CN103298104A - Wireless sensor network node three-dimensional positioner of leader intelligent choice mechanism - Google Patents
Wireless sensor network node three-dimensional positioner of leader intelligent choice mechanism Download PDFInfo
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Abstract
Disclosed is a wireless sensor network node three-dimensional positioner of a leader intelligent choice mechanism. The invention provides the wireless sensor network node three-dimensional positioner of the leader intelligent choice optimization based on received signal intensity distance measurement and on a biology heuristic method. A simple animal group leader preference mode is designed, a leader searcher obtains one leader candidate group, abilities of each candidate leader are evaluated, an optimal individual is selected as a leader, and the leader represents a globally optimal solution in positioner optimization problems. The problem of three-dimensional node positioning is abstracted into a nonlinearity unconstrained optimization problem by defining a three-dimensional node positioning objective function to which distance measurement is taken fully consideration, then the nonlinearity unconstrained optimization problem is solved by utilizing a leader intelligent choice optimization algorithm, and the solution is the three-dimensional coordinate estimated value of the node positioner. The wireless sensor network node three-dimensional positioner is easy to achieve, node positional accuracy is improved, calculation complexity is reduced, and the effect of range errors on the result is small.
Description
Technical field
The invention belongs to the self poisoning technical field of wireless sensor network node.
Background technology
(Wireless Sensor Networks, WSN) three-dimensional node locating is one of current research focus to wireless sensor network.In the WSN node locating, relatively Chang Yong distance measuring method has reception signal strength signal intensity (Received Signal Strength, RSS), the time of advent (Time Of Arrival, TOA) and the time of advent poor (Time Difference Of Arrival, TDOA) etc.But adopt the distance measuring method of TOA and TDOA to need the synchronous and extra hardware cost of correct time, range finding need not extra hardware cost and based on RSS, and communication overhead is little, computation complexity is low, therefore, based on the node locating technique of RSS range finding be use at most at present, one of node locating technique the most widely.
In order to improve the node locating precision, reduce the calculating power consumption of node simultaneously, multiple node locating device based on the RSS range finding has been proposed.Wherein more the having of research at present: estimate (Maximum Likelihood Estimation based on maximum likelihood, MLE) locator of algorithm, based on weighted least-squares (Weighted Least Squares, WLS) locator of algorithm, these locators are easier to realize, but can not obtain high orientation precision, be restricted in actual applications; (Multi-Dimensional Scaling MAP, MDS-MAP) the three-dimensional node locating device of technology owing to MDS matrix computations complexity, cause the loss of node calculating energy bigger based on multidimensional scaling; (the process randomness that produces next son generation among the GA is bigger, causes this locator can not obtain higher node locating precision equally for Genetic Algorithm, GA) the WSN node locating device of You Huaing based on genetic algorithm; Based on population (Particle Swarm Optimization, PSO) the WSN node locating device of algorithm optimization is to study more a kind of locator at present, though improved positioning accuracy to a certain extent, the positioning accuracy that improves is still very limited, and amount of calculation is bigger; Based on BFGS (Broyden, Fletcher, Goldfarb, though Shannon) the WSN node locating device of You Huaing can be avoided the calculating of inverse matrix in the least square localization method, relatively low by the positioning accuracy that this algorithm obtains.
Summary of the invention
The problem low at the three-dimensional node locating precision of current WSN based on the RSS range finding, that amount of calculation is big, the invention provides a kind of wireless sensor network node three-dimensional locator of head's Intelligence Selection mechanism, it is with didactic head's Intelligence Selection (Leader Intelligent Selection, LIS) positioning and optimizing algorithm is applied in the three-dimensional node locating of WSN, effectively improve the node locating precision, reduced computation complexity, and tested less apart from error effect.The present invention is achieved by the following technical solutions.
By the animal population head preference pattern of simplicity of design, obtain a head candidate colony by the head searchers, and each candidate head's ability evaluation is selected optimum individual as the head, this head represents the globally optimal solution in the locator optimization problem.By defining a three-dimensional node locating target function that takes into full account the range finding factor, with three-dimensional node locating problem abstract be non-linear unconstrained optimization problem, recycling head's Intelligence Selection is optimized algorithm and is found the solution this non-linear unconstrained optimization problem, and the gained solution is exactly the three-dimensional coordinate estimated value of node locating device.The specific implementation process is as follows.
