CN103220746A - Self-positioning method for network node of wireless sensor - Google Patents

Abstract

The invention relates to a self-positioning method for a network node of a wireless sensor. The self-positioning method comprises the following steps of flooding broadcast to internet by utilizing a distance vector exchange protocol so that all nodes obtain the light-flooding number of all anchor nodes; clustering the anchor nodes to form a plurality of subareas; judging a current sub-network topology structure by utilizing a network topology structure judgment factor according to light-flooding distance information among the anchor nodes; screening elements in a set one by one to find out nodes meeting promotion conditions and reference anchor nodes for positioning the nodes; promoting the nodes meeting the judging conditions; and repeatedly traversing all the nodes and carrying out a promotion strategy till all unknown nodes are positioned. Compared with the prior art, the self-positioning method for the network node of the wireless sensor, disclosed by the invention, has the advantages of adding the node promotion strategy based on a DV-distance algorithm, reducing the distance accumulation errors of route vectors among the nodes of an asymmetrical network and improving the integral positioning precision.

Description

A kind of wireless sensor network node method for self-locating

Technical field

The present invention relates to a kind of wireless sensor network node method for self-locating, be applicable to sensor network system with self-organizing characteristic.

Background technology

But advantages such as that wireless sensor network has is disguised strong, can dispose self-organizing fast, cost is low, can work under abominable and particular surroundings, at present, we also is in the starting stage to the research of wireless sensor network, still exists key technology problems to need solution badly.Massive wireless sensor all be usually by aircraft broadcast sowing, mode such as rocket ejection at random is dispersed in the area to be monitored, the positional information of node has very big uncertainty.And for the node of not determining positional information, its data message that collects does not almost have any using value.Therefore in the types of applications of wireless sensor network, the positional information of node is extremely important to the monitoring of network itself, the node location that can determine locale or release news all is one of basic function of wireless sensor network, and the practicality and the validity of sensor network is played key effect.Such as in the comprehensive safety protecting of important goal or sensitive target, need determine to take place unusual particular location based on the sensor node positional information, and the alarm grade of invasion; In target following, sensor node perceives the speed of moving target, needs in conjunction with own positional information, and the moving line of ability monitored object, and target of prediction movement locus have immeasurable meaning to the precision strike and the situation of battlefield supervision of target.

The massive wireless sensor location is general adopts non-distance-measuring and positioning method, and network topology structure is brought a lot of uncertain factors to non-range finding location algorithm.For regular network, existing algorithm can well realize node locating, but actual influence owing to other objective factors such as landform, environment and arrangements, the scramble network ubiquity causes many location algorithms not meet the demands.Owing in scramble network, have " node cavity " and " blind area ", promptly at a certain region memory in the route cavity, cause the multi-hop route vector between this zone interior nodes to form an arc route, the direct influence that the arc route is brought is exactly to cause the internodal estimated distance will be much larger than its actual value.Be difficult to adapt to this topological structure apart from method between traditional route vector meter operator node, must improve.

Summary of the invention

Goal of the invention: the present invention is directed to the distance that converts and obtain based on jumping figure information in traditional non-range finding position fixing process, be subjected to the network topology structure influence easily, especially more obvious this problem of error in scramble network has proposed a kind of new localization method,

Technical scheme: the technical solution adopted in the present invention is as follows:

A kind of node method for self-locating of wireless sensor network comprises following steps:

1) adopt the distance vector exchange agreement, flooded broadcast makes all nodes get access to the jumping hop count order of all anchor nodes to network;

2) according to the distribution of network size and anchor node, suitably anchor node is carried out cluster, form plurality of sub-regions.In each sub regions,, utilize network topology structure judgement factor delta that current sub network network topological structure is judged according to the jumping segment distance information between the anchor node;

3) when judging factor delta〉2, each unknown node is carried out ascending sort to the jumping figure of anchor node, choose the set of the anchor node of all jumping figure correspondences then, note is made { set:Hop, Num, ID}, and will gather interior Hop item and be initialized as { min _HopsSet, wherein the Hop representative is by the jumping figure of ascending sort, and Num represents anchor node number corresponding under the current jumping figure, and ID represents the anchor node numbering under the current jumping figure;

4) { the ID} interior element screens pair set one by one for set:Hop, Num, finds out the node that meets the promotion condition and to its reference anchor node that positions.

