CN111610490A - Sensing node positioning method for filtering RSSI and tabu search clustering - Google Patents
Sensing node positioning method for filtering RSSI and tabu search clustering Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
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- H04B17/309—Measuring or estimating channel quality parameters
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- H—ELECTRICITY
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
Abstract
The method is applied to a wireless sensor network, and in the RSSI measuring process, the method analyzes and optimizes the RSSI measured value by using an STF method and an MAF method, obtains the RSSI optimized measured value by combining the two filtering methods, avoids the interference of environmental factors to the signal intensity, and further obtains more accurate distance between nodes. In the node positioning process, the method converts the positioning problem into a global optimization problem, optimizes the position coordinates of the nodes by using a tabu search clustering method, reduces the positioning error and improves the accuracy of the node positioning result. In addition, the application also provides a sensor node positioning device, equipment and a readable storage medium for filtering RSSI and tabu search clustering, and the technical effect of the sensor node positioning device corresponds to the technology of the method.
Description
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for locating a sensor node of filtered RSSI and tabu search clusters.
Background
In the wireless sensor network, in order to ensure that the data can be maintained and monitored in time when the wireless sensor node has a fault and has position attributes, the positioning of the node is very critical, and the position information of the sensor node can also be used for routing protocols, data fusion, blind area processing and the like. Therefore, wireless sensor network node location technology is a hot spot of current research.
Many scholars have conducted research work on wireless sensor network positioning technology and have proposed some positioning methods. For example, the RSSI-based positioning method obtains the inter-node distance through RSSI measurement, and then obtains the coordinates of the positioning node using the maximum likelihood estimation method. However, the RSSI measurement is easily interfered by environmental factors, and the node coordinate error obtained by the maximum likelihood estimation method is large, resulting in low accuracy of the node positioning result.
In order to ensure that an accurate RSSI measurement value is obtained under a complex environmental noise, a correlative learner uses a Moving Average Filter (MAF) to process the measured RSSI, and the MAF method has strong adaptability to environmental changes and good inhibition effect on noise, but cannot converge under a severe environmental noise, and has a large positioning error. Some researchers propose to perform data optimization processing on the RSSI by using a Kalman Filter (KF), so that the true RSSI can be obtained well, but the KF method is weak in capability of tracking a sudden change state of a system, and when the RSSI generates an environmental noise sudden change, the processing effect is not ideal. In order to enhance the tracking capability of a KF and an improved Extended Kalman Filter (EKF) thereof on the system mutation state, some scholars propose a Strong Tracking Filter (STF), the method has stronger self-adaption capability than the KF or the EKF and is successfully applied in multiple fields of fault diagnosis, target tracking, energy consumption modeling and the like, but strong tracking filter weakening factors can only be selected by experience, the practical application difficulty is higher, and when the ratio of the RSSI jump amplitude to the environmental noise is smaller, the STF method is insensitive to the system mutation reaction and lower in reliability.
In conclusion, the positioning method based on the RSSI has large errors in both the RSSI measurement and the maximum likelihood estimation processes, which results in low accuracy of the node positioning result.
Disclosure of Invention
The application aims to provide a sensing node positioning method, a sensing node positioning device, sensing node positioning equipment and a readable storage medium for filtering RSSI and tabu search clustering, which are used for solving the problem that the node positioning result of an RSSI-based positioning method is not high in accuracy due to the fact that large errors exist in the RSSI measurement and maximum likelihood estimation processes. The specific scheme is as follows:
in a first aspect, the present application provides a method for locating a sensing node of filtering RSSI and tabu search clusters, which is applied to a wireless sensor network, and includes:
filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method and an MAF method to obtain an RSSI optimized measured value;
determining a distance value between the node to be positioned and the anchor node according to the RSSI optimized measured value;
generating a constraint function by utilizing a maximum likelihood estimation method according to the distance value between the node to be positioned and the anchor node, wherein the constraint function is used as a fitness function of a tabu search clustering method;
and converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by using a tabu search clustering method based on the fitness function to obtain a positioning result.
