CN114040473A - Clustering routing method for wireless sensor network - Google Patents

Clustering routing method for wireless sensor network Download PDF

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CN114040473A
CN114040473A CN202111435936.3A CN202111435936A CN114040473A CN 114040473 A CN114040473 A CN 114040473A CN 202111435936 A CN202111435936 A CN 202111435936A CN 114040473 A CN114040473 A CN 114040473A
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nodes
cluster head
wireless gateway
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刘洪�
陈锦山
谭冲
郑敏
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Shanghai Institute of Microsystem and Information Technology of CAS
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
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Shanghai Institute of Microsystem and Information Technology of CAS
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a clustering routing method of a wireless sensor network, which comprises the following steps: providing a wireless sensor network with a plurality of relay nodes; electing the current wireless gateway node from all the surviving relay nodes; taking the relay nodes which survive except the current wireless gateway node as common nodes, carrying out election of cluster head nodes and establishing clusters; the sensor is accessed to the nearest relay node and transmits data to the relay node, and the common nodes except the cluster head node transmit the data to the cluster head node corresponding to the cluster head node in a single-hop mode; the cluster head nodes perform data fusion on the data, and the data are transmitted among the cluster head nodes in a multi-hop networking mode so as to be transmitted to the wireless gateway nodes; the wireless gateway transmits the data to the monitoring platform; and repeating the steps. The wireless sensor network clustering routing method can solve the problems of overhigh energy consumption and too short network life cycle of the wireless sensor network in the power transmission, transformation and distribution scene.

Description

Clustering routing method for wireless sensor network
Technical Field
The invention relates to a wireless sensor network routing protocol, in particular to a wireless sensor network clustering routing method facing a transmission and transformation distribution scene based on an improved SSA algorithm.
Background
The transmission distance of the power transmission line is long, the monitoring area is wide, and the power transmission line is located outdoors and unattended. The wiring in the transformer substation is complex, and the long-distance transmission line is easily subjected to electromagnetic interference. The environment information monitoring and the equipment running state obtaining depend on a large number of sensors, including microclimate, pressure, waving, ice coating and other sensors, therefore, a Wireless Sensor Network (WSN)[1]Technology is an effective means of this problem. The wireless sensor network combines a sensor technology and a network communication technology, adopts low-cost nodes, automatically networks and dynamically configures, and has higher expansibility and robustness. Therefore, various sensors are widely used in power systems.
The problem of energy consumption in the wireless sensor network is still reflected in power transmission, transformation and distribution scenes, most wireless sensing nodes are powered by batteries, the wireless sensing nodes are located in unattended areas for a long time, the batteries are difficult to replace, even if the nodes use an energy obtaining technology to obtain electric energy (such as solar energy and mechanical vibration energy) from the surrounding environment, the influence of the environment is great, and the system is lowered, and the key is particularly. Meanwhile, in a power transmission and distribution environment, a monitoring area is large, a transmission distance is long, a large number of sensors are included, power supply is difficult, chain transmission causes 'hot point nodes' around a sink node to bear information forwarding of a large number of sensor nodes, environment information is collected by the sink node, energy is consumed rapidly, the nodes die too early, and the life cycle of the network is shortened seriously.
In a power transmission and distribution scene, the function of transmitting data to a monitoring platform is usually completed by a 4G module, the 4G module can send heartbeat and data to the monitoring platform at regular time, and the generated energy consumption is larger than that generated by communication with a single sensor at the same time. The relay nodes can also be networked in a mode of communicating with the sensor, and the generated energy consumption is less than that of the nodes directly transmitting data to the monitoring platform.
The efficient routing and scheduling algorithm plays an important role in reducing the energy consumption of the network and prolonging the life cycle of the network, integrates various factors such as position information and the like, distributes and schedules the forwarding of data messages of nodes, plans a better path, saves the energy consumption caused by each round of system transmission, considers the residual energy consumption of the nodes, ensures load balance, prevents the rapid death of special nodes and prolongs the life cycle of the network.
At present, although the existing sensor network clustering routing algorithm balances the load relative to the plane type routing, the survival time of the network is prolonged[3]. But also one or more of the following problems: 1) the situation that some sensors have no networking capability and the sensors are numerous is not considered. Specifically, in order to save cost, the computing power of some sensors is low, and the sensors do not have networking capability, cannot become "cluster head nodes" or relay nodes, and can only transmit data in a single-hop transmission mode. 2) The residual energy of the nodes is not considered, and the nodes with low energy consumption still have a certain probability of becoming the cluster head, so that the nodes die quickly. 3) Without considering the location factor, the elected cluster head may be an edge node, which results in that the structure distribution of the network is not uniform enough, the number of surrounding nodes is small, or the distance from the aggregation node is long, and the transmission cost is high. 4) The nodes are all transmitted in a single hop mode, for the nodes far away from the sink node, the transmitting power is improved, the cost of enlarging the transmission radius is high, the nodes are transmitted to the nearby nodes, and a lot of energy can be saved through multi-hop transmission. Therefore, the strategy of only one-hop transmission in network transmission is insufficient.
Therefore, by combining the characteristics of the transmission, transformation and distribution environment in the power internet of things, a new efficient, energy-saving and stable routing and scheduling algorithm is researched, and the method has important significance for a monitoring system of a transmission, transformation and distribution scene.
