CN107580355B - Position-based uniform clustering hierarchical routing method for wireless sensor network - Google Patents
Position-based uniform clustering hierarchical routing method for wireless sensor network Download PDFInfo
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Abstract
The invention discloses a position-based uniform clustering hierarchical routing method for a wireless sensor network, which comprises the following steps: 1. carrying out regular hexagon uniform clustering on the detection area, and layering the regular hexagon clusters according to the distance from the Sink nodes from the first layer to the mth layer until all the sensor nodes are covered; 2. considering the factors of residual energy and network load, establishing a reasonable cluster head selection mechanism: 3. fusing the data in the clusters and carrying out precoding processing; 4. establishing layer-by-layer routing; 5. data transmission and decryption. The invention has the advantages that: (1) the query directivity is high; (2) the life cycle is greatly prolonged; (3) a more efficient, optimized and energy-saving routing link is established for transmitting data; (4) the safety of the data is ensured.
Description
Technical Field
The invention relates to a routing method, in particular to a uniform clustering hierarchical routing method based on positions for a wireless sensor network, and belongs to the technical field of communication.
Background
A Wireless Sensor Network (WSN) is a multi-hop Wireless Sensor Network formed by a large number of Sensor nodes randomly distributed in a monitoring area in a self-organizing manner. The sensor nodes can sense the surrounding environment and have the functions of data acquisition, information processing fusion and short-distance data transmission. How to utilize the limited resources to achieve efficient collection and transmission of data is an important responsibility of WSN routing algorithms.
The WSN routing algorithms are various, and researches show that compared with a plane routing algorithm, a cluster head strategy and a fusion technology in a layered routing algorithm can reduce data transmission quantity, so that the WSN routing algorithm is suitable for a large-scale network and has better performance. Typical algorithms are LEACH algorithm, HEED algorithm, TEEN algorithm, PEGASIS algorithm, etc. Compared with a non-uniform clustering routing algorithm, the uniform clustering routing algorithm has the same clustering areas, similar node numbers in clusters and basically balanced energy consumption for data transmission, and is more favorable for the long-term operation of the network. Typical algorithms are the GAF algorithm, the GRID algorithm, the GeoGRID algorithm, etc.
The GRID algorithm is a uniform clustering routing algorithm based on location information. And establishing a square grid cluster in the monitoring area, and designating a node closest to the center of the grid as a gateway node. The arrangement of the gateway node and the routing query area reduces the transmission load, but the query directionality is weak.
Although the GeoGRID algorithm is improved in a route query mode, query directionality still needs to be improved.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a uniform clustering hierarchical routing method based on the position for the wireless sensor network, which has higher query directivity, can effectively save energy and ensure safe data transmission.
In order to achieve the above object, the present invention adopts the following technical solutions:
the method for uniformly clustering and hierarchically routing the wireless sensor network based on the position is characterized by comprising the following steps of:
step1, uniformly clustering regular hexagons in the detection area, layering the regular hexagons according to the distance from the Sink node, from the first layer to the mth layer until all sensor nodes are covered;
step2, considering the residual energy and network load factors, establishing a reasonable cluster head selection mechanism:
firstly, constructing a fitness function F (i) of a candidate node i in a cluster:
wherein α, γ is a weighting coefficient, α + β + γ is 1, Einitial(i) And Eremain(i) Are the initial energy and the residual energy, E, of the candidate node i in the cluster, respectivelyin(i) Is the sum of the energy consumptions from the candidate node i in the cluster to other nodes in the cluster, Einbest,iIs the minimum value of the sum of the energy consumptions from the node of the cluster where the candidate node i is located to other nodes in the cluster, Eout(i) Is the sum of the energy consumptions of the candidate node i in the cluster to six adjacent candidate nodes, Eoutbest,iThe energy consumption of the node of the cluster where the candidate node i in the cluster is located is the minimum value of the energy consumption sums of the six adjacent candidate nodes;
then, selecting a node with the maximum fitness function as a cluster head node;
step3, fusing the data in the cluster and carrying out precoding processing;
step4, establishing layer-by-layer routing:
the routing node is served by a cluster head, which is represented by a parameter (sn, rn, theta) when S (sn)S,rnS,θS) And D (sn)D,rnD,θD) When the nodes are respectively an information source node and an information sink node, firstly, a trapezoidal query strategy is adopted to determine a search area of a route according to the number of layers and the angle of the information source and the information sink, then two factors of a cluster head position and residual energy are considered to select the route node, and the calculation formula of a route selection factor for selecting a next-hop route node by the current route node is as follows by taking the hop count as a unit:
wherein λ and μ are equilibrium coefficients, λ + μ ═ 1 and λ > 0, μ > 0, pr0Is the trapezoidal coefficient between the source node and the target node, pr' is the trapezoidal coefficient between the next hop candidate route node and the target node, EinitialAnd EremainIs the initial energy and the residual energy of the next hop candidate route node;
selecting the cluster head corresponding to the minimum routing selection factor value as the routing node for sending data by the next hop by the current routing node;
step5, data transmission and decryption.