Preferably, the filtering processing is performed on the RSSI measurement values of the node to be located and the anchor node by using an STF method and a MAF method to obtain the RSSI optimized measurement value, and the filtering processing includes:
determining a strong tracking filtering weakening factor of the STF method by using the MAF method;
and filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method according to the strong tracking filtering weakening factor to obtain the RSSI optimized measured value.
Preferably, the optimizing the position coordinates of the node to be positioned by using a tabu search clustering method based on the fitness function to obtain a positioning result includes:
s11, determining an initial solution by using a clustering method according to the distance value between the node to be positioned and the anchor node, and taking the initial solution as an initial current optimal solution;
s12, carrying out tabu search in the neighborhood of the current optimal solution according to a tabu table to obtain a candidate solution set which is not tabu;
s13, determining a candidate solution with the maximum fitness value in the candidate solution set according to the fitness function; when the fitness value of the candidate solution is larger than that of the current optimal solution, updating the tabu table and updating the current optimal solution;
and S14, repeating S12 and S13 until the iteration times reach the preset times, and obtaining a target optimal solution as a positioning result.
Preferably, the performing tabu search in the neighborhood of the current optimal solution according to the tabu table to obtain a candidate solution set that is not tabu includes:
s21, carrying out tabu search in the neighborhood of the current optimal solution according to a tabu table to obtain a sampling solution which is not tabu;
s22, repeating S21 to obtain a sampling solution set;
and S23, carrying out local search on the sampling solutions in the sampling solution set to obtain a candidate solution set corresponding to each sampling solution.
Preferably, the determining, according to the fitness function, a candidate solution with a maximum fitness value in the candidate solution set includes:
and respectively determining the candidate solution with the maximum fitness value in the candidate solution set corresponding to each sampling solution according to the fitness function to obtain the local optimal solution corresponding to each sampling solution and obtain the global optimal solution.
Preferably, after determining the candidate solution with the maximum fitness value in the candidate solution set according to the fitness function, the method further includes:
and when the adaptability value of the local optimal solution is greater than the adaptability value of the global optimal solution, reducing the search step length, otherwise, increasing the search step length, wherein the search step length is preset with a minimum value.
Preferably, the performing tabu search in the neighborhood of the current optimal solution according to the tabu table to obtain a sampling solution that is not tabu includes:
and according to a tabu table, carrying out tabu search along the reverse direction of the last search direction in the neighborhood of the current optimal solution to obtain a sampling solution which is not tabu.
In a second aspect, the present application provides a sensor node positioning apparatus for filtering RSSI and tabu search clustering, which is applied to a wireless sensor network, and includes:
a filtering module: the RSSI measurement values of the nodes to be positioned and the anchor nodes are filtered by utilizing an STF method and an MAF method to obtain an RSSI optimized measurement value;
a distance determination module: the distance value between the node to be positioned and the anchor node is determined according to the RSSI optimized measured value;
a fitness function determination module: the maximum likelihood estimation method is used for generating a constraint function according to the distance value between the node to be positioned and the anchor node, and the constraint function is used as a fitness function of a tabu search clustering method;
a position determination module: and the method is used for converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by utilizing a tabu search clustering method based on the fitness function to obtain a positioning result.
In a third aspect, the present application provides a sensor node positioning device for filtering RSSI and tabu search clusters, including:
a memory: for storing a computer program;
a processor: for executing the computer program for implementing the steps of the method for sensor node location of filtered RSSI and tabu search clusters as described above.
In a fourth aspect, the present application provides a readable storage medium having stored thereon a computer program for implementing the steps of the method for sensor node location of filtered RSSI and tabu search clustering as described above when executed by a processor.
The application provides a sensing node positioning method for filtering RSSI and tabu search clustering, which is applied to a wireless sensor network and comprises the following steps: filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method and an MAF method to obtain an RSSI optimized measured value; determining a distance value between a node to be positioned and an anchor node according to the RSSI optimized measured value; generating a constraint function by using a maximum likelihood estimation method according to a distance value between a node to be positioned and an anchor node, wherein the constraint function is used as a fitness function of a tabu search clustering method; and (4) converting the positioning problem into an optimization problem, and optimizing the position coordinates of the nodes to be positioned by using a tabu search clustering method based on a fitness function to obtain a positioning result.
