CN106792531B - Node positioning method and device of sensor network - Google Patents
Node positioning method and device of sensor network Download PDFInfo
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- CN106792531B CN106792531B CN201611192686.4A CN201611192686A CN106792531B CN 106792531 B CN106792531 B CN 106792531B CN 201611192686 A CN201611192686 A CN 201611192686A CN 106792531 B CN106792531 B CN 106792531B
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
The invention relates to a node positioning method and a device thereof of a sensor network, and discloses a node positioning method and a device thereof, which comprises the steps of determining the position information of each beacon node and the minimum hop value between the unknown node and each beacon node according to the broadcast of each beacon node received by the unknown node; determining the average single-hop distance of a beacon node corresponding to the first broadcast received by the unknown node; calculating by adopting a multilateral measurement algorithm according to the position information, the minimum hop value and the average single-hop distance to obtain a position sample data set of the unknown node; a plurality of sample points are included within the data set; and determining the clustering centers of the sample points in the data set according to a clustering algorithm, and obtaining the position information of the unknown nodes according to the position information of each clustering center. The invention calculates the node position according to the clustering center of the data set on the position sample of the unknown node, can reduce the calculated amount and improve the positioning precision.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a node positioning method and device for a sensor network.
Background
A Wireless Sensor Network (WSN) is a network with functions of information acquisition, data processing, wireless transmission, and the like. The wireless sensor network consists of sensor nodes randomly distributed in the network, and the sensor nodes collect, analyze and process information around the nodes and accurately transmit analysis results back to the server. In monitoring activities, the sensor must specify its own position, and if there is no precise position information, the information obtained by the sensor is meaningless.
Currently, the commonly used positioning algorithm is DV-Hop algorithm, which is divided into three stages:
in the first stage, a node with a known position (hereinafter referred to as a beacon) continuously broadcasts information { hi, Xi, Yi } to the whole network, wherein hi is the hop number between the beacon and an unknown node and the initial value is 0, and (Xi, Yi) is the horizontal and vertical coordinates of the beacon. The unknown node records the position information from each beacon node and hi, and then adds 1 to hi and forwards the hi to the neighbor nodes. In this way, the unknown node selects the minimum hop count value corresponding to the beacon node from the information of the same beacon node received for multiple times;
and the second stage, calculating the sum of the distances between each beacon node and the rest beacon nodes and the sum of the hop count values, and dividing the sum of the distances by the sum of the hop count values to obtain the average single-hop distance of each beacon node.
And in the third stage, the beacon node broadcasts the average single-hop distance of the beacon node to the sensor network, the unknown node only records the average single-hop distance of the beacon node corresponding to the broadcast received for the first time, and then the unknown node utilizes the recorded minimum hop value, the received average single-hop distance and position information of the unknown node by a trilateration algorithm or a maximum likelihood estimation method.
However, since the distribution of sensor nodes in the wireless sensor network is often uneven, and the density degree of nodes in different areas is large, the average single-Hop distance of the beacon nodes is taken as the average single-Hop distance of the beacon nodes, which causes an error in the average single-Hop distance of the beacon nodes, so that the DV-Hop algorithm generates an inevitable error when calculating the distance from an unknown node to the beacon nodes. Resulting in low positioning accuracy.
Therefore, how to provide a node positioning method of a sensor network with high positioning accuracy and a device thereof is a problem that needs to be solved by those skilled in the art at present.
Disclosure of Invention
The invention aims to provide a node positioning method and a node positioning device, which can be used for calculating the node position according to the clustering center of a data set on a position sample of an unknown node, reducing the calculated amount and improving the positioning precision.
In order to solve the above technical problem, the present invention provides a node positioning method for a sensor network, including:
according to the broadcast of each beacon node received by an unknown node, determining the position information of each beacon node and the minimum hop value between the unknown node and each beacon node;
determining an average single-hop distance of a beacon node corresponding to the first broadcast received by the unknown node;
calculating by adopting a multilateration algorithm according to the position information, the minimum hop value and the average single hop distance to obtain a position sample data set of the unknown node; a plurality of sample points are included within the data set;
and determining the clustering centers of the sample points in the data set according to a clustering algorithm, and obtaining the position information of the unknown node according to the position information of each clustering center.