1. head's Intelligence Selection is optimized basic idea
The head of colony of animal selects behavior and memory behavior to be considered to senior behavior in the compoundanimal, and (Leader Intelligent Selection, LIS) algorithm is exactly these the two kinds of behaviors in the imitation animal population to head's Intelligence Selection.By the animal population head preference pattern of simplicity of design, can obtain the globally optimal solution of comparatively complicated positioning and optimizing problem.Comprise head (Leader), candidate head (Candidates) and searchers (Searcher) three class members in the LIS algorithm.Wherein the head represents the optimal solution of the problem of asking.Initial head utilizes neighbours' anchor node coordinate and ranging information to be obtained by least-squares algorithm, and offspring head then obtains by head's mechanism of campaigning for; Can candidate head represents head's candidate colony, become the head and mainly by the target function of definition in the invention ability of each candidate be estimated, and ability soprano then becomes the head in this generation; Searchers's main task is that initial head also is the searchers by the follow-on candidate head of search candidate head mechanism search.Having defined the head in the LIS algorithm campaigns for mechanism, searchers and selects mechanism and three kinds of behavior models of search candidate head mechanism.The LIS algorithm is in node locating, and each candidate is represented a possibility coordinate of sensor node, selects the head by the ability of estimating each candidate, and the final head who obtains is desired sensor node coordinate.
2. initial method
Colony intelligence optimization algorithm all takes to produce at random earlier population mostly, choose initial optimal value again from this initial population, because initially the randomness of time is bigger, the optimal value that obtains from initial population is generally relatively poor, this has not only increased the iterative process of algorithm, and has increased the overall calculation amount simultaneously.The LIS algorithm is taked the mode opposite with it, at first utilize neighbours' anchor node coordinate and ranging information to obtain a least square solution as the initial head of LIS algorithm by least square method, and obtain the initial candidate head by our following initial candidate head's mechanism model of design
Wherein,
Expression initial candidate head;
The initial head that the expression least-squares algorithm obtains;
The expression average is zero, and variance is
Normal distribution,
Value relevant with the number of anchor node among the whole WSN, can obtain by our following formula of design
Wherein,
The number of expression anchor node;
Be the initial candidate factor of influence, generally get a real number between 0 ~ 0.4.
3. the performing step of LIS algorithm
The initialization of step 1:LIS algorithm
According to the described initial method of last branch, obtain initial head and initial candidate head.Enter step (3);
Step 2: produce new candidate head
The searchers who obtains according to previous step produces new candidate head according to following formula
Wherein,
The searchers that the expression previous step obtains;
The expression average is zero, and variance is
Normal distribution,
Value defined by following formula
Wherein,
Be the initial variance that obtains by step (1);
Be the current iteration number of times;
Be maximum iteration time;
Be candidate's factor of influence, generally get an integer between 4 ~ 9;
Step 3: choose new head
Estimate the ability of each candidate by the target function of definition in the invention, choose the superior among this candidate head and the head of previous generation and compare, and be the new head in this generation with its superior's storing memory;
Step 4: choose the searchers
The number that is better than previous generation head among the candidate head is during more than one, and all are better than previous generation head's candidate head's arithmetic mean as candidate head's of future generation searchers; Otherwise, with the searchers of new head as candidate head of future generation;
Step 5: if the mean square deviation that reaches maximum iteration time or new head and previous generation head less than 0.001 o'clock, stops the final head's value that obtains of iteration and output; Otherwise enter step (2).
4. target function
Target function is the sole criterion of estimating LIS algorithm candidate individuality, and therefore, the accurate target function is one of key factor that improves positioning accuracy.Suppose that the coordinate of a certain unknown node is in a three dimensions
, it has
Individual neighbours' anchor node, the coordinate of neighbours' anchor node that it is corresponding is respectively
, be respectively by the anchor node of RSS range finding acquisition and the distance between the unknown node
Relation in the three dimensions between anchor node coordinate, unknown node coordinate and the range finding three can be expressed as:
In the range measurement of reality, internodal distance is more far away, the noise that adds in ranging process is also just more big, the internodal distance of measuring is also just more inaccurate, therefore, the mode of inverse weight of taking to find range is given prominence to the measuring distance with the nearer anchor node of unknown node, and adopts the outstanding effect of measuring distance in target function apart from the nearer anchor node of unknown node of range finding factor of influence, and the present invention has defined a kind of target function of following form
Wherein,
For greater than 1 distance measure;
Be the range finding factor of influence,
Can not be too big, generally get an integer between 2 ~ 5.
