] obtaining a network structure for sensor nodes;

[0008] dividing the network structure into at least two node regions according to a predetermined sink node;

[0009] calculating the distance factor and the residual energy factor of the nodes in each node region according to the divided node regions;

[0010] calculating the cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located;

[0011] According to a preset selection rule and a cluster head selection threshold of each node, a cluster head in a current period is determined, the remaining nodes are ordinary nodes, and the cluster head clusters the ordinary nodes;

[0012] A fusion task is performed on the clustered nodes until the next period.

[0013] Further, the network structure for the sensor nodes is obtained, comprising:

[0014] Deployment information of the sensor nodes is obtained, the deployment information at least including position information of the nodes;

[0015] According to the deployment information of the sensor nodes, the network structure for the sensor nodes is generated.

[0016] Further, the network structure is regionally divided according to the preset sink node, to obtain at least two node regions, comprising:

[0017] The preset sink node and a distance threshold are obtained, the distance threshold being calculated through an energy consumption coefficient of a free space attenuation model and an energy consumption coefficient of a multipath attenuation channel model;

[0018] The nodes in the network structure are divided with the sink node as the center and the distance threshold as the radius, to obtain at least two node regions.

[0019] Further, the distance factor and the residual energy factor are respectively obtained through the following formulas:

[0020]

[0021]

[0022] Wherein, ω is the distance factor, d i is the distance from the sensor node i to the sink node; d min ,d max respectively represent the nearest and farthest distances from the nodes in the deployment region to the sink node; E is the residual energy factor, E average is the average residual energy of all surviving nodes in the current period, E current is the residual energy of the current node.

[0023] Further, the cluster head selection threshold is obtained through the following formula:

[0024]

[0025] Wherein, λ1∈R+, λ2∈R+ are weighting coefficients, the values of which vary with the network size and application scenarios, r maxfor a set maximum number of cycles, r is the current cycle number / round number, E is the residual energy factor, and ω is the distance factor; ω can be used to determine the weighting factor, define λ1=1+ω, λ2=1+ω+r / r max ; assuming mod(·) is the modulo operation, p∈(0,1) is the probability of the sensor node becoming a cluster head node, rmod(1 / p) is the number of nodes that have been elected as cluster head nodes in this round of circulation, and G represents the set of nodes that have not been elected as cluster head nodes in this round of circulation.

[0026] Further, the cluster head in the current cycle is determined according to the preset selection rule and the cluster head selection threshold of each node, the remaining nodes are ordinary nodes, and the ordinary nodes are clustered according to the cluster head, including:

[0027] For each node, a random number is generated, and the random number is compared with the cluster head selection threshold of the node;

[0028] The node whose cluster head selection threshold is greater than the random number is determined as a cluster head, otherwise, the node is determined as an ordinary node;

[0029] The cluster head node broadcasts to the ordinary nodes in the network structure, so that the ordinary nodes join the corresponding cluster according to the principle of proximity.

[0030] Further, the fusion task is performed for the clustered nodes until the next cycle, including:

[0031] According to the cluster head and the ordinary node in each cluster, a routing path in each cluster is established, and the routing path at least includes a routing time slot of each ordinary node;

[0032] A hop path between each cluster head and the sink node is established;

[0033] In the stable transmission phase of the current cycle, the routing path and the hop path are used to process the transmission data.

[0034] Further, the sink node is determined by the following steps:

[0035] According to the deployment range of the sensor node, the distribution density of the node is determined;

[0036] According to the distribution density of the node, the position and the number of the sink node are determined, wherein more sink nodes are arranged in the area with larger distribution density, and the distance between any two sink nodes is greater than a preset value.

[0037] On the other hand, the present application also provides a sensor network clustering routing device based on coverage area division and energy consumption balancing, the device comprising:

[0038] an acquisition module configured to acquire a network structure for sensor nodes;

[0039] a region division module configured to divide the network structure into at least two node regions according to a preset sink node;

[0040] a first calculation module configured to calculate a distance factor and a residual energy factor of a node in each node region according to the divided node regions;

[0041] a second calculation module configured to calculate a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located;

[0042] a clustering module configured to determine a cluster head in a current period according to a preset selection rule and the cluster head selection threshold of each node, and determine ordinary nodes, and cluster the ordinary nodes according to the cluster head;

[0043] a transmission module configured to perform a fusion task for the clustered nodes until a next period.

[0044] Finally, the present document also provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method described above when executing the computer program.