Sparrow Search Algorithm (spark Search Algorithm, SSA)[2]The intelligent algorithm is an intelligent algorithm for simulating sparrow foraging and warning behaviors provided by Xue and the like in 2020, is excellent in the aspects of solving success rate, convergence speed and the like in the aspect of high-dimensional multimodal optimization, and is applied to scenes such as image segmentation, three-dimensional unmanned aerial vehicle aviation optimization and the like. The number of the nodes in the power transmission, transformation and distribution scene is numerous, and for the election problem of the optimal cluster head set, the SSA algorithm is high in optimization accuracy and has certain advantages, and the SSA algorithm is not applied to the election of the cluster heads.
Reference documents:
[1] mazu, sun yining, billow. overview of wireless sensor networks [ J ] proceedings of communications, 2004, 25 (4): 114-124.
[2]Xue J K,Shen B.A novel swarm intelligence optimization approach:Sparrow search algorithm[J].Systems Science&Control Engineering,2020,8(1):22-34.
[3]Heinzelman W R,Chandrakasan A,Balakrishnan H.Energy-efficient communication protocol for wireless microsensor networks[C]//Proceedings of the 33rd Annual Hawaii International Conference on System Sciences:Maui,HI,USA:IEEE,2000:1-10.
Disclosure of Invention
The invention aims to provide a wireless sensor network clustering routing method to solve the problems of overhigh energy consumption and too short network life cycle of a wireless sensor network in a power transmission, transformation and distribution scene.
In order to achieve the above object, the present invention provides a method for clustering routing in a wireless sensor network, including:
s1: providing a wireless sensor network with a plurality of relay nodes, wherein all the relay nodes can communicate with the sensor and the monitoring platform to perform network initialization;
s2: electing the current wireless gateway node from all the surviving relay nodes;
s3: taking all the surviving relay nodes except the current wireless gateway node as common nodes, carrying out election of a cluster head node in the common nodes, and establishing clustering;
s4: the sensor is accessed to the nearest relay node and transmits data to the relay node, and the common nodes except the cluster head node transmit the data to the cluster head node corresponding to the cluster head node in a single-hop mode; the cluster head nodes perform data fusion on the data, and the data are transmitted among the cluster head nodes in a multi-hop networking mode so as to be transmitted to the wireless gateway nodes; the wireless gateway transmits the data to the monitoring platform;
s5: the above-described steps S1-S4 are repeated.
In step S1, a network initialization is performed, which includes: presetting a relay node as a front wireless network joint point, and sending message information of each relay node to the front wireless network joint point; the message information includes a global ID, remaining energy and geographic location.
The step S2 includes:
step S21: determining a wireless gateway node weight function of each surviving relay node when the wireless gateway node elects;
wherein, in the wireless gateway node election, the wireless gateway node weight function p of the ith surviving relay nodeWG(i) Comprises the following steps:
Figure BDA0003381750060000031
Figure BDA0003381750060000032
Figure BDA0003381750060000041
Figure BDA0003381750060000042
wherein, alpha, beta and delta are wirelessWeight coefficient of gateway node weight function, α + β + δ being 1, α, β, δ being (0,1), Enode(i) Is the remaining energy of the ith surviving relay node, E (E)node) Is the average remaining energy, N, of all surviving relay nodesneighbor(i) The number of neighboring nodes of the ith surviving relay node, E (N)neigbor) Number of neighbor nodes averaged over all surviving relay nodes, dtoCT(i) Is the distance of the ith surviving relay node from the centroid of the surviving node set, E (d)toCT) Is the average of the distances of all surviving relay nodes from the centroid of the surviving node set, n is the number of all surviving relay nodes, GaliveIs a set of surviving nodes;
step S22: determining the current wireless gateway node according to the wireless gateway node weight function of each surviving relay node; the relay node corresponding to the maximum value of the weight function of the wireless gateway node is the current wireless gateway node.
In step S3, a sparrow search algorithm is used to elect a cluster head based on the low power consumption adaptive clustering hierarchical protocol.
In step S3, performing cluster head election in the common node includes:
s31: respectively taking the serial number arrays of the multiple groups of candidate cluster head nodes as vectors of the population to perform population initialization;
s32: determining the comprehensive fitness value of each population and sequencing;
s33: determining the position of a finder of the next iteration of the current iteration times;
s34: determining the position of a joiner of the next iteration of the current iteration times;
s35: determining the position of the alerter in the next iteration of the current iteration times;
s36: and taking the next iteration of the current iteration times as a new current iteration time, and repeating the steps S33-S35 until the iteration is completed, wherein the population corresponding to the optimal comprehensive fitness value of the current iteration times t of all the populations obtained at the moment is the election result of the cluster head node.
The step S31 specifically includes:
s311: determining a cluster head election weight function p of the ith common node when the candidate node is selectedcluster(i)；
S312: selecting weight function p according to cluster headcluster(i) Sorting common nodes from big to small, and selecting the first 50% nodes as candidate nodes; for each population, randomly extracting K unrepeated candidate nodes from the candidate nodes, taking the serial numbers of the K unrepeated candidate nodes as serial number arrays of a group of candidate cluster head nodes and as vectors of a single population, and repeating the random extraction for multiple times to construct multiple vectors as multiple population initial values; k is the total number of clusters;
s313: and marking UB and LB as the upper and lower bounds of the node number, and setting the number interval of the whole survival node according to the upper and lower bounds of the node number.
