A multi-constraint three-dimensional wireless sensor network energy-saving cluster head selection system and method

Abstract

The application provides a multi-constraint three-dimensional wireless sensor network energy-saving cluster head selection system and method, which comprises the following modules: a function calculation module, which is used to determine a sensor network energy-saving cost evaluation function based on constraint information affecting the energy consumption cost of the sensor network; a population generation module, which is used to randomly generate a group of populations; an adaptability statistical information acquisition module, which is used to acquire adaptability statistical information based on the adaptability of the populations, wherein the adaptability of the populations is the value of the sensor network energy-saving cost evaluation function; an optimal solution acquisition module, which is used to determine adaptive parameters based on the adaptability statistical information; based on the adaptive parameters, the populations are updated, and a global optimal solution is acquired; and a cluster head selection module, which is used to select the cluster head of the sensor network based on the global optimal solution. The application has wide potential in the fields of environmental monitoring, agriculture, transportation and health care, and can improve the network performance and energy utilization efficiency, promote the intelligentization and sustainable development.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving cluster head selection technology for Wireless Sensor Networks (WSNs), specifically relating to an energy-saving cluster head selection system and method for multi-constraint three-dimensional wireless sensor networks. Background Technology

[0002] Wireless Sensor Networks (WSNs) are networks composed of a large number of wireless sensor nodes distributed in a specific environment, used to collect, process, and transmit environmental information. Currently, WSNs have wide applications in various fields, including environmental monitoring, agriculture, transportation, and health monitoring. Among these, research on three-dimensional wireless sensor networks plays a particularly important role. By distributing sensor nodes in three-dimensional space, environmental data at different heights and locations can be acquired in real time, further improving the accuracy and coverage of various monitoring methods and enhancing resource utilization efficiency.

[0003] In wireless sensor networks, to extend the overall lifespan of the network and improve network performance, sensor nodes are often organized into clusters. Each cluster is coordinated and data is transmitted by a cluster head node. These cluster heads receive and aggregate data collected by other nodes within the cluster and transmit the data to the base station or other adjacent cluster head nodes.

[0004] The selection of the cluster head node is crucial in wireless sensor networks, directly impacting network performance and energy efficiency. This is because the cluster head node coordinates and manages communication activities within the entire cluster; therefore, selecting a suitable cluster head node is essential for achieving efficient data transmission, reducing energy consumption, and lowering communication overhead. Furthermore, the selection of the cluster head node also affects the network's energy balance. Sensor nodes consume energy during data acquisition, processing, and transmission, while the cluster head node typically bears a greater workload and communication tasks. An inappropriate cluster head selection may lead to some nodes consuming energy too quickly, thus shortening the overall network lifespan. By optimizing the cluster head selection, a more balanced distribution of energy can be achieved within the network, extending node lifespan and improving network sustainability. Therefore, selecting a suitable cluster head node is crucial for achieving efficient data transmission, reducing energy consumption, and lowering communication overhead.

[0005] Traditional cluster head selection methods are typically based on simple rules or basic algorithms, such as node distance and energy. However, these traditional methods have several problems when faced with multiple constraints. First, they often fail to fully consider the trade-offs between different constraints, such as network coverage, energy balance, and intra-cluster transmission efficiency. This leads to uneven energy consumption and shortened network lifetime. Second, these methods ignore the positional information of sensor nodes in three-dimensional space, selecting solely based on the distance between nodes, thus limiting the optimal selection of cluster heads. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a multi-constraint three-dimensional wireless sensor network energy-saving cluster head selection system and method. The technical solution of this invention has advantages such as balanced energy consumption, extended network lifetime, and the ability to fully consider multiple constraints, and is applicable to the field of wireless sensor networks.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A multi-constraint three-dimensional wireless sensor network energy-saving cluster head selection system includes:

[0009] The function calculation module is used to determine the energy-saving cost evaluation function of the sensor network based on the constraint information affecting the energy consumption cost of the sensor network;

[0010] A population generation module is used to randomly generate a population, wherein the individuals in the population are used to represent the possible values ​​of the cluster head node;

[0011] The fitness statistics information acquisition module is used to acquire fitness statistics information based on the fitness of the population, wherein the fitness of the population is the value of the sensor network energy-saving cost evaluation function;

[0012] The optimal solution acquisition module is used to determine the adaptive parameters based on the fitness statistics; update the population based on the adaptive parameters; and obtain the global optimal solution.

[0013] The cluster head selection module is used to select the cluster head of the sensor network based on the global optimal solution.

