CN107257565B - Wireless sensor network reliability calculation method based on energy and transmission - Google Patents

Wireless sensor network reliability calculation method based on energy and transmission Download PDF

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CN107257565B
CN107257565B CN201710407571.0A CN201710407571A CN107257565B CN 107257565 B CN107257565 B CN 107257565B CN 201710407571 A CN201710407571 A CN 201710407571A CN 107257565 B CN107257565 B CN 107257565B
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CN107257565A (en
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冯海林
董洁玉
马琳
梁伦
齐小刚
雷花
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Xidian University
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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
    • H04W40/04—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
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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
    • 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
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    • 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
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Abstract

The invention discloses a method for accurately evaluating the reliability of a wireless sensor network, relates to the technical field of wireless sensor networks, and aims to calculate the instantaneous reliability of the wireless sensor network by utilizing a base station to divide grids and then cluster and combining the working content of nodes and energy consumption characteristics such as perception, transmission and the like. The method comprises the steps of firstly calculating the probability that a node works normally and sent data are transmitted to a cluster head successfully according to application requirements, secondly calculating the working capacity of the cluster head node at a certain moment by using the number of the data transmitted to the cluster head by the nodes in the cluster and an energy consumption model of the cluster head node, and finally calculating the probability that the data in a final network reach a sink node by adopting a decomposition algorithm according to a routing mode among the clusters, namely the reliability of the whole network. Factors which have large influence on the method are fully considered: energy consumption and transmission; the service life of the network is prolonged by adopting an effective clustering and routing mode; the method has the advantages of simple and convenient calculation process and higher result accuracy, and has great reference value for the application needing to accurately evaluate the network capability.

Description

Wireless sensor network reliability calculation method based on energy and transmission
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a wireless sensor network reliability calculation method based on energy and transmission.
Background
The Wireless Sensor Network (WSN) is a wireless communication network which is formed by a large number of nodes in a mutual cooperation mode and is used for monitoring a target area, the main task is to sense an event, generate a data packet, send the data packet to a base station through single-hop or multi-hop wireless communication, and finally analyze the condition of the monitored area by a user. At present, the WSN has a wider application range and is one of important ways for acquiring information in the fields of industry, medicine, military and the like.
The nodes of the WSN are usually densely and randomly thrown in a target area with a severe environment, and the network structure is dynamically changed due to the adoption of a dormancy mechanism and fault tolerance which allows partial nodes to fail, so that the nodes form a network in a self-organizing manner to monitor the coverage of the area. Dense nodes can repeatedly cover the area to ensure the accuracy of information acquisition and enhance the fault tolerance of the network system. In order to improve the working capacity of the network, reliability evaluation can be performed on the network. Because the nodes with smaller volume and lower cost are usually selected when the network is deployed, the battery capacity of the nodes is smaller, so that the nodes have limited energy, and the energy is the guarantee of the normal work of the nodes in the working process of the nodes, so a great deal of research is carried out on the utilization condition of the network energy by students.
Firstly, a network topology structure is optimized, for example, the most common network topology model working in a clustering structure is adopted, so that the overall energy consumption of the WSN is reduced, and the reliability of the system is improved. Clustering is to group all nodes in a network according to a certain rule, each group of nodes cooperate with each other to be used as a small network subsystem to collect information in a region and send the information to a cluster head node. In general, the clustering of the network is that the adjacent nodes form a cluster according to the geographical positions of the nodes.
The first relatively mature algorithm proposed is L EACH, which is described in the document Energy-efficiency communication protocol for wireless microsensor networks by the authors, during the clustering stage, cluster heads are randomly generated, adjacent nodes are dynamically clustered, during the data communication stage, cluster member nodes send data to respective cluster heads, the cluster head nodes perform data fusion again, and the fused data is sent to a sink node, because the cluster head nodes need to receive the data of the member nodes, perform data fusion and communicate with the sink node at a far distance, the Energy consumption is large, the L EACH protocol provides that the cluster head nodes of EACH round need to rotate to balance the node Energy, and later, the researchers also propose a series of network clustering methods, such as PEGASIS algorithm, HEED algorithm, etc., but these algorithms have the problems that the single-hop transmission of the cluster head nodes and the sink nodes causes excessive and uneven Energy consumption and large communication overhead.
In the literature of the clustering routing algorithm based on the virtual grid in the WSN, an author proposes that a target monitoring area is firstly divided into grids, a base station calculates according to the side length of the grids to ensure that enough grids completely cover the whole area, and in the literature of the wireless sensor network clustering algorithm based on the base station grid, the author proposes that the area is directly divided into the grids by using signals transmitted by the base station, nodes in EACH grid form a cluster, and a multi-hop transmission mode is adopted among the clusters.