Preferably, the clustering algorithm is specifically a k-nodoids clustering algorithm.
Preferably, the determining the clustering centers of the sample points in the data set according to the clustering algorithm, and the obtaining the location information of the unknown node according to the location information of each clustering center specifically includes:
respectively determining the coordinates of each core object in the data set; the ordinate is the minimum reachable distance corresponding to the core objects, and the abscissa determines the order of the minimum reachable distance of each core object; the core object is sample points which take the core object as a center and are contained in a preset radius range, and the number of the sample points exceeds a first preset number;
connecting the coordinate points of the core objects to obtain a reachable oscillogram;
taking the core object corresponding to each wave trough on the reachable wave form graph as each cluster central point;
and calculating the average value of each clustering center point as the position information of the unknown node.
Preferably, after the taking the core object corresponding to each valley on the reachable waveform map as each cluster center point, the method further includes:
step s 301: distributing other sample points except the clustering center point in the data set to a cluster of the clustering center point closest to the sample point;
step s 302: and removing the clustering central points of which the number of the sample points contained in the cluster in which the clustering central points are located is less than a second preset number from each clustering central point.
Preferably, step s302 is followed by:
for each cluster with the number of the included sample points not less than the preset number, taking each included sample point as a current cluster central point, and determining the square sum of the distances from the other sample points except the current cluster central point to the current cluster central point in the cluster according to a square difference relational expression; wherein the square error relation is specifically as follows:
wherein L isjThe current cluster center point is the corresponding square sum of the jth current cluster center point; n is the total number of sample points within the cluster; piIs the position of the ith sample point, OjThe position of the jth current cluster center point is obtained;
and adjusting the current clustering center point corresponding to the minimum sum of squares as the clustering center point of the cluster.
Preferably, the determining the coordinates of each core object in the data set respectively, where the ordinate is the minimum reachable distance corresponding to the core object, and the abscissa determines the order of the minimum reachable distances of each core object specifically includes:
step s 501: randomly selecting a sample point from the data set, judging whether the sample point is a core object, if so, taking the sample point as a processing object, and entering a step s 502; if not, adding the sample point into the ending list, and repeating the step s 501;
step s 502: selecting direct density reachable points of the processing objects which are not added into the ordered list from the data set to be added into the ordered list, sequencing all sample points in the ordered queue according to the sequence of the reachable distance of the processing objects from small to large, determining the minimum reachable distance corresponding to the processing objects, and adding the processing objects into the ending list; wherein the abscissa is the order in which core objects are added to the end list; the direct density reachable points are sample points within a preset radius range corresponding to the core object;
step s 503: judging whether the sample point at the most front position in the ordered list is a core object, if so, taking the sample point as a processing object, and repeating the step s 502; if not, adding the sample point into the ending list, and repeating the step s 503; until all sample points in the data set are added to the end list.
Preferably, the reachable distance is a maximum value of a core distance of the core object and a euclidean distance between the core object and a corresponding direct density reachable point;
the core distance of the core object is a minimum radius threshold value for making itself a core object.
Preferably, the process of determining the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node specifically includes:
determining the minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and each of the other beacon nodes except the unknown node;
calculating the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node according to the minimum hop value among the beacon nodes, the position information of each beacon node and a single-hop distance relational expression; the single-hop distance relational expression is specifically as follows:
wherein e isk(x) a single-hop distance between a beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node0,y0) (x) location coordinates of a beacon node corresponding to the first broadcast received by the unknown nodek,yk) The position coordinate of the kth beacon node; h iskThe minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node is obtained; m is the total number of beacon nodes; w is akThe influence degree value of the beacon node corresponding to the first broadcast received by the unknown node is the kth beacon node; dkThe distance between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node; and e is the average single hop distance.