Therefore, in the RSSI measurement process, the method utilizes the STF method and the MAF method to analyze and optimize the RSSI measurement value, obtains the RSSI optimized measurement value by combining the two filtering methods, avoids the interference of environmental factors to the signal strength, and obtains more accurate distance between nodes. In the node positioning process, the method converts the positioning problem into a global optimization problem, optimizes the position coordinates of the nodes by using a tabu search clustering method, reduces the positioning error and improves the accuracy of the node positioning result.
In addition, the application also provides a sensor node positioning device, equipment and a readable storage medium for filtering RSSI and tabu search clustering, the technical effect of the sensor node positioning device corresponds to the technology of the method, and the details are not repeated.
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For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a first implementation of a method for locating a sensing node of filtering RSSI and tabu search clustering according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating an implementation of a second embodiment of a method for locating a sensing node of filtering RSSI and tabu search clusters according to the present disclosure;
fig. 3 is a flowchart of a tabu search clustering method in a second embodiment of a sensing node positioning method for filtering RSSI and tabu search clustering according to the present application;
fig. 4 is a functional block diagram of an embodiment of a sensor node positioning apparatus for filtering RSSI and tabu search clustering according to the present disclosure;
fig. 5 is a schematic structural diagram of an embodiment of a sensor node positioning device for filtering RSSI and tabu search clustering according to the present disclosure.
Detailed Description
The core of the application is to provide a method, a device, equipment and a readable storage medium for positioning the sensor node of filtering RSSI and tabu search clustering, which reduce the physical requirements of node hardware, can adapt to the positioning of the sensor node in a complex environment and have higher positioning precision and stability.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. 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 application.
Referring to fig. 1, a first embodiment of a method for positioning a sensing node of filtered RSSI and tabu search clusters provided in the present application is described below, where the first embodiment is applied to a wireless sensor network, and the method includes:
s101, filtering RSSI measured values of a node to be positioned and an anchor node by utilizing an STF method and an MAF method to obtain an RSSI optimized measured value;
s102, determining a distance value between the node to be positioned and the anchor node according to the RSSI optimized measured value;
s103, generating a constraint function by using a maximum likelihood estimation method according to a distance value between the node to be positioned and the anchor node, wherein the constraint function is used as a fitness function of a tabu search clustering method;
and S104, converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by using a tabu search clustering method based on the fitness function to obtain a positioning result.
The principle of node positioning is that during the period of transmitting position information between nodes, the distance between anchor nodes which are distributed randomly and nodes to be positioned is obtained, and then the position coordinates of the nodes to be positioned are calculated according to the distance optimization between the nodes. In order to improve the positioning accuracy, on one hand, the anti-interference capability of the RSSI (Received Signal Strength Indication) needs to be improved, and the self-adaptive accurate measurement capability of the RSSI needs to be improved; on the other hand, the error of determining the position coordinates of the nodes according to the distance between the nodes needs to be reduced.
Currently, both the STF and MAF methods can be used to filter RSSI measurements, but both have their own drawbacks. For example, MAF cannot converge under severe environmental noise, and has a large error; the strong tracking filtering weakening factor of the STF can be selected only by experience, the practical application difficulty is high, and when the RSSI jump amplitude and the environmental noise ratio are small, the STF method is insensitive to the system mutation reaction and low in reliability.
In view of the above problems, this embodiment provides a Novel Strong Tracking Filter (NSTF), that is, the RSSI measurement value is filtered by combining the advantages of the STF method and the MAF method, so as to obtain a more accurate distance between the anchor node and the random node to be located. The method comprises the steps of firstly, performing tracking filtering processing on signal strength information received by an anchor node and a node to be positioned, overcoming signal strength weakening and deviation caused by environmental noise change, improving stability, and then obtaining a distance value between nodes according to a logarithmic fading model related to RSSI and distance.