The other method is to plan the node working mode, for example, a mode of alternately working node active and dormant is adopted, so that the energy consumption in the working process can be effectively reduced. In the document Energy control in dependent wireless sensor Networks, a model perspective, an author analyzes the Energy consumption problem of the network redundant node according to the state of the network redundant node working mode in an alternating manner, and obtains the WSN working capacity according to the internal topology structure of the network. But as a communication network, the transmission capability between nodes in the network is also an important factor affecting its operation capability.
Currently, there are more techniques for improving the reliability of WSN transmission, but there are few precise evaluations thereof, and there is no uniform definition. In the document Ester, Event-to-sink reliable transport in wireless sensoretworks, an author estimates the reliability of the WSN by taking the number of data transmitted to a sink node as an index. In the literature, authors in the literature, the probability that a node successfully sends data to a sink node is used as an index for evaluating the reliability of a network. In these documents, a reliability evaluation method is established in connection with the aspects of selection of network components, key technologies, application models, and the like. However, these reliability definitions all have certain limitations, and are not suitable for common network applications, and the actual relationship between components and various applications in the literature is not clearly explained, and is not suitable for large-scale network reliability simulation applications.
The WSN is used as a complex system, the working modes in practical application are various, the topological structure is variable, and the method for improving the working capacity of the WSN is also various, so that certain difficulty exists in accurately evaluating the reliability of a network system both in theory and calculation, and huge challenges are brought to the reliability analysis and evaluation of the network.
Disclosure of Invention
The embodiment of the invention provides a wireless sensor network reliability calculation method based on energy and transmission, which can solve the problems in the prior art.
A wireless sensor network reliability calculation method based on energy and transmission comprises the following steps:
(1) the method comprises the following steps that N nodes are randomly and uniformly thrown in a plane target area of M × M, signals with equal difference properties, which are transmitted in two mutually perpendicular directions by a base station, divide a network into N virtual grids;
(2) the nodes in the area judge which power level grids belong to according to the received base station signals, broadcast the ID and energy information of the nodes, receive and compare the information sent by the other nodes, and judge the cluster in which the nodes are located and other members in the cluster;
(3) after each cluster is determined, comparing the received information by the nodes, selecting the node with the maximum residual energy in the cluster as a cluster head node of the current cluster, and performing identity broadcast on the nodes in the cluster after the cluster head node is determined;
(4) evaluating the capability of the nodes in the cluster according to the working states of the nodes in the cluster, which specifically comprises the following steps:
(4a) the cluster nodes sense data and send the sensed data to the cluster head nodes, the sensing number and the data sending number are the same, and the energy consumption of sensing the data and sending the data by the cluster nodes can be calculated by combining an energy consumption model;
(4b) after the energy consumption of each link of the node work is obtained, calculating the residual energy at a certain moment, and analyzing the probability that the residual energy is greater than the energy required by the node to sense and send data, namely the instantaneous reliability of the node;
(5) the instantaneous reliability analysis and calculation of the cluster head nodes specifically comprises the following steps:
(5a) the cluster head node receives data sent by all nodes in the cluster, and the data received by the cluster head node is calculated according to the data sent by the nodes in the cluster;
(5b) performing data fusion on the cluster head nodes, fusing the p data into one data, calculating the number of the processed data to be the number of the data sent by the cluster head, and calculating by using an energy consumption model to obtain the consumption energy;
(6) aiming at the star-shaped topological structure in the cluster, at least k nodes are required to successfully transmit data to a cluster head in each cluster according to application requirements, and the cluster transmission reliability is calculated by combining the node working probability and the transmission probability;
(7) the inter-cluster routing adopts a high-reliability routing mode, and the reliability of the whole network is deduced by using a decomposition algorithm and a recursion method.
The wireless sensor network reliability calculation method based on energy and transmission in the embodiment of the invention has the following advantages:
the network adopts a grid-based clustering method and a high-reliability routing mode adopted among clusters, so that the network can balance energy consumption, enhance the fault tolerance and prolong the working time;
according to the invention, the network reliability under certain practical application requirements is accurately evaluated aiming at the network model with prolonged service life;
the invention divides the network work, and defines the reliability of each part work by combining the energy consumption and the transmission probability according to the work content of each part;
the method for deducing the reliability of calculation by combining the probability distribution with the network decomposition algorithm is also applicable to the reliability calculation of the large-scale wireless sensor network.