In order to solve the above technical problem, the present invention further provides a node positioning apparatus for a sensor network, including:
the system comprises a hop number determining module, a position determining module and a hop number determining module, wherein the hop number determining module is used for determining the position information of each beacon node and the minimum hop number value between the unknown node and each beacon node according to the broadcast of each beacon node received by the unknown node;
the single-distance determining module is used for determining the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node;
the data set calculation module is used for calculating a position sample data set of the unknown node by adopting a multilateration algorithm according to the position information, the minimum hop value and the average single hop distance; a plurality of sample points are included within the data set;
and the position calculation module is used for determining the clustering centers of the sample points in the data set according to a clustering algorithm and obtaining the position information of the unknown node according to the position information of each clustering center.
The invention provides a node positioning method and a device thereof, which are used for calculating a position sample data set of an unknown node by adopting a multilateration algorithm after determining a minimum hop value between the unknown node and each beacon node, an average single-hop distance of the beacon node corresponding to a first broadcast received by the unknown node and position information of each beacon node, and then determining a clustering center of the sample point in the data set according to the clustering algorithm so as to obtain the position information of the unknown node. Each sample point in the data set represents possible position information of an unknown node, the clustering center is located at the dense position of the sample points, and the accurate position information of the unknown node is located in the dense area of the sample points.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the prior art and the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart illustrating a process of a node location method of a sensor network according to the present invention;
fig. 2 is a flowchart illustrating a process of another node location method of a sensor network according to the present invention;
fig. 3 is a flowchart illustrating a process of another node location method of a sensor network according to the present invention;
fig. 4 is a schematic structural diagram of a node positioning device of a sensor network according to the present invention.
Detailed Description
The core of the invention is to provide a node positioning method and a device thereof, which can reduce the calculated amount and improve the positioning precision by calculating the node position according to the clustering center of the data set on the position sample of the unknown node.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are 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 present invention provides a node positioning method of a sensor network, referring to fig. 1, fig. 1 is a flow chart of a process of the node positioning method of the sensor network provided by the present invention; the method comprises the following steps:
step s 101: according to the broadcast of each beacon node received by the unknown node, determining the position information of each beacon node and the minimum hop value between the unknown node and each beacon node;
step s 102: determining the average single-hop distance of a beacon node corresponding to the first broadcast received by the unknown node;
step s 103: calculating by adopting a multilateral measurement algorithm according to the position information, the minimum hop value and the average single-hop distance to obtain a position sample data set of the unknown node; a plurality of sample points are included within the data set;
step s 104: and determining the clustering centers of the sample points in the data set according to a clustering algorithm, and obtaining the position information of the unknown nodes according to the position information of each clustering center.
Preferably, the clustering algorithm is specifically k-nodoids clustering algorithm.
Referring to fig. 2, fig. 2 is a flowchart illustrating a process of another node location method for a sensor network according to the present invention; the process of step s104 specifically includes:
step s 201: respectively determining the coordinates of each core object in the data set; the ordinate is the minimum reachable distance corresponding to the core objects, and the abscissa determines the order of the minimum reachable distance of each core object; the core object is sample points which take the core object as a center and are contained in a preset radius range, and the number of the sample points exceeds a first preset number;
and if one sample point is within the preset radius range of one core object, the sample point is the direct density reachable point of the core object. The minimum value of the reachable distances of the direct density reachable points and the core object is the minimum reachable distance corresponding to the core object.
Step s 202: connecting the coordinate points of each core object to obtain a reachable oscillogram;
it is understood that the ordinate is the minimum reachable distance, and the abscissa indicates the arrangement order, and the finally obtained coordinate points of the core objects can be connected by a smooth line to obtain a waveform diagram, which includes peaks and valleys, wherein the valleys indicate the parts of the core objects with smaller minimum reachable distances.
The core objects are sample points at the dense positions of the sample points, and partial core objects with smaller minimum reachable distances in the core objects are selected, so that partial core objects with higher surrounding density degree can be further selected from the core objects, and the core objects are used as clustering centers, so that the positioning accuracy can be greatly improved, and the final calculated amount is reduced.
