CN110536373B - Optical wireless sensor network clustering method based on copula theory - Google Patents
Optical wireless sensor network clustering method based on copula theory Download PDFInfo
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Abstract
The invention discloses an optical wireless sensor network clustering method based on copula theory, which comprises the following steps: determining the position of a Cluster Head (CH) by utilizing a hierarchical maximum likelihood estimation method so as to form an initial cluster; when a node outside a cluster requests to join the cluster, a Cluster Head (CH) requires the node outside the cluster to send an observed value of the node outside the cluster, and the Cluster Head (CH) carries out correlation analysis on the observed value of the node outside the cluster according to a copula theory; under the constraint of energy load, whether the cluster external nodes can be added is determined according to whether the information content in the cluster can be improved by adding the cluster external nodes, and a new cluster is formed when the cluster external nodes are added. The clustering method can simplify the computation complexity of the network, reduce the computation overhead and time, and balance the power requirement of each cluster in the network.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a copula theory-based optical wireless sensor network clustering method which can be used for an optical wireless sensor network.
Background
The Wireless Sensor Network (WSNS) is an ad hoc network formed by spatially distributed sensor nodes, and completes global tasks in a wireless communication mode in a cooperative manner. The WSNS is mainly divided into a planar network structure and a clustered network structure. Compared with a planar network structure, the clustering network structure has better expansibility and robustness. Therefore, in order to improve the overall performance of the network, it is a key problem in WSNS to make a good clustering strategy.
The basic idea of the clustering algorithm is to divide a network into a plurality of sub-regions according to a formulated strategy, the sub-regions are called as 'clusters', each cluster is composed of a cluster head node (CH) and a cluster member node (CM), and the tasks are completed through cooperation. Currently, there are many Clustering algorithms, such as LEACH algorithm (Low Energy Adaptive Clustering Hierarchy), TEEN algorithm (Threshold sensitive Energy Sensor Network Protocol), PEGASIS algorithm (Power Efficient heating in Sensor Information Systems), GAF (Geographic Adaptive Clustering), etc. However, most of these algorithms focus on balancing network energy consumption, ignoring the amount of information in the transmitted data. The LEACH algorithm is a typical distributed clustering algorithm, and selects CH randomly according to probability in the process of forming clusters, which may cause the uneven distribution of the CH; the GAF algorithm is a clustering algorithm based on the geographical location of the nodes, but the data transmitted to the CH may be the same between neighboring nodes, which results in redundancy of data. Therefore, in the clustering process, in order to improve the performance of the network, the energy efficiency and the data information amount must be fully considered.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a clustering method based on power requirements in a wireless sensor network.
The technical scheme adopted by the invention is as follows:
a copula theory-based optical wireless sensor network clustering method comprises the following steps:
step 1, determining the position of a Cluster Head (CH) by utilizing a layered maximum likelihood estimation method, and further forming an initial cluster;
and 2, when the cluster external node requests to join the cluster, the Cluster Head (CH) requires the cluster external node to send the observed value of the cluster external node, and the Cluster Head (CH) performs correlation analysis on the observed value of the cluster external node according to a copula theory.
And 3, under the constraint of energy load, determining whether the cluster external nodes can be added according to whether the information content in the cluster can be improved by adding the cluster external nodes, and forming a new cluster when the cluster external nodes are added.
Further, the method for analyzing the correlation in step 2 comprises:
step 2.1, represent the signal a (k) received by the sensor node as an observation V = (V) 1 ,...,v i ,...,v j ,...,v k ) The useful information in the observation value V is theta, and the joint Probability Density Function (PDF) of the node observation value V is represented as f n (V|θ)；
2.2, the dependency relationship between two sensor nodes is described by a gaussian copula function, and a kendall rank correlation coefficient represents an observation value correlation metric value, so that the correlation between the observation values in the multivariate distribution can be described as follows:
C(V|Σ)＝Φ Σ (Φ -1 (v 1 ),...,Φ -1 (v k ))
wherein C (V | Sigma) is a Gaussian copula function, phi Σ Representing normal distribution of variables following a standard form, sigma being a measure of the correlation of the observed values, phi -1 (v k ) Obey an inverse distribution;
step 2.3, the Gaussian copula function is subjected to differential processing to obtain a copula density function:
wherein E is an identity matrix;
step 2.4, using the joint theory to represent the joint PDF of the observed value between the sensor nodes by using the edge PDFs and the copula density function, and calculating to obtain the information quantity I (theta):
wherein, f (v) i [ theta ]) as the edge PDFs, c (v) 1 ,v 2 ,...,v k | θ) is a coupula density function of useful information θ in the observed value;
further, the observed value correlation measure value
Wherein, tau (v) i ,v j ) Is the kendall rank correlation coefficient of the gaussian copula function,
v i is an in-cluster node observed value, v j As an off-cluster node observation, C (v) i ,v j ) For representing between two variables by a Gaussian copula functionCorrelation;
further, the energy load constraint, written as:
wherein E is i Is the energy of the ith node in the cluster, E ave Is the average energy of all nodes in the cluster,
E L is the energy load threshold.