Therefore, in the process of analyzing and calculating the network working capacity, the invention fully considers two factors which have larger influence on the network working capacity: energy consumption and transmission; the service life of the network is prolonged by adopting an effective clustering and routing mode; the method has the advantages of simple and convenient calculation process and higher result accuracy, and has great reference value for the application needing to accurately evaluate the network capability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the steps of a method provided in an embodiment of the present invention;
FIG. 2 is a diagram of a clustering operation mode of a wireless sensor network;
FIG. 3 is an ID presentation diagram of a cluster head node;
FIG. 4 is a diagram of inter-cluster data transmission;
FIG. 5 is a graph of node reliability over time within a cluster;
FIG. 6 is a reliability diagram of a cluster node completing application requirements under different transmission probabilities;
FIG. 7 is a graph of cluster head reliability when the number of nodes in different clusters is taken;
fig. 8 is a reliability diagram of the entire network when the transmission probabilities between clusters are different.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the method for calculating the instantaneous reliability of the network according to the network operation mode shown in fig. 2 in combination with energy consumption and transmission of the present invention includes the following specific steps:
step 100, randomly and uniformly throwing N nodes in a plane target area of M × M, dividing a network into N virtual grids by signals with equal difference property, which are transmitted by a base station in two mutually vertical directions;
step 200: the nodes in the area judge which power level grids belong to according to the received base station signals, broadcast the ID and energy information of the nodes, receive and compare the information sent by the other nodes, and judge the cluster in which the nodes are located and other members in the cluster;
step 300: after each cluster is determined, comparing the received information by the nodes, selecting the node with the maximum residual energy in the cluster as a cluster head node of the current cluster, and performing identity broadcast on the nodes in the cluster after the cluster head node is determined as shown in fig. 3;
step 400: evaluating the capability of the nodes in the cluster according to the working state of the nodes in the cluster;
(4a) and the cluster nodes sense data and send the sensed data to the cluster head nodes. Suppose the number of data perceived by node i at time t
Figure BDA0001311370710000071
Obeying the Poisson distribution with the parameter of lambda, as the nodes in the cluster only carry out perception transmission tasks, the quantities of perception and transmission data are the same, namely
Figure BDA0001311370710000072
Then, the energy consumption of sensing data and sending data by the nodes in the cluster is respectively as follows:
Figure BDA0001311370710000073
wherein E iss(l) Is composed ofSensing the energy required by a data of length l bit, Et(d, l) is the energy consumed by the node to transmit data with length of l bit;
(4b) after the energy consumption of each link of the node work is obtained, the residual energy of the node at a certain moment is calculated, the probability that the residual energy is larger than the energy required by the node to sense and send data is analyzed, and the probability is the instantaneous reliability of the node i at the moment t:
Figure BDA0001311370710000081
after finishing has
Figure BDA0001311370710000082
Figure BDA0001311370710000083
Wherein E is0Is the initial energy of the node;
step 500: analyzing and calculating the instantaneous reliability of the cluster head node j;
(5a) there are m nodes in each cluster, and according to the application requirements, at least k nodes in the cluster are required to work normally and data is successfully transmitted to the cluster head, and the cluster is considered to be in accordance with the requirements, that is, the structure in the cluster can be regarded as a k-out-of-m structure. Because the nodes in the cluster work in a star topology structure, the number of the nodes receiving data by the cluster head node j is equal to
Figure BDA0001311370710000084
Therefore, the energy consumed by the cluster head node j for receiving the data is as follows:
Figure BDA0001311370710000085
wherein E isr(l) Representing the energy required to receive data of length l bit;
(5b) the cluster head node fuses the received data, fuses p data into one data, the number of the data after calculation is the number of the data sent by the cluster head, and the energy consumption is as follows:
Figure BDA0001311370710000086
wherein E isDARepresenting the energy required to fuse p data;
(5c) obtaining the reliability of the cluster head node j according to the residual energy:
Figure BDA0001311370710000087
after finishing has
Figure BDA0001311370710000091
Wherein the content of the first and second substances,
Figure BDA0001311370710000092
F＝(Es(l)+lEDA)+λEt(d,l)+fEr(l)+fEDA
step 600: the application requires that at least k nodes in each cluster are required to successfully transmit data to the cluster head, and under the condition that the energy of the node i is sufficient and the transmission process is successful, the data collected by the node can reach the cluster head node, so the capability of the cluster head j meeting the application requirement and working normally can be expressed as follows:
Figure BDA0001311370710000093
wherein P isijProbability of successfully transmitting data to cluster head j for node i;
step 700: the inter-cluster routing adopts a high-reliability routing mode as shown in fig. 4, and the decomposition algorithm is utilized to deduce the reliability of the whole network by adopting a recursion method:
R(G)＝R(G*e)Pe+R(G-e)(1-Pe)
wherein P iseIs the probability of successful data transmission between cluster heads whenWhen a contraction edge e enables a certain cluster head node to be overlapped with a sink node, R (G) e) is equal to 1, and when the deletion edge e enables all cluster heads not to be communicated with the sink node, R (G-e) is equal to 1.