In step S32, the overall fitness value of the population is:
Figure BDA0003381750060000051
wherein, the ratio of phi,
Figure BDA0003381750060000052
gamma is the weight of the fitness function of the energy, the fitness function of the distance between clusters and the fitness function of the distance from the cluster head node to the wireless gateway node respectively,
Figure BDA0003381750060000053
Figure BDA0003381750060000054
f1、f2、f3respectively is a fitness function of energy, a fitness function of inter-cluster distance and a fitness function of the distance from a cluster head node to a wireless gateway node;
fitness function f of energy1Comprises the following steps:
Figure BDA0003381750060000055
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; n is the total number of common nodes; k is the total number of clusters; enode(i) Is the energy of the ith common node in the population, ECH(j) The energy of a cluster head node of the jth cluster in the population;
fitness function f of inter-cluster distance2Comprises the following steps:
Figure BDA0003381750060000056
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; d (j, i) represents the distance from the ith common node in the jth cluster in the population to the cluster head node, nc(j) Representing the number of common nodes in the jth cluster in the population;
fitness function f of distance from cluster head node to wireless gateway node3Comprises the following steps:
Figure BDA0003381750060000057
wherein j is the ordinal number of the cluster; k is the total number of clusters; dtoWG(i) The distance from the cluster head node of the jth cluster in the cluster to the wireless gateway node.
In step S33, determining the finder position of the next iteration of the current iteration number by using a Levy flight algorithm;
the finder position for the next iteration of the current iteration number is:
Figure BDA0003381750060000061
Figure BDA0003381750060000062
wherein t is the currentThe number of iterations is,
Figure BDA0003381750060000063
is the finder position at the current iteration number t of the ith element in the jth population,
Figure BDA0003381750060000064
the position of a finder at the next iteration t +1 of the current iteration times of the I element in the J-th population; i is the ordinal number of the element; t is the total iteration number; μ is a random value from 0 to 1; v (V is equal to [0, 1]])，ST(ST∈[0.5,1]) Respectively an early warning value and a safety value; q is a normally distributed random number, W is 1 xdpopAll 1 matrices.
The step S2 further includes a step S23: the previous wireless gateway node informs all the surviving relay nodes of the current wireless gateway node in a broadcasting mode; and in the step S3, establishing a cluster including: the current wireless gateway node broadcast informs all the surviving relay nodes of the election result of the cluster head node in a broadcasting mode; then, the common nodes except the cluster head node select the cluster head node or the wireless gateway node closest to the cluster head node for access;
alternatively, in step S3, creating a cluster includes: the former wireless gateway node informs all the surviving relay nodes of the election results of the current wireless gateway node and the cluster head node in a broadcasting mode; and then, the common nodes except the cluster head node select the cluster head node or the wireless gateway node closest to the cluster head node for access.
In step S4, the cluster head node and the wireless gateway node calculate a minimum spanning tree according to the prim algorithm, and the wireless gateway node serves as a root node to determine the spanning tree between the cluster head nodes as a multi-hop transmission path between the cluster head nodes.
According to the wireless sensor network clustering routing method, the relay nodes are arranged, the energy consumption is saved through the multi-modulation networking among the relay nodes, and the network life cycle is prolonged; the invention also provides a routing protocol for rotating the wireless gateway node by using the relay node as the wireless gateway node, so as to prevent the wireless gateway node from dying out early.
Furthermore, the wireless sensor network clustering routing method optimizes the selection of the wireless gateway nodes according to the residual energy of the relay nodes, the number of the adjacent nodes of the relay nodes and the distance from the relay nodes to the center of mass of the surviving node set.
In addition, residual energy, the number of adjacent nodes and position information are considered on the basis of the existing clustering protocol, and a sparrow searching algorithm is introduced to optimize cluster head election. Meanwhile, in order to avoid the sparrow searching algorithm from being trapped in local convergence, a Levy flight strategy is added, so that better cluster head distribution is obtained, and the network life cycle is prolonged.
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Fig. 1 is a diagram illustrating an application scenario and a schematic diagram of a wireless sensor network clustering routing method according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In the power system scene, most scenes are outdoors or the wiring is complex, and each node of the wireless sensor network is powered by a battery. As shown in fig. 1, the applicable scenario of the wireless sensor network clustering routing method of the present invention is a wireless sensor network such as a transformer substation or a transmission line, which is different from a common wireless sensor network, and the wireless sensor network does not include a sink node with infinite energy, but a plurality of nodes with high computing power are deployed as relay nodes of a sensor. The relay node is similar to a Sink node in a common wireless sensor network, and has the following functions: 1) communicating with the sensor 2) transmitting the data to the monitoring platform. That is, all relay nodes are capable of communicating with the sensors and have the function of communicating with the monitoring platform.
As shown in fig. 1, the present invention is based on the following principle: after the sensor collects the environmental information of the transformer substation or the power transmission line, the sensor selects to access the nearest relay node according to the signal intensity of the received signal and transmits monitoring information. And networking is carried out among the relay nodes, one node is selected from the relay nodes in each turn of the network as a gateway node of the whole network, namely a Wireless gateway node (Wireless gateway), the other nodes are degenerated into common nodes, a cluster head node is selected from the common nodes, and the other common nodes are selected to be added into the nearest cluster. After the information of the sensor is collected by the nodes, the data are transmitted to the cluster head nodes in the cluster for data fusion, the cluster head nodes transmit the data to the wireless network joint points through the relay node network, then the wireless network joint points upload the information of the whole network to the monitoring platform in a wireless mode (such as a 4G module), and the other relay nodes close the uploading function. Therefore, the invention can solve the problems of overhigh energy consumption and too short network life cycle of the wireless sensor network in the power transmission, transformation and distribution scene.
