CN113965948A - Sensor data acquisition method based on self-adaptive clustering network - Google Patents
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Abstract
The invention discloses a sensor data acquisition method based on a self-adaptive clustering network, which comprises the following steps: step one, interactively triggering network nodes to finish self-adaptive clustering by utilizing clustering control information; step two, clustering information control is carried out, and the method comprises the following steps: cluster head weight, clustering state, destination node ID, connected cluster head set and neighbor list of each node; step three, in the multi-hop self-organizing network, after any node receives clustering control information of a neighbor node, different states of the node are extracted by a self-learning function module for analysis; step four, in the multi-hop self-organizing network, the network nodes transfer conditions according to the clustering roles and the clustering states of the nodes; after the cluster is built, the Sink transmits Rup in the current round of virtual backbone network in the transmission range outside the cluster until all cluster heads have grades; the method comprises the following steps that monitoring information is sent to a cluster head by member nodes in a cluster in a TDMA time slot, and the member nodes detect a channel after sending the information: the invention can lead the average routing overhead of the whole network to be converged quickly.
Description
Technical Field
The invention relates to a sensor data acquisition method based on a self-adaptive clustering network, and belongs to the technical field of network protocols and topology control.
Background
The self-adaptive clustering network has very wide application prospects in the aspects of military and national defense, environmental monitoring and the like, and is a sensor network integrating three technologies of a sensor, a micro-electromechanical system and a network, and has very wide application prospects in the aspects of environmental monitoring and the like.
However, the uncertainty of the external environment often results in the need of hundreds of sensors to work in cooperation, so the research on the sensor network formed by the large-scale wireless integrated network sensor nodes is considered as a challenging hot research topic in this century. Compared with the traditional Adhoc network, the sensor network has the following characteristics: 1) nodes are distributed densely; 2) the node is easy to lose efficacy; 3) node resources (energy, storage and computational power, etc.) are limited; 4) since a large number of nodes do not necessarily have global information. Therefore, reactive or pre-routing protocols such as DSR and DSDV, which are commonly used in Adhoc networks, cannot be directly applied to sensor networks, and new algorithms must be studied for their characteristics. The invention provides a sensor data acquisition method based on a self-adaptive clustering network.
Disclosure of Invention
The invention designs and develops a sensor data acquisition method based on a self-adaptive clustering network, which can realize rapid convergence and ensure that the average routing overhead of the whole network is rapidly converged, and the rapid convergence of the self-adaptive network is further applied to water quality prediction modeling and material noise cancellers.
The technical scheme provided by the invention is as follows:
a sensor data acquisition method based on an adaptive clustering network comprises the following steps:
step one, self-adaptive clustering stage: according to the area of the network monitoring area, the network monitoring area is equally divided into M0 sub-areas, and the change of the number of nodes in each sub-area is monitored, so that self-adaptive clustering is realized;
wherein M0 is more than or equal to 1;
step two, clustering information control is carried out, and the method comprises the following steps: cluster head weight, clustering state, destination node ID, connected cluster head set and neighbor list of each node;
step three, node clustering state transfer conditions, in the multi-hop self-organizing network, after any node receives clustering control information of a neighbor node, a self-learning function module extracts neighbor list information, clustering state information, connected cluster head information and cluster head weight information, and cluster state change information of a local network in a two-hop range of the node is analyzed according to the node clustering state transfer conditions;
step four, the node clustering state transfer process: in the multi-hop self-organizing network, a network node self-adaptively executes a clustering routing process according to the clustering role of the node and the clustering state transfer condition to finish a clustering state updating behavior;
step five, route establishment stage: after the cluster is built, the Sink transmits Rup in the current round of virtual backbone network in an out-of-cluster transmission range until all cluster heads have grades;
wherein R is more than R, R is the transmission range outside the cluster, R is the transmission range inside the cluster, the rank of Sink is 0, and the rank of the cluster head of the next hop neighbor is 1;
step six, data transmission stage: the method comprises the following steps that monitoring information is sent to a cluster head by member nodes in a cluster in a TDMA time slot, and the member nodes detect a channel after sending the information:
when communication conflict is found, the nodes with high levels stop sending data, a channel is given out, the nodes with low levels communicate with the cluster heads of the nodes, and data acquisition is completed;
wherein, the member node and the cluster head, and the cluster head and the base station are in single-hop communication.