[0014] Preferably, it also includes the initialization module, used to initialize the parameters of the wireless sensor network in three-dimensional space;

[0015] The initialization parameters include: the set of all node locations in the wireless sensor network in three-dimensional space, receiver location, cluster head selection time period, initial node energy, node sensing radius, and number of node clusters; where a node represents a sensor in the wireless sensor network.

[0016] Preferably, it also includes a cluster head selection module for selecting the cluster head of the wireless sensor network node based on the initialized parameters;

[0017] The cluster head selection module selects one node within each node cluster as the cluster head.

[0018] Preferably, the function calculation module includes:

[0019] An energy consumption constraint unit is used to obtain the energy consumption cost based on the total number of nodes in the wireless sensor network, the initial energy of each node, and the remaining energy.

[0020] The intra-cluster data transmission efficiency constraint unit is used to obtain the intra-cluster data transmission efficiency cost based on the number of node clusters in the wireless sensor network, the total transmission coarseness in each cluster, and the effective transmission count.

[0021] The communication distance constraint unit is used to obtain the communication distance cost between the cluster head and the receiver based on the total number of nodes, node locations, and receiver locations of the wireless sensor network.

[0022] The load balancing constraint unit is used to obtain the load balancing cost based on the time period selected by the cluster head and the load balancing.

[0023] The function calculation unit is used to obtain a wireless sensor network energy-saving cost evaluation function based on the energy consumption cost, the intra-cluster data transmission efficiency cost, the communication distance cost, the load balancing cost, and the weighting coefficients of each cost.

[0024] This invention also provides a method for selecting energy-saving cluster heads in a multi-constraint three-dimensional wireless sensor network, used in the aforementioned system, comprising the following steps:

[0025] Based on the constraint information affecting the energy consumption cost of sensor networks, a sensor network energy-saving cost evaluation function is determined.

[0026] A population is randomly generated, wherein the individuals in the population are used to represent the possible values ​​of the cluster head node;

[0027] Based on the fitness of the population, fitness statistics are obtained, wherein the fitness of the population is the value of the energy-saving cost evaluation function of the sensor network.

[0028] Based on the fitness statistics, the adaptive parameters are determined; based on the adaptive parameters, the population is updated, and the global optimal solution is obtained.

[0029] Based on the globally optimal solution, the cluster head of the sensor network is selected.

[0030] Preferably, the constraints affecting the energy consumption cost of the sensor network include energy consumption cost, intra-cluster data transmission efficiency cost, communication distance cost, and load balancing cost;

[0031] The method for calculating the energy-saving cost assessment function of wireless sensor networks is as follows:

[0032] Based on the total number of nodes in the wireless sensor network, the initial energy of each node, and the remaining energy, the energy consumption cost is obtained.

[0033] Based on the number of node clusters in the wireless sensor network, the total transmission coarseness in each cluster, and the number of effective transmissions, the data transmission efficiency cost within the cluster is obtained.

[0034] Based on the total number of nodes, node locations, and receiver locations in the wireless sensor network, the communication distance cost between the cluster head and the receiver is obtained.

[0035] Based on the cluster head selection time period and load balancing, the load balancing cost is obtained;

[0036] The energy consumption cost, the intra-cluster data transmission efficiency cost, the communication distance cost, the load balancing cost, and the weighting coefficients of each cost are used to obtain the energy-saving cost evaluation function for wireless sensor networks.

[0037] Compared with existing technologies, the beneficial effects of this invention are as follows: The method proposed in this invention can simultaneously consider multiple constraints, including factors such as energy consumption, data transmission efficiency, communication distance, and load balancing, thereby achieving a balanced distribution of energy consumption and effectively extending the network's lifespan. Compared with traditional methods, the technical solution of this invention fully considers the trade-offs between multiple constraints and optimizes the selection of the cluster head through a genetic algorithm, achieving efficient data transmission, reducing energy consumption and communication overhead, improving the sustainable operation of the network, and obtaining optimal results. Furthermore, this invention is applicable to the field of three-dimensional wireless sensor networks, has significant application value, and is suitable for various sensor network application scenarios, such as urban, field, and underwater environmental monitoring, demonstrating broad applicability. Attached Figure Description

[0038] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the structure of the energy-saving cluster head selection system for a multi-constraint three-dimensional wireless sensor network according to an embodiment of the present invention;

[0040] Figure 2This describes the load balancing calculation process in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Example 1

[0044] like Figure 1 As shown, a multi-constraint three-dimensional wireless sensor network energy-saving cluster head selection system includes:

[0045] The initialization module is used to initialize the parameters of the wireless sensor network in three-dimensional space.

