CN111107603B - Clustering management method for improving availability of distributed multi-hop network - Google Patents
Clustering management method for improving availability of distributed multi-hop network Download PDFInfo
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Abstract
The invention discloses a clustering management method for improving the availability of a distributed multi-hop network, which mainly solves the problems of disordered networking of large-scale network nodes and high control overhead in the prior art. The implementation scheme comprises the following steps: the method comprises the following steps of cluster generation and cluster maintenance, wherein the cluster generation step comprises the following steps: firstly, the nodes randomly participate in competition cluster heads according to the electric quantity percentage of the nodes; secondly, selecting cluster head nodes in the network according to a weight optimization principle; then, periodically broadcasting cluster domain information by the cluster head node and the clustered nodes to enable the clustered nodes to cluster; the maintenance steps of the cluster are as follows: 1) selecting standby cluster head nodes by the cluster head nodes according to the similarity optimal principle, and realizing information synchronization of the master cluster head node and the slave cluster head node in a differential synchronization mode; 2) and the standby cluster head node detects the survival condition of the cluster head node and performs the function of the cluster head node as required. The invention improves the network performance, ensures the stability of the network, and can be used for the hierarchical division and maintenance of a large-scale network organization structure.
Description
Technical Field
The invention belongs to the technical field of communication, and further relates to a hierarchical network clustering management method which can be used for hierarchical division and maintenance of a large-scale network organization structure.
Background
The traditional self-organizing network organization structure is an equality, the functions and the positions of nodes in the network are equal, the data transmission in the network depends on node forwarding, but under the large-scale networking and multi-hop range networking scenes, the traditional self-organizing network is disordered in organization, large in control overhead and short in time slot resources, and is difficult to adapt to the large-scale network node scene, so that the expandability is poor. In recent years, a hierarchical network structure is constructed by using a clustering algorithm, the application of network bandwidth is optimized, the utilization rate of a channel and network performance can be improved, and a high-performance large-scale ad hoc network becomes possible. Under the outdoor environment, the large-scale distributed networking still faces the severe conditions of node electric quantity limitation, barrier shielding, node failure and the like, and higher requirements are put forward on the stability and the robustness of the network; by adopting a master-slave cluster head node management network mechanism, the influence of cluster head node failure on network data transmission can be effectively reduced, the stability of the network is enhanced, and the usability of the clustering algorithm is greatly improved.
In the patent of Beijing technology university, which is filed as an assistant-based clustering method in Ad Hoc network (patent application No. 200810106381.6, publication No. CN 101267404A), a clustering method using minimum ID is proposed, the cluster is initialized and cluster heads are selected by using the minimum ID of nodes, and in the aspect of cluster domain maintenance, the node with the minimum weight in the cluster is selected as an assistant node. According to the method, a traditional and conservative mode is adopted to select the cluster head, and an assistant node is selected to deal with the scene that the electric quantity of the cluster head node is low, so that the hidden danger caused by the condition that the electric quantity of the cluster head node is too low is avoided. However, the method has the disadvantages that the cluster head nodes selected by the node minimum ID clustering method are not necessarily reasonable, cluster head collision is easily caused in a multi-hop network topology scene, and meanwhile, the selection of the assistant nodes has some defects.
The patent of Beijing Xinwei communication technology corporation ' applied for ' a static formation clustering-based ad hoc network system ' (patent application number: 201510075784.9, publication number: CN 105992301A) provides an ad hoc network method for static clustering by using the static distribution characteristic of network nodes, and has good performance in reducing the control overhead of the whole network. The method utilizes a static formation structure to perform cluster division, and virtual routing tables among cluster heads are established and maintained by all the cluster heads to realize network communication. However, this method has a disadvantage that since it uses only the static distribution characteristics of the nodes as cluster division elements and limits the movement characteristics of the nodes, the cluster organization structure is disturbed and the inter-cluster information transmission is unstable in a dynamic node movement scenario.
Disclosure of Invention
The invention aims to provide a clustering management method for improving the availability of a distributed multi-hop network aiming at the defects of the prior art so as to ensure the stability of a cluster structure, reduce the probability of cluster recombination and reduce the control overhead of networking.