The position-based uniform clustering hierarchical routing method for the wireless sensor network is characterized in that in Step1, in order to facilitate data acquisition, a regular hexagon cluster numbering table is firstly established, specifically as follows:
assuming that the side length of a regular hexagon is L, a coordinate system is established by taking a Sink node as an original point, the number of layers and the coordinate angle of a centroid of the regular hexagon are respectively expressed by rn and theta, cluster numbers are arranged along the inner layer to the outer layer and in the anticlockwise direction, the number of the Sink node is 0, and the number sn of a regular hexagon grid (rn, theta) is as follows:
in the formula, k is a superposition factor.
The position-based uniform clustering hierarchical routing method for the wireless sensor network is characterized in that in Step2, after the selected cluster head nodes work for a period of time, a cluster head automatic resignation mechanism is used for automatically resignating certain non-conditional cluster head nodes, and the principles of the cluster head automatic resignation mechanism are two:
the first principle is as follows: the remaining energy of the cluster head must be greater than Einitial(i) If not, automatically quitting the cluster head, enabling the node meeting the conditions to serve as the cluster head, and if no node meeting the conditions exists, selecting the optimal cluster head according to a selection mechanism until the node dies;
the second principle is as follows: the remaining energy when the node acts as a cluster head is Eremain(i) When, if Eremain(i) Greater than Einitial(i) 10, after working for a period of time, when the rest energy of the cluster head is Eremain(i) And/2, the cluster head automatically quits.
The position-based uniform clustering hierarchical routing method for the wireless sensor network is characterized in that in Step3, the process of fusing data in a cluster is as follows:
firstly, sending a brief report to a cluster head by a node in a cluster;
then, the cluster head judges whether the data collected by the nodes are the same through the received node brief report, and if the data collected by the nodes are repeated, one of the corresponding nodes is randomly selected to receive the information;
and finally, establishing a TDMA time schedule table by the cluster head to acquire data, wherein the data obtained after data fusion is carried out by the information source cluster head is X ═ X1,x2,...,xk]T。
The position-based uniform clustering hierarchical routing method for the wireless sensor network is characterized in that in Step3, the process of performing pre-coding processing on the fused data is as follows:
first, assuming that the Sink node is always safe and reliable, an m × m dimensional precoding matrix H of the same full rank is stored before sensor placement, and any dimensional master sub-matrix of the precoding matrix H is full rank:
then, the precoding matrix H is adjusted to HkThe adjusting method comprises the following steps:
(1) when k is m, H does not need to be resized;
(2) when k is<When m is needed, H takes own k-dimensional main subarray to obtain Hk：
(3) When k is>m, expanding H by using a full 1 matrix II to obtain Hk：
Finally, linear coding is carried out to obtain Y ═ XHk。
The location-based uniform clustering hierarchical routing method for the wireless sensor network is characterized in that in Step4, the trapezoidal query strategy is as follows:
if abs (. theta.) (θ)S-θD) If < pi, the search area is ((rn)S,rnD),(θS,θD))；
If abs (. theta.) (θ)S-θD) Greater than pi and thetaSIf < pi, the search area is ((rn)S,rnD),(θD,2π+θS))；
If thetaSIf > pi, the search area is ((rn)S,rnD),(θS,2π+θD))。
The method for uniform clustering hierarchical routing based on the position of the wireless sensor network is characterized in that in Step5, the data transmission and decryption processes are as follows:
according to the route established in Step4, the cluster head where the information source is located sends information to the information sink node, the information sink decodes the received encoded information Y, and the precoding matrix H is adjusted to HkThen decoding Y
And finally obtaining the source data X.