[0046] A further implementation method is that the parameters initialized in the initialization module include: the set of all node locations in the wireless sensor network in three-dimensional space, the receiver location, the cluster head selection time period, the initial energy of the node, the sensing radius of the node, and the number of node clusters; wherein, the node represents the sensor in the wireless sensor network.

[0047] In this embodiment, the linear sensor network has 100 nodes in three-dimensional space, numbered S0...S 99 The receiver is numbered S 100 The initial energy of the sensor is 100. The nodes are divided into five clusters, and the time period (round) for selecting the cluster head is N.

[0048] The cluster head selection module is used to select the cluster head of the wireless sensor network node based on the initialized parameters.

[0049] A further implementation involves the cluster head selection module selecting one node within each node cluster as the cluster head.

[0050] The function calculation module is used to determine the energy-saving cost evaluation function of the sensor network based on the constraint information affecting the energy consumption cost of the sensor network;

[0051] A population generation module is used to randomly generate a population, wherein the individuals in the population are used to represent the possible values ​​of the cluster head node;

[0052] The fitness statistics information acquisition module is used to acquire fitness statistics information based on the fitness of the population, wherein the fitness of the population is the value of the sensor network energy-saving cost evaluation function;

[0053] The optimal solution acquisition module is used to determine the adaptive parameters based on the fitness statistics; update the population based on the adaptive parameters; and obtain the global optimal solution.

[0054] The cluster head selection module is used to select the cluster head of the sensor network based on the global optimal solution.

[0055] A further implementation method is that the function computation module includes: an energy consumption constraint unit, an intra-cluster data transmission efficiency constraint unit, a communication distance constraint unit, a load balancing constraint unit, and a function computation unit.

[0056] In this embodiment, in order to link the goal of selecting the optimal cluster head with the above-mentioned influencing factors, a linear function (wireless sensor network energy saving cost evaluation function) was designed as shown in equation (1):

[0057] F cost =αF e +βF t +γF d +δF l (1)

[0058] F cost It is the sensor network cost evaluation function, F e F t F d and F l These are energy consumption cost, intra-cluster data transmission efficiency cost, distance cost, and load balancing cost. α, β, γ, and δ are weighting coefficients between 0 and 1, used to balance the impact of each cost on the total cost, and their values ​​are 0.3, 0.3, 0.2, and 0.2, respectively.

[0059] An energy consumption constraint unit is used to obtain the energy consumption cost based on the total number of nodes in the wireless sensor network, the initial energy of each node, and the remaining energy, as shown in equation (2):

[0060]

[0061] Where i = 1, 2, ..., N represents the number of nodes in the entire sensor network, E s E represents the initial energy of each node. r(i) This represents the remaining energy of the i-th node.

[0062] The intra-cluster data transmission efficiency constraint unit is used to obtain the intra-cluster data transmission efficiency cost based on the number of node clusters in the wireless sensor network, the total transmission coarsening in each cluster, and the effective transmission count, as shown in equation (3):

[0063]

[0064] Where j = 1, 2, ..., M represents the number of clusters in the entire sensor network, and T (j) T represents the total number of transmissions in each cluster. e(j) This indicates the number of valid transmissions in each cluster.

[0065] The communication distance constraint unit is used to obtain the communication distance cost between the cluster head and the receiver based on the total number of nodes, node locations, and receiver locations of the wireless sensor network, as shown in equation (4):

[0066]

[0067] Where i = 1, 2, ..., M represents the number of cluster heads, (x i ,y i ,z i ) represents the position of node i, (x s ,y s ,z s ) indicates the location of the receiver node.

[0068] The load balancing constraint unit is used to obtain the load balancing cost based on the cluster head selection time period and load balancing, as shown in equation (5):

[0069]

[0070] Where k = 0.1, 0.2, ..., 1 is a value that varies with the number of rounds N, and L(k) represents the load balance.

[0071] The function computation unit is used to obtain the energy-saving cost evaluation function of wireless sensor network based on energy consumption cost, intra-cluster data transmission efficiency cost, communication distance cost, load balancing cost, and the weight coefficients of each cost.

[0072] Load balancing prevents too many energy nodes from being repeatedly elected as cluster heads (CHs), even when they are out of energy. Similar to CHs, load balancing nodes can elect a node at any time within their ten-round time period. Nodes that are not yet CHs still have a chance to become CHs before the end of the time period. Initially, each node competing to become a CH (cluster head) is assigned a value of 0.1. This value increases by 0.1 with each round until it reaches 1, as shown below. Figure 2As shown. Once it reaches 1, the node will become a CH. For a node, the higher this value, the greater its chance of becoming a CH.