The technical idea of the invention for realizing the above purpose is as follows: by introducing a management and maintenance mechanism of the cluster, the stability of the cluster structure is ensured, and the cluster recombination probability is reduced; by introducing an effective control overhead method, the control overhead of networking is reduced. The method comprises the following specific steps:
(1) nodes in the network randomly participate in competition cluster heads according to the self electric quantity percentage information;
(2) selecting cluster head nodes in the network according to the interactive information of the nodes and the weight optimal principle;
(3) the cluster head node executes the steps (4) and (5) simultaneously;
(4) diffusion of cluster domain information and clustering of nodes outside the cluster:
(4a) cluster head nodes broadcast cluster domain information, wherein the information comprises cluster head node IDs, the number of nodes in a cluster, the cluster domain range of the cluster and the clock level in the node cluster;
(4b) after receiving and storing the broadcasted cluster domain information, the nodes outside the cluster determine whether the nodes can enter the cluster set of the cluster according to the cluster domain range of the cluster, and then select the optimal cluster from the cluster sets which can enter the cluster according to the principle of minimum number of nodes inside the cluster to enter the cluster;
(4c) periodically broadcasting cluster domain information after the cluster external nodes enter the cluster so as to enable the cluster domain information to be diffused outwards;
(5) maintaining a cluster domain:
(5a) selecting standby cluster head nodes by the cluster head nodes according to the similarity optimal principle, and realizing information synchronization of the master cluster head node and the slave cluster head node in a differential synchronization mode;
(5b) and the standby cluster head node detects the survival condition of the cluster head node and performs the function of the cluster head node as required.
Compared with the prior art, the invention has the following advantages:
firstly, as the invention adopts a weighted clustering algorithm, the invention fully considers the factors of node electric quantity, network load balance, cluster domain range size and cluster scale, and forms a cluster processing mechanism to divide the network into different sub-networks, thereby improving the network node capacity and transmission rate; meanwhile, a cluster domain range self-adaptive rectification process is adopted, so that the number of members in a cluster is controlled in a controllable range, and the occurrence probability of cluster splitting and recombination is reduced.
Secondly, because the invention adopts the master cluster head node and the slave cluster head node to manage the network, the invention overcomes the defect that the network is only managed by the master cluster head node in the prior art, and avoids the risk of cluster structure reconstruction caused by the failure of the cluster head node, so that the invention has better performance in the aspect of ensuring the network stability.
Thirdly, because the invention adopts a differential synchronization mode to synchronize the network information of the master and slave cluster head nodes, the problem of inconsistent data of the master and slave nodes at a longer time interval caused by sending data at a fixed time interval in the prior art is solved, the cluster head failure detection of the invention is more sensitive, and the consistency of the data is ensured.
Drawings
FIG. 1 is a general flow chart of an implementation of the present invention;
FIG. 2 is a sub-flowchart of the invention in which nodes randomly participate in competing cluster heads;
FIG. 3 is a sub-flow diagram of selecting cluster head nodes in the network according to the present invention;
FIG. 4 is a sub-flow diagram of cluster domain maintenance in the present invention;
fig. 5 is a diagram of an exemplary cluster structure of the present invention.
Detailed Description
Embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the embodiment is divided into two major parts, namely, generation of a cluster and maintenance of the cluster, and the specific implementation steps are as follows:
generation of clusters
Step 1, nodes in the network randomly participate in competition cluster heads according to the percentage information of the electric quantity of the nodes.
Referring to fig. 2: the specific implementation of this step is as follows:
1.1) calculating the electric quantity percentage s of the node:
1.2) calculating a clustering detection threshold P according to the electric quantity percentage s of the nodes:
p ═ exp [ in (c) × s ], where in (c) is an amplification factor, such that the cluster detection threshold P increases exponentially with the charge percentage s, i.e., the higher the charge percentage, the higher the cluster detection probability of nodes;
1.3) carrying out clustering randomness detection:
1.3.1) generating a random number R of 0- (t × c) by means of a random number function rand ():
r ═ rand ()% (t × C), where the rand () function is a random function in C language that generates random numbers,
t is selected from the range of 0.6-0.8;
1.3.2) obtaining a randomness detection result according to the random number R and the clustering detection threshold P:
where result is a boolean data type, true indicates that the result is true, and false indicates that the result is false.
