CN114071631A - Distributed sensor network data fusion method and system - Google Patents
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Abstract
The invention provides a distributed sensor network data fusion method and a system, comprising the following steps: acquiring a plurality of sensor nodes and sink nodes; acquiring a plurality of cluster head nodes and a plurality of cluster internal nodes based on a preset node data fusion algorithm; based on a distributed clustering fusion algorithm for improving the residual energy and distance, respectively carrying out data fusion on a plurality of cluster internal nodes and a plurality of cluster head nodes to obtain cluster head fusion data and cluster head fusion data; based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink node. According to the invention, through a low-energy-consumption clustering algorithm based on the improvement of residual energy and distance, cooperative nodes are randomly selected by the cluster heads in each round of data fusion process, and the intermediate nodes are matched with the cluster heads to perform data protection fusion, so that the calculated amount and the communication amount of the nodes are effectively reduced, and finally, the calculated amount, the communication amount and the fusion precision are greatly improved.
Description
Technical Field
The invention relates to the technical field of communication engineering analysis and verification, in particular to a distributed sensor network data fusion method and system.
Background
The modern communication network and the high-quality wireless communication network bring great convenience to the modern society, and the construction quality of the wireless network is always a key concern. Among them, a Wireless Sensor Network (WSN) is a multi-hop ad hoc Network system, which not only provides an attacker with opportunities for eavesdropping and tampering data, but also causes unnecessary bandwidth waste, and therefore, cross research on data security and data fusion of the WSN has gradually become one of the current research hotspots.
With the continuous and intensive research, researchers have proposed various privacy protection data fusion methods for wireless sensor networks, and although these algorithms also perform fusion processing on information collected by wireless sensors and then transmit the information to users, the purpose of fusion is to obtain data information capable of more accurately reflecting an observed object so as to achieve better understanding of the observed data object, and the energy efficiency of sensor nodes is not taken as a primary optimization target.
Disclosure of Invention
The invention provides a distributed sensor network data fusion method and a distributed sensor network data fusion system, which are used for solving the defect that the energy efficiency of a sensor node is not systematically optimized aiming at a wireless sensor network in the prior art.
In a first aspect, the present invention provides a distributed sensor network data fusion method, including:
acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set;
acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm;
respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data;
based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink nodes.
Further, the obtaining a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm specifically includes:
preprocessing the nodes in the clusters based on a time-series data fusion algorithm;
and preprocessing the plurality of cluster head nodes based on a data fusion algorithm of matrix analysis.
Further, the preprocessing the plurality of intra-cluster nodes by the time-series-based data fusion algorithm specifically includes:
acquiring a preset time sequence, and dividing the preset time sequence into a preset number of segments, wherein each segment comprises single data or two adjacent data;
extracting any two adjacent segments in the preset segment number, and respectively calculating the merging cost of any two adjacent segments;
and selecting two segments which meet preset merging conditions and have the minimum merging cost for merging until all the segments which meet the preset merging conditions are merged.
Further, the matrix analysis-based data fusion algorithm preprocesses the plurality of cluster head nodes, and specifically includes:
determining a confidence distance measure and a confidence distance matrix, and respectively determining a relation matrix and an optimal fusion number;
and realizing the optimal fusion of data based on the confidence distance measure, the confidence distance matrix, the relation matrix and the optimal fusion number.
Further, the distributed clustering fusion algorithm based on the improved residual energy and distance performs data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes respectively to obtain cluster head fusion data and cluster head fusion data, and specifically includes:
initializing the plurality of cluster internal nodes and the plurality of cluster head nodes respectively;
after the first round of cluster selection and the cluster grouping are executed, each node saves current residual energy, next round of cluster selection and cluster grouping are executed, the current residual energy and the next residual energy are compared, if the next residual energy of any node is smaller than the product of the current residual energy and a preset proportion, cluster selection and cluster grouping are restarted, and if not, the current stable state is maintained.
Further, the plurality of intra-cluster nodes and the plurality of cluster head nodes are respectively initialized, specifically including:
marking the plurality of cluster nodes by adopting preset cluster marks respectively, and marking the plurality of cluster head nodes by adopting preset cluster head marks;
by inquiring node mark bits, the plurality of cluster internal nodes and the plurality of cluster head nodes respectively acquire respective corresponding data fusion algorithms;
each cluster head node sends a query command to a corresponding cluster node, and confirms the state of the corresponding cluster node;
the corresponding intra-cluster node returns a confirmation message, enters a monitoring state and starts a timer;
and after receiving the confirmation message, each cluster head node carries out a low energy consumption mode and waits for the corresponding cluster internal node to carry out data transmission.
Further, based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink node, which specifically includes:
based on a time heartbeat mechanism, acquiring the time sequence length of data acquisition of the plurality of cluster nodes, data fusion time, data transmission time to a cluster head node and a transmission time slot allocated to each cluster node by the cluster head node;
acquiring a node closest to a cluster head node, calculating the distance between the closest node and the transmission time of the closest node, distributing a time sequence to the closest node, and acquiring the fusion time of the closest node based on the time sequence;
respectively calculating to obtain a time sequence corresponding to each intra-cluster node, acquiring data and performing data fusion by each intra-cluster node according to the corresponding time sequence, and transmitting the fused data to a cluster head node;
and after all the intra-cluster nodes finish the current round of data transmission, calculating to obtain the time interval from the next round of data transmission according to the current intra-cluster node, the fusion time of the current intra-cluster node and the transmission time from the current intra-cluster node to the cluster head node.