Further, comparing the added intra-cluster information amount I '(theta) of the node outside the cluster with the information amount I (theta) before the addition according to the correlation analysis result, and if I' (theta) > I (theta), receiving a node addition request to form a new cluster; if I' (θ). Ltoreq.I (θ), the addition is rejected.
The invention has the beneficial effects that:
compared with the prior art, the clustering method has the advantages that the data information amount in the clusters is maximized in the process of forming the clusters; according to the invention, by setting the constraint condition of network energy consumption, the energy consumption and the data information amount can be effectively balanced, and the energy efficiency is improved. Meanwhile, the copula theory-based optical wireless sensor network clustering method can be used for the optical wireless sensor network.
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FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a network structure diagram according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
With reference to fig. 1, the method for clustering an optical wireless sensor network based on copula theory according to the present invention is as follows:
step 1: with the power spectral density PSD of the signal as a consideration object, identifying the cluster head CH by utilizing the layered maximum likelihood estimation HML, and the specific process is as follows:
assuming that there are k different log-likelihood functions in the network to find the CH location (maximum log-likelihood at a particular level), the signal power spectral density PSD in the n channels is taken as a sample object X k ＝{x 1 ,x 2 ,...,x n D, its sample dimension.
1) Calculating the mean of the sample objects:
2) Calculating the covariance of the sample object:
3) Obtaining a log-likelihood function according to the mean and the covariance:
wherein L is k For the kth group of sample objects X k And the corresponding log-likelihood function is the position of the cluster head CH when the log-likelihood ratio is maximum.
And 2, step: when a node outside a cluster requests to join a cluster, as shown in fig. 2, a Cluster Head (CH) requests the node outside the cluster to send an observed value of the node outside the cluster, and the Cluster Head (CH) performs correlation analysis on the observed value of the node outside the cluster according to a copula theory; the specific process is as follows:
step 2.1, representing the signal a (k) received by the sensor node as an observation value V = (V) 1 ,...,v i ,...,v j ,...,v k ) The joint Probability Density Function (PDF) of the observed value V is denoted as f n (V|θ)；
And 2.2, in a specific implementation process, describing the dependency relationship between two sensor nodes by Gaussian copula, and representing the correlation metric value of the observed value by a kendall rank correlation coefficient. Thus, the correlation between multivariate distributions of different observations is represented as:
C(V|Σ)＝Φ Σ (Φ -1 (v 1 ),...,Φ -1 (v k )) (1)
where C (V | Σ) is a Gaussian copula function, Φ Σ Representing normal distribution of variables following a standard form, sigma being a measure of the correlation of the observed values, phi -1 (v k ) Obey an inverse distribution;
step 2.3, the Gaussian copula function is subjected to differential processing to obtain a copula density function:
wherein the observed value is a measure of the correlation
Kendall rank correlation coefficient of Gaussian copula function
v i Is an in-cluster node observation, v j Is an observed value of an out-of-cluster node, E is an identity matrix, C (v) i ,v j ) The correlation between two variables is expressed by a Gaussian copula function;
and 2.4, when the measurement value of the observed value of the sensor node is less than or equal to 1, data redundancy and resource waste are caused. Therefore, the amount of Information in a cluster is calculated from the correlation during clustering, and Fisher Information (Fisher Information) is used to represent the amount of Information in the cluster, written as:
and then, using a joint theory to represent joint PDF of the observed value between the sensor nodes by using edge PDFs and copula density functions, and calculating to obtain information quantity I (theta):
wherein, f (v) i [ theta ]) are edge PDFs, c (v) 1 ,v 2 ,...,v k | θ) is a coupula density function of useful information in the observed value;
and 3, step 3: under the constraint of energy load, whether the cluster external nodes can be added is determined according to whether the information content in the cluster can be improved by adding the cluster external nodes, and a new cluster is formed when the cluster external nodes are added.