The wireless sensor network clustering routing method mainly researches the networking of the relay nodes and the corresponding routing algorithm, and the access of the sensors is not considered for the moment. The wireless sensor network clustering routing method is divided into 4 stages, namely a network initialization stage, a wireless gateway node election stage, a cluster head election and clustering stage and a routing transmission stage.
The clustering routing method of the wireless sensor network specifically comprises the following steps:
step S1: and carrying out network initialization. Namely, a wireless sensor network with a plurality of relay nodes is provided, and all the relay nodes can communicate with the sensors and the monitoring platform to perform network initialization.
Performing network initialization, including: a relay node is preset to serve as a front wireless network joint point, and each relay node sends message information of the relay node to the front wireless network joint point. The message information includes a global ID, remaining energy, geographical location, and the like.
Step S2: the election of the current wireless gateway node is made among all surviving relay nodes.
The step S2 specifically includes:
step S21: determining a wireless gateway node weight function of each surviving relay node when the wireless gateway node elects;
wherein, in the wireless gateway node election, the wireless gateway node weight function p of the ith surviving relay nodeWG(i) Comprises the following steps:
Figure BDA0003381750060000081
Figure BDA0003381750060000082
Figure BDA0003381750060000083
Figure BDA0003381750060000084
wherein, α, β, δ are weight coefficients of wireless gateway node weight function, α + β + δ is 1, α, β, δ belongs to (0,1), Enode(i) Is the remaining energy of the ith surviving relay node, E (E)node) Is the average remaining energy, N, of all surviving relay nodesneighbor(i) The number of neighboring nodes of the ith surviving relay node, E (N)neigbor) Number of neighbor nodes averaged over all surviving relay nodes, dtoCT(i) Is the distance of the ith surviving relay node from the centroid of the surviving node set, E (d)toCT) Is the average of the distances of all surviving relay nodes from the centroid of the surviving node set, n is the number of all surviving relay nodes, GaliveIs a set of surviving nodes, i.e. a set of all surviving relay nodes.
Step S22: and determining the current wireless gateway node according to the wireless gateway node weight function of each surviving relay node.
And the relay node corresponding to the maximum value of the weight function of the wireless gateway node is the current wireless gateway node. That is, the current wireless gateway node is numbered ioptAnd is numbered ioptSatisfies the following formula:
iopt∈max(pWG(i),i∈Galive) (5)
wherein p isWG(i) The weight function of the ith relay node when the wireless gateway node elects; galiveIs a set of surviving nodes, i.e. a set of all surviving relay nodes.
Step S23: the pre-wireless gateway node informs all surviving relay nodes of the current wireless gateway node by means of broadcasting.
Furthermore, in some other embodiments, this step S23 may also be omitted, and instead the current election results of the wireless gateway node and the cluster head node are notified simultaneously below.
Thus, the number is ioptBecomes the current wireless gateway node and the current wireless gateway node election is completed.
Step S3: and (4) carrying out cluster head election and clustering, namely, taking all the surviving relay nodes except the current wireless gateway node as common nodes, carrying out cluster head node election in the common nodes, and establishing clustering.
And after the wireless gateway node election is completed, the relay node which is not selected as the WG gateway is changed back to the common node. The structure of the whole network can be similar to that of a common sensor network, and the energy consumption of the network can be balanced by adopting a clustering routing mechanism, so that hot point nodes around a wireless gateway are prevented from rapidly dying, namely, the network is formed by clustering of a second-layer network, namely common nodes.
In this embodiment, a Sparrow Search Algorithm (SSA) is used to elect a cluster head based on an existing low-power adaptive clustering hierarchical protocol (LEACH), so that the distribution of the cluster head is optimized, the optimization target is to improve the occupation ratio of the residual energy of the cluster head node in all surviving nodes, and reduce the energy consumption of each round of transmission. In order to accelerate convergence, population initialization is optimized, and three factors are considered: the residual energy of the node, the number of adjacent nodes of the node and the distance from the node to the wireless gateway node. Although the Sparrow Search Algorithm (SSA) has strong local search capability, it is easy to get into local optima similar to the multi-population intelligent optimization algorithm, and therefore, the Levy flight strategy is also used to improve the Sparrow Search Algorithm (SSA).
Therefore, in step S3, the electing a cluster head in the common node specifically includes:
step S31: respectively taking the serial number arrays of the multiple groups of candidate cluster head nodes as vectors of the population to perform population initialization;
in order to balance the energy consumption and accelerate the convergence of the intelligent algorithm, the population initialization needs to be optimized in step S31. Therefore, through optimization, the step S31 includes: selecting candidate nodes according to a cluster head election weight function obtained by 3 factors including the residual energy of the nodes, the distance between the nodes and the base station and the number of adjacent nodes of the nodes, extracting the candidate nodes to obtain a group of candidate cluster head nodes, taking the serial number array of the candidate cluster head nodes as a vector of a single population, and repeating random extraction for multiple times to construct multiple vectors as multiple initial population values.
Therefore, the step S31 specifically includes:
step S311: determining a cluster head election weight function p of the ith common node when the candidate node is selectedcluster(i)；
Similar to the wireless network node election, when selecting a candidate node, the cluster head election weight function of the ith common node is as follows:
Figure BDA0003381750060000101
Figure BDA0003381750060000102
wherein, i is the ordinal number of the common node; o, ρ, and θ are weighting coefficients set empirically, o + ρ + θ ═ 1, o, ρ, θ ∈ (0,1), Enode(i) Is the ith ordinary nodeResidual energy of, E (E)node) Is the average residual energy, N, of all common nodesneighbor(i) The number of adjacent nodes of the ith common node, E (N)neigbor) The average number of adjacent nodes of all common nodes, dtoWG(i) Is the distance from the ith ordinary node to the wireless gateway node, E (d)toWG) Is the average of the distances of all common nodes from the wireless gateway node, GnodeIs the set of all ordinary nodes that includes all surviving relay nodes except the wireless gateway node.