In a second aspect, the present invention further provides a distributed sensor network data fusion system, including:
the acquisition module is used for acquiring a plurality of sensor nodes and sink nodes from the distributed sensor network node set;
the first processing module is used for obtaining a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm;
the second processing module is used for respectively carrying out data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data;
and the third processing module is used for transmitting the in-cluster fusion data to the corresponding cluster head nodes by the plurality of in-cluster nodes based on a preset fusion mechanism, and transmitting the cluster head fusion data to the sink node by the plurality of cluster head nodes.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the distributed sensor network data fusion method described in any one of the above are implemented.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which computer program, when executed by a processor, performs the steps of the distributed sensor network data fusion method as described in any one of the above.
According to the distributed sensor network data fusion method and system provided by the invention, the cooperative nodes are randomly selected by the cluster heads in each round of data fusion process through a low-energy-consumption clustering algorithm based on the improved residual energy and distance, and the data is protected and fused by matching the intermediate nodes with the cluster heads, so that the calculated amount and the communication amount of the nodes are effectively reduced, and finally, the calculated amount, the communication amount and the fusion precision are greatly improved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a distributed sensor network data fusion method provided by the present invention;
FIG. 2 is a block diagram of a wireless sensor network provided by the present invention;
FIG. 3 is a block diagram of a low energy clustering implementation of the present invention to improve the remaining energy and distance;
FIG. 4 is a schematic diagram of a low energy distributed embodiment of the present invention for improving the remaining energy and distance;
FIG. 5 is a schematic diagram of a data transmission process of a member node in a cluster according to the present invention;
FIG. 6 is a graph comparing the number of nodes provided by the present invention with the FND;
FIG. 7 is a graph comparing the number of nodes provided by the present invention versus LNDs;
FIG. 8 is a comparison graph of the life cycle of the network provided by the present invention;
FIG. 9 is a comparison of the number of nodes versus energy consumption provided by the present invention;
FIG. 10 is a schematic structural diagram of a distributed sensor network data fusion system provided in the present invention;
fig. 11 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
The invention provides a low-energy-consumption distributed algorithm strategy based on improved residual energy and distance, which is characterized in that data security fusion operation is advanced to a data acquisition node, data of a certain time is accumulated and is locally subjected to data security fusion, the data is transmitted to a cluster head node, and the cluster head node performs distributed security fusion on data from different member nodes again. Finally, qualitative analysis shows that the distributed data security fusion strategy can reduce the redundancy of data and reduce the data transmission quantity on the premise of ensuring the data security, thereby further reducing the computational dimension and the communication quantity of network nodes.
Fig. 1 is a schematic flow chart of a distributed sensor network data fusion method provided by the present invention, as shown in fig. 1, including:
s1, acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set;
s2, acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm;
s3, respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and distance to obtain cluster head fusion data and cluster head fusion data;
s4, based on a preset fusion mechanism, the cluster nodes transmit the cluster fusion data to the corresponding cluster head nodes, and the cluster head nodes transmit the cluster head fusion data to the sink node.
Specifically, the distributed clustering fusion algorithm for improving the residual energy and the distance considers that the task of the cluster head node is heavy, the cluster head node is responsible for broadcasting messages to all nodes, receiving data transmitted by member nodes in the cluster after clustering, fusing the related data, and finally sending fused useful information to the base station by the cluster head node, so that the base station can monitor the remote environment change and the like in real time. And through message sending triggering, the node is converted into a cluster head node after receiving the information and continues to send the information to nearby nodes, and other network nodes in the distributed network are in a waiting state until receiving the information sent from the nearby nodes, or else, the node is added into the cluster after receiving the information sent by any cluster head. After the topological structure is formed, the key distribution of the nodes is carried out, and a random key pre-distribution method is adopted according to the safe multiparty calculation principle, so that the probability of cracking the key of the nodes can be reduced as much as possible, and the safety protection of data is higher under the condition that the base station and the nodes are not trusted. Therefore, the cluster head nodes usually consume more energy than the non-cluster head nodes, and how to reduce the energy consumption of the cluster head nodes and balance the energy of the nodes in the network is very important for prolonging the life cycle of the whole network.
The optimal cluster number is obtained on the basis of analyzing the energy consumed by the network, on one hand, the calculated amount of the nodes in the cluster is considered, on the other hand, the calculated amount of the cluster head nodes is also considered, the calculated amount comprises arithmetic operations, namely noise interference processing, encryption and decryption operations and data fusion operations, the basic idea is that after the clustering is finished, the nodes in the cluster start to sense data and carry out fusion operation, and the selected cluster head number enables the total energy consumed by each round of network to be minimum.
All intra-cluster nodes execute the following steps in the intra-cluster fusion process: after each node collects data, interference operation is carried out by utilizing the public numerical value of the nodes in the cluster and a plurality of private random values, namely, the data are converted into a polynomial of degree 2. In the whole calculation process, each node encrypts a plurality of noise interference values and sends the noise interference values to the cluster head nodes and the intermediate nodes, and after the cluster head nodes and the intermediate nodes receive interference processing results sent by other nodes, the cluster head nodes and the intermediate nodes decrypt the values by using a shared key, and then arithmetic operation is carried out to combine polynomials, so that the calculation formula of the optimal cluster head number is as follows:
where N represents the total number of nodes, the nodes are uniformly distributed in an M × M region, dtoBSIs the distance, ε, from the cluster head node to the signal sourcefs＝10pJ/bit/m2，εamp＝0.0013pJ/bit/m4Is a constant.