Step s 203: taking the core object corresponding to each wave trough on the reachable oscillogram as each clustering center point;
step s 204: and calculating the average value of each cluster center point as the position information of the unknown node.
Since the clustering centers may be the actual positions of the unknown nodes, averaging the clustering centers can obtain more accurate position information.
Preferably, step s203 is followed by:
step s 301: distributing other sample points except the clustering center point in the data set to the cluster of the clustering center point closest to the sample point;
i.e. all sample points in the data set are divided into a number of clusters, the number of clusters depending on the number of cluster centers.
Step s 302: and removing the clustering central points from each clustering central point, wherein the number of the sample points contained in the cluster where the clustering central point is located is less than a second preset number.
It can be understood that if the number of sample points included in the cluster is too small, it indicates that the number of sample points gathered around the cluster center is not large, and the coordinates of the cluster center are generally not the real position information of the unknown node, so that the calculation accuracy can be further improved by removing the coordinates.
Preferably, referring to fig. 3, fig. 3 is a flowchart illustrating a process of a node location method of another sensor network according to the present invention; step s302 is followed by:
step s 303: for each cluster with the number of the included sample points not less than the preset number, taking each included sample point as a current cluster central point, and determining the square sum of the distances from the other sample points except the current cluster central point to the current cluster central point in the cluster according to a square difference relational expression; wherein, the square error relation is specifically as follows:
wherein L isjThe current cluster center point is the corresponding square sum of the jth current cluster center point; n is the total number of sample points within a cluster; piIs the position of the ith sample point, OjThe position of the jth current clustering center point is taken as the position of the jth current clustering center point;
step s 304: and adjusting the current clustering center point corresponding to the minimum sum of squares as the clustering center point of the cluster.
It can be understood that, after the clusters are allocated, the true cluster center in each cluster may not be the cluster center obtained in step s203, and therefore, the square sum of the distances between each sample point in the cluster and the other sample points except for the sample point itself needs to be determined, and the square sum is the smallest, that is, the true cluster center in the cluster. The operation improves the accuracy of the selected clustering center, and further improves the positioning precision.
Preferably, the process of step s201 is specifically:
step s 501: randomly selecting a sample point from the data set, judging whether the sample point is a core object, if so, taking the sample point as a processing object, and entering a step s 502; if not, adding the sample point into the ending list, and repeating the step s 501;
step s 502: selecting direct density reachable points of the processing objects which are not added into the ordered list from the data set, adding the direct density reachable points into the ordered list, sequencing the sample points in the ordered list according to the sequence of the reachable distances of the processing objects from small to large, determining the minimum reachable distance corresponding to the processing objects, and adding the processing objects into the ending list; wherein, the abscissa is the order of adding the core object into the ending list; the direct density reachable points are sample points within a preset radius range corresponding to the core object;
step s 503: judging whether the sample point at the most front position in the ordered list is a core object, if so, taking the sample point as a processing object, and repeating the step s 502; if not, adding the sample point into the ending list, and repeating the step s 503; until all sample points in the data set are added to the end list.
Wherein, the reachable distance is the maximum value of the core distance of the core object and the Euclidean distance between the core object and the corresponding direct density reachable point; the core distance of a core object is the minimum radius threshold that makes itself a core object.
In a preferred embodiment, the process of step s102 is specifically:
determining the minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and each of the other beacon nodes except the unknown node;
calculating the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node according to the minimum hop value among the beacon nodes, the position information of each beacon node and the single-hop distance relational expression; the relationship of the single hop distance is as follows:
wherein e iskThe single-hop distance between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node (x)0,y0) Location coordinates of the beacon corresponding to the first broadcast received for unknown nodes, (x)k,yk) Position coordinates of a kth beacon node; h iskThe minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node; m is the total number of beacon nodes; w is akThe influence degree value of the beacon node corresponding to the first broadcast received by the unknown node is set for the kth beacon node; dkThe distance between a beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node; e is the average single hop distance.