[0073] A further implementation method involves the following specific applications of genetic algorithms:

[0074] Randomly generate a group of individuals in the population and obtain the possible values ​​of the cluster head;

[0075] In this embodiment, node encoding is based on an adaptive genetic algorithm. Each chromosome corresponds to a cluster head selection method, and the chromosome length is equal to a binary array equal to the total number of sensor nodes (each bit corresponds to one sensor node). Each bit in the array with a value of 1 indicates that its corresponding node has been selected as a cluster head, and 0 indicates a normal node. A set of chromosomes is randomly generated.

[0076] The value of the energy-saving cost assessment function is used as the fitness of the population, and the fitness statistics are updated; that is, for each chromosome, the value of equation (1) is used as the fitness. The average fitness F of this set of chromosomes is calculated. ave Optimal fitness F max .

[0077] Update the adaptive parameters based on the updated fitness statistics;

[0078] In this embodiment, the probabilities and parameters of selection, crossover, and mutation are adjusted based on fitness statistics. Selection: Based on the updated selection probabilities, some individuals are selected as parents. Methods such as roulette wheel selection or tournament selection can be used. Crossover: Two individuals are selected from the chosen parents, and offspring are generated through crossover. Single-point crossover or multi-point crossover can be used. Mutation: The generated offspring are mutated to increase population diversity. A random location on the chromosome can be selected, and the gene value at that location can be randomly changed.

[0079] Based on the individual's current fitness F j With optimal fitness F max And the worst global fitness value F min The relative performance of selection, crossover, and mutation probabilities is adjusted as shown in Table 1.

[0080] Table 1

[0081]

[0082] Here, f is the relative fitness, which refers to the difference between an individual's fitness and the global best fitness. ΔF represents the difference between the global best fitness and the global worst fitness, and P... s P c and P mThese represent the probabilities of the selection, crossover, and mutation operations, respectively.

[0083] a. Selection operation: Select some superior individuals from the parent generation to form a new population.

[0084] b. Crossover operation: Select two individuals from the selected parent generation and generate offspring through crossover operation.

[0085] c. Mutation operation: Randomly select a location in the chromosome and randomly change the gene value at that location.

[0086] Based on the updated adaptive parameters, the population is updated, and a globally optimal solution is obtained based on a preset termination condition. The preset termination condition is reaching the maximum number of iterations predetermined by the genetic algorithm. That is, each time the genetic algorithm is run, a fixed number of iterations n is preset (e.g., 100 generations, 500 generations, etc.). When the algorithm reaches the set upper limit of iterations, it will stop running regardless of whether an optimal solution has been found or whether it is still improving.

[0087] Based on the globally optimal solution, the optimal cluster head selection scheme is obtained.

[0088] In this embodiment, the generated offspring are combined with the original population to form a new population. A termination condition is determined: if the termination condition has not been met, the iteration returns to the fitness update unit to continue; if the termination condition has been met, the globally optimal solution is output as the final solution.

[0089] In this example, the optimal sensor cluster head selection result obtained by the adaptive genetic algorithm is 12, 28, 42, 64, 88, with a cost of 46.5.

[0090] Experimental results show that the energy-saving cluster head selection method for three-dimensional wireless sensor networks under multiple constraints proposed in this invention can effectively elect cluster heads for three-dimensional sensor network nodes, thereby obtaining the most energy-efficient cluster head set.

[0091] Example 2

[0092] This invention also provides a method for selecting energy-saving cluster heads in multi-constraint three-dimensional wireless sensor networks, for use in systems, comprising the following steps:

[0093] This invention also provides a method for selecting energy-saving cluster heads in a multi-constraint three-dimensional wireless sensor network, used in the aforementioned system, comprising the following steps:

[0094] Based on the constraint information affecting the energy consumption cost of sensor networks, a sensor network energy-saving cost evaluation function is determined.

[0095] A population is randomly generated, wherein the individuals in the population are used to represent the possible values ​​of the cluster head node;

[0096] Based on the fitness of the population, fitness statistics are obtained, wherein the fitness of the population is the value of the energy-saving cost evaluation function of the sensor network.

[0097] Based on the fitness statistics, the adaptive parameters are determined; based on the adaptive parameters, the population is updated, and the global optimal solution is obtained.

[0098] Based on the globally optimal solution, the cluster head of the sensor network is selected.

[0099] A further implementation method is that the constraint information affecting the energy consumption cost of the sensor network includes energy consumption cost, intra-cluster data transmission efficiency cost, communication distance cost, and load balancing cost.

[0100] The method for calculating the energy-saving cost assessment function of wireless sensor networks is as follows:

[0101] Based on the total number of nodes in the wireless sensor network, the initial energy of each node, and the remaining energy, the energy consumption cost is obtained.