The optimal number of cluster heads obtained is also a more ideal number due to the assumption of over-idealization, and many other factors need to be considered in the application. By comprehensively considering a plurality of factors such as the area of an observation region, the total number of nodes, the position of a signal source and the like, and adopting a typical energy consumption model, the model is based on two assumptions:
(1) all nodes in the wireless communication network are completely the same;
(2) the energy consumption of the network information in all directions is the same.
The wireless communication sensor node sends and receives lbit information d distance, and the consumed energy is respectively as follows:
ER(l)＝ER-elec(l)＝lEelec
if the distance between the receiver and the transmitter is less than the critical value d0Then the vector model is used; if it is larger than the critical value d0Then the multiple choice model is used. EelecIs the energy consumed by the transmit and receive circuits, which depends on factors such as digital coding, modulation, and filtering of the signal, in this model both the transmit and receive signals are identical.
Firstly, assuming that N total nodes are approximately uniformly distributed, multiple factors need to be considered, and especially when wireless sensor nodes are generally deployed, it is impossible to ensure that each node is uniformly distributed, so that the nodes have the same interval and density, and the number of nodes which cannot be placed on the same area is too different, or some nodes are too dense and some nodes are too loose, which is not only unfavorable for collecting information, but also unfavorable for clustering, routing and transmission, and also can waste a large amount of energy, but generally, we consider that the nodes are uniformly distributed in an M × M area.
If the wireless sensor communication network comprises k clusters, the number of nodes in each cluster is N/k, wherein each cluster comprises a cluster head node and N/k-1 member nodes, and the number of bits contained in each data transmission in the wireless communication process is l.
The energy formula of the wireless signal source model is adopted, and the distance between a signal source and a cluster head is long in general, so that the energy consumption is calculated by adopting a multipath model. The energy consumed by each cluster head node comprises the following parts: and receiving the data packet of the member node, performing data fusion processing, and sending the data packet subjected to the fusion processing to a remote receiving end. The energy consumed by one cluster head node can be expressed as:
generally, the non-cluster-head nodes are closer to the respective cluster-head nodes, so the energy consumption for sending data can use a free space model, and the energy consumed by each non-cluster-head node comprises two parts: receiving (collecting) the data packet and sending the data packet to the cluster head node, the energy consumed by a non-cluster head node can be expressed as:
Enon-CH＝lEelec+lεfsd2 toCH
wherein d istoBSIs the distance from the cluster head node to the base station, dtoCHIndicating the distance from a non-cluster head node to a cluster head node, EDAEnergy consumed for data fusion, EelecRepresenting the energy consumed by the transmitting and receiving circuits, ∈fs，εampBoth represent the signal source enhanced amplification factor, and two models are respectively applied, and the parameters are constants which are generally respectively taken as EDA＝5nJ/bit/signal，Eelec＝50nJ/bit，εfs＝10pJ/bit/m2，εamp＝0.0013pJ/bit/m4，dtoCHIndicating the distance from the non-cluster head node to the cluster head node.
The energy consumed by all nodes in each cluster when lbit data is transmitted in each round of circulation consists of two parts: energy consumed by cluster head node ECHAnd energy consumed by member nodes Enon-CHThus, the total energy consumed by the entire network per round is obtained as:
after finishing, obtaining:
ETotal＝l[(2N-k)Eelec+NEDA+kεampd4 toBS+(N-k)εfsd2 toCH]
due to EDA，Eelec，εfs，εampThese several parameters are constants, so it is desirable to solve for ETotleThe key point is that the handle dtoBSAnd dtoCHAnd (5) finding out. The distance is solved below by the distribution density and mathematical expectation.
The entire monitoring region R is approximately denoted M2The distance between the base station and the sensor node is expected to be:
this distance expectation depends mainly on the position coordinates (x, y) of the base stations, which represent the position coordinates of the sensor nodes, defined by the expectation, dtoBSIs equal to E [ d ]toBS]。
The area occupied by each cluster is approximately denoted as M2K, then the distance d from the cluster head node to the member node is defined according to the mathematical expectationtoBSThe expectation of the square of (d) should be:
where ρ (x, y) is the distribution density of sensor nodes in each cluster, since the optimal cluster structure should be a circle, it is assumed that this region is a circle with a radius of
And by corresponding coordinate transformation:
where ρ (r, θ) is constant with respect to r, θ, and ρ (r, θ) is 1/(M)2K), thus obtaining:
let ETotle＝fs(k) To f fors(k) The first derivative of k is taken and made equal to zero, i.e.:
since the second derivative f of the function to ks (2)(k) Is always positive, so the function has a minimum value, which is the optimal cluster head number kopt(is a function of N, M):
when k is the above value, the network consumesEnergy ETotalIs the minimum value.
The clustering method commonly used by most network protocols is that once cluster head nodes are selected, they actively send information that they become cluster heads to all nodes. According to the strength of the emission source, the node selects a cluster to be added, and informs a corresponding cluster head node; or the node selects which cluster to join according to the distance between the node and the cluster head, and the distance calculation method is
The following considerations are made by taking into account the problems of cluster head cycling and re-clustering: and when the cluster head node continuously works for a period of time or the residual energy of the cluster head node is lower than a certain value, announcing that the cluster is disassembled, and then carrying out cluster selection and cluster grouping again according to a clustering algorithm. It is not desirable to reselect a cluster every time data transmission (from a node to a cluster head to a base station, once per round), and frequent cluster selection increases energy consumption.
According to the invention, through a low-energy-consumption clustering algorithm based on the improvement of residual energy and distance, cooperative nodes are randomly selected by the cluster heads in each round of data fusion process, and the intermediate nodes are matched with the cluster heads to perform data protection fusion, so that the calculated amount and the communication amount of the nodes are effectively reduced, and finally, the calculated amount, the communication amount and the fusion precision are greatly improved.
