CN106211256B - A kind of Unmanned Aerial Vehicle Data collection method based on data critical node - Google Patents
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Abstract
The invention discloses a kind of Unmanned Aerial Vehicle Data collection methods based on data critical node to improve sink node motion limitation problem by the way that sink node to be mounted on unmanned plane；By the collection to key node datas certain in network, the completion of science is carried out to whole network data, ensure that unmanned plane normally completes the collection work to whole network data situation under limited electricity, ensure that monitoring region is long-term and effective data collection effort.The theory of the data proposed by the present invention acquired with data critical node on behalf whole sensing node, it can not only be applied to Unmanned Aerial Vehicle Data acquisition aspect, also available good application, plays reduction cost, improves the effect of working efficiency under large-scale network environment.
Description
Technical Field
The invention belongs to the technical field of data collection, and relates to an unmanned aerial vehicle data collection method based on data key nodes.
Background
Data collection is the most important task of the sensor network, and is a bridge connecting a sensing layer and an application layer. The effective data collection method can reduce the workload of the sensing node, avoid the regional death of the network and prolong the life cycle of the network.
Generally, according to whether a sink node in a sensing network is fixed, the main data collection methods in the current sensing network can be divided into two types: the first type is a data collection method based on a fixed sink node, and the second type is a data collection method based on a mobile sink. The mobile sink-based data method means that a sink node moves in a monitoring area according to a certain rule or freely, and data collected by a data sensing node are collected. The method can completely avoid the problem of energy holes, thereby prolonging the life cycle of the network.
However, when the sink node moves in the monitoring area, the method defaults that the sink node can move freely in the monitoring area, and the application can allow the sink node to move in the monitoring area. However, under a complex environment, the existing data collection method based on the mobile sink node ignores the problem of sink movement limitation and the problem of application scene limitation. Thus, the application of such methods in a practical environment will be severely limited. Meanwhile, under the condition of huge network scale, receipts are collected by the sink nodes in the whole network in a moving mode, time and energy cost is huge, and for some mobile devices carrying the sink nodes, the huge time and energy cost is possibly hard to bear, so that the application of the method in a real scene is limited again.
Disclosure of Invention
Aiming at the problems or defects in the prior art, the invention aims to provide an unmanned aerial vehicle data collection method based on data key nodes, and the problem of limited movement of a sink node is solved by carrying the sink node on an unmanned aerial vehicle; through the collection to some key node data in the network, carry out scientific completion to the whole network data, guaranteed that unmanned aerial vehicle normally accomplishes the collection work to the whole network data condition under limited electric quantity, guaranteed that the monitoring area is long-term and effectual data collection work.
In order to achieve the purpose, the invention adopts the following technical scheme:
an unmanned aerial vehicle data collection method based on data key nodes comprises the following steps:
the method comprises the following steps: acquiring all data collected by all nodes in a wireless sensor network within a certain time period [ t, t + alpha ];
step two: judging any two nodes n in all nodesiAnd njWhether the nodes are similar to each other or not is judged, and the similar conditions of all the nodes in the wireless sensor network are obtained; judging any two nodes niAnd njThe method of similarity is as follows:
node niAnd njAt a time period [ t, t + alpha ]]The data sequence collected in the device isAndif the data sequenceAndthe distance betweenIs less thanThen node niAnd node njAt [ t, t + alpha ]]The above is similar, otherwise, node niAnd node njAt [ t, t + alpha ]]Are dissimilar; wherein epsilon is an application error;
step three: selecting data key nodes from all nodes in the wireless sensor network according to the similarity conditions of all nodes obtained in the step two;
step four: and the unmanned aerial vehicle acquires data of the data key nodes to obtain the data conditions of all the nodes in the whole wireless sensor network.
Specifically, the step three includes selecting a data critical node from all nodes in the wireless sensor network, which includes the following steps:
step 3.1, giving a historical data set D of all nodes in the wireless sensor networkpreAnd applying the error e through the given historical node data DpreAnd applying the error epsilon to calculate a graph Sim of similar nodes in the network by using a DTW algorithmgraph；
Step 3.2, map Sim for similar nodesgraphAll the nodes in (2) are calculated to obtain eachA worth attribute of the individual node;
step 3.3, map SimgraphEach node in the graph Sim is traversed to find out the node with the maximum word attribute, the node is a data critical node, the node is added into a set cnodeset { } and simultaneously in the graph SimgraphThe neighbor node Sim of the data key node is foundgraph[cn]Neibor, in scheme SimgraphIn the node set, the data key node and the neighbor nodes thereof are deleted to obtain a new graph Simgraph；
Step 3.4, obtaining new graph SimgraphRepeating the step 3.2 to the step 3.4 until the graph SimgraphThe set of middle nodes is empty;
and 3.5, the nodes in the set cNodeset { } are all the obtained data key nodes.