1.3.3) determining node competition cluster head operation according to the randomness detection result:
if the result of the randomness detection result is true, the node participates in the competition cluster head;
otherwise, 1.3.4) is executed;
1.3.4) judging whether the node meets the continuous 3-5 rounds of competition protection conditions without participating in competition cluster heads:
if the competition protection condition is met, the nodes participate in competition cluster heads;
otherwise, the node does not participate in the competition cluster head;
and 2, selecting cluster head nodes in the network according to the interactive information of the nodes and the weight optimal principle.
Referring to fig. 3: the specific implementation of this step is as follows:
2.1) determining the clustering weight parameter values of the nodes:
firstly, determining the number D of nodes which are not clustered in neighbor nodes of the nodes, and calculating the absolute value of the difference between the degree of the nodes and the ideal node degree D, namely D '═ D-D';
secondly, determining the number M of nodes which are not clustered in the two-hop range of the nodes, estimating the cluster range of the nodes as cluster heads according to the value M, and calculating the absolute value of the difference between the estimated cluster node number M ' and the ideal cluster node number M, namely M ' ═ M ' -M |, wherein:
in the embodiment, a cluster domain range self-adaptive increasing mode is adopted, the range size is calculated according to the number m of nodes which are not clustered in a two-hop range during clustering, and the cluster head self-adaptively changes the range value according to the number of nodes which are not clustered outside the cluster after clustering;
then, according to the adjacent node information of the node, calculating a clustering balance factor S':
wherein K is the number of adjacent nodes of the node, xiDegree of the ith adjacent node of the node, and D is degree of an ideal node;
2.2) calculating the clustering weight W according to the clustering weight parameter value:
W＝a×d'+b×m'+c×S'+x，
wherein, a, b, c are weighting factors with different values, and a + b + c is 1, d ', m ', S ' is the clustering weight parameter obtained in 2.1); in a network, clustering weights W calculated by nodes with a close distance are likely to be the same, and in order to deal with the situation that homogeneous nodes compete for cluster heads, a parameter x is introduced, wherein the parameter is obtained by multiplying 0.000001 by a node id, namely, the node id is smaller and preferentially becomes a cluster head under the condition that the weights are the same;
2.3) clustering weight W of the node and clustering minimum weight W of the node and the adjacent nodesminPutting the service frame into a service frame, and periodically broadcasting the service frame to the periphery;
the service frame is a hello data packet of information interaction between nodes, is used for adding useful information of node interaction, and is carried out through periodic broadcasting;
2.4) non-clustered node receives service frame of adjacent node, and stores clustering minimum weight W in the adjacent nodeminAnd calculating the minimum clustering weight W of the two-hop range with the node as the center2_minI.e. from the saved clustering minimum weight W of the node and all its neighborsminFinding the minimum weight as the minimum clustering weight W of the two-hop range with the node as the center2_min；
2.5) judging the clustering weight W of the node and the minimum clustering weight W of the two-hop range by the non-clustered node2_minWhether the cluster weights are equal to or not and whether the cluster weight W is less than or equal to a set cluster threshold WHTTwo of themIndividual clustering detection conditions:
if the two clustering detection conditions are both met, cluster head competition is successful, namely, the node meeting the two clustering detection conditions is taken as a cluster head node;
otherwise, the competing cluster heads fail.
The clustering threshold WHTIs a clustering protection threshold, when the clustering weight W of the node is greater than WHTIndicating that this node is not suitable to act as a cluster head.
And 3, simultaneously executing the following steps 4 and 5 by the cluster head node.
And 4, diffusing cluster domain information and clustering nodes outside the clusters.