Step 3.1, because the energy load of the network is too high and may affect the life cycle of the whole network, an energy load constraint condition is set, which is written as:
wherein, E i Is the energy of the ith node in the cluster, E ave Is the average energy of all nodes within the cluster,
E L is the energy load threshold.
Step 3.2, under the constraint of energy load, comparing the added intra-cluster information amount I '(theta) of the node outside the cluster with the information amount I (theta) before the addition according to the correlation analysis result, and if I' (theta) > I (theta), receiving a node addition request to form a new cluster; if I' (θ) ≦ I (θ), then the addition is denied, i.e.:
the above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (6)
1. A copula theory-based optical wireless sensor network clustering method is characterized by comprising the following steps:
step 1, determining the position of a cluster head by utilizing a layered maximum likelihood estimation method, and further forming an initial cluster;
step 2, when the cluster external node requests to join the cluster, the cluster head requires the cluster external node to send the observed value of the cluster external node, and the cluster head performs correlation analysis on the observed value of the cluster external node according to a copula theory;
step 3, under the constraint of energy load, determining whether the cluster external nodes can be added according to whether the addition of the cluster external nodes can improve the information content in the cluster, and forming a new cluster when the cluster external nodes are added;
the method for analyzing the correlation in the step 2 comprises the following steps:
step 2.1, representing the signal a (k) received by the sensor node as an observation value V = (V) 1 ,...,v i ,...,v j ,...,v k ) The useful information in the observed value V is theta, and the joint probability density function of the observed values V of the nodes is represented as f n (V|θ)；
Step 2.2, the dependency relationship between two sensor nodes is described by a gaussian copula function, and a kendall rank correlation coefficient represents an observation value correlation metric value, so that the correlation between the observation values and the multivariate distribution can be described as follows:
C(V|Σ)＝Φ Σ (Φ -1 (v 1 ),...,Φ -1 (v k ))
wherein C (V | Sigma) is a Gaussian copula function, phi Σ Representing normal distribution of variables following a standard form, sigma being a measure of the correlation of the observed values, phi -1 (v k ) Obey an inverse distribution;
step 2.3, the Gaussian copula function is subjected to differential processing to obtain a copula density function:
wherein E is an identity matrix;
step 2.4, using the joint theory to represent the joint PDF of the observed value between the sensor nodes by using the edge PDFs and the copula density function, and calculating to obtain the information quantity I (theta):
wherein, f (v) i [ theta ]) are edge PDFs, c (v) 1 ,v 2 ,...,v k | θ) is a coupul density function of useful information θ in the observed values.
2. The copula theory-based optical wireless sensor network clustering method according to claim 1, wherein the observed value correlation metric value
Wherein, tau (v) i ,v j ) Kendall rank correlation coefficient, v, being a function of Gaussian copula i Is an in-cluster node observation, v j And observing values for nodes outside the cluster.
3. The copula theory-based optical wireless sensor network clustering method according to claim 2, wherein kendall rank correlation coefficient of the gaussian copula function
Wherein, C (v) i ,v j ) The correlation between two variables is expressed by a Gaussian copula function.
4. The copula theory-based optical wireless sensor network clustering method according to claim 1, wherein the energy load constraint condition is written as:
wherein E is i Is the energy of the ith node in the cluster, E ave Is the average energy of all nodes in the cluster, E L Is the energy load threshold.
5. The copula theory-based optical wireless sensor network clustering method according to claim 4, wherein the average energy of all nodes in a cluster
6. The copula theory-based optical wireless sensor network clustering method according to claim 1, wherein the added intra-cluster information amount I '(θ) of the off-cluster node is compared with the information amount I (θ) before the addition according to the correlation analysis result, and if I' (θ) > I (θ), the node addition request is accepted to form a new cluster; if I' (θ). Ltoreq.I (θ), the addition is rejected.
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| CN101841884A (en) * | 2010-04-30 | 2010-09-22 | 北京航空航天大学 | Wireless sensor network cluster head inheritance clustering method based on energy |
| CN107529201A (en) * | 2017-08-29 | 2017-12-29 | 武汉中原电子信息有限公司 | A kind of wireless sensor network data transmission method based on LEACH agreements |
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