Based on the above embodiment, the obtaining a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on the preset node data fusion algorithm specifically includes:
preprocessing the nodes in the clusters based on a time-series data fusion algorithm;
and preprocessing the plurality of cluster head nodes based on a data fusion algorithm of matrix analysis.
The time-sequence-based data fusion algorithm is used for preprocessing the nodes in the clusters, and specifically comprises the following steps:
acquiring a preset time sequence, and dividing the preset time sequence into a preset number of segments, wherein each segment comprises single data or two adjacent data;
extracting any two adjacent segments in the preset segment number, and respectively calculating the merging cost of any two adjacent segments;
and selecting two segments which meet preset merging conditions and have the minimum merging cost for merging until all the segments which meet the preset merging conditions are merged.
The matrix analysis-based data fusion algorithm is used for preprocessing the cluster head nodes, and specifically comprises the following steps:
determining a confidence distance measure and a confidence distance matrix, and respectively determining a relation matrix and an optimal fusion number;
and realizing the optimal fusion of data based on the confidence distance measure, the confidence distance matrix, the relation matrix and the optimal fusion number.
Specifically, for the wireless sensor network, the network structure is shown in fig. 2, and includes: the wireless sensor network consists of a large number of sensor nodes and a convergent node (base station node), and the specifications of the sensor nodes are the same; the network is a clustering structure, the network forms a plurality of clusters, and the cluster structure is not changed once determined; each cluster is provided with only one cluster head node, and the cluster contains a large number of member nodes; single-hop communication is carried out between the member nodes in the cluster and the cluster head node; the base station only communicates with the cluster head nodes of each cluster and cannot receive data from the member nodes; the cluster head node can obtain the position information of all member nodes in the cluster.
Further, in the clustered network structure, the member nodes and the cluster head nodes in the cluster play different roles, and the assignment work is also different, which affects the selection of the data fusion algorithm in the proposed hierarchical data fusion strategy. The working states of the nodes are respectively as follows:
first, member nodes in a cluster: initializing, automatically entering a monitoring state, starting to acquire data, transmitting data information to a cluster head node in a wireless mode, simultaneously entering monitoring, and starting the next round of data acquisition; and considering the time factor to realize data fusion on the member nodes in the cluster, and adopting a data fusion algorithm based on a time sequence as a fusion algorithm on the member nodes in the cluster.
Here, the time-series based data fusion algorithm is as follows:
1) dividing the whole time sequence into m (m is more than or equal to n/2) segments, wherein each segment comprises one data or two adjacent data;
2) respectively calculating the merging cost of two adjacent segments, wherein the merging cost is mainly determined by the following two factors: firstly, errors are caused after two sections are combined; secondly, the number of data contained in the corresponding sub-time sequence of the segments after combination;
3) selecting two segments which meet the requirement of merging conditions and have the minimum merging cost for merging;
4) and repeating the process, and combining all the segments meeting the combination condition.
By adding a timer, the aim is to make it possible for the node to control the size of the time sequence itself.
Secondly, clustering the head nodes: the method comprises the steps of initializing, then entering a low-energy consumption mode, enabling cluster head nodes not to be responsible for collecting data, only receiving data transmitted by member nodes in a cluster, enabling the cluster head nodes to enter a receiving state when the data arrive, receiving and storing the data, and performing data fusion after receiving the data of all the cluster nodes, wherein the data fusion is used in a general energy consumption-based network design. And after the data fusion processing, sending the data to the base station. The data fusion of the cluster head nodes is greatly different from that of the member nodes in the cluster, and the member nodes in the cluster only fuse data collected within a certain time, which can be determined according to the storage capacity of the nodes. The cluster head node is to merge data transmitted by member nodes in the cluster, the cluster generally includes a large number of nodes, and relatively speaking, the workload of the cluster head node is much larger than that of the member nodes in the cluster, and much more energy is consumed, so the complexity and the merging effect of the data merging itself must be considered. A data fusion algorithm based on matrix analysis is selected, so that on one hand, the algorithm is simpler, the calculated amount is less, and the time consumption is less; on the other hand, the fusion precision is comparable to the data fusion result of the D-S evidence combination.
Here, the data fusion algorithm based on matrix analysis is as follows:
1) determining a confidence distance measure and a confidence distance matrix;
2) determining a relation matrix and an optimal fusion number;
3) and (4) optimally fusing data.
Based on any of the above embodiments, the distributed clustering fusion algorithm based on improved residual energy and distance performs data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes, respectively, to obtain cluster head fusion data and cluster head fusion data, and specifically includes:
initializing the plurality of cluster internal nodes and the plurality of cluster head nodes respectively;
after the first round of cluster selection and the cluster grouping are executed, each node saves current residual energy, next round of cluster selection and cluster grouping are executed, the current residual energy and the next residual energy are compared, if the next residual energy of any node is smaller than the product of the current residual energy and a preset proportion, cluster selection and cluster grouping are restarted, and if not, the current stable state is maintained.
Wherein, the plurality of intra-cluster nodes and the plurality of cluster head nodes are respectively initialized, and the method specifically comprises the following steps:
marking the plurality of cluster nodes by adopting preset cluster marks respectively, and marking the plurality of cluster head nodes by adopting preset cluster head marks;
by inquiring node mark bits, the plurality of cluster internal nodes and the plurality of cluster head nodes respectively acquire respective corresponding data fusion algorithms;
each cluster head node sends a query command to a corresponding cluster node, and confirms the state of the corresponding cluster node;
the corresponding intra-cluster node returns a confirmation message, enters a monitoring state and starts a timer;
and after receiving the confirmation message, each cluster head node carries out a low energy consumption mode and waits for the corresponding cluster internal node to carry out data transmission.