5) for the node that satisfies decision condition it is promoted;

6) travel through all nodes again, carry out and promote strategy, finish the location until all unknown node.

Described network topology structure is judged the factor $\mathrm{\δ}=\frac{\underset{\mathrm{ij}}{\mathrm{\Σ}}\underset{x=1}{\overset{{\mathrm{hop}}_{\mathrm{ij}}}{\mathrm{\Σ}}}{\mathrm{hopdist}}_x}{\underset{\mathrm{ij}}{\mathrm{\Σ}}{\mathrm{dist}}_{\mathrm{ij}}},$ Wherein ${\mathrm{dist}}_{\mathrm{ij}}=\sqrt{{(x_i-x_j)}^2+{(y_i-y_j)}^2},$ (x _i, y _i), (x _j, y _j) be node i, the coordinate of j, hop _IjFor jumping hop count, hopdist _xBe every section jumping distance.

Element screening step is as follows in the step 4:

1) chooses a threshold value Num in the set set _ThAs decision condition;

2) Hop item that will be corresponding with it is as { min _HopsGather and upgrade;

Whether 3) anchor node to each ID under this set carries out the judgement of range difference least square, promptly calculate and satisfy

$\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{({\mathrm{hop}}_i-{\mathrm{hop}}_j)}^2=\min ;$

Num element number sum 〉=3 in the described set set.

Beneficial effect: the present invention adds node and promotes strategy on DV-distance algorithm basis, adopts the DV-distance location in local isotropism dense network, and the node of finishing the location is promoted as anchor node.By increasing the jumping hop count that the way of promoting anchor node reduces the route vector, reduce the possibility that the arc route occurs with this, reach the effect of improving big radian multi-hop route vector.Along with the carrying out of position fixing process, anchor node density constantly increases, reduced in the nonuniform network route vector between the node apart from accumulated error, improved whole positioning accuracy.

Description of drawings

Fig. 1 is a flow chart of the present invention.

Fig. 2 is the regular network topological diagram.

Fig. 3 is a C type scramble network topological diagram.

Fig. 4 is an algorithm flow chart of the present invention.

Embodiment

The invention will be further described below in conjunction with accompanying drawing:

As shown in Figure 1, wireless sensor network method for self-locating provided by the invention comprises following steps:

Step 1: adopt the distance vector exchange agreement, flooded broadcast makes all nodes get access to the jumping hop count order of all anchor nodes to network.

Step 2: according to the distribution of network size and anchor node, suitably anchor node is carried out cluster, form plurality of sub-regions.In each sub regions,, utilize network topology structure judgement factor delta that current sub network network topological structure is judged according to the jumping segment distance information between the anchor node.

For massive wireless sensor, interstitial content is too much in the network, if adopt global calculation, at first causes very big of amount of calculation, and next brings network topology structure identification inaccurate.Can carry out area dividing to global network, be divided into the experimental process network, then each sub-network be carried out Distributed Calculation.

Because anchor node is the node of location aware, can carry out dynamic clustering to them, forms the sub-network that several are organized into by anchor node, then each sub-network is carried out topological structure identification.

C-mean cluster method is as the classic algorithm of dynamic clustering, implements more conveniently, and fast convergence rate can obtain good cluster effect.Weak point is must specify the classification number before the cluster.This algorithm basis is the error sum of squares criterion, and core concept is as follows:

(1), from sample set, selects N proper sample by rule of thumb as initial cluster center according to particular problem;

(2) get a sample, it is included in that class of the cluster centre nearest with it, recomputate sample average, upgrade cluster centre.Take off a sample then, repetitive operation is included in the respective class until all samples;

(3) adopt the error sum of squares criterion function to judge whether cluster is reasonable, unreasonable then the modification classified.Circulation is judged, is revised until reaching the algorithm end condition.

Under the very big situation of network size, when the anchor node distribution was C-network, it was 3 that the cluster number is set; When distribution was O type network, the cluster number was set to 4.