4.1) cluster head nodes broadcast cluster domain information, wherein the information comprises cluster head node IDs, the number of nodes in a cluster, the cluster domain range of the cluster and the clock level in the node cluster;
4.2) after the cluster outside node receives the broadcasted cluster domain information and stores the cluster domain information, determining whether the cluster outside node can enter the cluster set of the cluster according to the cluster domain range of the cluster, namely, the cluster outside node calculates the nearest distance hop count reaching the cluster head according to the locally stored adjacent node clock grade information, if the nearest distance hop count reaching the cluster head by the node is less than or equal to the cluster domain range of the cluster domain, the cluster is a cluster which the node can join;
in this example, the rule for determining the node clock level is: setting the clock grade of cluster head nodes to be 0, finding out the node with the minimum clock grade in the adjacent nodes of the same cluster by the cluster nodes except the cluster head, taking the node as an upstream node, carrying out cluster domain data synchronization by the cluster nodes according to the upstream node, and adding one to the clock grade value of the cluster nodes, wherein the clock grade value of the cluster nodes is equal to the clock grade value of the upstream node; the distance between the cluster head and the nodes in the cluster is equal to the absolute value of the difference between the clock levels of the two nodes, the clock level of the same cluster adjacent node of the cluster head node is 1, namely the distance between the same cluster adjacent node of the cluster head and the cluster head is 1 hop;
4.3) the nodes outside the cluster select the optimal cluster from the cluster set capable of entering the cluster according to the principle that the number of the nodes inside the cluster is the minimum, namely for the cluster set capable of entering the cluster, the cluster with the minimum number of the nodes inside the cluster is selected as the optimal cluster to enter the cluster, and cluster information is sent to the cluster head;
4.4) periodically broadcasting cluster domain information after the cluster external nodes enter the cluster so as to enable the cluster domain information to be diffused outwards;
maintenance of clusters
Step 5, maintaining the cluster field:
referring to fig. 4: the specific implementation of this step is as follows:
5.1) selecting standby cluster head nodes according to the similarity optimal principle by the cluster head nodes:
5.1.1) determining the number n of adjacent nodes of the cluster head node;
5.1.2) calculating the same cluster neighbor node of the cluster head nodeiSimilarity ratio same _ ratei：
same_ratei＝m/n
Wherein m is a cluster head node and a cluster-shared neighbor nodeiThe number of overlapping nodes;
5.1.3) according to the same cluster of neighbor nodesiSimilar rate of (sa me _ rate)iAnd percentage of electric quantity siCalculating a backup node weight Ws_i：
Ws_i＝g×same_ratei+l×si，
Wherein g, l are weighting factors with different values, and g + l is 1;
5.1.4) finding the maximum spare node weight W in the same cluster adjacent nodes by cluster head nodess_maxCorresponding standby node slave _ idnew；
5.1.5) setting a hard threshold rate for update detection of the standby nodeHTAnd soft threshold rateSTThe following two update detection conditions are set according to the threshold:
condition 1: standby node slave _ idnewWith the existing standby node slave _ idoldNot equal;
condition 2: ws_max＞＝rateHTAnd Ws_max-Ws_old＞＝rateST；
Wherein, Ws_maxIs standby node slave _ idnewCorresponding backup node weight, Ws_oldIs an existing standby node slave _ idoldCorresponding backup node weights;
5.1.6) cluster head node judging standby node slave _ idnewWhether two update detection conditions in 5.1.5) are simultaneously satisfied:
if two updating detection conditions are met simultaneously, the cluster head node informs the node slave _ idoldCancel to become a standby node and inform the node of the slave _ idnewBecoming a standby node;
otherwise, the cluster head node does not update the standby node;
5.2) realizing information synchronization of the master cluster head node and the slave cluster head node in a differential synchronization mode:
5.2.1) the main cluster head node sends the synchronous information to the standby cluster head node simultaneously in the following two ways:
mode 1: the main cluster node sends complete cluster data information to the standby cluster head node at regular time in a period T1;
mode 2: the main cluster node sends data change information in intervals to the standby cluster head node at a fixed time in a period T2;
wherein, the value of T1 is far greater than that of T2, namely the value of T1 is 3-6 times of that of T2;
5.2.2) the standby cluster head node compares the size of the time t1 of generating the synchronization information by the cluster head with the size of the local data updating time t2 according to the received synchronization information:
if t1> t2, then 5.2.3) is executed, and let t2 be t 1;
otherwise, the data updating operation is not executed;
5.2.3) according to the type of the synchronous information, the following updating operations are executed:
if the synchronous information is complete intra-cluster data information of the cluster head node, directly replacing local intra-cluster data through the synchronous information;
and if the synchronous information is data change information in the interval of the cluster head nodes, changing the data in the local cluster through the synchronous information.