Step S312: selecting weight function p according to cluster headcluster(i) Sorting common nodes from big to small, and selecting the first 50% nodes as candidate nodes; for each population, randomly extracting the serial numbers of K non-repeated candidate nodes from the candidate nodes, taking the serial numbers as a serial number array of a group of candidate cluster head nodes and as a vector of a single population, and then repeating the random extraction for multiple times to construct multiple vectors as multiple population initial values. Wherein K is the total number of clusters.
Preferably, the total number of clusters K is[4]：
Figure BDA0003381750060000111
Wherein n is a set G of common nodesnodeNumber of nodes in (i.e. total number of common nodes), εfsAnd εmpRespectively are power amplification factor parameters of a free space model and a multipath attenuation model, M is the side length of a monitoring area, EelecFor circuit losses of energy, dtoWGDistance expectation for node to wireless gateway[5]。
The value of K is sized according to computing power, here abstracted as K; of course, the first 50% is also an example percentage number, and in other embodiments, the 50% ratio may also vary depending on the computational capabilities of the nodes in the network and the network itself.
See in particular reference [4] [ king gold wei, grand china, grand german ] study of optimal cluster headcount of wireless sensor network based on energy consumption [ J ]. sensor and microsystem, 2011, 30 (7): 45-47,50 ] and reference [5] [ Liangqing, Lizeran, Konhomentum, et al. 3622- & lt3624 ].
Step S313: after the initial value of the population is constructed, UB and LB are recorded as the upper and lower bounds of the node number in order to prevent local optimization, and the number interval of the whole survival node except the wireless gateway node is set according to the upper and lower bounds of the node number. The upper and lower bounds are only used to determine the range of the optimal solution.
Step S32: and determining the comprehensive fitness value of each population and sequencing.
The determination of the fitness function is the most important step in the algorithm. In the invention, the lower fitness function value obtained by continuous iteration is the optimization target of the sparrow search algorithm.
The optimization target of the electing cluster head is the proportion of the residual energy of the cluster head node in all the surviving nodes, so the influence factors of the optimization target include the following three: the energy occupation ratio of the cluster head node, the inter-cluster distance between the common node and the cluster head node and the distance between the cluster head node and the wireless gateway node.
Fitness function f of energy1Is defined as:
Figure BDA0003381750060000112
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; n is a set G of common nodesnodeThe number of nodes in the node; k is the total number of clusters; enode(i) Is the energy of the ith common node in the population, ECH(j) The energy of the cluster head node of the jth cluster in the population.
Fitness function f of inter-cluster distance2Is defined as:
Figure BDA0003381750060000121
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; k is the total number of clusters; d (j, i) represents the distance from the ith common node in the jth cluster in the population to the cluster head node, nc(j) And the number of the common nodes in the jth cluster in the population is shown (the common nodes comprise cluster head nodes).
Fitness function f of distance from cluster head node to wireless gateway node3Is defined as:
Figure BDA0003381750060000122
wherein j is the ordinal number of the cluster; k is the total number of clusters; dtoWG(i) The distance from the cluster head node of the jth cluster in the cluster to the wireless gateway node.
Thus, the overall fitness value of the population is:
Figure BDA0003381750060000123
wherein, the ratio of phi,
Figure BDA0003381750060000124
gamma is the weight of the fitness function of the energy, the fitness function of the distance between clusters and the fitness function of the distance from the cluster head node to the wireless gateway node respectively,
Figure BDA0003381750060000125
Figure BDA0003381750060000126
f1、f2、f3respectively, a fitness function of energy, a fitness function of inter-cluster distance, and a fitness function of the distance from the cluster head node to the wireless gateway node.
Step S33: updating the position of the finder; namely, determining the position of a finder in the common node of the next iteration of the current iteration times;
according to the prior art, the formula of the finder position for the next iteration of the current iteration number is as follows:
Figure BDA0003381750060000127
wherein t is the current iteration number,
Figure BDA0003381750060000128
is the finder position at the current iteration number t of the ith element in the jth population,
Figure BDA0003381750060000129
the position of a finder at the next iteration t +1 of the current iteration times of the I element in the J-th population; i is the ordinal number of the element; t is the total iteration number; μ is a random value from 0 to 1; v (V is equal to [0, 1]])，ST(ST∈[0.5,1]) Respectively an early warning value and a safety value; q is a normally distributed random number, W is 1 xdpopAll 1 matrices of dpopIs the optimization dimension. Each population has a clustering mode as an input value of the algorithm.
In this embodiment, a Levy flight algorithm is employed to update the finder position (i.e., determine the finder position for the next iteration of the current iteration). In particular, Levy flight belongs to the markov process, which is a special random walk strategy that combines short range searches with occasional long jumps. Thus, the Levy flight strategy may increase population diversity. Since the discoverer often guides other sparrows to update the location, and experiments show that Levy flights have little impact on other roles, only the Levy flight strategy is used to update the discoverer's location.
The formula of the finder's position for the next iteration of the current iteration number is updated to the following formula:
Figure BDA0003381750060000131
Figure BDA0003381750060000132
where ξ is the step size.