Specifically, an RSSI state space model is established, an STF method is applied to RSSI measurement value optimization, the STF method is analyzed by utilizing the sampling process of Gaussian distribution data, a strong tracking filtering weakening factor is determined, an MAF method is introduced, the inaccuracy of the traditional RSSI state space model is overcome, and the RSSI self-adaptive measurement capability is improved. After the RSSI measurement is optimized, a more accurate distance value between nodes can be obtained according to a logarithmic fading model.
As a specific embodiment, the combination of the STF method and the MAF method is: determining a strong tracking filtering weakening factor of the STF method by using the MAF method; and filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method according to the strong tracking filtering weakening factor to obtain the RSSI optimized measured value.
In the node positioning process, the traditional method is to directly calculate the coordinates of the nodes by adopting a maximum likelihood estimation method, but the positioning precision is low, so the positioning problem is converted into an optimization problem in the embodiment, a tabu search clustering method is applied to carry out global and local optimization, the optimized node coordinates obtained by the maximum likelihood estimation method are referred to during local optimization, the optimal value of each iteration is substituted into a fitness function for comparison, and when the fitness function obtains the optimal value, the corresponding coordinates are the optimal coordinates.
Specifically, a fitness function is generated as a constraint condition according to a maximum likelihood estimation method. By the method, the positioning problem is converted into an optimization problem under the constraint condition, and an intelligent optimization method is adopted to seek the optimal solution of the fitness function. The tabu search clustering method partially simulates a positioning scene, a series of coordinate values can be obtained through calculation, the coordinate values are substituted into the fitness function, and the larger the fitness function value is, the better the coordinate values are. And finally outputting the optimal coordinate value which is the positioning coordinate after multiple iterations.
As a specific implementation manner, according to a distance value between the node to be positioned and the anchor node, determining an initial solution by using a clustering method to serve as an initial current optimal solution; performing tabu search in the neighborhood of the current optimal solution according to a tabu table to obtain a candidate solution set which is not tabu; determining a candidate solution with the maximum fitness value in the candidate solution set according to a fitness function; when the fitness value of the candidate solution is larger than that of the current optimal solution, updating the tabu table and updating the current optimal solution; and repeating the searching and updating processes until the iteration times reach preset times to obtain a target optimal solution, namely the position coordinates of the node to be positioned.
The method for positioning the sensing node of filtering RSSI and tabu search clustering is applied to a wireless sensor network, and in the RSSI measuring process, the method analyzes and optimizes the RSSI measured value by using an STF method and a MAF method, obtains the RSSI optimized measured value by combining the two filtering methods, avoids the interference of environmental factors on the signal strength, and further obtains more accurate distance between nodes. In the node positioning process, the method converts the positioning problem into a global optimization problem, optimizes the position coordinates of the nodes by using a tabu search clustering method, reduces the positioning error and improves the accuracy of the node positioning result.
The second embodiment of the sensing node positioning method for filtering RSSI and tabu search clustering provided by the present application is described in detail below, and is implemented based on the first embodiment, and is expanded to a certain extent on the basis of the first embodiment.
Before the implementation of the second embodiment, a related art background is described.
First, the signal strength indication model is described. The distance between sensor nodes needs to be indirectly reflected by a signal strength indication. In a complex non-coal mine underground environment, the RSSI is influenced by the height of a roadway, the height of a middle section, environmental changes and the like, compared with an open and spacious environment, the phenomenon of signal transmission interference jitter is obvious, and environmental noise disturbs the RSSI prominently. RSSI generally follows a logarithmic fading model, as shown in equation (1):
wherein RSSI (d) is the RSSI value at d from the emission source, and has the unit of dBm; RSSI (d)0) Is a distance d0Processing the RSSI value; n is a path attenuation exponent; xσObeying a gaussian distribution with a mean of 0 and a standard deviation of σ.