Preferably, the third step includes:
cluster head replacement bars, successful cluster entry conditions, cluster head switching conditions, cluster fission conditions, cluster merging conditions and adjacent cluster merging states.
Preferably, the clustering routing process in the fourth step includes:
applying for cluster state updating behaviors of cluster entering, cluster head lifting, cluster head replacement, cluster head switching, cluster fission and cluster merging.
Preferably, the data transmission phase includes:
and 2, mi is responsible for data fusion.
It is preferable that the first and second liquid crystal layers are formed of,
the data fusion process in the step 2 comprises the following steps:
selecting a neighbor cluster head with NT being 0 and the minimum level in the neighbor table by mi to forward data;
when the level values of a plurality of neighbor cluster heads are equal and are all minimum, selecting to ensure that the CHi +2, CHi+1，CHiMaximum CHiForwarding;
when < CHi+2，CHi+1，CHiIf not, selecting a distance straight line LSink,SourceForwarding the nearest cluster head;
selecting one of the conditions for forwarding until the data reaches Sink;
wherein, CHiThe definition is expressed as the ith cluster head, i represents the level corresponding to the cluster head, and i is y, y-1.
Preferably, in the step 1, the network monitoring area is equally divided from left to right-from top to bottom.
The invention has the following beneficial effects:
(1) node networking adaptivity: after the WSNs are deployed, a large number of nodes are randomly deployed in a monitoring area, so that the situation that information interaction cannot be directly performed among some nodes necessarily exists, and if the positions of some nodes are remote, the nodes even drop into a pit, and the participation in networking is directly influenced. In this case, the nodes can be divided into two types, one type is a node set which can directly communicate with the base station, and the other type is a node set which can not directly communicate with the base station. Other nodes can be selected as agents, the transfer of information is completed by the agent nodes, so that the nodes can work normally, the resource utilization rate and the reliability of the network are improved, the scale of a network monitoring area is enlarged, and the nodes have stronger self-adaptability in the networking process.
(2) Self-adaptability of node energy: generally, the lifetime of a network is determined by the energy consumption rate of the nodes, and different criteria exist for measuring the lifetime of the network. At present, a common measurement method is to declare network death when nodes in a network cannot normally form a network and work after certain energy consumption occurs. In this way, if the energy difference between any node and its neighbor nodes is analyzed, if the difference is large, an unbalanced energy consumption method is adopted for clustering.
(3) Adaptivity of node transmission radius: the related experiment shows that the power consumption of the CPU to execute the 3 Mb command and the power consumption of the communication to transmit the 1Kb packet on the 100m communication line are almost the same. Therefore, the remote data transmission should be reduced as much as possible while focusing on the fusion processing of the local data, and the communication energy consumption burden is reduced. Therefore, the node automatically adjusts the suitability of the transmission radius according to the distance between the node and the neighbor node, and can avoid unnecessary energy loss caused by redundant transmission radius.
(4) Adaptivity of inter-cluster routing: considering the characteristics of the WSNs, in the process of constructing the inter-cluster route, reasonable calculation is needed, the selection range of the relay node is accurately determined, and a multi-hop route with the minimum energy consumption is conveniently formed. In addition, the energy consumption rate of the nodes should be noted, because the cluster head which is closer to the base station is often subjected to the data forwarding task for a plurality of times, so that the energy consumption rate is higher.
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Fig. 1 is a flowchart of a sensor data acquisition method based on an adaptive clustering network according to the present invention.