By analyzing all parts of the network work, the overall accurate reliability of the network can be finally obtained.
The analytical calculations of the present invention can be further illustrated by the following simulation calculations:
simulation conditions
The method comprises the steps that n nodes with the same initial energy are randomly and uniformly deployed in a square plane area of M × M, each node is provided with a base station which belongs to a unique ID. monitoring area and is sufficient in energy and high in computing capacity, once all the nodes are deployed, the nodes do not move any more, and a TDMA rule, namely a time division multiple access protocol, is adopted during data transmission.
Other simulation parameters are shown in table 1:
TABLE 1 parameter settings
Parameter(s) Value of Parameter(s) Value of
Length of area side a (m) 100m Base station location (50，150)
Initial energy of node E0(J) 0.5 Packet length l (bit) 4000
Eelec(nJ/bit) 50 εfs(pJ/bit/m2) 10
γ(nJ/bit) 50 EDA(nJ/bit/signal) 5
Minimum number k of working nodes in a cluster 5 Poisson distribution parameter lambda 1
Number of clusters N 4
Emulated content
Combining the above parameters and simulation conditions, under the condition that the network is divided into 4 clusters, and the number of nodes in each cluster is 10, based on a node energy consumption model, the reliability of the nodes in each cluster is calculated by using the calculation method of the present invention, then the working capacity of each cluster when the application requirements are met is calculated by combining different node data transmission probabilities, the influence of different transmission probabilities on the working capacity is analyzed, the reliability of the cluster head nodes when the number of nodes in different clusters is calculated by comparison through the number of data transmission in the clusters, and finally the reliability of the whole network is calculated according to an inter-cluster routing mode, and the result is shown in fig. 5-8.
In fig. 5, after the node operates for 300 rounds, its reliability will decrease rapidly because the node has enough residual energy in the initial stage of operation, and can completely satisfy the energy required by the node to sense and transmit data, so that its normal operation capability is strong, but after operating for a while, the node consumes more energy, and its residual energy capability to continue operating will gradually decrease. In view of the data transmission capability of a node to a cluster head, fig. 6 shows the change over time in the capability of a node within a cluster to transmit data to a cluster head. Under the condition that the number of nodes in the cluster is not changed, the larger the transmission probability from the nodes to the cluster head is, the better the working capacity of the whole cluster is. The cluster head is different from the common node in that it needs to fuse processing data and therefore consumes a lot of energy, and as can be seen from fig. 7, the high reliability retention time of the cluster head is short, which is due to the excessive consumption of energy consumption. Due to the limitation of application requirements, when the number of nodes in a cluster is smaller, the reliability of receiving the required data by the cluster head is lower, but as the number of nodes in the cluster is increased, the more energy consumed by the nodes in the cluster for receiving the data is, the more energy is consumed by the nodes in the cluster, and the remaining energy is reduced, so that the reliability is reduced. When data transmission is performed between cluster head nodes, the data transmission is interfered by an external environment, and as shown in fig. 8, when the number of nodes in a cluster is 10, network reliability under different inter-cluster transmission probabilities is considered. Compared with the reliability of the cluster head node when m is 10 in fig. 7, the external environment has less influence on data transmission relative to the energy of the node. Under the same cluster head reliability, the greater the inter-cluster transmission probability is, the higher the overall reliability of the network is.