[0045] With the above technical solution, the method for clustering routing of a sensor network based on division of a coverage region and balancing of energy consumption includes: acquiring a network structure for sensor nodes; dividing the network structure into at least two node regions according to a preset sink node; calculating a distance factor and a residual energy factor of a node in each node region according to the divided node regions; calculating a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located; determining a cluster head in a current period according to a preset selection rule and the cluster head selection threshold of each node, and determining ordinary nodes, and clustering the ordinary nodes according to the cluster head; and performing a fusion task for the clustered nodes until a next period. The method processes the network structure in layers, can effectively manage sensor nodes, reduce network energy consumption, and optimize network resources.

[0046] To make the above and other purposes, features and advantages of the present document more apparent and understandable, the following describes preferred embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only constitute some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained based on these accompanying drawings without creative effort.

[0048] Figure 1 A step schematic diagram of the sensor network clustering routing method based on coverage area division and energy consumption balancing provided by the embodiments is shown;

[0049] Figure 2 A network clustering situation diagram in a period in the simulation experiment of the embodiments is shown;

[0050] Figure 3 A network surviving node number change diagram when the sink node is located at the area center in the simulation experiment of the embodiments is shown;

[0051] Figure 4 A total residual energy diagram when the sink node is located at the area center in the simulation experiment of the embodiments is shown.

[0052] Figure 5 A network surviving node number comparison result diagram when the sink node is located at the area edge and the area center in the simulation experiment of the embodiments is shown;

[0053] Figure 6 A total residual energy diagram comparison result diagram when the sink node is located at the area edge and the area center in the simulation experiment of the embodiments is shown;

[0054] Figure 7 A structure schematic diagram of the sensor network clustering routing device based on coverage area division and energy consumption balancing provided by the embodiments is shown;

[0055] Figure 8 A structure schematic diagram of the computer device provided by the embodiments is shown.

[0056] Explanation of the drawing symbols:

[0057] 710, an acquisition module; 720, an area division module; 730, a first calculation module; 740, a second calculation module; 750, a clustering module; 760, a transmission module. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments herein, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0059] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, device, product, or apparatus including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or apparatus.

[0060] In the prior art, coverage optimization for sensor network nodes has always been a hot issue. The coverage optimization effect directly affects the quality of the perception service provided by the sensor network. Taking certain strategies to optimize the network coverage performance is of great significance for reasonably allocating network resources, better completing the perception of target areas, data collection, and improving network operation efficiency. For the coverage problem of large-scale sensor networks, centralized processing structures have high requirements for network communication bandwidth and computing and storage resources, and are difficult to be applied on a large scale. Distributed processing structures need to consider the problem of mutual communication between nodes. Therefore, how to solve the coverage problem of large-scale sensor networks has become a technical problem to be solved at present.

[0061] To solve the above problems, the embodiments of the present application provide a sensor network clustering routing method based on coverage area division and energy consumption balancing, which can effectively realize the management of large-scale sensor networks and reduce network energy consumption. Figure 1 is a step schematic diagram of a sensor network clustering routing method based on coverage area division and energy consumption balancing provided by the embodiments of the present application. The present specification provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps can be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one of the many execution orders of the steps, and does not represent the only execution order. In actual system or device product execution, the method order shown in the embodiments or drawings can be executed in sequence or in parallel. Specifically, as shown in the figure, the method can include: Figure 1

[0062] ​S101: Obtain a network structure for sensor nodes;

[0063] S102: According to a preset sink node, divide the network structure into regions to obtain at least two node regions;

[0064] S103: According to the node regions obtained by the division, calculate the distance factor and the residual energy factor of the nodes in each node region;

[0065] S104: According to the distance factor and the residual energy factor of each node, and the node region where each node is located, calculate the cluster head selection threshold of each node;

[0066] S105: According to a preset selection rule and the cluster head selection threshold of each node, determine the cluster head in the current period, the remaining nodes are ordinary nodes, and cluster the ordinary nodes according to the cluster head;

[0067] S106: Perform fusion tasks for the clustered nodes until the next period.

[0068] It can be understood that, the embodiments of the present specification dynamically divide the entire network into several independent sub-network structures (i.e. node regions) by adjusting the cluster head election method, these sub-networks are individually processed by their own cluster heads, and finally the sub-network information is aggregated to obtain the final fusion data.