5.3) the nodes in the cluster update the subnet network sequence number seq according to three rules generated by the typical cluster domain structure characteristics:
rule 1: initializing the network serial number seq of the nodes in the cluster to be 0, and selecting an upstream node by each node in the cluster except the cluster head according to a rule determined by the node clock grade in 4.2) to synchronize the network serial number seq, namely enabling the network serial number seq of the node to be equal to the network serial number seq in the service frame broadcast by the upstream node after the nodes in the cluster receive the service frame broadcast by the upstream node;
rule 2: when the cluster head node broadcasts the service frame, firstly adding one to the network serial number seq of the node, and then adding the network serial number seq of the node to the service frame to broadcast;
rule 3: when broadcasting the service frame for the nodes in the cluster except the cluster head, directly adding the network serial number seq of the node into the service frame and broadcasting without changing the network serial number seq of the node;
referring to fig. 5, the typical cluster structure is composed of a plurality of multi-branch trees, a cluster head node is a root node of the multi-branch trees, and synchronization of network serial numbers seq on macro-scale of cluster nodes and cluster head nodes in the network and synchronization of network serial numbers seq on micro-scale of cluster nodes and upstream nodes in the network can be ensured according to a subnet network serial number seq updating rule;
5.4) the spare cluster head node detects the survival condition of the cluster head node, and the cluster head node functions as required:
5.4.1) setting three detection conditions for the survival of the main cluster head node:
condition 1: the standby cluster head node detects that the main cluster head node is not in the neighbor node;
condition 2: the standby cluster head node subnet network serial number seq stops updating, and the adjacent nodes of the standby cluster head node subnet network serial number seq contain more than 3-6 same cluster nodes;
condition 3: the standby cluster head node continuously sends heartbeat detection information to the cluster head for 3 times at a time interval T, but does not receive cluster head response within 6T time;
after the subnet sequence number seq of the standby node stops updating in the above condition 2, the network may have two situations: 1.
when the cluster head node fails, seq stops updating; 2. the standby node leaves the subnet network and cannot receive the service frame broadcast by other nodes in the cluster. Therefore, case 1 needs to be discriminated on the basis, and the adopted additional conditions are as follows: the adjacent nodes of the standby cluster head node comprise more than 3-6 same cluster nodes.
5.4.2) the spare cluster head node detects whether the main cluster head node sequentially meets three detection conditions of survival:
if yes, judging that the cluster head node is invalid, executing the cluster head node function by the standby cluster head node, and broadcasting a message which becomes the main cluster head node;
otherwise, the cluster head node is still valid.