Specifically, after the sensor nodes are disseminated and the network cluster structure is determined, the cluster head nodes and the member nodes in the cluster need to complete initialization work on the nodes:
1) the cluster head node and the member nodes in the cluster are distinguished by a node zone bit, the mark Si of the cluster head node is set to be 1, and the mark Si of the member nodes in the cluster is set to be 0;
2) loading the corresponding data fusion algorithms through the flag bits of the query nodes;
3) the cluster head node sends a query command to the member nodes in the cluster to see whether the member nodes are ready;
4) the member nodes in the cluster return a confirmation message, enter a monitoring state and simultaneously open a timer;
5) and after receiving the confirmation message, the cluster head node enters a low energy consumption mode and waits for data transmission of member nodes in the cluster.
The method comprises the following steps of integrating two levels of data of member nodes and cluster head nodes in a cluster, and starting the wireless sensor network after initialization.
For member nodes within a cluster: the cluster head node sends a command when the timer reaches a preset time, the cluster head node performs data fusion on the collected data, sends a data result to the cluster head node after the fusion is completed, and starts a new round of data collection and timing.
For the cluster head node, in order to save energy, the cluster head node enters a low energy consumption mode after initialization until the member nodes in the cluster transmit data, then data fusion is started, and after the fusion processing is finished, the data result is sent to the base station while continuing to wait and receive the arrival of the data of the member nodes in the cluster.
For the base station, after receiving the data from the cluster head node, the data can be directly transmitted to the required user, or the data can be transmitted to the user after being fused in a decision-making way.
As shown in fig. 3, the low-energy-consumption clustering fusion method for improving the remaining energy and distance not only can save the energy consumption of sending data by member nodes in a cluster, but also greatly saves the energy consumption of receiving data by a cluster head node. Because the data fusion of the member nodes in the cluster is carried out, the data is not sent once every time the data is collected, and the data is sent once in a time period through the fusion, so that the times of data receiving and data fusion of the cluster head nodes are reduced, the energy consumption of the nodes is saved, and the life cycle of the network is effectively prolonged.
It will be appreciated that the strategy is implemented by setting specific numerical proportions, as shown in figure 4. Each node stores the residual energy E of the node at the end of the previous roundn_current-1Then the data is transmitted to a receiving end through cluster selection, cluster formation and data transmission, and after one round, the residual energy E of the receiving end is storedn_currentWhen E isn_current≤0.5En_current-1Namely, more than half of the residual energy of the node is consumed in the round, cluster reselection is needed to prevent the node energy from being too low to enable the next round of operation to be normally performed. Whenever a node E appearsn_current≤0.5En_current-1The cluster is reselected.
Based on any of the above embodiments, based on the preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink node, which specifically includes:
based on a time heartbeat mechanism, acquiring the time sequence length of data acquisition of the plurality of cluster nodes, data fusion time, data transmission time to a cluster head node and a transmission time slot allocated to each cluster node by the cluster head node;
acquiring a node closest to a cluster head node, calculating the distance between the closest node and the transmission time of the closest node, distributing a time sequence to the closest node, and acquiring the fusion time of the closest node based on the time sequence;
respectively calculating to obtain a time sequence corresponding to each intra-cluster node, acquiring data and performing data fusion by each intra-cluster node according to the corresponding time sequence, and transmitting the fused data to a cluster head node;
and after all the intra-cluster nodes finish the current round of data transmission, calculating to obtain the time interval from the next round of data transmission according to the current intra-cluster node, the fusion time of the current intra-cluster node and the transmission time from the current intra-cluster node to the cluster head node.
Specifically, a plurality of data packets are fused at the source node and then transmitted, and the fusion is performed again at the cluster head, which has the obvious disadvantage of large delay. Due to the fact that distances from member nodes in the cluster to the cluster head are different, time for transmitting data is different, and therefore even though data fusion time of the member nodes in the cluster is equivalent, the time for the data to reach the cluster head node is quite different. In this case, having the cluster head node either wait for the data packets of the remaining nodes will increase the delay; or the time is strictly controlled, and the data packet of the member node is listed in the subsequent data fusion before arriving in the specified time, so the data fusion effect is poor.
In order to solve the delay problem caused by low energy consumption clustering for improving residual energy and distance, a time heartbeat mechanism is introduced, and the specific method is as follows:
(1) the time sequence length of the data collected by the member nodes in the cluster is siThe data fusion time is ti(depending on the length of the time series, s can be used for calculationiPresentation), the time of data transmission to the cluster head node is Ti(related to transmission distance, i.e. distance d from member node in cluster to cluster head nodei) The cluster head node allocates a transmission time slot w to each member node, the transmission time slot w represents the time required by the cluster head node to receive a data packet transmitted by a non-cluster head node, communication is allowed to be carried out with only one node in one time slot, wherein i represents the number of member nodes in the cluster, and i is 1, 2. The member nodes in the cluster transmit data from data acquisition to cluster head nodes, and the time usedThe inter-allocation is shown in fig. 5.