The invention has the advantages that:
(1) the method adopts a route query strategy and a route selection strategy, sets a route query area and a route selection factor on the basis of comprehensively considering two factors of a cluster head node position factor and a node residual energy, and enables the selected next hop route node to be closer to a target node more optimally, so that the method has higher query directivity;
(2) the method adopts a cluster head selection mechanism and a maintenance mechanism, ensures network balance and long-term work of the nodes (GeoGRID algorithm takes the nearest node to the center of the grid as a cluster head until the node dies, so that the node dies too early), and has great promotion in the aspect of prolonging the life cycle;
(3) the method adopts a data fusion strategy and a hierarchical routing strategy to establish a more efficient, optimized and energy-saving routing link for transmitting data;
(4) the method of the invention adopts the data coding technology to encrypt the data, thereby ensuring the safety of the data.
Drawings
FIG. 1 is a flow chart of a routing method of the present invention;
FIG. 2 is a network topology architecture diagram;
FIG. 3 is a grid numbering diagram;
FIG. 4 is a schematic diagram of a ladder routing query strategy;
FIG. 5 is a graph of the number of surviving nodes over time;
FIG. 6 is a graph of network energy consumption over time;
FIG. 7 is a graph of average energy consumption of WSN nodes as a function of network size.
Detailed Description
The invention provides a uniform clustering hierarchical safe routing method based on position on the basis of comprehensively considering the energy consumption, the life cycle and the network safety of a wireless sensor network. According to the method, on the basis of regular hexagon division of the region, a cluster head selection and maintenance mechanism is established, data transmission is carried out by using a data fusion technology and a routing strategy, and the safe transmission of data is ensured by adopting a pre-coding technology.
The invention is described in detail below with reference to the figures and the embodiments.
Referring to fig. 1, the method for uniform clustering hierarchical routing based on location in a wireless sensor network of the present invention includes the following steps:
step 1: regular hexagon uniform clustering is carried out on detection area
Regular hexagons have good area coverage and we exploit this property of regular hexagons to cluster the detection areas uniformly, as shown in figure 2. And layering the regular hexagon clusters according to the distance from the Sink node, from the first layer to the mth layer, until all the sensor nodes are covered.
(1) Regular hexagon cluster numbering table
In order to facilitate data acquisition, a regular hexagon cluster numbering table is established at first. Assuming that the side length of the regular hexagon is L, L is 1. And establishing a coordinate system by taking the Sink node as an origin, respectively representing the number of layers and the coordinate angle of the centroid of the regular hexagon by rn and theta, and numbering clusters along the inner layer to the outer layer and in the counterclockwise direction. As shown in fig. 3, let the number of the Sink node be 0, and the number sn of the regular hexagonal grid (rn, θ) be:
in the formula, k is a superposition factor.
(2) Judgment of regular hexagon cluster where sensor node is located
And (4) setting the communication radius of the sensor node as R, and making the side length L of the regular hexagon equal to R/4 in order to ensure that the adjacent clusters can be directly communicated.
The Sink node position is (x)0,y0) The broadcast packet is (Sk, x)0,y0L), all sensor nodes broadcast the packet only once. Setting coordinates (x, y) of the sensor node A, and calculating relative coordinates (x ', y') after receiving the data packet:
(x',y')＝(x-x0,y-y0)
and calculating the distance l 'and the azimuth angle theta' from the sensor node A to the sink node:
analyzing the centroid of the grid:
when the abscissa of the centroid is an odd multiple of sqrt (3)/2 × L, the corresponding ordinate is an odd multiple of 3/2 × L;
when the abscissa of the centroid is an integer multiple of sqrt (3) × L, the corresponding ordinate is an integer multiple of 3 × L.
Thus, the sensor node can find the nearest regular hexagonal cluster (sn, l ', θ', x ', y').
Step 2: automatic selection and resignation of cluster head (cluster head)
(1) Establishing a reasonable cluster head automatic selection mechanism
In the WSN, the energy consumption of the cluster head is large, so that the residual energy and network load factors need to be considered, and a reasonable cluster head selection mechanism is established.
Firstly, constructing a fitness function F (i) of a candidate node i in a cluster:
wherein α, γ is a weighting coefficient, α + β + γ is 1. Einitial(i) And Eremain(i) The initial energy and the remaining energy of the candidate node i in the cluster, respectively. Ein(i) Is the sum of the energy consumptions from the candidate node i in the cluster to other nodes in the cluster, Einbest,iIs the minimum value of the sum of the energy consumptions from the node of the cluster where the candidate node i is located to other nodes in the cluster, Eout(i) Is the sum of the energy consumptions of the candidate node i in the cluster to six adjacent candidate nodes, Eoutbest,iThe energy consumption of the node of the cluster in which the candidate node i is located in the cluster to six adjacent candidate nodes is the minimum value.