Fig. 2 is a flow chart of node clustering state transition according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1-2, the present invention provides a sensor data acquisition method based on an adaptive clustering network, including: the method comprises the following steps of self-adaption clustering stage, clustering control information, node clustering state transfer conditions, node clustering state transfer process, route establishment stage and data transmission stage, and specifically comprises the following steps:
step one, a self-adaptive stage: during initialization, according to the size of a network monitoring area, the network monitoring area is equally divided into M0 sub-areas according to the principle of from left to right to top to bottom, all nodes in each sub-area form a cluster, self-adaptive clustering is realized by monitoring the number change of the nodes in each sub-area, and the clustering control information is utilized to interactively trigger the network nodes to complete self-adaptive clustering, so that the situation that the energy attenuation of part of the nodes is too fast due to uneven cluster head distribution and unreasonable cluster scale, and the whole service life of the network is shortened is prevented;
wherein M0 is more than or equal to 1;
the design principles of the adaptive clustering scheme include:
Wi＝c1Di+c2Numi+c3ERi+c4Energyi+c5Dij
In the formula, c1、c2、c3、c4、c5Is a weight factor, and
Di＝|di-M |, where diIs niM is the maximum number of nodes in the cluster that can be processed by the cluster head;
Numiis niThe times of making cluster heads;
ERiis niEnergy consumption Rate (EnergyExpenddiureRate) and ERi＝Energyi/round；
EnergyiIs niThe energy used;
dijrepresents niThe first cluster head n when bonding with the clusterjThe distance between them;
from the above, it is easy to know that the smaller the weight, the more suitable the node is for serving as a cluster head;
step 2, passing niCalculating WiThen, broadcasting NIP to the adjacent street by using an intra-cluster transmission range r (which refers to the transmission range when the cluster head communicates with the intra-cluster nodes), recording related contents into NT by a receiving node and determining the degree of the receiving node;
step two, clustering control information: the cluster head weight value, the clustering state and the target node ID of the node, a connected cluster head set and a neighbor list are included;
the clustering state comprises 7 states of applying for clustering, cluster members, cluster heads, cluster head replacement, cluster head switching, cluster fission and cluster combination, and the clustering state and the target node ID indicate the clustering role of the neighbor node and the clustering operation being executed by the neighbor node and the target node matched with the neighbor node to complete the clustering operation;
step three, node clustering state transfer conditions: in the multi-hop self-organizing network, after any node receives clustering control information of a neighbor node, a self-learning function module extracts neighbor list information, clustering state information, connected cluster head information and cluster head weight information, and analyzes clustering state change information of a local network in a two-hop range of the node according to a node clustering state transfer condition, and the method comprises the following steps:
cluster head replacement conditions: the neighbor node of the node and the set formed by the node are a true superset of the set formed by a certain neighbor cluster head and the neighbor node;
successful clustering conditions: the clustering state of the cluster head appointed by the node is the cluster head, and the neighbor list information of the cluster head comprises the ID of the node;
cluster head switching conditions: all the neighbor cluster heads of the node are communicated, and the union of all the neighbor cluster heads and the neighbor nodes thereof is a superset of the set formed by the node and the neighbor nodes of the node;
clustering fission conditions: the node is a cluster head and receives a new cluster entering application, and the sum of the number of the current cluster members and the number of the new cluster entering members reaches the preset maximum cluster member capacity limit;
clustering and merging conditions: the minimum node ID in the connected cluster head set of the adjacent node exists in the current control period and is not equal to the minimum node ID in the connected cluster head set of the node;
adjacent cluster merging state: the clustering state of the cluster heads with the adjacent clusters in the current control period is clustering combination, and the judgment result of the adjacent cluster combination state is that the adjacent clusters are in the combination state;
step four: node clustering state transfer flow: in the multi-hop self-organizing network, a network node executes a clustering routing process in a self-adaptive manner according to the clustering role of the node and the clustering state transfer condition, completes clustering state updating behaviors such as applying for entering a cluster, lifting a cluster head, replacing the cluster head, switching the cluster head, splitting the cluster, merging the cluster and the like, and optimizes a whole network clustering routing structure as shown in fig. 2;
step five: route establishment phase: after the cluster is built, the Sink transmits Rup in the current round of virtual backbone network in an out-of-cluster transmission range R (which refers to the maximum transmission range of nodes and is used for communication between cluster heads and between the cluster heads and the Sink, R is larger than R), the rank of the Sink is 0, the rank of the next-hop neighbor cluster head is 1, and so on until all cluster heads have the rank;
step six: and (3) a data transmission stage: the member nodes and the cluster head, and the cluster head and the base station all adopt single-hop communication. And the member nodes in the cluster send monitoring information to the cluster head in the TDMA time slot of the member nodes. In order to avoid TDMA time slot conflicts of member nodes in different clusters, the member nodes detect the channel after sending information, if communication conflicts are found, the nodes with high levels stop sending data, the channel is given out, the nodes with low levels preferentially communicate with the cluster heads of the members, and the nodes with low levels are ensured to successfully communicate with the cluster heads of the members as soon as possible.