The foregoing description is only an example of the present invention and is not intended to limit the invention, so that it will be apparent to those skilled in the art that various changes and modifications in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (5)
1. A clustering management method for improving the availability of a distributed multi-hop network is characterized in that: comprises the following steps:
(1) the nodes in the network randomly participate in competition cluster heads according to the percentage information of the electric quantity of the nodes, and the method is realized as follows:
(1a) calculating the electric quantity percentage s of the nodes:
wherein e represents the current electric quantity of the node battery, and v represents the capacity of the node battery;
(1b) calculating a clustering detection threshold P according to the electric quantity percentage s of the nodes:
P＝exp[In(c)*s]
in (c) is an amplification factor, so that the clustering detection threshold P is exponentially increased along with the electric quantity percentage s, namely the clustering detection probability of the nodes with higher electric quantity percentage is higher;
(1c) performing clustering randomness detection:
(1c1) a random number R of 0- (t × c) is generated by means of a random number function rand (): r ═ rand ()% (t × c);
wherein, the rand () function is a random function generating random numbers in C language, and t is a value in the range of 0.6-0.8;
(1c2) obtaining a randomness detection result according to the random number R and the clustering detection threshold P:
wherein result is a boolean data type, true indicates that the result is true, and false indicates that the result is false;
(1c3) determining node competition cluster head operation according to the randomness detection result:
if the result of the randomness detection result is true, the node participates in the competition cluster head;
otherwise, performing (1c 4);
(1c4) judging whether the node meets the continuous 3-5 rounds of competition protection conditions without participating in competition cluster heads:
if the competition protection condition is met, the nodes participate in competition cluster heads;
if the competition protection condition is not met, the node does not participate in the competition cluster head;
(2) selecting cluster head nodes in the network according to the interaction information of the nodes and the weight optimal principle, and realizing the following steps:
(2a) determining the clustering weight parameter values of the nodes:
(2a1) determining the number D of nodes which are not clustered in neighbor nodes of the nodes, and calculating the absolute value of the difference between the degree of the nodes and the ideal node degree D, namely D' ═ D-D |;
(2a2) determining the number M of nodes which are not clustered in a two-hop range of the nodes, estimating a cluster area range of the nodes as cluster heads according to the value M, and calculating the absolute value of the difference between the estimated cluster area node number M ' and the ideal cluster area node number M, namely M ' ═ M ' -M |, wherein:
(2a3) calculating a clustering balance factor S' according to the adjacent node information of the nodes:
(2b) calculating a clustering weight W according to the clustering weight parameter value:
W＝a×d'+b×m'+c×S'+x
wherein a, b and c are weighting factors with different values, a + b + c is 1, d ', m ' and S ' are clustering weight parameters obtained in the step (2a), and x is an auxiliary parameter;
(2c) the node clusters the weight W and the minimum cluster weight W between the node and the adjacent nodeminPutting the service frame into a service frame, and periodically broadcasting the service frame to the surrounding, wherein the service frame is a hello data packet of information interaction between nodes and is used for adding useful information of the node interaction;
(2d) the non-clustered node receives the service frame of the adjacent node and stores the W of the adjacent nodeminAnd calculating the minimum clustering weight W of the two-hop range with the node as the center2_min；
(2e) The non-clustered node firstly judges whether the clustering weight W of the node is the same as the minimum clustering weight W of the two-hop range2_minAnd then judging whether the clustering weight W is less than or equal to the set clustering threshold WHT：
If the two clustering detection conditions are both met, cluster head competition is successful, namely, the node meeting the two clustering detection conditions is taken as a cluster head node;
otherwise, the competition cluster head fails;
(3) the cluster head node executes the steps (4) and (5) simultaneously;
(4) diffusion of cluster domain information and clustering of nodes outside the cluster:
(4a) cluster head nodes broadcast cluster domain information, wherein the information comprises cluster head node IDs, the number of nodes in a cluster, the cluster domain range of the cluster and the clock level in the node cluster;
(4b) after receiving and storing the broadcasted cluster domain information, the nodes outside the cluster determine whether the nodes can enter the cluster set of the cluster according to the cluster domain range of the cluster, and then select the optimal cluster from the cluster sets which can enter the cluster according to the principle of minimum number of nodes inside the cluster to enter the cluster;
(4c) periodically broadcasting cluster domain information after the cluster external nodes enter the cluster so as to enable the cluster domain information to be diffused outwards;
(5) maintaining a cluster domain:
(5a) selecting standby cluster head nodes by the cluster head nodes according to the similarity optimal principle, and realizing information synchronization of the master cluster head node and the slave cluster head node in a differential synchronization mode; the method is realized as follows:
(5a1) determining the number n of adjacent nodes of the cluster head node;
(5a2) same cluster neighbor node of computing cluster head nodeiSimilarity ratio same _ ratei：
same_ratei＝m/n
Wherein m is a cluster head node and a cluster-shared neighbor nodeiThe number of overlapping nodes;
(5a3) according to the same cluster of adjacent nodesiSimilar rate of (sa me _ rate)iAnd percentage of electric quantity siCalculating a backup node weight Ws_i：
Ws_i＝g×same_ratei+l×si
Wherein g, l are weighting factors with different values, and g + l is 1;
(5a4) the cluster head node finds the maximum standby node weight W in the same cluster of adjacent nodess_maxCorresponding standby node slave _ idnew；
(5a5) Standby node slave _ idnewThe update is performed under two conditions that are satisfied simultaneously:
condition 1: standby node slave _ idnewWith the existing standby node slave _ idoldNot equal;
condition 2: ws_max＞＝rateHTAnd Ws_max-Ws_old＞＝rateST；
Wherein, rateHTAnd rateSTUpdating the hard threshold and soft threshold set for detection separately for the standby nodeThe threshold is used for preventing the standby cluster head node from being frequently changed in the node moving scene; ws_maxIs standby node slave _ idnewCorresponding backup node weight, Ws_oldIs an existing standby node slave _ idoldCorresponding backup node weights;
(5a6) the main cluster head node simultaneously sends the synchronous information to the standby cluster head node in the following two ways:
mode 1: the main cluster node sends complete cluster data information to the standby cluster head node at regular time in a period T1;
mode 2: the main cluster node sends data change information in intervals to the standby cluster head node at a fixed time in a period T2;
wherein, T1 is a period for the main cluster head node to transmit complete intra-cluster data, T2 is a period for the main cluster head node to change data transmission in an interval, and the value of T1 is much larger than that of T2, namely the value of T1 is 3-6 times of that of T2;
(5a7) the standby cluster head node compares the size of the cluster head synchronization information generation time t1 with the size of the local data updating time t2 according to the received synchronization information:
if t1> t2, (5a8) is performed, and let t2 be t 1;
otherwise, the data updating operation is not executed;
(5a8) and performing the following updating operation according to the type of the synchronous information:
if the synchronous information is complete intra-cluster data information of the cluster head node, directly replacing local intra-cluster data through the synchronous information;
if the synchronous information is data change information in the interval of the cluster head nodes, changing the data in the local cluster through the synchronous information;
(5b) and the standby cluster head node detects the survival condition of the cluster head node and performs the function of the cluster head node as required.
2. The method according to claim 1, wherein the cluster head node in (4a) broadcasts cluster domain information, which means that the cluster head node sends out information of cluster head node ID of its cluster, the number of nodes in the cluster, the cluster domain range of the cluster, and the clock level in the node cluster through a wireless channel, so as to facilitate discovery of the cluster domain.
3. The method according to claim 1, wherein the determining whether the cluster set in (4b) can be clustered or not according to the cluster domain range of the cluster means that the node calculates the nearest hop count to the cluster head according to the locally stored clock level information of the neighboring nodes, and if the nearest hop count to the cluster head by the node is less than or equal to the cluster domain range of the cluster domain, the cluster is a cluster that the node can join.
4. The method according to claim 1, wherein the step (4b) of selecting the optimal cluster from the cluster sets capable of being clustered according to the principle that the number of nodes in the cluster is the minimum, is to select the cluster with the minimum number of nodes in the cluster as the optimal cluster for clustering, and send cluster information to the cluster head.
5. The method of claim 1, wherein the (5b) is embodied as follows:
(5b1) setting three detection conditions for the survival of the main cluster head node:
condition 1: the standby cluster head node detects that the main cluster head node is not in the neighbor node;
condition 2: the standby cluster head node subnet network serial number seq stops updating, and the adjacent nodes of the standby cluster head node subnet network serial number seq contain more than 3-6 same cluster nodes;
condition 3: the standby cluster head node continuously sends heartbeat detection information to the cluster head for 3 times at a time interval T, but does not receive cluster head response within 6T time;
(5b2) the standby cluster head node detects whether the main cluster head node sequentially meets three detection conditions of survival:
if yes, judging that the cluster head node is invalid, executing the cluster head node function by the standby cluster head node, and broadcasting a message which becomes the main cluster head node;
otherwise, the cluster head node is still valid.
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