It can be understood that, because the densities of the node distributions are different, the average single-hop distance between the beacon nodes is directly adopted to have a larger error, so that the influence degree value between the beacon nodes is taken as a weight coefficient and added to the calculation relation of the average single-hop distance, and the influence on the average single-hop distance of the beacon nodes due to the density of the nodes can be reduced as much as possible.
The invention provides a node positioning method, which comprises the steps of determining the minimum hop value between an unknown node and each beacon node, the average single-hop distance of the beacon node corresponding to a first broadcast received by the unknown node and the position information of each beacon node, calculating a position sample data set of the unknown node by adopting a multilateration algorithm, then determining the clustering center of the sample point in the data set according to the clustering algorithm, and further obtaining the position information of the unknown node. Each sample point in the data set represents possible position information of an unknown node, the clustering center is located at the dense position of the sample points, and the accurate position information of the unknown node is located in the dense area of the sample points.
The invention further provides a node positioning device of the sensor network, which is shown in fig. 4, and fig. 4 is a schematic structural diagram of the node positioning device of the sensor network provided by the invention. The device includes:
the hop count determining module 1 is configured to determine, according to the broadcast of each beacon node received by the unknown node, position information of each beacon node and a minimum hop count value between the unknown node and each beacon node;
the single-distance determining module 2 is used for determining the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node;
the data set calculating module 3 is used for calculating a position sample data set of the unknown node by adopting a multilateration algorithm according to the position information, the minimum hop count value and the average single-hop distance; a plurality of sample points are included within the data set;
and the position calculation module 4 is used for determining the clustering centers of the sample points in the data set according to a clustering algorithm and obtaining the position information of the unknown nodes according to the position information of each clustering center.
The invention provides a node positioning device, which is characterized in that after the minimum hop value between an unknown node and each beacon node, the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node and the position information of each beacon node are determined, a position sample data set of the unknown node is calculated by adopting a multilateral measurement algorithm, then the clustering center of the sample point in the data set is determined according to a clustering algorithm, and further the position information of the unknown node is obtained. Each sample point in the data set represents possible position information of an unknown node, the clustering center is located at the dense position of the sample points, and the accurate position information of the unknown node is located in the dense area of the sample points.
It is to be noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A node positioning method of a sensor network is characterized by comprising the following steps:
according to the broadcast of each beacon node received by an unknown node, determining the position information of each beacon node and the minimum hop value between the unknown node and each beacon node;
determining an average single-hop distance of a beacon node corresponding to the first broadcast received by the unknown node;
calculating by adopting a multilateration algorithm according to the position information, the minimum hop value and the average single hop distance to obtain a position sample data set of the unknown node; a plurality of sample points are included within the data set;
respectively determining the coordinates of each core object in the data set; the ordinate is the minimum reachable distance corresponding to the core objects, and the abscissa determines the order of the minimum reachable distance of each core object; the core object is sample points which take the core object as a center and are contained in a preset radius range, and the number of the sample points exceeds a first preset number;
connecting the coordinate points of the core objects to obtain a reachable oscillogram;
taking the core object corresponding to each wave trough on the reachable wave form graph as each cluster central point;
and calculating the average value of each clustering center point as the position information of the unknown node.
2. Method according to claim 1, characterized in that the clustering algorithm is in particular a k-medoids clustering algorithm.
3. The method of claim 2, wherein the taking the core object corresponding to each valley on the reachable waveform map as each cluster center point further comprises:
step s 301: distributing other sample points except the clustering center point in the data set to a cluster of the clustering center point closest to the sample point;
step s 302: and removing the clustering central points of which the number of the sample points contained in the cluster in which the clustering central points are located is less than a second preset number from each clustering central point.