[0069] Among them, the main energy consumption in the sensor network is in the data transmission and processing process, therefore the main energy consumption model includes the sending end energy consumption model E t and the receiving end energy consumption model E r . When the transmission distance is d, the energy consumption model of the sensor node sending and receiving kbits of data is as follows:

[0070]

[0071] Among them, E0 is the circuit energy consumption of the node processing each bit of data, ∈ fs is the energy consumption coefficient of the free space attenuation model, ∈ mp is the energy consumption coefficient of the multipath fading channel model, is the distance threshold, the energy consumption of the signal in the wireless channel transmission is proportional to the distance d r . When the short distance wireless transmission is d≤d0, r=2, and when the long distance wireless transmission is d>d0, r=4. Therefore, according to the transmission distance between the node and the base station, the sending node can use different energy consumption models for calculation.

[0072] In the embodiments of the present specification, the network structure for the sensor nodes is obtained, including:

[0073] obtaining deployment information of the sensor nodes, the deployment information comprising at least position information of the nodes;

[0074] generating a network structure for the sensor nodes according to the deployment information of the sensor nodes.

[0075] The network structure of the nodes can form a distribution map of the sensor nodes, wherein information of each node is included, and further, information of residual energy of the nodes, capability of obtaining surrounding environment, data processing capability of the nodes, etc.

[0076] Further, the network structure is divided into at least two node regions according to a predetermined sink node, comprising:

[0077] obtaining a predetermined sink node and a distance threshold, the distance threshold being calculated by an energy consumption coefficient of a free space attenuation model and an energy consumption coefficient of a multipath fading channel model;

[0078] dividing the nodes in the network structure into at least two node regions with the sink node as the center and the distance threshold as the radius.

[0079] It can be understood that, by dividing the coverage area of the sensor nodes into a plurality of sub-regions, the hierarchical processing of the coverage area is realized, and thus the effective management of each sub-region is realized. Specifically, the distance threshold is used to ensure that each sub-region can be effectively managed, and the sink node can perform wireless communication with all the sensor nodes and receive and transmit corresponding data.

[0080] The calculation of the distance factor, the residual energy factor and the cluster head selection threshold will be described in detail below.

[0081] Further, the cluster head in the current period is determined according to a predetermined selection rule and the cluster head selection threshold of each node, and the remaining nodes are normal nodes, and the normal nodes are clustered according to the cluster head, comprising:

[0082] generating a random number for each node, and comparing the random number with the cluster head selection threshold of the node;

[0083] determining the node as the cluster head if the cluster head selection threshold is greater than the random number, otherwise, determining the node as the normal node;

[0084] The cluster head node broadcasts to the normal nodes in the network structure, so that the normal nodes join the corresponding clusters according to the principle of proximity.

[0085] Through the above manner, the cluster head in each node area can be quickly determined, and it should be noted that one node area can have multiple cluster heads. Then, clustering of nodes is implemented based on the cluster heads, that is, each normal node enters the clustering corresponding to the nearest cluster head pair.

[0086] In a further embodiment, the fusion task is performed for the clustered nodes until the next period, including:

[0087] According to the cluster head and normal node in each cluster, a routing path in each cluster is established, and the routing path at least includes a routing time slot of each normal node.

[0088] A hop path between each cluster head and the sink node is established.

[0089] In the stable transmission phase of the current period, the transmission data is processed according to the routing path and the hop path.

[0090] In a specific implementation, it is assumed that the node a uses a free space model for communication, and the communication cost between nodes is proportional to the square of the distance between nodes. Node b wants to transmit data to the sink node, and can send data to the cluster head of the cluster where it is located, and the cluster head processes and fuses the data and sends the processed data to the sink node.

[0091] The sink node is determined by the following steps:

[0092] According to the deployment range of the sensor node, the distribution density of the node is determined.

[0093] According to the distribution density of the node, the position and number of the sink node are determined, wherein more sink nodes are arranged in the area with larger distribution density, and the distance between any two sink nodes is greater than a preset value.

[0094] It should be noted that a larger distribution density indicates that the nodes in the area are relatively active, that is, the data flow is relatively large and dense. In order to ensure the efficiency of data processing, more sink nodes can be arranged to ensure the efficiency of data processing in the area, and the distance between any two sink nodes is greater than a preset value, so that each sink node can be fully utilized, the energy consumption is evenly distributed, and invalid nodes are avoided.

[0095] Further, multiple sink nodes can also be arranged in the area with a larger coverage range, so that the data processing in the larger coverage range can be ensured to be efficient.