Carry out anti-interference optimization to RSSI, with formula (1) split, constitute by spacious environment standard RSSI and the extra RSSI two parts that the environment disturbance caused in the pit caused, promptly:
wherein RSSI (d)0,n0) Is a standard RSSI part; RSSI (d, n, σ) is the additional RSSI component; n is0The transmission attenuation index of the open environment path is taken as value [1.8, 2.2%](ii) a n is the transmission attenuation index of the underground environment path, and takes the value of [2,6 ]]。
Through the formula (1) and the formula (2), it can be found that the path attenuation index n and the standard deviation of underground environment wireless transmission are randomly changed due to environmental interference, even if the RSSI of a fixed node is unstable and even jumps occur, the characteristics enable the reliability of the underground RSSI to be low, the observation result difference to be large, and the ranging precision to be low. The problem to be solved by this embodiment is to improve the RSSI anti-interference capability and improve the adaptive accurate estimation capability thereof, thereby improving the ranging accuracy.
The maximum likelihood estimation method is described below. The positioning principle of the maximum likelihood estimation method is as follows: assuming that a region to be monitored has 1 random node to be positioned and q anchor nodes, the coordinates of the anchor nodes are known, and the distance between each anchor node and each node to be positioned is measured through RSSI (received signal strength indicator), so that a distance equation between each node to be positioned and each anchor node is obtained. Let each anchor node coordinate be (x)1,y1)，(x2,y2)，…，(xq,yq) To stand forThe coordinates of the positioning random node are (x, y), and the measuring distance between the anchor node and the random node to be positioned is d1,d2,…,dqThen, there is a system of equations:
during the solution, the system of equations is converted into:
AX＝B (4)
simplifying A and B, and solving X as follows:
and solving by adopting a least square method to obtain an estimated value X, and obtaining a more accurate solution when the sum of squares of errors between the measured value B and the corresponding estimated value AX is minimum. The accuracy of the least squares solution is limited by the error of the reference equation itself, so it is necessary to develop equation (4) into equation (6), where eiThe distance measurement error between the anchor node and the random node to be positioned is as follows:
i.e. least squares
And taking the minimum value to obtain the node coordinate with higher positioning precision.
Through the analysis, the coordinates of the random node to be positioned, which are obtained by the positioning method combining the RSSI and the maximum likelihood estimation method, have large errors. Therefore, in the embodiment, the RSSI is processed by using the NSTF method, so that a more accurate distance between the anchor node and the random node to be positioned can be obtained, and the positioning accuracy is better improved by further optimizing the coordinates estimated by the maximum likelihood estimation method by using an intelligent method.
Referring to fig. 2, the second embodiment is applied to a wireless sensor network, and includes:
s201, establishing an RSSI state space model of an underground environment;
in the underground environment, RSSI is continuously sampled, the true RSSI of two fixed nodes is influenced by obstacles, personnel movement and the like, and the obtained RSSI can generate frequent fluctuation. When new environmental noise is generated between the fixed nodes, the RSSI jumps and keeps a stable state, and the RSSI is not influenced by the environmental noise any more. Establishing an RSSI state space model according to the formula (7):
in the formula, h is an RSSI value; z is the RSSI measurement and k represents time of day. Both the system noise ω and the measurement noise υ obey the equation (8):
where Q and R obey the standard noise variance.