And then, selecting the node with the maximum fitness function as a cluster head node.
(2) Establishing reasonable automatic cluster head quitting mechanism
The principle of the automatic quit mechanism of the cluster head is two:
the first principle is as follows: the remaining energy of the cluster head must be greater than Einitial(i) If not, automatically quitting the cluster head, enabling the node meeting the conditions to serve as the cluster head, and if no node meeting the conditions exists, selecting the optimal cluster head according to a selection mechanism until the node dies;
the second principle is as follows: the remaining energy when the node acts as a cluster head is Eremain(i) When, if Eremain(i) Greater than Einitial(i) 10, after working for a period of time, when the rest energy of the cluster head is Eremain(i) And/2, the cluster head automatically quits.
Step 3: fusing and precoding data in clusters
When the cluster head collects data, a data fusion strategy is adopted to reduce data redundancy.
Firstly, the nodes in the cluster send brief reports to the cluster head, and the brief reports contain brief information such as node ID, data type, residual energy and the like.
And then, the cluster head judges whether the data collected by the nodes are the same through the received node brief report, and if the data collected by the nodes are repeated, one of the corresponding nodes is randomly selected to receive the information.
And finally, establishing a TDMA time schedule table by the cluster head to acquire data.
The data after the data fusion of the information source cluster head is X ═ X1,x2,...,xk]TIn order to ensure the safe transmission of data, a precoding technology is introduced to perform precoding processing on the data.
The Sink node is assumed to be always safe and reliable. Storing a precoding matrix H of the same full rank before sensor placement, and the main subarray of any dimension of the precoding matrix H is full rank:
the implementation of the precoding technique:
the source data is X ═ X1,x2,...,xk]TAdjusting the precoding matrix H to Hk(the adjustment method is described below), linear encoding Y ═ XH is performedk。
The precoding matrix H adjusting method is as follows:
(1) when k is m, H does not need to be resized;
(2) when k is<When m is needed, H takes own k-dimensional main subarray to obtain Hk：
(3) When k is>m, expanding H by using a full 1 matrix II to obtain Hk：
H can be obtained according to the initial setting of HkFor a full rank matrix, a coding operation may be performed.
Step 4: establishing layer-by-layer routing
Route establishment between any of the nodes is discussed below.
The routing nodes are served by cluster heads, which can be represented by parameters (sn, rn, θ).
In order to ensure the quick search of the route, the method designs a route query strategy and a route selection strategy.
(1) Route query policy
When S (sn)S,rnS,θS) And D (sn)D,rnD,θD) When the nodes are the information source node and the information sink node, firstly, the search range (namely the search area) of the route is determined according to the layer number and the angle of the information source and the information sink.
Ladder query strategy: as shown in FIG. 4, node S (sn)S,rnS,θS) And D (sn)D,rnD,θD) If abs (θ)S-θD) If < pi, the search area is ((rn)S,rnD),(θS,θD) ); if abs (. theta.) (θ)S-θD) Greater than pi and thetaSIf < pi, the search area is ((rn)S,rnD),(θD,2π+θS) ); if thetaSIf > pi, the search area is ((rn)S,rnD),(θS,2π+θD))。
(2) Routing strategy
And (4) selecting the routing node by considering two factors of the cluster head position and the residual energy.
And establishing a search area according to a trapezoidal query strategy, and setting delta theta as the angle difference of the search area. Here, in units of hop counts, the height of the trapezoid is | rnS-rnDThe upper bottom and the lower bottom are respectively:
wherein rnmin＝min{rnS,rnD}，rnmax＝max{rnS,rnD}。
Define the relevant trapezoid base and high proportionality coefficient pr:
the calculation formula of the routing factor for selecting the next hop routing node by the current routing node is as follows:
wherein λ and μ are equilibrium coefficients, λ + μ ═ 1 and λ > 0, μ > 0; pr (total reflection)0Is the trapezoidal coefficient between the source node and the target node, pr' is the trapezoidal coefficient between the next hop candidate route node and the target node, EinitialAnd EremainIs the initial energy and the remaining energy of the next hop candidate routing node.
And the current routing node selects the cluster head corresponding to the minimum routing selection factor value as the routing node for sending data by the next hop.