Step S34: updating the position of the joiner; namely, determining the position of an adder in the common node of the next iteration of the current iteration times;
location of joiner
Figure BDA0003381750060000133
Comprises the following steps:
Figure BDA0003381750060000134
wherein the content of the first and second substances,
Figure BDA0003381750060000135
is the position of the joiner at the current iteration number t of the ith element in the jth population,
Figure BDA0003381750060000136
the position of the joiner under the next iteration t +1 of the current iteration times of the I element in the J-th population is determined; i is the ordinal number of the element; n is a radical ofpopThe population number; q is a normally distributed random number; xbest(t +1) is the optimal position of the next iteration t +1 of the current iteration number, Xworst(t) is the worst position of the current iteration number t; a. the+Is 1 xdpopThe matrix has values of 1 and-1 randomly distributed and satisfies A+＝AT(AAT)-1W is 1 xdpopAll 1 matrices.
Step S35: updating the position of the warner; namely, determining the position of the alerter in the next iteration of the current iteration times;
the position updating formula of the alerter in the node is as follows:
Figure BDA0003381750060000141
wherein, Xbest(t) is the optimal position of the current iteration number t, Xworst(t) is the worst position of the current iteration number t;
Figure BDA0003381750060000142
taking x-N (0,1) as a step length, and H is a random number from-1 to 1, wherein the position of the alerter is the position of the current iteration time t of the I element in the J-th population; f. ofi，fw，fgThe comprehensive fitness values of the sparrows in the current population, the worst comprehensive fitness value of the current iteration times t of all the populations and the best comprehensive fitness value of the current iteration times t of all the populations are respectively, and epsilon is an energy consumption coefficient.
Step S36: electing to obtain a cluster head; namely, taking the next iteration of the current iteration times as a new current iteration time, and repeating the steps S33 to S35 until the iteration is completed, wherein the population corresponding to the optimal comprehensive fitness value of the current iteration times t of all the populations obtained at this time is the election result of the cluster head node.
In the step S3, creating a cluster includes: the current wireless gateway node broadcast informs all the surviving relay nodes of the election result of the cluster head node in a broadcasting mode; and then, the common nodes except the cluster head node select the cluster head node or the wireless gateway node closest to the cluster head node for access.
In addition, in other embodiments, the current wireless gateway node broadcast notifies all surviving relay nodes of the election result of the cluster head node in a broadcast manner, which may be changed to: the former wireless gateway node informs all the surviving relay nodes of the current wireless gateway node and the election result of the cluster head node in a broadcasting mode.
Step S4: and carrying out routing transmission.
The step S4 includes: the sensor is accessed to the nearest relay node and transmits data to the relay node, common nodes except the cluster head nodes transmit the data to the cluster head nodes corresponding to the cluster head nodes in a single-hop mode, the cluster head nodes perform data fusion on the data, the cluster head nodes transmit the data in a multi-hop networking mode to transmit the data to wireless gateway nodes, and the wireless gateway transmits the data to a monitoring platform in a wireless mode (such as a 4G module).
If the cluster head is too far away from the wireless gateway node, a large amount of energy consumption is generated, and therefore, data are transmitted between the cluster head nodes in a multi-hop mode.
In step S4, the cluster head node and the wireless gateway node calculate a minimum spanning tree according to the prim algorithm, and the wireless gateway node serves as a root node to determine the spanning tree between the cluster head nodes as a multi-hop transmission path between the cluster head nodes.
And the energy consumption of transmission and residual energy between cluster head nodes are considered, so that the relay nodes are prevented from consuming too low energy and also bear the energy consumption of forwarding.
Therefore, the weight w (i, j) between the cluster head nodes i, j is defined as:
Figure BDA0003381750060000151
wherein i and j are ordinal numbers of cluster head nodes, ETx(i, j) is the energy transferred from cluster head node i to cluster head node j, Enode(i*)，Enode(j) is the residual energy of node i, j. And forming a spanning tree between the cluster head nodes according to the weight values w (i, j) between the cluster head nodes.
Further, step S5 may be further included: repeating the steps S2-S4 to enable the network to start the next round of flow and prevent premature death of the wireless gateway node.
Thus, the present invention also provides a routing protocol (LEACH-WGR-SSA) for a round-robin gateway node to prevent premature death of the gateway node.
The above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the scope of the present invention, and various changes may be made in the above embodiments of the present invention. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.

Claims (10)

1. A clustering routing method for a wireless sensor network is characterized by comprising the following steps:
step S1: providing a wireless sensor network with a plurality of relay nodes, wherein all the relay nodes can communicate with the sensor and the monitoring platform to perform network initialization;
step S2: electing the current wireless gateway node from all the surviving relay nodes;
step S3: taking all the surviving relay nodes except the current wireless gateway node as common nodes, carrying out election of a cluster head node in the common nodes, and establishing clustering;
step S4: the sensor is accessed to the nearest relay node and transmits data to the relay node, and the common nodes except the cluster head node transmit the data to the cluster head node corresponding to the cluster head node in a single-hop mode; the cluster head nodes perform data fusion on the data, and the data are transmitted among the cluster head nodes in a multi-hop networking mode so as to be transmitted to the wireless gateway nodes; the wireless gateway transmits the data to the monitoring platform;
step S5: repeating the steps S2-S4.
2. The method for clustering routing according to claim 1, wherein in step S1, performing network initialization comprises: presetting a relay node as a front wireless network joint point, and sending message information of each relay node to the front wireless network joint point; the message information includes a global ID, remaining energy and geographic location.