Step 3: for regular network, the fine solution orientation problem of traditional APS algorithm energy, node does not need to promote.For non-regular network, then need node to promote.How the recognition network topological structure can be judged as follows:

The topological structure of analysis rule network (shown in Figure 2) and non-regular network (is that representative is shown in Figure 3 with the C-network) according to actual range between the anchor node and the ratio between the measuring distance, can suitably be distinguished as can be seen.If anchor node i, the j coordinate is respectively (x _i, y _i), (x _j, y _j), the actual range between them ${\mathrm{dist}}_{\mathrm{ij}}=\sqrt{{(x_i-x_j)}^2+{(y_i-y_j)}^2},$ The jumping hop count is hop _Ij, every section jumping distance is hopdist _xNote δ is that network topology structure is judged the factor, $\mathrm{\δ}=\frac{\underset{\mathrm{ij}}{\mathrm{\Σ}}\underset{x=1}{\overset{{\mathrm{hop}}_{\mathrm{ij}}}{\mathrm{\Σ}}}{\mathrm{hopdist}}_x}{\underset{\mathrm{ij}}{\mathrm{\Σ}}{\mathrm{dist}}_{\mathrm{ij}}}.$ In the actual conditions, the cumulative measurement distance between the anchor node all is greater than air line distance, i.e. δ〉1, but the δ value in regular network will be provided with δ=2 and be decision threshold much smaller than the δ value in the non-regular network among the present invention.When δ≤2 are judged to be regular network, otherwise be non-regular network.

When judging factor delta〉2, current network is non-regular network as can be known, should adopt node to promote strategy.At first each unknown node is carried out ascending sort to the jumping figure of anchor node, choose the set of the anchor node of all jumping figure correspondences then, note is made { set:Hop, Num, ID}, and will gather interior Hop item and be initialized as { min _HopsSet, wherein the Hop representative is by the jumping figure of ascending sort, and Num represents anchor node number corresponding under the current jumping figure, and ID represents the anchor node numbering under the current jumping figure.

Step 4: in the recognition network topological structure,, can find out whether there is multi-hop arc route between them by the δ value between the test anchor node.δ among Fig. 3 for example ₁₂, δ ₁₃Will be much larger than δ ₂₃, the A1 of UNICOM then, A2, the localized network of A3 are irregular certainly.If X node among the figure can be promoted as anchor node, then by X, A2, the localized network approximate regulation network of A3 UNICOM can position by the APS algorithm.In fact, the X node can be unique definite by following method:

If unknown node X to be promoted is to anchor node i, the jumping figure of j is respectively hop _i, hop _j, guarantee hop _i, hop _j∈ { min _Hops, and

Promptly satisfy the range difference quadratic sum minimum of X to each anchor node, then can unique definite X.{ min wherein _HopsRepresent the set that less hop forms, the node that can promote is always preferentially searched for anchor node in this set, utilize range difference least square rule then, filters out concrete anchor node and positions.

Estimating to illustrate theoretically that the node locating precision of above selection is the highest according to the maximum likelihood in the node location evaluation method, is the optimum node promoted.The coordinate of supposing n point is respectively (x ₁, y ₁), (x ₂, y ₂), (x ₃, y ₃) ..., (x _n, y _n), corresponding distance is d ₁, d ₂, d ₃..., d _n, the coordinate of unknown node X be (x, y), formula (1) is always set up so:

(x-x _i) ²+(y-y _i) ²=d _i ²,i=1,2,...,n…(1)

The employing maximum-likelihood method is found the solution, and can get the coordinate of X

, wherein

$A=2\left[\begin{array}{cc}{x}_1-x_2& y_1-y_2\\ \·& \·\\ \·& \·\\ \·& \·\\ x_1-x_n& y_1-y_n\end{array}\right],$ $b=\left[\begin{array}{c}{x_1}^2-{x_2}^2+{y_1}^2-{y_2}^2+{d_1}^2-{d_2}^2\\ \·\\ \·\\ \·\\ {x_1}^2-{x_n}^2+{y_1}^2-{y_n}^2+{d_1}^2-{d_n}^2\end{array}\right],$ $\hat{X}=\left[\begin{array}{c}x\\ y\end{array}\right]\·\·\·\left(2\right)$

According to formula (2), if

Error minimum, then d _i ²-d _j ²Minimum.And d _i ²-d _j ²=(d _i+ d _j) (d _i-d _j), wherein

(d _i-d _j)=|| hop _i-hop _j||, get (hop _i-hop _j) ²Minimum.Thereby proved that the node of selecting promoted is optimum node.

{ the ID} interior element screens pair set one by one for set:Hop, Num.The Num number sum that at first guarantees set set interior element equals three at least to satisfy the demand of location, can choose a threshold value Num _Th〉=3 as decision condition, and with the Hop item of correspondence as { min _HopsGather and upgrade, whether the anchor node to each ID under this set carries out the judgement of range difference least square then, promptly calculate and satisfy By this processing, can find out the node that meets the promotion condition and to its reference anchor node that positions.