(2) The cluster head node calculates the distance d between the cluster head node and the member node according to the position information of the member node in the clusteri(i 1, 2.. k), the time T required for each member node to transmit data to the cluster head node can then be determinedi。
(3) Finding the nearest node x with the distance dxThe time taken for transmission is TxTo which a time series s is assignedxBecause the energy and the storage space of the sensor node are limited and the real-time requirement is generally met, the time sequence is not suitable to be too long. The fusion time t needed by the node x can be obtained from the time sequencex. After these times are determined, other nodes are referenced to the node x.
(4) As shown in FIG. 5, the time series s corresponding to the member nodes in each cluster can be obtained by the following formulax,s1,s2,…,sk-1。
s1+t1+T1＝sx+tx+Tx+w
s2+t2+T2＝s1+t1+T1+w
...
sk-1+tk-1+Tk-1＝sk-2+tk-2+Tk-2+w
(5) And the member nodes in the cluster start to acquire data and perform data fusion according to respective time sequences, and then transmit the data to the cluster head node.
(6) After one round of transmission is finished, because member nodes in each cluster have a time slot difference in time arrangement, the data transmission at the cluster head nodes can be finished in sequence. The acquisition information of the next round is automatically entered after transmission, but the time sequence from this round must be calculated according to the following calculation formula:
s1+t1+T1＝sx+tx+Tx
s2+t2+T2＝s1+t1+T1
...
sk-1+tk-1+Tk-1＝sk-2+tk-2+Tk-2
only need to keep s lateri，ti，TiThe three time sums are equal, and the member nodes in the cluster can transmit data to the cluster head node in sequence. That is, the whole process needs to be calculated twice: start and end of the first round.
The heartbeat mechanism can ensure that the data of the member nodes in the cluster are sequentially transmitted to the cluster head nodes, and the cluster head nodes can immediately perform data fusion after receiving the data, so that the speed is improved, the data fusion effect is optimized, the energy consumption of data transmission is greatly reduced, and the serious consequences of channel contention and data loss caused by the fact that the member nodes in the cluster transmit the data to the cluster head nodes at the same time are avoided.
Further performing qualitative analysis on the network, firstly analyzing energy consumption, setting k clusters in the network, and setting n clusters in each clusterkOne node, i.e. 1 cluster head node and nk1 intra-cluster member node. The intra-network data are respectively fused by adopting intra-cluster member nodes and cluster heads, each intra-cluster member node can collect a plurality of data packets within a period of time, and then fusion is carried out according to a data fusion algorithm based on a time sequence to synthesize one data packet, so that only one output data packet is provided; and after the cluster head node receives the data of the member nodes in the cluster, the data fusion based on matrix analysis is carried out, and after a time heartbeat mechanism is added, the data of the member nodes can be sequentially sent to the cluster head node. The number of data packets required to be sent in each network is
Compared with the situation that data fusion is not adopted in the network or only carried out on the cluster head nodes, the energy consumption of the network is obviously reduced.
Secondly, network congestion is reduced, time-series data fusion is performed through member nodes in the cluster, so that the original situation that data needs to be transmitted for multiple times in a period of time is changed into the situation that the data is transmitted for only once, and the contention of data independent transmission on a channel and the resource waste are effectively dispersed.
Finally, delay is caused, and due to the fact that distances between the cluster head and member nodes in the cluster are unequal, time of data reaching the cluster head nodes is not synchronous, and therefore data fusion effect on the cluster head nodes is affected, and large delay is brought. However, a time heartbeat mechanism is introduced later, so that the member nodes in the cluster can send the data packets to the cluster head in a basically close time, and the problem of originally larger delay is solved.
Based on any of the above embodiments, the simulation is performed through a virtual environment, and an NS2 tool, namely Network Simulator Version 2, is an object-oriented, discrete event-driven Network environment Simulator, and is mainly used for solving the problem in the aspect of Network research. The NS2 provides simulation of multiple protocols such as TCP, routing, multicast, etc. over a wireless or wired network, and can completely simulate the entire network environment, it implements most common network protocols and link layer models using a complete set of C + + class libraries, and can build up a model of the entire network using instances of these classes, and includes detailed implementation.
According to the hierarchical clustering routing algorithm, in order to carry out experiments and performance comparison, a hierarchical clustering routing algorithm protocol is utilized, firstly, source codes of the hierarchical clustering routing algorithm protocol are added to NS2 successfully, then, corresponding source codes of LEACH are modified respectively according to a DCHS clustering algorithm and an EDCA clustering algorithm, and finally, the LEACH protocol and the modified low-energy-consumption clustering protocol for improving residual energy and distance are compared and analyzed.
And (3) simulation environment configuration:
(1) randomly distributing 100 nodes of the same type in a 200m x 200m area, wherein the area range is an abscissa x (0-200), an ordinate y (0-200), and the coordinate position of a Sink point is Sink (50, 200);
(2) assuming that the initial energy of each node is consistent and 2 joules (J);
(3) adopting a continuous sending mode, wherein each node has 4000 bits (500 bytes) of data to be transmitted in each round;
(4)Eelec50nJ/bit, representing the power consumption of the receiver circuitry and transmitter circuitry to process 1-bit data;
εfs＝10pJ/bit/m2the power consumption of the transmitter signal amplification circuit for transmitting 1-bit data to a single area is represented;
εamp＝0.0013pJ/bit/m4the power consumption of the transmitter signal amplification circuit under the dual-path model for transmitting 1-bit data to a single area is suitable for the transmitting power of a transmitter amplifier when the distance is long;
(5) the energy consumption when the data fusion is carried out is EDAThe energy loss required by processing 1 bit of data when the cluster head node performs data fusion is represented by 5nJ/bit/signal, and in order to consider the problem of compromise between communication overhead and processing overhead, the model takes the energy loss as a parameter for statistics.