Wherein, the data transmission stage comprises:
step 2, mi is responsible for data fusion;
step 2.1, selecting the neighbor cluster head with NT 0 and minimum level in the neighbor table by the mi to forward data;
step 2.2, when the levels of a plurality of neighbor cluster heads are equal and are all the minimum, selecting to ensure that the < CHi+2，CHi+1，CHiMaximum CHiForwarding, CHiDefining as the ith cluster head;
when < CHi+2，CHi+1，CHiIf not, selecting a distance straight line LSink,SourceForwarding the nearest cluster head;
when a plurality of records meeting the conditions exist, selecting one of the conditions to forward until the data reaches the Sink;
wherein, i represents the level corresponding to the cluster head, and i is y, y-1.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (6)
1. A sensor data acquisition method based on a self-adaptive clustering network is characterized by comprising the following steps:
step one, self-adaptive clustering stage: according to the area of the network monitoring area, the network monitoring area is equally divided into M0 sub-areas, and the change of the number of nodes in each sub-area is monitored, so that self-adaptive clustering is realized;
wherein M0 is more than or equal to 1;
step two, clustering information control is carried out, and the method comprises the following steps: cluster head weight, clustering state, destination node ID, connected cluster head set and neighbor list of each node;
step three, node clustering state transfer conditions, in the multi-hop self-organizing network, after any node receives clustering control information of a neighbor node, a self-learning function module extracts neighbor list information, clustering state information, connected cluster head information and cluster head weight information, and cluster state change information of a local network in a two-hop range of the node is analyzed according to the node clustering state transfer conditions;
step four, the node clustering state transfer process: in the multi-hop self-organizing network, a network node self-adaptively executes a clustering routing process according to the clustering role of the node and the clustering state transfer condition to finish a clustering state updating behavior;
step five, route establishment stage: after the cluster is built, the Sink transmits Rup in the current round of virtual backbone network in an out-of-cluster transmission range until all cluster heads have grades;
wherein R is more than R, R is the transmission range outside the cluster, R is the transmission range inside the cluster, the rank of Sink is 0, and the rank of the cluster head of the next hop neighbor is 1;
step six, data transmission stage: the method comprises the following steps that monitoring information is sent to a cluster head by member nodes in a cluster in a TDMA time slot, and the member nodes detect a channel after sending the information:
when communication conflict is found, the nodes with high levels stop sending data, a channel is given out, the nodes with low levels communicate with the cluster heads of the nodes, and data acquisition is completed;
wherein, the member node and the cluster head, and the cluster head and the base station are in single-hop communication.
2. The method for acquiring sensor data based on the adaptive clustering network according to claim 1, wherein the third step comprises:
cluster head replacement bars, successful cluster entry conditions, cluster head switching conditions, cluster fission conditions, cluster merging conditions and adjacent cluster merging states.
3. The method for acquiring sensor data based on the adaptive clustering network according to claim 2, wherein the clustering routing process in the fourth step comprises:
applying for cluster state updating behaviors of cluster entering, cluster head lifting, cluster head replacement, cluster head switching, cluster fission and cluster merging.
4. The adaptive clustering network based sensor data collection method of claim 3, wherein the data transmission phase comprises:
step 1, the Source transmits the data from the mobile phone to the cluster head mi；
Step 2, miAnd (4) taking charge of data fusion.
5. The method of claim 4, wherein the adaptive clustering network-based sensor data collection method,
the data fusion process in the step 2 comprises the following steps:
miselecting a neighbor cluster head with NT being 0 and the minimum level in the neighbor table to forward data;
when a plurality of neighbor cluster heads have equal and minimum level values, selecting to ensure that the CHi+2，CHi+1，CHiMaximum CHiForwarding;
when < CHi+2，CHi+1，CHiIf not, selecting a distance straight line LSink,SourceForwarding the nearest cluster head;
selecting one of the conditions for forwarding until the data reaches Sink;
wherein, CHiThe definition is expressed as the ith cluster head, i represents the level corresponding to the cluster head, and i is y, y-1.
6. The method for acquiring the sensor data based on the adaptive clustering network as claimed in claim 5, wherein the network monitoring area is equally divided from left to right-from top to bottom in the step 1.
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