3. The method for clustering routing according to claim 1, wherein the step S2 includes:
step S21: determining a wireless gateway node weight function of each surviving relay node when the wireless gateway node elects;
wherein the ith survives at the time of wireless gateway node electionWireless gateway node weight function p of relay nodeWG(i) Comprises the following steps:
Figure FDA0003381750050000011
Figure FDA0003381750050000012
Figure FDA0003381750050000013
Figure FDA0003381750050000021
wherein, α, β, δ are weight coefficients of wireless gateway node weight function, α + β + δ is 1, α, β, δ belongs to (0,1), Enode(i) Is the remaining energy of the ith surviving relay node, E (E)node) Is the average remaining energy, N, of all surviving relay nodesneighbor(i) The number of neighboring nodes of the ith surviving relay node, E (N)neigbor) Number of neighbor nodes averaged over all surviving relay nodes, dtoCT(i) Is the distance of the ith surviving relay node from the centroid of the surviving node set, E (d)toCT) Is the average of the distances of all surviving relay nodes from the centroid of the surviving node set, n is the number of all surviving relay nodes, GaliveIs a set of surviving nodes;
step S22: determining the current wireless gateway node according to the wireless gateway node weight function of each surviving relay node; the relay node corresponding to the maximum value of the weight function of the wireless gateway node is the current wireless gateway node.
4. The method for clustering and routing according to claim 1, wherein in step S3, a sparrow search algorithm is used to perform cluster head election based on a low power consumption adaptive clustering hierarchical protocol.
5. The method for clustering and routing according to claim 4, wherein in the step S3, a cluster head election is performed in a common node, and the method comprises:
step S31: respectively taking the serial number arrays of the multiple groups of candidate cluster head nodes as vectors of the population to perform population initialization;
step S32: determining the comprehensive fitness value of each population and sequencing;
step S33: determining the position of a finder of the next iteration of the current iteration times;
step S34: determining the position of a joiner of the next iteration of the current iteration times;
step S35: determining the position of the alerter in the next iteration of the current iteration times;
step S36: and taking the next iteration of the current iteration times as a new current iteration time, and repeating the steps S33-S35 until the iteration is completed, wherein the population corresponding to the optimal comprehensive fitness value of the current iteration times t of all the populations obtained at the moment is the election result of the cluster head node.
6. The method for clustering routing according to claim 5, wherein the step S31 specifically includes:
step S311: determining a cluster head election weight function p of the ith common node when the candidate node is selectedcluster(i)；
Step S312: selecting weight function p according to cluster headcluster(i) Sorting common nodes from big to small, and selecting the first 50% nodes as candidate nodes; for each population, randomly extracting K non-repeated candidate nodes from the candidate nodes, taking the serial numbers of the K non-repeated candidate nodes as the serial number array of one group of candidate cluster head nodes and as the vector of a single population, and repeating the random extraction for multiple times to construct multiple vectors as multiple populationsAn initial value; k is the total number of clusters;
step S313: and marking UB and LB as the upper and lower bounds of the node number, and setting the number interval of the whole survival node according to the upper and lower bounds of the node number.
7. The method for clustering routing according to claim 5, wherein in step S32, the overall fitness value of the population is:
Figure FDA0003381750050000033
wherein, the ratio of phi,
Figure FDA0003381750050000034
gamma is the weight of the fitness function of the energy, the fitness function of the distance between clusters and the fitness function of the distance from the cluster head node to the wireless gateway node respectively,
Figure FDA0003381750050000035
Figure FDA0003381750050000036
f1、f2、f3respectively is a fitness function of energy, a fitness function of inter-cluster distance and a fitness function of the distance from a cluster head node to a wireless gateway node;
fitness function f of energy1Comprises the following steps:
Figure FDA0003381750050000031
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; n is the total number of common nodes; k is the total number of clusters; enode(i) Is the energy of the ith common node in the population, ECH(j) The energy of a cluster head node of the jth cluster in the population;
fitness of inter-cluster distanceFunction f2Comprises the following steps:
Figure FDA0003381750050000032
wherein, i is the ordinal number of the common node; j is the cluster ordinal number; d (j, i) represents the distance from the ith common node in the jth cluster in the population to the cluster head node, nc(j) Representing the number of common nodes in the jth cluster in the population;
fitness function f of distance from cluster head node to wireless gateway node3Comprises the following steps:
Figure FDA0003381750050000041
wherein j is the ordinal number of the cluster; k is the total number of clusters; dtoWG(i) The distance from the cluster head node of the jth cluster in the cluster to the wireless gateway node.
8. The method for clustering routing according to claim 5, wherein in step S33, a Levy flight algorithm is used to determine the finder position of the next iteration of the current iteration number;
the finder position for the next iteration of the current iteration number is:
Figure FDA0003381750050000042
Figure FDA0003381750050000043
wherein t is the current iteration number,
Figure FDA0003381750050000044
is the finder position of the I element in the J group under the current iteration number t，
Figure FDA0003381750050000045
The position of a finder at the next iteration t +1 of the current iteration times of the I element in the J-th population; i is the ordinal number of the element; t is the total iteration number; μ is a random value from 0 to 1; v (V is equal to [0, 1]])，ST(ST∈[0.5,1]) Respectively an early warning value and a safety value; q is a normally distributed random number, W is 1 xdpopAll 1 matrices.
9. The method for clustering routing according to the wireless sensor network of claim 1, wherein the step S2 further comprises the step S23: the previous wireless gateway node informs all the surviving relay nodes of the current wireless gateway node in a broadcasting mode; and in the step S3, establishing a cluster including: the current wireless gateway node broadcast informs all the surviving relay nodes of the election result of the cluster head node in a broadcasting mode; then, the common nodes except the cluster head node select the cluster head node or the wireless gateway node closest to the cluster head node for access;
alternatively, in step S3, creating a cluster includes: the former wireless gateway node informs all the surviving relay nodes of the election results of the current wireless gateway node and the cluster head node in a broadcasting mode; and then, the common nodes except the cluster head node select the cluster head node or the wireless gateway node closest to the cluster head node for access.