Step 5: data transmission and decryption
According to the route established in Step4, the cluster head where the information source is located sends information to the information sink node, the information sink decodes the received encoded information Y, and the precoding matrix H is adjusted to HkThen decoding Y
And finally obtaining the source data X.
Note that: since the information X and Y are of the same dimension, the precoding matrix H is adjusted here to HkThe method is the same as that in Step 3.
The method comprises the following steps:
we compared the simulation of the method of the present invention (for ease of reading, the method of the present invention is referred to as the HUCR method) with the GeoGRID method using the MATLAB simulation tool.
200 nodes are randomly generated in a circular simulation area with the radius of 160m, and a Sink node is positioned in the center. See table 1 for detailed simulation parameters.
Table 1 simulation parameter settings
We compare the simulation of the method in terms of life cycle, energy consumption and network scale.
(1) Life cycle
A method comparison graph of the number of WSNs surviving nodes as a function of time at a certain time of network node distribution is shown in fig. 5.
As can be seen from fig. 5: compared with the GeoGRID method, the curve of the HUCR method is slower in descending speed, the GeoGRID method node dies completely in 290 rounds, and the HUCR method node dies completely in 345 rounds.
Therefore, the HUCR method prolongs the life cycle of the network.
(2) Energy consumption
A method comparison graph of the average energy consumption of WSN nodes versus time is shown in fig. 6.
As can be seen from fig. 6: the HUCR method consumes much less network energy at any one time than the GeoGRID method, and retains approximately two-fifths of the energy when the GeoGRID method has exhausted the energy.
Therefore, the HUCR method greatly saves the energy of the network nodes.
(3) Network scale
At a certain node density and connectivity, the variation of the average energy consumption of the WSN nodes (communication at the 200 th round) when the network scale is changed from 100 to 500 by the two methods is studied as shown in fig. 7.
As can be seen from fig. 7: when the network scale is increased, the average node energy consumption of the two methods is in an increasing trend, the average node energy consumption of the GeoGRID method is high, and the average node energy consumption of the HUCR method is low.
Therefore, the HUCR method is more suitable for large-scale networks.
From the above three sets of experiments we can conclude that: the HUCR method has better performance, effectively reduces the network energy consumption, prolongs the network life cycle, and is suitable for large-scale networks.
The main reasons were analyzed as follows:
(1) the GeoGRID method takes a node closest to a grid center as a cluster head until the node dies, so that the node dies too early, the routing query directivity is weak, and the HUCR method adopts a cluster head selection mechanism and a quit mechanism to ensure network balance and long-term effective work of the node;
(2) the data fusion strategy, the hierarchical routing strategy and the automatic query routing strategy establish a more efficient, optimized and energy-saving routing link for transmitting data, the energy consumption of 1-bit information transmission of 100m is generally equal to that of 1000 calculation commands, the communication consumption between nodes is far greater than that of node information processing and calculation in the data transmission process, and the HUCR method also has good advantages in a large-scale network.
It should be noted that the above-mentioned embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the protection scope of the present invention.
Claims (3)
1. The method for uniformly clustering and hierarchically routing the wireless sensor network based on the position is characterized by comprising the following steps of:
step1, establishing a regular hexagon cluster numbering table, uniformly clustering regular hexagons in a detection area, layering the regular hexagon clusters according to the distance from the Sink node, from the first layer to the mth layer until all sensor nodes are covered, wherein the method for establishing the regular hexagon cluster numbering table specifically comprises the following steps:
assuming that the side length of a regular hexagon is L, a coordinate system is established by taking a Sink node as an original point, the number of layers and the coordinate angle of a centroid of the regular hexagon are respectively expressed by rn and theta, cluster numbers are arranged along the inner layer to the outer layer and in the anticlockwise direction, the number of the Sink node is 0, and the number sn of a regular hexagon grid (rn, theta) is as follows:
in the formula, k is a superposition factor;
step2, considering the residual energy and network load factors, establishing a reasonable cluster head selection mechanism:
firstly, constructing a fitness function F (i) of a candidate node i in a cluster:
wherein α, γ is a weighting coefficient, α + β + γ is 1, Einitial(i) And Eremain(i) Are the initial energy and the residual energy, E, of the candidate node i in the cluster, respectivelyin(i) Is the sum of the energy consumptions from the candidate node i in the cluster to other nodes in the cluster, Einbest,iIs the minimum value of the sum of the energy consumptions from the node of the cluster where the candidate node i is located to other nodes in the cluster, Eout(i) Is the sum of the energy consumptions of the candidate node i in the cluster to six adjacent candidate nodes, Eoutbest,iThe energy consumption of the node of the cluster where the candidate node i in the cluster is located is the minimum value of the energy consumption sums of the six adjacent candidate nodes;
then, selecting a node with the maximum fitness function as a cluster head node;
step3, fusing the data in the cluster and carrying out precoding processing, wherein:
(1) the process of fusing the data in the cluster is as follows:
firstly, sending a brief report to a cluster head by a node in a cluster;
then, the cluster head judges whether the data collected by the nodes are the same through the received node brief report, and if the data collected by the nodes are repeated, one of the corresponding nodes is randomly selected to receive the information;
and finally, establishing a TDMA time schedule table by the cluster head to acquire data, wherein the data obtained after data fusion is carried out by the information source cluster head is X ═ X1,x2,...,xk]T；
(2) The process of performing pre-coding processing on the fused data specifically comprises the following steps:
first, assuming that the Sink node is always safe and reliable, an m × m dimensional precoding matrix H of the same full rank is stored before sensor placement, and any dimensional master sub-matrix of the precoding matrix H is full rank:
then, the precoding matrix H is adjusted to HkThe adjusting method comprises the following steps:
(1) when k is m, H does not need to be resized;
(2) when k is<When m is needed, H takes own k-dimensional main subarray to obtain Hk：
(3) When k is>m, expanding H by using a full 1 matrix II to obtain Hk：
Finally, linear coding is carried out to obtain Y ═ XHk；
Step4, establishing layer-by-layer routing:
the routing node is served by a cluster head, which is represented by a parameter (sn, rn, theta) when S (sn)S,rnS,θS) And D (sn)D,rnD,θD) When the nodes are respectively an information source node and an information sink node, firstly, a trapezoidal query strategy is adopted to determine a search area of a route according to the number of layers and angles of the information source and the information sink, wherein the trapezoidal query strategy is as follows:
if abs (. theta.) (θ)S-θD) If < pi, the search area is ((rn)S,rnD),(θS,θD))；
If abs (. theta.) (θ)S-θD) Greater than pi and thetaSIf < pi, the search area is ((rn)S,rnD),(θD,2π+θS))；
If thetaSIf > pi, the search area is ((rn)S,rnD),(θS,2π+θD))；
Then, considering two factors of cluster head position and residual energy to select the routing node, taking hop number as a unit, and the calculation formula of the routing factor for selecting the next hop routing node by the current routing node is as follows:
wherein λ and μ are equilibrium coefficients, λ + μ ═ 1 and λ > 0, μ > 0, pr0Is the trapezoidal coefficient between the source node and the target node, pr' is the trapezoidal coefficient between the next hop candidate route node and the target node, EinitialAnd EremainIs the initial energy and the residual energy of the next hop candidate route node;
selecting the cluster head corresponding to the minimum routing selection factor value as the routing node for sending data by the next hop by the current routing node;
step5, data transmission and decryption.
2. The method as claimed in claim 1, wherein after the selected cluster node has been operated for a certain period of time in Step2, the cluster node is automatically resigned to some unqualified cluster nodes by using a cluster automatic resignation mechanism, and the principle of the cluster automatic resignation mechanism is two:
the first principle is as follows: the remaining energy of the cluster head must be greater than Einitial(i) If not, automatically quitting the cluster head, enabling the node meeting the conditions to serve as the cluster head, and if no node meeting the conditions exists, selecting the optimal cluster head according to a selection mechanism until the node dies;
the second principle is as follows: the remaining energy when the node acts as a cluster head is Eremain(i) When, if Eremain(i) Greater than Einitial(i) 10, after working for a period of time, when the rest energy of the cluster head is Eremain(i) And/2, the cluster head automatically quits.
3. The method for uniform clustering hierarchical routing based on location in the wireless sensor network according to claim 1, wherein in Step5, the data transmission and decryption process is as follows:
according to the route established in Step4, the cluster head where the information source is located sends information to the information sink node, the information sink decodes the received encoded information Y, and the precoding matrix H is adjusted to HkThen decoding Y
And finally obtaining the source data X.
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| CN111698706B (en) * | 2020-06-10 | 2022-05-13 | 长春师范大学 | Improved LEACH routing method of wireless sensor network based on chaos inheritance |
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| CN113504432B (en) * | 2021-07-08 | 2022-12-30 | 广西电网有限责任公司电力科学研究院 | Transformer substation grounding grid monitoring system |
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