5. the performing step of the three-dimensional locator of the wireless sensor network node of head's Intelligence Selection mechanism
Step 1: seek orientable unknown node
In the network starting stage, give each sensor node distribute one ID number, and anchor node and unknown node carried out mark, anchor node sends message to the unknown node in oneself jumping scope then, message content comprises ID number and the D coordinates value of oneself.Unknown node is noted the message information that receives, and judges the number of self neighbours' anchor node, positions estimation if the number of anchor node is not less than 4;
Step 2: by the distance between wireless channel model estimation unknown node and the anchor node;
Step 3: the node locating estimation problem is converted to unconstrained optimization problem;
Step 4: find the solution the minimum of target function by the LIS algorithm, the final head of acquisition is the estimated value of desired unknown node coordinate.
Beneficial effect of the present invention is.
1. positioning accuracy
Positioning accuracy is the most important performance evaluating of location algorithm, represents the average position error of node with following formula usually
In the formula,
It is the true coordinate of node;
It is the estimated coordinates of node;
With
Be respectively node sum and anchor node number;
Be the node communication radius.
2. target function
This locator target function (LIS Objective Function, LIS-OF) with traditional weighting but consider locator target function (the Conventional Weighted No Factor Objective Function of range finding factor of influence, CWNF-OF) consider locator target function (the No Weighted Factor Objective Function of range finding weighted sum range finding factor of influence and among the present invention, NWF-OF) calculate and to be respectively the average time that a unknown node coordinate spends: 0.0175510s, 0.0155420s, 0.0034478s.Fig. 2 is respectively that these three kinds different locator target functions are along with the comparison diagram of the variation position error of anchor node number, node communication radius and interchannel noise variance.From Fig. 2 and spended time as can be seen, though this locator target function amount of calculation is bigger, but owing to added this parameter of range finding factor of influence, not only can obtain higher positioning accuracy, tested influence apart from error simultaneously is significantly less than other target function, especially relatively poor when node environment of living in, when noise was big, it was tested apart from the less advantage of error effect more to demonstrate this locator target function.
3. initial method
Traditional locator initialization of population (Conventional Initialization, Con-Init) generally take to produce population at random earlier, chooses initial optimal value again from this initial population by method.(though added least-squares algorithm, too much do not increase the computing time of algorithm for LIS Initialization, LIS-Init) method to the invention provides a kind of more efficiently locator initialization.Be respectively the average time that this locator initial method and unknown node coordinate of traditional locator initial method calculating spend: 0.018838s, 0.021338s.Fig. 3 is respectively the different initial methods of these two kinds of locators along with the comparison of the variation position error of anchor node number, node communication radius and interchannel noise variance.From Fig. 3 and spended time as can be seen, this locator initial method has reduced the computing time of algorithm, and can obtain higher positioning accuracy, especially when the node communication radius hour, more can demonstrate the high advantage of this locator initial method positioning accuracy.
4. LIS algorithm performance
Be respectively the average time that LIS algorithm, ABC (Artificial Bee Colony) algorithm, PSO algorithm and unknown node coordinate of GA algorithm calculating spend: 0.016796s, 0.040900s, 0.038664s, 0.016970s.Fig. 4 is respectively these four kinds different locators and optimizes algorithm along with the comparison of the variation positioning performance of anchor node number, node communication radius and interchannel noise variance.From figure and spended time as can be seen, optimizing algorithm with other locator compares, the LIS algorithm can obtain higher positioning accuracy, and effectively reduced the computing time of algorithm, namely reduced the computation complexity of whole node navigation system, especially when the node communication radius hour, more can demonstrate the high advantage of this locator positioning accuracy; Though the computing time of genetic algorithm is basic identical with the LIS algorithm, positioning accuracy is lower.