Specifically, the formula adopted in the step 3.2 for computing the worth attribute is as follows:
wherein v represents a node, the numerator of the formula represents the number of neighbor nodes of the node v, and the denominator represents the average value of the distances between the node v and all the existing data critical nodes.
Compared with the prior art, the invention has the following advantages:
1. through the analysis of the sensing data, a small number of sensing nodes with high data value, namely data key nodes, are selected, and the data collected by the sensing nodes can represent the data collected by all the sensing nodes under the requirement of application errors. Then, regard as unmanned aerial vehicle data collection's conventional target with key node of data to outstanding unmanned aerial vehicle is at the key task of data collection in-process, reduced unmanned aerial vehicle at the work load of data collection in-process, finally improved unmanned aerial vehicle's work efficiency.
2. The invention uses the data key node to represent the theory of the data collected by all the perception nodes, not only can be applied to the data collection aspect of the unmanned aerial vehicle, but also can be well applied in the large-scale network environment, and has the effects of reducing the cost and improving the working efficiency.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a physical distribution diagram of data critical nodes;
fig. 3 is a data critical node distribution diagram corresponding to the outdoor data set when the application error is 0.01.
The embodiments of the invention will be explained and explained in further detail with reference to the figures and the detailed description.
Detailed Description
According to the technical scheme, referring to fig. 1, the unmanned aerial vehicle data collection method based on the data key nodes comprises the following steps:
the method comprises the following steps: all data collected by all nodes in the wireless sensor network within a certain time period [ t, t + alpha ] are obtained. Where α represents the length of the time period.
Step two: judging any two nodes n in all nodes in wireless sensor networkiAnd njAnd if so, obtaining the similar conditions of all the nodes in the wireless sensor network. The method for judging whether the nodes are similar is as follows:
node niAnd njAt a time period [ t, t + alpha ]]The internally collected data sequence with time sequence significance isAndif the data sequenceAndthe distance betweenIs less thanNamely:then node niAnd node njAt [ t, t + alpha ]]The above is similar, otherwise, node niAnd node njAt [ t, t + alpha ]]The above are dissimilar.
Wherein,wherein d isi,tRepresenting a node niData collected at time t, and t < t1＜...＜t+α。
ε is the application error, which is defined as the data error that the application can accept. If the application error epsilon is a, the application is acceptable when the difference between the final collected data and the real collected data is between-a, a. There is no fixed rule for setting epsilon to be circulated, and the setting is mainly set according to the requirements of the application.
When in the time period [ t, t + alpha ]]Inner, node niAnd node njDistance between nodes when clock system is completely synchronousThe calculation formula is as follows:
if during the time period t, t + alpha]Inner, node niAnd node njIf the clock system is not completely synchronized or the data loss occurs, the DTW algorithm is used for calculatingAndthe distance betweenThe essence of the DTW algorithm is to "stretch" the shorter-dimension vectors and then solve for distance from the longer-dimension vectors.
The following pairs of nodes niAnd node njWhen the clock system is completely synchronous, the correctness of the similarity judging method of the two nodes is proved:
if during the time period t, t + alpha]Inner, node niAnd node njThe clock system being fully synchronised with node njThe clock systems are completely synchronous, and no data is lost in both, the data collected by the two nodes in the time period are equal in number, i.e. the data collected by the two nodes in the time period are equal in numberSuppose that in a time period t, t + α]In total, there are H acquisition cycles. Let beta denote during the time period t, t + alpha]For node n at each acquisition cycle iniAnd node njThe collected data, the absolute value of the phase difference between them form a set.
β＝{β1,β2,...,βH}(2)
Let z be the number of elements smaller than ε in β, and Q be the number of elements larger than ε.