4. The method of claim 3, further comprising, after step s 302:
for each cluster with the number of the included sample points not less than the preset number, taking each included sample point as a current cluster central point, and determining the square sum of the distances from the other sample points except the current cluster central point to the current cluster central point in the cluster according to a square difference relational expression; wherein the square error relation is specifically as follows:
wherein L isjThe current cluster center point is the corresponding square sum of the jth current cluster center point; n is the total number of sample points within the cluster; piIs the position of the ith sample point, OjThe position of the jth current cluster center point is obtained;
and adjusting the current clustering center point corresponding to the minimum sum of squares as the clustering center point of the cluster.
5. The method according to any one of claims 1 to 4, wherein the step of determining the coordinates of each core object in the data set separately, wherein the ordinate is the minimum reachable distance corresponding to the core object, and the abscissa determines the order of the minimum reachable distances of each core object by:
step s 501: randomly selecting a sample point from the data set, judging whether the sample point is a core object, if so, taking the sample point as a processing object, and entering a step s 502; if not, adding the sample point into the ending list, and repeating the step s 501;
step s 502: selecting direct density reachable points of the processing objects which are not added into the ordered list from the data set to be added into the ordered list, sequencing all sample points in the ordered queue according to the sequence of the reachable distance of the processing objects from small to large, determining the minimum reachable distance corresponding to the processing objects, and adding the processing objects into the ending list; wherein the abscissa is the order in which core objects are added to the end list; the direct density reachable points are sample points within a preset radius range corresponding to the core object;
step s 503: judging whether the sample point at the most front position in the ordered list is a core object, if so, taking the sample point as a processing object, and repeating the step s 502; if not, adding the sample point into the ending list, and repeating the step s 503; until all sample points in the data set are added to the end list.
6. The method according to any of claims 1-4, wherein the reachable distance is the maximum of a core distance of the core object and a Euclidean distance between the core object and a corresponding direct density reachable point;
the core distance of the core object is a minimum radius threshold value for making itself a core object.
7. The method according to any one of claims 1 to 4, wherein the determining the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node is specifically:
determining the minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and each of the other beacon nodes except the unknown node;
calculating the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node according to the minimum hop value among the beacon nodes, the position information of each beacon node and a single-hop distance relational expression; the single-hop distance relational expression is specifically as follows:
wherein e isk(x) a single-hop distance between a beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node0,y0) (x) location coordinates of a beacon node corresponding to the first broadcast received by the unknown nodek,yk) The position coordinate of the kth beacon node; h iskThe minimum hop count value between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node is obtained; m is the total number of beacon nodes; w is akThe influence degree value of the beacon node corresponding to the first broadcast received by the unknown node is the kth beacon node; dkThe distance between the beacon node corresponding to the first broadcast received by the unknown node and the kth beacon node; and e is the average single hop distance.
8. A node positioning apparatus of a sensor network, comprising:
the system comprises a hop number determining module, a position determining module and a hop number determining module, wherein the hop number determining module is used for determining the position information of each beacon node and the minimum hop number value between the unknown node and each beacon node according to the broadcast of each beacon node received by the unknown node;
the single-distance determining module is used for determining the average single-hop distance of the beacon node corresponding to the first broadcast received by the unknown node;
the data set calculation module is used for calculating a position sample data set of the unknown node by adopting a multilateration algorithm according to the position information, the minimum hop value and the average single hop distance; a plurality of sample points are included within the data set;
a coordinate determination module for determining the coordinates of each core object in the data set respectively; the ordinate is the minimum reachable distance corresponding to the core objects, and the abscissa determines the order of the minimum reachable distance of each core object; the core object is sample points which take the core object as a center and are contained in a preset radius range, and the number of the sample points exceeds a first preset number;
the oscillogram obtaining module is used for connecting the coordinate points of the core objects to obtain a reachable oscillogram;
a cluster center point obtaining module, configured to use a core object corresponding to each trough on the reachable waveform diagram as each cluster center point;
and the position calculation module is used for calculating the average value of each clustering central point as the position information of the unknown node.
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| CN105635964A (en) * | 2015-12-25 | 2016-06-01 | 河海大学 | Wireless sensor network node localization method based on K-medoids clustering |
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