In sum, the present invention proposes a kind of new three-dimensional node intelligent positioner of the WSN based on the optimization of head's Intelligence Selection, select this simple behavior model to realize the comparatively complicated three-dimensional node locating problem of WSN by imitation animal population head, the raising of node locating precision and the reduction of computation complexity there is bigger contribution, and the tested influence apart from error of this locator is less, for the development of the three-dimensional node locating technique of WSN provides new theoretical method.
Description of drawings
Fig. 1 is the schematic diagram of head's Intelligence Selection algorithm of intelligent positioner of the present invention.
Fig. 2-(a), Fig. 2-(b), Fig. 2-(c) is respectively that these three kinds different locator target functions of LIS-OF, CWNF-OF and NWF-OF are along with the comparison diagram of the variation position error of anchor node number, node communication radius and interchannel noise variance.
Fig. 3-(a), Fig. 3-(b), Fig. 3-(c) is respectively that these two kinds different locator initial methods of Con-Init and LIS-Init are along with the comparison diagram of the variation position error of anchor node number, node communication radius and interchannel noise variance.
Fig. 4-(a), Fig. 4-(b), Fig. 4-(c) is respectively that these four kinds different locators of LIS algorithm, ABC algorithm, PSO algorithm and GA algorithm are optimized algorithms along with the comparison diagram of the variation position error of anchor node number, node communication radius and interchannel noise variance.
Embodiment
The present invention is further detailed explanation below in conjunction with drawings and the specific embodiments.
The wireless sensor network node three-dimensional locator of a kind of head's Intelligence Selection of the present invention mechanism, by defining a three-dimensional node locating target function that takes into full account the range finding factor, with the three-dimensional localization problem of wireless sensor network node abstract be non-linear unconstrained optimization problem, head's Intelligence Selection that recycling proposes is optimized algorithm and is found the solution this non-linear unconstrained optimization problem, and the gained solution is exactly the three-dimensional coordinate estimated value of node locating device.
In conjunction with Fig. 1, three-dimensional locator of the present invention passes through following steps successively.
Step 1: seek orientable unknown node
In the network starting stage, give each sensor node distribute one ID number, and anchor node and unknown node carried out mark, anchor node sends message to the unknown node in oneself jumping scope then, message content comprises ID number and the D coordinates value of oneself.Unknown node is noted the message information that receives, and judges the number of self neighbours' anchor node, positions estimation if the number of anchor node is not less than 4.
Step 2: by the distance between wireless channel model estimation unknown node and the anchor node.
Step 3: the node locating estimation problem is converted to unconstrained optimization problem.Namely ask the minimum of following target function
Step 4: initialization
The initial value of setup parameter, and utilize neighbours' anchor node coordinate and ranging information to obtain a least square solution as the initial head of LIS algorithm by least square method, and obtain the initial candidate head by our following initial candidate head's mechanism model of design
Wherein,
Expression initial candidate head;
The initial head that the expression least-squares algorithm obtains;
The expression average is zero, and variance is
Normal distribution,
Value relevant with the number of anchor node among the whole WSN, can obtain by our following formula of design
Wherein,
The number of expression anchor node;
Be the initial candidate factor of influence, generally get a real number between 0 ~ 0.4.Enter step (6).
Step 5: produce new candidate head
The searchers who obtains according to previous step produces new candidate head according to following formula
Wherein,
The searchers that the expression previous step obtains;
The expression average is zero, and variance is
Normal distribution,
Value defined by following formula
Wherein,
Be the initial variance that obtains by step (1);
Be the current iteration number of times;
Be maximum iteration time;
Be candidate's factor of influence, generally get an integer between 4 ~ 9.
Step 6: choose new head
Estimate the ability of each candidate by the target function of definition in the invention, choose the superior among this candidate head and the head of previous generation and compare, and its superior's storing memory is the new head in this generation.
Step 7: choose the searchers
The number that is better than previous generation head among the candidate head is during more than one, with all arithmetic means of candidate head that are better than previous generation head as candidate head's of future generation searchers, otherwise, with the searchers of new head as candidate head of future generation.
Step 8: if the mean square deviation that reaches maximum iteration time or new head and previous generation head stopped iteration less than 0.001 o'clock, the final head who obtains is exactly the optimal solution of unknown node coordinate; Otherwise enter step (5).