When Q is 0, then all elements in β are less than epsilon. It is expressed in a physical sense in a time period t, t + alpha]For node n in any same acquisition periodiAnd node njThe acquired data, the phase difference between them is within the applied error range, so at any time niThe collected data can replace njCollected data, so node niAnd node njAre similar.
When z > Q, then substantially all elements in β are less than ε. It is expressed in a physical sense in a time period t, t + alpha]For node n during most of the acquisition periodiAnd node njThe collected data, the phase difference between them is within the applied error range, in this case we also consider niThe collected data can replace njCollected data, so node niAnd node njAre similar.
When z < Q, then almost all elements in β are greater than ε. Assuming that the sum of all elements smaller than epsilon in beta is betasmallThen:
the physical significance of equation (5) is that while almost all elements in β are larger than ε, they differ from ε by very little, almost 0. In this case we also consider n to beiThe collected data can replace njCollected data, so node nkAnd node njAre similar.
When z is equal to Q, the number of elements larger than ∈ in β is substantially the same as the number of elements smaller than ∈. In a stable environment, the situation that the data acquired by the nodes are high and low can not occur, so that n can be in half continuous timeiCollected data and njThe difference between the acquired data is within the application error range, and the difference between the acquired data and the acquired data in the other half time is not within the application error range. Thus, when z is of comparable size to Q, the physical significance lies in node njCollected data and node niThe acquired data have similarity during the first half (second half) and have no similarity during the second half (first half). This is not possible because the environment in which the node is located is stable in most cases and does not change significantly.
In view of the above, it is desirable to provide,if during the time period t, t + alpha]Inner, node niAnd node njThe clock systems are fully synchronized, neither of which loses data, and the data acquisition sequenceAndis less than ε × H, node niAnd node njAre similar.
If during the time period t, t + alpha]Inner, node niAnd node njIf the clock system is not completely synchronized or the data loss occurs, the DTW algorithm is used for calculatingAndthe distance between them. The essence of the DTW algorithm is to "stretch" the shorter-dimension vectors and then solve for distance from the longer-dimension vectors. Thus, the same can be obtained: if the data acquisition sequenceAndis less thanThen node niAnd node njAre similar.
If node niThe collected data can replace the node njThe collected data is called node niAnd njAt a time period [ t, t + alpha ]]Internally similar, also called node niAnd njAt a time period [ t, t + alpha ]]Similar relationships exist within the memory. If [ t, t + α ]]The data in the node n can sufficiently represent the environment change around the node, and the node n is callediAnd njSimilar throughout the life cycle. In the following part of the invention it is assumed that the data over the selected time period is sufficient to represent the change in the environment.
Step three: selecting key nodes of all nodes in the wireless sensor network according to the similar conditions of all nodes in the wireless sensor network; the specific method comprises the following steps:
step 3.1, giving a historical data set D of all nodes in the wireless sensor networkpreAnd an acceptable data error in the algorithm, i.e., an application error epsilon. By given historical node data DpreAnd the applied error epsilon is calculated by using a DTW algorithm to obtain a graph Sim of similar nodes in the networkgraph；
Step 3.2, map Sim of similar nodesgraphAll the nodes in the network are calculated to obtain the worth attribute of each node, and the specific formula for calculating the worth attribute is as follows:
wherein v represents a node, the numerator of the formula represents the number of neighbor nodes of the node v, and the denominator represents the average value of the distances between the node v and all the existing data critical nodes. And the worth attribute is a data value, and the data value is an attribute of each data perception node and specifically represents a use value of the data collected by the data perception node. In the invention, the following assumptions are made: under normal conditions, the data collected by all sensing nodes has the same value of use, which is set to 1 for simplicity. Thus, for a data critical node nkIf the coverage of the corresponding node is Γ, the data value is | Γ |. Because | Γ | represents in a physical sense: within the applied error range, the data collected by the nodes can be used as a representation to replace the data collected by all the nodes in the set gamma and is used by the application, so that the data value of the nodes is the sum of the data values of all the nodes in the gamma, namely, the sum is the gamma. Therefore, the worth attribute of a node is proportional to the number of neighbor nodes and inversely proportional to the distance of the existing data critical node.