(6) The number of the nodes is from 50 to 1000, and 50 nodes are added each time, so that the relation between the algorithm performance and the network scale is convenient to know, and the relation is shown in table 1.
TABLE 1
The following are some key functions involved in the algorithm:
(1) initializing a node: initsensor ();
(2) number of nodes not dead: LiveNum ();
(3) optimal number of cluster heads per round: OptNum ();
(4) judging whether all nodes are selected once: allelect ();
(5) energy consumption of cluster head nodes: EneCluster ();
(6) energy consumption of non-cluster head nodes: EneNon ().
The following is a multidimensional verification of the service:
(1) FIG. 6 is a comparison of the time of death of the first node of a network employing a hierarchical cluster routing algorithm, DCHS and EDCA in a context of different node numbers (node numbers from 50-1000).
Experiments show that as the number of nodes increases, the death time of the first node of the hierarchical clustering routing algorithm, the DCHS and the low-energy-consumption clustering for improving the residual energy and distance tends to decrease slowly, and the death time of the first node is shorter because the increase of the number of nodes means the increase of cluster members and the energy consumption of a cluster head is larger for a clustering-based protocol. However, at node number 50, the first node to die of the low energy cluster to improve the remaining energy and distance occurs earlier, closer to DCHS, mainly due to the sparser distribution of nodes.
(2) FIG. 7 is a comparison of the dead time of all nodes in a low-energy-consumption clustered network under the environment of different node numbers (the node numbers are 50-1000) by adopting a hierarchical clustering routing algorithm, DCHS and improved residual energy and distance.
Experiments show that with the increase of the number of nodes, the FND time of the hierarchical clustering routing algorithm, the DCHS and the low-energy-consumption clustering method for improving the residual energy and distance tends to increase slowly, because the higher the number of nodes, the higher the ratio of the nodes with relatively high energy in the later life of the network, the longer the death time of all the nodes.
(3) Fig. 8 is a graph of the relationship between the number of node deaths and the network operating time (number of rounds) with a scenario of 200m × 200m and a node number of 100.
From fig. 8, it can be seen that the death time of the first node of the low energy-consumption clustering method for improving the residual energy and distance is three times as long as that of the hierarchical clustering routing algorithm, the death time of all nodes is two times as long as that of LEACH, and the difference from DCHS is not very large, but a certain energy-saving effect is achieved after improvement. This shows that the low energy clustering method for improving the remaining energy and distance is better designed in network load balancing, thereby avoiding the situation that the nodes die prematurely due to excessive energy loss.
(4) Fig. 9 is a simulation result of average energy consumption of three algorithms, namely, a hierarchical clustering routing algorithm, a DCHS algorithm, and a low-energy-consumption clustering method for improving remaining energy and distance, at different network scales (the number of nodes), which is a result of averaging each algorithm by running 10 times at each different network scale (the number of nodes is from 50 to 1000).
As can be seen from the figure, compared with the other two algorithms, the average energy consumption of the low-energy-consumption clustering method for improving the residual energy and the distance is the minimum, because it considers not only the residual energy of the nodes but also the distance from the nodes to the base station, thereby better balancing the network load, reducing the energy difference between the nodes, thereby prolonging the life cycle of the network, and basically achieving the purpose of reducing the energy consumption as much as possible and maximizing the life cycle of the network by the wireless sensor network protocol.
The invention achieves the aim of balancing network energy consumption by controlling the number of clusters and selecting nodes with residual energy as much as possible close to a base station as cluster heads, and finally utilizes the existing hierarchical clustering routing algorithm protocol to carry out analog simulation and performance analysis; the hierarchical data fusion strategy advances data fusion operation to a data acquisition node, accumulates data for a certain time, performs data fusion locally, and transmits the data to a cluster head node, wherein the cluster head node performs data fusion again from different member nodes; finally, qualitative analysis shows that the data fusion strategy can reduce the redundancy of data and reduce the data transmission quantity, thereby prolonging the life cycle of the network.
The distributed sensor network data fusion system provided by the present invention is described below, and the distributed sensor network data fusion system described below and the distributed sensor network data fusion method described above may be referred to in correspondence with each other.
Fig. 10 is a schematic structural diagram of a distributed sensor network data fusion system provided in the present invention, as shown in fig. 10, including: an acquisition module 1001, a first processing module 1002, a second processing module 1003 and a third processing module 1004
The acquisition module 1001 is configured to acquire a plurality of sensor nodes and sink nodes from a distributed sensor network node set; the first processing module 1002 is configured to obtain a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm; the second processing module 1003 is configured to perform data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes respectively based on a distributed clustering fusion algorithm for improving remaining energy and distance, so as to obtain cluster head fusion data and cluster head fusion data; the third processing module 1004 is configured to, based on a preset fusion mechanism, transmit the in-cluster fusion data to the corresponding cluster head node by the plurality of in-cluster nodes, and transmit the cluster head fusion data to the sink node by the plurality of cluster head nodes.
According to the invention, through a low-energy-consumption clustering algorithm based on the improvement of residual energy and distance, cooperative nodes are randomly selected by the cluster heads in each round of data fusion process, and the intermediate nodes are matched with the cluster heads to perform data protection fusion, so that the calculated amount and the communication amount of the nodes are effectively reduced, and finally, the calculated amount, the communication amount and the fusion precision are greatly improved.
Fig. 11 illustrates a physical structure diagram of an electronic device, and as shown in fig. 11, the electronic device may include: a processor (processor)1110, a communication interface (communication interface)1120, a memory (memory)1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other via the communication bus 1140. Processor 1110 may invoke logic instructions in memory 1130 to perform a distributed sensor network data fusion method comprising: acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set; acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm; respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data; based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink nodes.