[0102] Based on the number of node clusters in the wireless sensor network, the total transmission coarseness in each cluster, and the number of effective transmissions, the data transmission efficiency cost within the cluster is obtained.

[0103] Based on the total number of nodes, node locations, and receiver locations in the wireless sensor network, the communication distance cost between the cluster head and the receiver is obtained.

[0104] Based on the cluster head selection time period and load balancing, the load balancing cost is obtained;

[0105] The energy consumption cost, the intra-cluster data transmission efficiency cost, the communication distance cost, the load balancing cost, and the weighting coefficients of each cost are used to obtain the energy-saving cost evaluation function for wireless sensor networks.

[0106] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A multi-constrained three-dimensional wireless sensor network energy-saving cluster head selection system, characterized in that, The system comprises: a function calculation module configured to determine a sensor network energy saving cost evaluation function based on constraint information affecting a sensor network energy consumption cost; a population generation module configured to randomly generate a group of populations, wherein individuals of the populations are used to represent possible values of cluster head nodes; an adaptability statistical information acquisition module configured to acquire adaptability statistical information based on adaptabilities of the populations, wherein the adaptabilities of the populations are values of the sensor network energy saving cost evaluation function; an optimal solution acquisition module configured to determine adaptive parameters based on the adaptability statistical information, update populations based on the adaptive parameters, and acquire a global optimal solution; a cluster head selection module configured to select cluster heads of the sensor network based on the global optimal solution; The system further comprises an initialization module configured to initialize parameters of a wireless sensor network in a three-dimensional space. The initialized parameters include: a set of all node positions of the wireless sensor network in the three-dimensional space, a receiver position, a cluster head selection time period, node initial energy, a node sensing radius, and a node cluster number; wherein a node represents a sensor in the wireless sensor network. The cluster head selection module selects cluster heads of nodes of the wireless sensor network based on the initialized parameters. The cluster head selection module selects one node in each node cluster as a cluster head. The function calculation module comprises: an energy consumption constraint unit configured to obtain an energy consumption cost based on a total number of nodes of the wireless sensor network, initial energy and residual energy of each node; an intra-cluster data transmission efficiency constraint unit configured to obtain an intra-cluster data transmission efficiency cost based on a number of node clusters of the wireless sensor network, a total transmission number in each cluster, and an effective transmission number; a communication distance constraint unit configured to obtain a communication distance cost between the cluster heads and the receiver based on the total number of nodes of the wireless sensor network, node positions, and the receiver position; The load balancing constraint unit is configured to obtain a load balancing cost based on the cluster head selection time period and load balancing. The calculation formula is as follows: ， wherein is a value that varies with cycle N; a function calculation unit configured to obtain a wireless sensor network energy saving cost evaluation function based on the energy consumption cost, the intra-cluster data transmission efficiency cost, the communication distance cost, the load balancing cost, and weight coefficients of the respective costs.

2. A method for selecting energy-efficient cluster heads in a multi-constrained three-dimensional wireless sensor network, the method comprising: The system of claim 1 comprises the following steps: determining a sensor network energy saving cost evaluation function based on constraint information affecting a sensor network energy consumption cost; randomly generating a group of populations, wherein individuals of the populations are used to represent possible values of cluster head nodes; acquiring adaptability statistical information based on adaptabilities of the populations, wherein the adaptabilities of the populations are values of the sensor network energy saving cost evaluation function; determining adaptive parameters based on the adaptability statistical information, updating populations based on the adaptive parameters, and acquiring a global optimal solution; selecting cluster heads of the sensor network based on the global optimal solution.

3. The method of claim 2, wherein, The constraint information affecting the sensor network energy consumption cost includes an energy consumption cost, an intra-cluster data transmission efficiency cost, a communication distance cost, and a load balancing cost. The method for calculating a wireless sensor network energy saving cost evaluation function is: obtaining an energy consumption cost based on a total number of nodes of the wireless sensor network, initial energy and residual energy of each node; obtaining a cluster-in data transmission efficiency cost based on a number of node clusters of the wireless sensor network, a total number of transmissions in each cluster, and an effective number of transmissions; obtaining a communication distance cost between the cluster head and the receiver based on a total number of nodes of the wireless sensor network, node positions, and a receiver position; obtaining a load balancing cost based on a cluster head selection time period and load balancing; obtaining a wireless sensor network energy saving cost evaluation function based on the energy consumption cost, the cluster-in data transmission efficiency cost, the communication distance cost, the load balancing cost, and weight coefficients of the respective costs.

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