Step 3.3, map SimgraphEach node in the graph Sim is traversed to find out the node with the maximum word attribute, the node is a data critical node, the node is added into a set cnodeset { } and simultaneously in the graph SimgraphThe neighbor node Sim of the data key node is foundgraph[cn]Neibor, in scheme SimgraphIn the node set, the data key node and the neighbor nodes thereof are deleted to obtain a new graph Simgraph。
Step 3.4, obtaining new graph SimgraphRepeating the step 3.2 to the step 3.4 until the graph SimgraphThe set of middle nodes is empty.
And 3.5, setting the nodes in the set cNodeset { }, namely all the finally obtained data key nodes.
The specific flow and use of the above method can be summarized as the following chart:
a set of nodes N selected from the networkxAs data critical nodes: it satisfies the following conditions: 1. ensuring that each node remaining in the network is at least equal to NxIs similar; 2. when N is presentxWhen all nodes in the node group are in a straight line, the key length between two nodes with the farthest physical distance is the minimum (the key length is the total length of a connecting line between the two nodes); when N is presentxWhen all nodes in the node are not in a straight line, the node is guaranteed to be composed of NxThe key area of the external graph corresponding to all the nodes in the graph is the minimum (the key area is the area of the graph formed by the connecting lines among the nodes); 3. for any node set meeting the condition one and the condition two, redundant nodes are deleted, and N is guaranteedxIs the node set with the least number of elements and satisfying the above two conditions.
Converting the selection problem of the data key nodes into a mathematical problem according to the definition of the data key nodes:
assuming that the whole network is a graph G, all the sensing nodes in the network are vertices in the graph G. If the perception data is analyzed, the two nodes have a similar relation under the requirement of an applied error, an edge exists between the two corresponding vertexes, and if the similarity does not exist, the edge does not exist between the two corresponding vertexes. We refer to graph G as a graph of similarity relationships for the nodes of the whole network. How to select data critical nodes from all data-aware nodes in the network can now be understood as finding a collection of vertices N in the graph GxSuch that each remaining vertex in the network has at least one neighbor vertex at NxIn, ensure NxThe number of vertices in (a) is minimal and the critical length or critical area corresponding to these vertices is minimal.
The selection problem of the data key nodes is the problem of the minimum dominating set in graph theory, and the solving difficulty is NP-hard. The solution of the minimum dominating set problem of the related documents is to solve by adopting a greedy strategy algorithm.
1) Proving and solving formula
When the selection problem of the data key nodes is the problem of the minimum dominating set in graph theory, the difficulty of solving the problem is the proving of NP-hard
And (3) proving that: the solution difficulty of the minimum dominating set problem in graph theory is NP-hard. The specific definition is as follows: for an undirected graph G (V, E), a batch of nodes h belonging to V are found out from G, so that the vertex in h and the vertex in V-h have edge relation, and the minimum number of the vertices in h is ensured. If for each node at the edge of the network there is no affinity with other nodes in the network. In this case, the nodes in the edge region are definitely data critical nodes, and the critical lengths or critical areas corresponding to all the data critical nodes are also fixed. Therefore, the selection work for the data critical node at this time can be understood as: on-netFinding the least number of nodes N in the networkxSuch that each node remaining in the network has at least one neighbor node at NxThis is exactly the minimum dominating set problem in graph theory. Thus, it can be seen that the minimum dominating set problem is a special case of the selection problem of data-critical nodes in the network. The choice of data critical node in the network is therefore NP-hard.
2) The method is mainly composed of two parts. The first part is to construct a similarity relation graph of nodes of the whole network by using a DTW algorithm. In the process of constructing the similarity relation graph, whether similarity exists between each node and all other nodes is judged, so that the time complexity is O (n)2) Assuming that the average length of the data acquisition sequence corresponding to the node is a, the overall time complexity of the first part is O (a)2*n2). The second part is to select the data key nodes according to the similarity relation graph, and the core part is to perform twice loop judgment process on all the nodes as can be seen from the algorithm. Thus, the temporal complexity of the second part is O (n)2). So overall the temporal complexity of algorithm 1 is O (a)2*n2) Although overall the time complexity of algorithm 1 is O (n)2) Level, however, if the length of the data acquisition sequence is large, i.e. the value of a is large, the constant coefficient of the time complexity of the algorithm 1 will be very large, so how to select the appropriate data acquisition sequence is very important.