[0096] In the embodiments of the present disclosure, a sensor network clustering routing method based on coverage area division and energy consumption balancing is also provided, and the method includes the following steps:

[0097] Step one: Area division of deployment area

[0098] Considering that the sensor nodes will select the communication mode according to the threshold distance d0, the present patent divides the deployment area into two parts according to d0: a sub-area S1 within the radius d0 from the sink node, and another area S2 within the deployment range outside the radius d0 from the sink node. It is noted that in order to increase the coverage, we can set multiple sink nodes according to the area size, and the mutual distance can also be determined separately.

[0099] Step two: Calculation of distance factor ω

[0100] According to step one, the node distribution in the network is obtained, and the distance factor ω of each node is further calculated according to formula (2). Wherein, d i is the distance from the sensor node i to the sink node; d min , d max respectively represent the nearest and farthest distances from the nodes in the deployment area to the sink node.

[0101]

[0102] Step three: Calculation of the node's own residual energy factor E

[0103] The node calculates its own residual energy factor E through formula (3) according to its own residual energy. Wherein, E average is the average residual energy of all surviving nodes in the current period, and E current is the residual energy of the current node. The energy consumption model is shown in formula (1). By subtracting the average residual energy of the network from the residual energy of the node, the probability of the current node becoming a cluster head is dynamically changed.

[0104]

[0105] Before the deployment of the sensor network, the initial energy of each node is generally the same, but as the network transmits data, some nodes are selected as cluster head nodes, which correspond to larger energy consumption. If a certain node is frequently selected as a cluster head, it will lead to rapid energy consumption of these nodes, and in severe cases, it will cause the network transmission to be paralyzed. Therefore, the residual energy of the node needs to be considered when selecting the cluster head.

[0106] Step four: Calculation of cluster head selection threshold T PE (n)

[0107] The node calculates the cluster head selection threshold T PE (n) according to formula (4) according to its own distributed area and residual energy factor and distance factor. Wherein, λ1∈R+, λ2∈R+ are weighting coefficients, whose values vary with the network size and application scenario. r maxFor the set maximum cycle number, r is the current cycle number / round, E is the residual energy factor calculated by formula (3), and ω is the distance factor calculated by formula (2). ω can be used to determine the weighting coefficient, and λ1=1+ω and λ2=1+ω+r / r are defined max . Assuming that mod(·) is a remainder operation, p∈(0,1) is the probability that the sensor node becomes a cluster head node, rmod(1 / p) is the number of nodes that are elected as cluster heads in this round of circulation, and G represents the set of nodes that are not elected as cluster heads in this round of circulation.

[0108]

[0109] Step five: determining a cluster head and completing clustering.

[0110] The node randomly generates a number μ between 0 and 1, and compares the number μ with the threshold value T PE (n) calculated in step four. If μ PE (n), the node is elected as a cluster head, otherwise, the node is a common node. The common node selects a cluster head to join according to the principle of proximity according to the notification received from the cluster head (the position information of the node is known). After completing the clustering, the sensor network enters a stable stage to perform a data fusion task until the next cycle to reselect a cluster head.

[0111] The sensor network clustering routing method based on coverage area division and energy consumption balancing provided in the application divides the deployment area into partitions, performs regional optimization or task division according to different partitions, can reasonably allocate the limited energy resources in the network, and prolongs the service life of the sensor network.

[0112] Exemplarily, after the cluster head is determined in each cycle, the cluster head allocates a routing time slot for each cluster member according to the number of common nodes in the cluster and the positional relationship. Then, the cluster head sends the allocated time slot table to the cluster members, and the cluster head broadcasts its own information to establish an inter-cluster routing path. After the cluster establishment stage is completed, the path between the cluster heads is established, the cluster member nodes continuously collect surrounding environment information, and then transmit the collected data to the cluster head of the cluster to which the member node belongs in one hop in the time slot allocated to the member node. The node turns off the communication module in the transmission time of the node to reduce energy consumption. After receiving the data of all the nodes in the cluster, the cluster head performs data fusion on the collected data, and then transmits the fused data to the sink node. In the time period of each working cycle, the stable data transmission stage is longer than the cluster establishment stage, so that the efficiency and quantity of each data transmission can be ensured, and the cluster head selection process is reduced. The data collected in each cluster is one frame, and multiple frames of data are collected in each transmission stage, so that the number of cluster establishment is reduced, more energy is used for data transmission, and the energy utilization rate is improved.

[0113] The embodiments of the present specification also simulate and verify the provided method by using MATLAB. The computer configuration used in the simulation experiment is as follows: Intel Core i7-6700HQ CPU@2.6GHzx8, 8GB RAM. In the experiment, it is assumed that 100 nodes are randomly placed in a regular area of 100x100 m for coverage, the sink node coordinates are located at the center of the coverage area (50, 50), the initial energy of each node is set to 0.5J, and some other experimental parameters are shown in Table 1.