Step 5: for the node that satisfies decision condition, can think that this node meets strict positioning requirements, position error is local minimum, allows its promotion.So far, first round node is promoted and is finished.

The unknown node that meets the promotion condition, owing to itself need satisfy strict condition, so in the middle of all unknown node, their positioning accuracy is the highest.In Fig. 3, the X node satisfies the promotion condition, preferentially adopts the maximum-likelihood method location.Though estimated position that obtains and actual position be deviation to some extent, for the location of follow-up unknown node, the arc route accumulated error of comparing and selecting the A1 anchor node to bring as the reference node, this departure is negligible.

Generally, can significantly reduce the influence of multi-hop route accumulated error behind the anchor node replacement part actual anchors node of promoting, select this moment to promote anchor node as the reference node, cooperate other actual anchors nodes, adopt the DV-distance algorithm to obtain range information, utilize optimized Algorithm such as maximum-likelihood method or simulated annealing to position then.If unknown node is found to the jumping hop count of promoting anchor node when differing very little with jumping hop count to the actual anchors node, consider that promoting node exists the position error of self after all, should abandon utilizing the promotion anchor node to select the actual anchors node to replace it as the reference node.

Step 6: travel through all nodes again, carry out and promote strategy, finish the location until all unknown node.

If exist many wheels to promote in the network, need set priority to the anchor node of promoting, the anchor node priority that attains promotion at first is the highest.When selecting the promotion anchor node as the reference node, at first select the highest node of priority, can reduce the accumulated error of when selecting to promote the anchor node location, bringing like this.As shown in Figure 4, the node of the present invention's proposition is promoted the complete flow chart of strategy.

Claims (3)

1. the node method for self-locating of a wireless sensor network is characterized in that, comprises following steps:

1) adopt the distance vector exchange agreement, flooded broadcast makes all nodes get access to the jumping hop count order of all anchor nodes to network;

2) according to the distribution of network size and anchor node, suitably anchor node is carried out cluster, form plurality of sub-regions, in each sub regions, according to the jumping segment distance information between the anchor node, utilize network topology structure judgement factor delta that current sub network network topological structure is judged;

3) when judging factor delta〉2, each unknown node is carried out ascending sort to the jumping figure of anchor node, choose the set of the anchor node of all jumping figure correspondences then, note is made { set:Hop, Num, ID}, and will gather interior Hop item and be initialized as { min _HopsSet, wherein the Hop representative is by the jumping figure of ascending sort, and Num represents anchor node number corresponding under the current jumping figure, and ID represents the anchor node numbering under the current jumping figure;

4) { the ID} interior element screens pair set one by one for set:Hop, Num, finds out the node that meets the promotion condition and to its reference anchor node that positions.

5) for the node that satisfies decision condition it is promoted;

6) travel through all nodes again, carry out and promote strategy, finish the location until all unknown node.

2. method for self-locating according to claim 1 is characterized in that: described network topology structure is judged the factor $\mathrm{\δ}=\frac{\underset{\mathrm{ij}}{\mathrm{\Σ}}\underset{x=1}{\overset{{\mathrm{hop}}_{\mathrm{ij}}}{\mathrm{\Σ}}}{\mathrm{hopdist}}_x}{\underset{\mathrm{ij}}{\mathrm{\Σ}}{\mathrm{dist}}_{\mathrm{ij}}},$ ${\mathrm{dist}}_{\mathrm{ij}}=\sqrt{{(x_i-x_j)}^2+{(y_i-y_j)}^2},$ (x _i, y _i), (x _j, y _j) be node i, the coordinate of j, hop _IjFor jumping hop count, hopdist _xBe every section jumping distance.

3. method for self-locating according to claim 1 is characterized in that: element screening step is as follows in the step 4:

1) chooses a threshold value Num in the set set _ThAs decision condition;

2) Hop item that will be corresponding with it is as { min _HopsGather and upgrade;

Whether 3) anchor node to each ID under this set carries out the judgement of range difference least square, promptly calculate and satisfy

$\underset{i}{\mathrm{\Σ}}\underset{j}{\mathrm{\Σ}}{({\mathrm{hop}}_i-{\mathrm{hop}}_j)}^2=\min ;$

Num element number sum 〉=3 in the described set set.

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