Since the data critical node is a regular target node for data collection by the drone, it will communicate frequently with the drone. So if a data critical node is ever unchanged, its energy will be quickly exhausted, causing the network to enter a "bogus" state. In response to this problem, the solution proposed herein is: and setting an energy threshold value for the data critical node, and when the energy of the data critical node is less than the energy threshold value, finding the node with the minimum average distance to the existing data critical node in the node coverage range, and setting the node as a new data critical node.
If based on similarities between nodesThe constructed whole network node similarity relation graph SimgraphIn (2), there are N vertices and M edges. Assuming that the number of data critical nodes finally calculated by the algorithm is k, the relevant literature can know thatTherefore, if the data key node is used as the target node of unmanned aerial vehicle data collection, the task amount of unmanned aerial vehicle data collection can be reduced at minimum
Step four: the unmanned aerial vehicle collects data of data key nodes in the network, and accordingly data conditions of all the nodes in the whole network are obtained.
The effects of the present invention can be further illustrated by the following simulations.
And simulation 1, verifying the correctness of the data key nodes in the method.
The program in the whole experiment and simulation is completely written by self, the development language is c + +, and the parameters of all simulation environments are set by referring to the parameters in the real environment. The data sets used in this simulation were respectively (1) the outdoor data set: the 41 data perception nodes deployed in the site of the great wall of north taiwan of the town of elm collect data between 12 and 1 days of 2015 and 20 days of 2015 and 12 and 2015. The bottom layer of the 41 nodes adopts a CC2530 hardware structure and is responsible for collecting the temperature value of 15cm depth inside the soil of the great wall site. (2) Indoor data set: data collected by 54 data-aware nodes deployed in the Intel berkeley research laboratory between 28/2004 and 5/4/2004. The bottom layer of the 54 nodes adopts a TinyOS operating system to collect temperature, humidity, illumination and self voltage data in the surrounding environment. In the experiments herein, only the temperature data collected at the nodes was utilized for the 4 data collected. The correctness of the selection algorithm for the data critical nodes proposed in step 3c) is verified below using the outdoor and indoor data sets, respectively. The experimental results of the simulation are described below:
table 1 shows the critical data nodes and their corresponding node coverage areas for the outdoor data set when the application error is set to 0.01. As is clear from table 1, there are 6 data-critical nodes in the entire network, and therefore, the amount of tasks in the entire data collection process would be reduced by 85.37% without the exception of the perception of anomalous data. The coverage areas of the nodes corresponding to the data critical nodes 56, 493 and 495 in the 6 data critical nodes are relatively large, and are respectively 19, 12 and 7, which account for 92.67 percent of the number of the nodes of the whole network.
TABLE 1 critical node situation for data corresponding to outdoor data set with 0.01 application error
Fig. 3 shows the real distribution of data critical nodes and their node coverage: the pentagram shape in the graph represents the data critical nodes and the circle represents the data trivial nodes.
Different nodes are represented in fig. 3 by different symbols (A, B and C), corresponding to the node coverage of different data critical nodes:
● A node indicates the node coverage corresponding to data critical node number 56. These nodes are mainly distributed: the outside of the west wall, the inside and outside of the south wall, and the inside of the east wall.
● node B indicates the coverage of the node corresponding to the 493 number data critical node. These nodes are mainly distributed: the inside of the west wall and the outside of the east wall.
● C node indicates the node coverage corresponding to No. 495 data critical node. These nodes are mainly distributed: the outside of the north wall.
And the coverage range of the node corresponding to the data critical node No. 56. They are located in positions just where the sun shines each day. Therefore, the soil temperature in these places is relatively high. The data collected by these nodes have great similarity.
For the coverage of the node corresponding to the 493 number data critical node. The west wall topography of the northtown is higher, and the east wall topography is lower. The outer side of the west wall and the inner side of the east wall both block the sun substantially, and the wall bodies are thick, so the soil temperature values collected on the inner side of the west wall and the outer side of the east wall are both low. The data collected by these nodes have great similarity.
And for number 495, the coverage of the node corresponding to the critical node of the data. Because the outer side of the north wall is provided with the road, the temperature of the soil outside the north wall is greatly influenced by the outside and has certain difference with other wall bodies. The data collected by these nodes have great similarity.
Table 2 shows the critical data nodes and their corresponding node coverage for the indoor data set with an application error of 0.0001. As can be seen from table 2: the total number of the data critical nodes is 7, the node coverage range of the data critical node No. 45 is maximum 42, and the data critical node accounts for 70.77% of the total number of the nodes of the whole network.