[0114] Table 1 Network clustering simulation experiment parameter setting

[0115] Parameter Specific value Probability p that a common node becomes a cluster head 0.1 The circuit energy E0 consumed by the node to process each bit of data 50 nJ / bit Free space attenuation coefficient ∈ fs ]]> 10 pJ / bit / m2 Multipath channel attenuation coefficient ∈ mp ]]> 0.0013 pJ / bit / m 4 ]] maximum number of periods r max ]] 5000 rounds Data packet size 4000 bit

[0116] Then, we complete the sensor network clustering based on coverage area division and energy consumption balancing according to the operations of steps one to five. In the experimental process, the network clustering result of a certain round is as shown in Figure 2 The improved sensor network clustering routing method based on area division and energy consumption balancing proposed in the present application is compared with the classic LEACH algorithm in terms of network surviving node number and total residual energy. It can be seen from Figure 3 that the first dead node in the sensor network clustering routing method based on coverage area division and energy consumption balancing appears about 200 rounds later than the classic LEACH algorithm, from Figure 4 it can be seen that the performance in terms of overall energy consumption is better than the classic LEACH, and the life cycle can be prolonged.

[0117] Further, the sink node position is modified to the network edge position, and the coordinates are (100, 100). It is noted that the only difference from the above experiment is the coordinates of the sink node, and the rest of the experimental parameters remain unchanged, including the consistent distribution of sensor nodes. The experimental results are as shown in Figure 5 and Figure 6 It can be seen that the position of the network sink node has a great influence on the network surviving life. Compared with the result of the sink node located at the center, the first dead node appears earlier, and the energy consumption is faster. The reason is that the number of nodes far away from the sink node increases, which leads to the relatively increased wireless transmission energy consumption. When the sink node is located at the edge of the area, the nodes in the entire network partition that are close to the sink node adopt the threshold value of S1 area, and only consider the energy consumption effect; at the same time, the nodes far away from the sink node adopt the threshold value of S2 area, and consider the distance factor and energy consumption balancing, so that the network life is prolonged.

[0118] The present application also provides a sensor network clustering routing device based on coverage area division and energy consumption balancing, as shown in Figure 7 The device comprises:

[0119] The acquisition module 710 is configured to acquire a network structure for a sensor node.

[0120] The region division module 720 is configured to divide the network structure into at least two node regions according to a preset sink node.

[0121] The first calculation module 730 is configured to calculate a distance factor and a residual energy factor of a node in each node region according to the divided node regions.

[0122] The second calculation module 740 is configured to calculate a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located.

[0123] The clustering module 750 is configured to determine a cluster head in a current period according to a preset selection rule and the cluster head selection threshold of each node, and the remaining nodes are normal nodes, and to cluster the normal nodes according to the cluster head.

[0124] The transmission module 760 is configured to perform a fusion task for the clustered nodes until the next period.

[0125] The beneficial effects achieved by the above device are consistent with the beneficial effects achieved by the above method, and the embodiments of the present application will not be described in detail.

[0126] The present embodiment provides a computer device, the internal structure diagram of which can be as shown in the figure. Figure 8 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a computer device running surface covering identification method.

[0127] Those skilled in the art can understand that Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0128] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the above method embodiments.

[0129] In an embodiment, a computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0130] In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0131] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to a memory, database or other medium used in the embodiments provided in the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0132] It should also be understood that, in the embodiments herein, the term "and / or" only describes an association relationship of associated objects, and indicates that there can be three relationships. For example, A and / or B can represent three cases of A existing alone, A and B existing together, and B existing alone. In addition, the character " / " in the present embodiment generally represents an "or" relationship between the front and rear associated objects.

[0133] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in a general manner. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present embodiment.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0135] In several embodiments provided herein, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, and the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can also be electrical, mechanical or other forms of connection.

[0136] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments herein.

[0137] The principles and implementation manners of the present embodiment are described herein, and the above embodiment description is only used to help understand the method and its core idea; at the same time, for those skilled in the art, according to the idea of the present embodiment, the specific implementation manner and application range will be changed, and the above description should not be understood as limiting the present embodiment.