In addition, the logic instructions in the memory 1130 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer being capable of executing the distributed sensor network data fusion method provided by the above methods, the method including: acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set; acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm; respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data; based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink nodes.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the distributed sensor network data fusion method provided in the above, the method comprising: acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set; acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm; respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data; based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink nodes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A distributed sensor network data fusion method is characterized by comprising the following steps:
acquiring a plurality of sensor nodes and sink nodes from a distributed sensor network node set;
acquiring a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm;
respectively performing data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data;
based on a preset fusion mechanism, the plurality of intra-cluster nodes transmit the intra-cluster fusion data to the corresponding cluster head nodes, and the plurality of cluster head nodes transmit the cluster head fusion data to the sink nodes.
2. The method according to claim 1, wherein the obtaining a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm specifically includes:
preprocessing the nodes in the clusters based on a time-series data fusion algorithm;
and preprocessing the plurality of cluster head nodes based on a data fusion algorithm of matrix analysis.
3. The distributed sensor network data fusion method according to claim 2, wherein the time-series-based data fusion algorithm preprocesses the plurality of intra-cluster nodes, and specifically comprises:
acquiring a preset time sequence, and dividing the preset time sequence into a preset number of segments, wherein each segment comprises single data or two adjacent data;
extracting any two adjacent segments in the preset segment number, and respectively calculating the merging cost of any two adjacent segments;
and selecting two segments which meet preset merging conditions and have the minimum merging cost for merging until all the segments which meet the preset merging conditions are merged.
4. The distributed sensor network data fusion method according to claim 2, wherein the preprocessing the plurality of cluster head nodes by the data fusion algorithm based on matrix analysis specifically comprises:
determining a confidence distance measure and a confidence distance matrix, and respectively determining a relation matrix and an optimal fusion number;
and realizing the optimal fusion of data based on the confidence distance measure, the confidence distance matrix, the relation matrix and the optimal fusion number.
5. The distributed sensor network data fusion method according to claim 1, wherein the distributed clustering fusion algorithm based on the improved residual energy and distance performs data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes respectively to obtain cluster head fusion data and cluster head fusion data, and specifically includes:
initializing the plurality of cluster internal nodes and the plurality of cluster head nodes respectively;
after the first round of cluster selection and the cluster grouping are executed, each node saves current residual energy, next round of cluster selection and cluster grouping are executed, the current residual energy and the next residual energy are compared, if the next residual energy of any node is smaller than the product of the current residual energy and a preset proportion, cluster selection and cluster grouping are restarted, and if not, the current stable state is maintained.
6. The distributed sensor network data fusion method according to claim 5, wherein the initializing of the plurality of cluster internal nodes and the initializing of the plurality of cluster head nodes are performed respectively, and specifically includes:
marking the plurality of cluster nodes by adopting preset cluster marks respectively, and marking the plurality of cluster head nodes by adopting preset cluster head marks;
by inquiring node mark bits, the plurality of cluster internal nodes and the plurality of cluster head nodes respectively acquire respective corresponding data fusion algorithms;
each cluster head node sends a query command to a corresponding cluster node, and confirms the state of the corresponding cluster node;
the corresponding intra-cluster node returns a confirmation message, enters a monitoring state and starts a timer;
and after receiving the confirmation message, each cluster head node carries out a low energy consumption mode and waits for the corresponding cluster internal node to carry out data transmission.
7. The distributed sensor network data fusion method according to claim 1, wherein based on a preset fusion mechanism, the cluster nodes transmit the cluster fusion data to corresponding cluster head nodes, and the cluster head nodes transmit the cluster head fusion data to the sink node, specifically including:
based on a time heartbeat mechanism, acquiring the time sequence length of data acquisition of the plurality of cluster nodes, data fusion time, data transmission time to a cluster head node and a transmission time slot allocated to each cluster node by the cluster head node;
acquiring a node closest to a cluster head node, calculating the distance between the closest node and the transmission time of the closest node, distributing a time sequence to the closest node, and acquiring the fusion time of the closest node based on the time sequence;
respectively calculating to obtain a time sequence corresponding to each intra-cluster node, acquiring data and performing data fusion by each intra-cluster node according to the corresponding time sequence, and transmitting the fused data to a cluster head node;
and after all the intra-cluster nodes finish the current round of data transmission, calculating to obtain the time interval from the next round of data transmission according to the current intra-cluster node, the fusion time of the current intra-cluster node and the transmission time from the current intra-cluster node to the cluster head node.
8. A distributed sensor network data fusion system, comprising:
the acquisition module is used for acquiring a plurality of sensor nodes and sink nodes from the distributed sensor network node set;
the first processing module is used for obtaining a plurality of cluster head nodes and a plurality of intra-cluster nodes corresponding to each cluster head node based on a preset node data fusion algorithm;
the second processing module is used for respectively carrying out data fusion on the plurality of intra-cluster nodes and the plurality of cluster head nodes based on a distributed clustering fusion algorithm for improving the residual energy and the distance to obtain cluster head fusion data and cluster head fusion data;
and the third processing module is used for transmitting the in-cluster fusion data to the corresponding cluster head nodes by the plurality of in-cluster nodes based on a preset fusion mechanism, and transmitting the cluster head fusion data to the sink node by the plurality of cluster head nodes.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the distributed sensor network data fusion method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the distributed sensor network data fusion method according to any one of claims 1 to 7.
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