10. The wireless sensor network clustering routing method of claim 1, wherein in the step S4, the cluster head node and the wireless gateway node calculate a minimum spanning tree according to a prim algorithm, and the wireless gateway node serves as a root node to determine the spanning tree between the cluster head nodes as a multi-hop transmission path between the cluster head nodes.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114885379A (en) * 2022-04-29 2022-08-09 西北核技术研究所 Large-scale unmanned aerial vehicle cluster self-adaptive clustering networking method
CN115134835A (en) * 2022-08-30 2022-09-30 成都星联芯通科技有限公司 Internet of things networking system and gateway selection method
CN115209425A (en) * 2022-07-15 2022-10-18 沈阳航空航天大学 Unmanned aerial vehicle deployment method based on wireless sensor distribution
CN116761225A (en) * 2023-08-17 2023-09-15 湖南天联城市数控有限公司 Reliable transmission method for wireless sensor network
CN117320112A (en) * 2023-10-26 2023-12-29 陕西思极科技有限公司 Dual-mode communication network energy consumption balancing method and system

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050099943A1 (en) * 2003-11-12 2005-05-12 Nokia Corporation Traffic and radio resource control in a wireless communication device
US20110188378A1 (en) * 2007-03-12 2011-08-04 Sandra Collins Arrangement and Method Relating to Network Management
CN103781143A (en) * 2014-02-25 2014-05-07 东南大学 Cluster tree hierarchical wireless sensor network routing method with optimized energy efficiency
CN108696926A (en) * 2018-05-09 2018-10-23 河海大学常州校区 A kind of underwater wireless sensor network cross-layer reliable data transmission method
CN112105072A (en) * 2020-10-21 2020-12-18 国网思极紫光(青岛)微电子科技有限公司 Internet of things communication system and construction method thereof
CN112492661A (en) * 2020-12-10 2021-03-12 中南民族大学 Wireless sensor network clustering routing method based on improved sparrow search algorithm
CN112822653A (en) * 2020-12-30 2021-05-18 国网甘肃省电力公司信息通信公司 Clustering routing method in wireless sensor network
CN112954763A (en) * 2021-02-07 2021-06-11 中山大学 WSN clustering routing method based on goblet sea squirt algorithm optimization
CN113395660A (en) * 2021-06-18 2021-09-14 河南大学 WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050099943A1 (en) * 2003-11-12 2005-05-12 Nokia Corporation Traffic and radio resource control in a wireless communication device
US20110188378A1 (en) * 2007-03-12 2011-08-04 Sandra Collins Arrangement and Method Relating to Network Management
CN103781143A (en) * 2014-02-25 2014-05-07 东南大学 Cluster tree hierarchical wireless sensor network routing method with optimized energy efficiency
CN108696926A (en) * 2018-05-09 2018-10-23 河海大学常州校区 A kind of underwater wireless sensor network cross-layer reliable data transmission method
CN112105072A (en) * 2020-10-21 2020-12-18 国网思极紫光(青岛)微电子科技有限公司 Internet of things communication system and construction method thereof
CN112492661A (en) * 2020-12-10 2021-03-12 中南民族大学 Wireless sensor network clustering routing method based on improved sparrow search algorithm
CN112822653A (en) * 2020-12-30 2021-05-18 国网甘肃省电力公司信息通信公司 Clustering routing method in wireless sensor network
CN112954763A (en) * 2021-02-07 2021-06-11 中山大学 WSN clustering routing method based on goblet sea squirt algorithm optimization
CN113395660A (en) * 2021-06-18 2021-09-14 河南大学 WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
TIANKAI LIU等: "SSA-Based WSN Clustering Routing Algorithm for Power Grid", 《2021 2ND INFORMATION COMMUNICATION TECHNOLOGIES CONFERENCE (ICTC)》, pages 1 - 3 *
YIN LEI等: "Improved Sparrow Search Algorithm based DV-Hop Localization in WSN", 《2020 CHINESE AUTOMATION CONGRESS (CAC)》, pages 2 *
阎新芳;张永琦;王志龙;李锡刚;: "无线传感器网络中基于网关的多级簇树维护更新算法", 传感技术学报, no. 02 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114885379A (en) * 2022-04-29 2022-08-09 西北核技术研究所 Large-scale unmanned aerial vehicle cluster self-adaptive clustering networking method
CN115209425A (en) * 2022-07-15 2022-10-18 沈阳航空航天大学 Unmanned aerial vehicle deployment method based on wireless sensor distribution
CN115134835A (en) * 2022-08-30 2022-09-30 成都星联芯通科技有限公司 Internet of things networking system and gateway selection method
CN115134835B (en) * 2022-08-30 2022-12-20 成都星联芯通科技有限公司 Internet of things networking system and gateway selection method
CN116761225A (en) * 2023-08-17 2023-09-15 湖南天联城市数控有限公司 Reliable transmission method for wireless sensor network
CN116761225B (en) * 2023-08-17 2023-11-14 湖南天联城市数控有限公司 Reliable transmission method for wireless sensor network
CN117320112A (en) * 2023-10-26 2023-12-29 陕西思极科技有限公司 Dual-mode communication network energy consumption balancing method and system

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