Claims

1. A sensor network clustering routing method based on coverage area division and energy consumption balancing, characterized in that, The method comprises: obtaining a network structure of sensor nodes; dividing the network structure into at least two node regions according to a pre-set sink node; calculating a distance factor and a residual energy factor of nodes in each node region according to the divided node regions; calculating a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located; determining a cluster head in a current period according to a pre-set selection rule and the cluster head selection threshold of each node, the remaining nodes being ordinary nodes, and clustering the ordinary nodes according to the cluster head; performing a fusion task on the clustered nodes until the next period; wherein the cluster head selection threshold is obtained by the following formula: ; wherein, is a weighting coefficient, whose value varies with the network size and application scenario, defined as , is the set maximum cycle number, r is the current cycle number / round number, E is the residual energy factor, is the distance factor, is used to determine the weighting coefficient; suppose is the modulo operation, is the probability of the sensor node becoming a cluster head node, is the number of elected cluster head nodes in this round of circulation, S1 is the area within the radius d0 from the sink node, S2 is the area from outside the radius d0 to the deployment range, G represents the set of nodes that have not been elected as cluster heads in this round of circulation.

2. The method of claim 1, wherein, the obtaining of the network structure of sensor nodes comprises: obtaining deployment information of sensor nodes, the deployment information at least comprising position information of nodes; generating a network structure of sensor nodes according to the deployment information of the sensor nodes.

3. The method of claim 1, wherein, the dividing of the network structure into at least two node regions according to a pre-set sink node comprises: obtaining a pre-set sink node and a distance threshold, the distance threshold being calculated by an energy consumption coefficient of a free space attenuation model and an energy consumption coefficient of a multipath fading channel model; dividing nodes in the network structure into at least two node regions with the sink node as the center and the distance threshold as the radius.

4. The method of claim 1, wherein, the distance factor and the residual energy factor are obtained by the following formulas respectively: ； ； wherein, is a distance factor, is a sensor node i to the sink node; denote the nearest and farthest distance of a node within the deployment area to the sink node, respectively; E is a residual energy factor, is the average residual energy of all surviving nodes within the current period, is the residual energy of the current node.

5. The method of claim 1, wherein, the determination of a cluster head in a current period according to a pre-set selection rule and the cluster head selection threshold of each node, the remaining nodes being ordinary nodes, and the clustering of the ordinary nodes according to the cluster head comprises: for each node, generating a random number and comparing the random number with the cluster head selection threshold of the node; determining the node as a cluster head if the cluster head selection threshold is greater than the random number, otherwise, determining the node as an ordinary node; the cluster head node broadcasts to ordinary nodes in the network structure so that the ordinary nodes join the corresponding clusters according to the nearest principle.

6. The method of claim 1, wherein, the performance of a fusion task on the clustered nodes until the next period comprises: establishing a routing path in each cluster according to the cluster head and the ordinary nodes in each cluster, the routing path at least comprising a routing time slot of each ordinary node; establishing a hop path between each cluster head and the sink node; in a stable transmission stage of the current period, processing transmission data according to the routing path and the hop path.

7. The method of claim 1, wherein, the sink node is determined by the following steps: determining the distribution density of nodes according to the deployment range of sensor nodes; determining the position and number of sink nodes according to the distribution density of nodes, wherein more sink nodes are arranged in regions with larger distribution density, and the distance between any two sink nodes is greater than a pre-set value.

8. A sensor network clustering routing device based on coverage area division and energy consumption balancing, characterized in that, the device comprises: an obtaining module for obtaining a network structure of sensor nodes; a region division module for dividing the network structure into at least two node regions according to a pre-set sink node; The first calculation module is configured to calculate a distance factor and a residual energy factor of each node in each node region according to the divided node regions; The second calculation module is configured to calculate a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node region where each node is located; The cluster head selection threshold is obtained by the following formula: ; wherein, is a weighting coefficient, whose value varies with network size and application scenario, defined as , is a set maximum cycle number, r is a current cycle number / round number, E is a residual energy factor, is a distance factor, is used to determine the weighting coefficient; suppose is a modulo operation, is the probability of the sensor node becoming a cluster head node, is the number of elected cluster head nodes in this round of circulation, S1 is an area within a radius d0 from the sink node, S2 is an area outside the radius d0 from the sink node, G represents a set of nodes that have not been elected as cluster heads in this round of circulation; The clustering module is configured to determine a cluster head in the current period and ordinary nodes according to a preset selection rule and the cluster head selection threshold of each node, and cluster the ordinary nodes according to the cluster head; The transmission module is configured to perform a fusion task for the clustered nodes until the next period.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1 to 7 when executing the computer program.

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