Description of the symbols:
a: length of sides of target area
E0: initial energy of node
l: size of each data packet
Pij: probability of successful transmission of data sensed by node i to node j in a network
N: number of network clusters
k: minimum number of successfully transmitted data nodes required in a cluster
m: number of nodes in each cluster
R (G): wireless sensor network reliability
WSN Wireless sensor network
L EACH L ow-Energy Adaptive Clustering Hierarchy low-power consumption Adaptive Clustering layering protocol
Power-Efficient heating in Sensor Information Systems Efficient energy-saving clustering protocol of PEGASIS
Time Division Multiple Access
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (1)

1. A wireless sensor network reliability calculation method based on energy and transmission is characterized by comprising the following steps:
(1) the method comprises the following steps that N nodes are randomly and uniformly thrown in a plane target area of M × M, signals with equal difference properties, which are transmitted in two mutually perpendicular directions by a base station, divide a network into N virtual grids;
(2) the nodes in the area judge which power level grids belong to according to the received base station signals, broadcast the ID and energy information of the nodes, receive and compare the information sent by the other nodes, and judge the cluster in which the nodes are located and other members in the cluster;
(3) after each cluster is determined, comparing the received information by the nodes, selecting the node with the maximum residual energy in the cluster as a cluster head node of the current cluster, and performing identity broadcast on the nodes in the cluster after the cluster head node is determined;
(4) evaluating the capability of the nodes in the cluster according to the working states of the nodes in the cluster, which specifically comprises the following steps:
(4a) carrying out data perception on nodes in the cluster, sending perception data to cluster head nodes, and assuming the number of data perceived by the node i at the moment t
Figure FDA0002465632230000011
Obeying the Poisson distribution with the parameter of lambda, as the nodes in the cluster only carry out perception transmission tasks, the quantities of perception and transmission data are the same, namely
Figure FDA0002465632230000012
Then, the energy consumption of sensing data and sending data by the nodes in the cluster is respectively as follows:
Figure FDA0002465632230000013
wherein E iss(l) To sense the energy required for a data length of l bits, Et(d, l) the energy consumed by the node to transmit a data with the length of lbit;
(4b) after the energy consumption of each link of the node work is obtained, the residual energy of the node at a certain moment is calculated, the probability that the residual energy is larger than the energy required by the node to sense and send data is analyzed, and the probability is the instantaneous reliability of the node i at the moment t:
Figure FDA0002465632230000021
after finishing has
Figure FDA0002465632230000022
Figure FDA0002465632230000023
Wherein E is0Is the initial energy of the node;
(5) the instantaneous reliability analysis and calculation of the cluster head nodes specifically comprises the following steps:
(5a) each cluster is provided with m nodes, at least k nodes in the cluster are required to work normally according to application requirements and data is transmitted to a cluster head successfully, the cluster is considered to meet the requirements, namely the cluster structure can be regarded as a k-out-of-m structure, and as the cluster nodes work in a star topology structure, the number of the nodes receiving the data is equal to that of the nodes receiving the data by a cluster head node j
Figure FDA0002465632230000024
Therefore, the energy consumed by the cluster head node j for receiving the data is as follows:
Figure FDA0002465632230000025
wherein E isr(l) Representing the energy required to receive data of length l bit;
(5b) the cluster head node fuses the received data, fuses p data into one data, the number of the data after calculation is the number of the data sent by the cluster head, and the energy consumption is as follows:
Figure FDA0002465632230000026
wherein E isDARepresenting the energy required to fuse p data;
(5c) obtaining the reliability of the cluster head node j according to the residual energy:
Figure FDA0002465632230000027
after finishing has
Figure FDA0002465632230000028
Wherein the content of the first and second substances,
C＝Es(l)+Et(d,l)+Er(l)+Eda-fu，
Figure FDA0002465632230000031
F＝(Es(l)+lEDA)+λEt(d,l)+fEr(l)+fEDA
(6) aiming at a star topology structure in a cluster, at least k nodes required in each cluster are used for successfully transmitting data to a cluster head according to application requirements, and the cluster transmission reliability is calculated by combining the node working probability and the transmission probability, and the method specifically comprises the following steps:
the application requires that at least k nodes in each cluster are required to successfully transmit data to the cluster head, and under the condition that the energy of the node i is sufficient and the transmission process is successful, the data collected by the node can reach the cluster head node, so the capability of the cluster head j meeting the application requirement and working normally can be expressed as follows:
Figure FDA0002465632230000032
wherein P isijProbability of successfully transmitting data to cluster head j for node i;
(7) the inter-cluster routing adopts a high-reliability routing mode, the reliability of the whole network is deduced by using a decomposition algorithm and a recursion method, and the concrete process of deducing the reliability of the whole network is as follows:
the inter-cluster routing adopts a high-reliability routing mode, and the reliability of the whole network is deduced by using a decomposition algorithm and a recursion method:
R(G)＝R(G*e)Pe+R(G-e)(1-Pe)
wherein P iseFor the probability of successful data transmission between cluster heads, when a certain cluster head node is overlapped with a sink node due to contraction of an edge e, R (G) e is 1, and when the edge e is deleted, all cluster heads are not communicated with the sink node, and then R (G-e) is 1.
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