TABLE 2 data Critical node case for indoor data set with 0.0001 application error
Fig. 2 is a physical distribution diagram of data critical nodes, and it is apparent from the diagram that the node coverage of data critical node number 45 is distributed almost in each corner of the network, and the coverage of other data critical nodes is scattered in the lower left corner and the upper right corner of the monitoring area. The data-critical nodes and their corresponding node coverage areas as a whole do not have any significant features in terms of geographical distribution. This is because the environment is very stable in an indoor environment, and the indoor air is also circulated, so that the air temperature values are the same almost everywhere, and thus the data are substantially similar to each other, so that the above-described result occurs.
Claims (3)
1. An unmanned aerial vehicle data collection method based on data key nodes is characterized by comprising the following steps:
the method comprises the following steps: acquiring all data collected by all nodes in a wireless sensor network within a certain time period [ t, t + alpha ];
step two: judging any two nodes n in all nodesiAnd njWhether the nodes are similar to each other or not is judged, and the similar conditions of all the nodes in the wireless sensor network are obtained; judging any two nodes niAnd njThe method of similarity is as follows:
node niAnd njAt a time period [ t, t + alpha ]]The data sequence collected in the device isAndif the data sequenceAndis less thanThen node niAnd node njAt [ t, t + alpha ]]The above is similar, otherwise, node niAnd node njAt [ t, t + alpha ]]Are dissimilar; wherein epsilon is an application error;
step three: selecting data key nodes from all nodes in the wireless sensor network according to the similarity conditions of all nodes obtained in the step two;
step four: and the unmanned aerial vehicle acquires data of the data key nodes to obtain the data conditions of all the nodes in the whole wireless sensor network.
2. The data-critical-node-based unmanned aerial vehicle data collection method of claim 1, wherein the step three of selecting the data-critical node from all nodes in the wireless sensor network specifically comprises the following steps:
step 3.1, giving a historical data set D of all nodes in the wireless sensor networkpreAnd applying the error e through the given historical node data DpreAnd applying the error epsilon to calculate a graph Sim of similar nodes in the network by using a DTW algorithmgraph；
Step 3.2, map Sim for similar nodesgraphAll the nodes in the network are calculated to obtain the worth attribute of each node;
step 3.3, map SimgraphEach node in the graph Sim is traversed to find out the node with the maximum word attribute, the node is a data critical node, the node is added into a set cnodeset { } and simultaneously in the graph SimgraphThe neighbor node Sim of the data key node is foundgraph[cn]Neibor, in scheme SimgraphIn the node set, the data key node and the neighbor nodes thereof are deleted to obtain a new graph Simgraph；
Step 3.4, obtaining new graph SimgraphRepeating the step 3.2 to the step 3.3 until the graph SimgraphThe set of middle nodes is empty;
and 3.5, the nodes in the set cNodeset { } are all the obtained data key nodes.
3. A method as claimed in claim 2, wherein the formula used in the step 3.2 of computing the worth attribute is:
wherein v represents a node, the numerator of the formula represents the number of neighbor nodes of the node v, and the denominator represents the average value of the distances between the node v and all the existing data key nodes; if the set cnodeset { } is empty, the value of the worth attribute is 1.
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| WO2019032091A1 (en) * | 2017-08-07 | 2019-02-14 | Ford Global Technologies, Llc | Locating a vehicle using a drone |
| CN108718459A (en) * | 2018-05-22 | 2018-10-30 | 南京邮电大学 | A kind of wireless sense network method of data capture based on unmanned plane |
| CN109033125B (en) * | 2018-05-31 | 2022-05-13 | 黑龙江大学 | Time sequence data domination set information extraction method |
| CN108645769B (en) * | 2018-06-05 | 2020-05-05 | 广州市荣盛环保科技有限公司 | Environment air quality monitoring method based on unmanned aerial vehicle |
| CN109885598B (en) * | 2019-01-25 | 2021-03-02 | 沈阳无距科技有限公司 | Fault identification method and device, computer readable storage medium and electronic equipment |
| CN110493844B (en) * | 2019-09-24 | 2021-05-11 | 广州大学 | Data fusion alliance game method and system for wireless sensor network |
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