It should be noted that at last; above embodiment is only unrestricted in order to technical scheme of the present invention to be described; although with reference to preferred embodiment the present invention is had been described in detail; but protection scope of the present invention is not limited thereto; anyly be familiar with those skilled in the art in the technical scope that the present invention discloses; the modification that can expect easily or be equal to replacement, and do not break away from the spirit and scope of technical solution of the present invention, all should be encompassed within protection scope of the present invention.
Claims (3)
1. the wireless sensor network node three-dimensional locator of head's Intelligence Selection mechanism is characterized in that: according to the heuristic thought of biology, proposed head's Intelligence Selection positioning and optimizing algorithm; This optimizes algorithm by the animal population head preference pattern of simplicity of design, obtains a head candidate colony by the head searchers, and each candidate head's ability evaluation is selected optimum individual as the head, the globally optimal solution of expression optimization problem; In order to accelerate head's Intelligence Selection algorithm later stage head's election contest and evolutionary rate, utilize anchor node coordinate and ranging information to try to achieve an initial coordinate as the initial head of head's Intelligence Selection algorithm by least-squares algorithm; By defining a three-dimensional node locating target function that takes into full account the range finding factor, with the three-dimensional localization problem of wireless sensor network node abstract be non-linear unconstrained optimization problem, head's Intelligence Selection that recycling proposes is optimized algorithm and is found the solution this non-linear unconstrained optimization problem, and the solution of trying to achieve is exactly the estimated value of WSN node three-dimensional coordinate.
2. head's intelligent three-dimensional locator according to claim 1, it is characterized in that: according to the form that receives actual channel model in the signal strength signal intensity range finding, consider that range finding and the relation of range error have obtained this parameter of range finding factor of influence, to the target function inverse weight of finding range, and by the outstanding less effect of measuring distance in target function of range error of range finding factor of influence; Consider the actual noise form in the channel model, the three-dimensional node intelligent positioner that the present invention is based on the optimization of head's Intelligence Selection can solve range error just to affect positioning, improves positioning accuracy and the convergence of algorithm speed of node.
3. 3 D intelligent locator according to claim 1 and 2 is characterized in that: specifically pass through following steps:
Step 1: seek orientable unknown node
Step 2: by the distance between wireless channel model estimation unknown node and the anchor node;
Step 3: the node locating estimation problem is converted to unconstrained optimization problem;
Namely ask the minimum of following target function;
Step 4: initialization
The initial value of setup parameter, and utilize neighbours' anchor node coordinate and ranging information to obtain a least square solution as the initial head of LIS algorithm by least square method, and obtain the initial candidate head by our following initial candidate head's mechanism model of design
Wherein,
Expression initial candidate head;
The initial head that the expression least-squares algorithm obtains;
The expression average is zero, and variance is
Normal distribution,
Value relevant with the number of anchor node among the whole WSN, can obtain by our following formula of design
Wherein,
The number of expression anchor node;
Be the initial candidate factor of influence, generally get a real number between 0 ~ 0.4;
Enter step (6);
Step 5: produce new candidate head
The searchers who obtains according to previous step produces new candidate head according to following formula
Wherein,
The searchers that the expression previous step obtains;
The expression average is zero, and variance is
Normal distribution,
Value defined by following formula
Wherein,
Be the initial variance that obtains by step (1);
Be the current iteration number of times;
Be maximum iteration time;
Be candidate's factor of influence, generally get an integer between 4 ~ 9;
Step 6: choose new head
Estimate the ability of each candidate by the target function of definition in the invention, choose the superior among this candidate head and the head of previous generation and compare, and be the new head in this generation with its superior's storing memory;
Step 7: get the searchers
The number that is better than previous generation head among the candidate head is during more than one, with all arithmetic means of candidate head that are better than previous generation head as candidate head's of future generation searchers, otherwise, with the searchers of new head as candidate head of future generation;
Step 8: if the mean square deviation that reaches maximum iteration time or new head and previous generation head stopped iteration less than 0.001 o'clock, the final head who obtains is exactly the optimal solution of unknown node coordinate; Otherwise enter step (5).
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