S202, filtering RSSI measured values of a node to be positioned and an anchor node by using an NSTF method to obtain an RSSI optimized measured value, and further determining a distance value between the node to be positioned and the anchor node;
in the embodiment, the principle of the NSTF method is to fuse the STF method and the MAF method, and firstly apply the NSTF method to an RSSI state space model (7) to obtain the NSTF method for measuring the downhole RSSI, wherein the method mainly calculates the following formula:
N(k+1)＝S0(k+1)-Q-βR (12)
M(k+1)＝P(k|k) (13)
P(k+1|k)＝λ(k+1)P(k|k)+Q (16)
P(k+1|k+1)＝[1-K(k+1)]P(k+1|k) (19)
N(k+1)＝S0(k+1)-Q-6R (20)
in the formula (I), the compound is shown in the specification,
the predicted RSSI at time k +1 for reference to the RSSI at time k,
estimated RSSI output by a new STF method at the time k; gamma is the RSSI estimation residual error; s0For estimating residual variance, p is forgetting factor, p is 0.95, N () represents normal distribution function, Q, R represents scalar noise variance, weakening factor β is greater than or equal to 1, λ is fading factor, λ is0Represents a scale factor; m () represents a Gaussian distribution function, P is a state covariance, P (k +1| k) represents a state covariance at the time k +1 and a state covariance at the time k, respectively, obtained using the state covariance at the time k; k is a Kalman gain;
and is
The fusion of MAF method and STF method. In the STF method, when the attenuation factor β is set to a value, β is generally set to 6, and when the system stability is to be improved, only the environmental noise standard deviation can be dealt with. The ambient noise standard deviation is determined by environmental factors and data processing may be employed to reduce the ambient noise standard deviation. Applying the MAF method to the measurement process of the underground RSSI to obtain:
where θ is the sliding window time. For the z function, z (k-theta +1), z (k-theta +2), …, z (k) are independent of each other and respectively follow a normal distribution
(
Is a mean value), equation (21) follows a normal distribution
The MAF method has stronger tracking capability on signal change and can reduce the variance of environmental noise to a greater extent. Therefore, equation (21) of the MAF method is fused to the STF method, i.e., equation (10) of the STF method is changed to:
in summary, the entire calculation process of the NSTF method is to calculate the expressions (9), (22), (11), (20) and (13) - (19) in sequence. The complexity of the calculation process is low, and the method can be well applied to the distance measurement optimization of the wireless sensor network node. The distance value between the sensor nodes with higher precision is obtained by performing tracking filtering processing on the RSSI and substituting the RSSI into the expressions (1) and (2).
S203, generating a constraint function by using a maximum likelihood estimation method according to the distance value between the node to be positioned and the anchor node, wherein the constraint function is used as a fitness function of a tabu search clustering method;
s204, converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by using a tabu search clustering method based on the fitness function to obtain a positioning result.
Basic idea of tabu search clustering: and obtaining a group of optimal solutions based on a clustering method, performing tabu search in the neighborhood, judging tabu after performing clustering iteration on the domain candidate solutions, continuously updating a search center and a tabu table, and taking the optimal solution memorized in the search process as the solution of the problem when the search times reach the maximum iteration times.
Regarding the tabu table, the tabu length and the tabu judgment, in this embodiment, the tabu table adopts a combination of a short-term table and a long-term table, the short-term table is memorized to reach a point, the long-term table is memorized to be an iterative process, and the iteration can be immediately reset when the iteration is captured by a memorized sequence. The taboo length can adopt a fixed value or a variable value, and when the variable value is used, the taboo length is inversely related to the current iteration number. The taboo judgment considers taboo on a local convergence region, and in addition, the cluster sequence is irrelevant to the clustering effect, so that the taboo judgment needs to be carried out by a fixed rule in order to eliminate the influence of a secondary sequence.
Neighborhood: the centroid V is a c-s matrix (c is the number of clusters, s is the number of record attributes), and its neighborhood usually refers to the region with radius r, and is defined as the hypersphere of equation (23).
N(V,r)＝{V'|V'-V|≤r} (23)
Where radius r is the search step.
Specifically, in this embodiment, a variable step size searching manner is used, and when the optimal result of the current search is poor in comparison with the reference, the step size is increased to search in a wider range, otherwise, the step size is decreased to search locally. Considering the diversified strategy of searching, r should be given a minimum value to prevent the candidate solution from moving too small, and reduce the global searching capability. According to the inspiration of the inertia phenomenon, the hypersphere neighborhood is strengthened, and the neighborhood is sampled in the opposite direction by referring to the moving track of the centroid in the last iteration during the neighborhood generation. Neighborhood resampling time-warpingEntering a chaos optimization strategy, setting a chaos sequence a as { a ═ a0,a1,…,agIn which a is0∈[-1,1]The sequence is obtained from the chaotic self-mapping function equation (24):
and determining the moving distance of the neighborhood sampling clustering center V' relative to the origin according to the chaos sequence a and the step length r.
As shown in fig. 3, the specific flow of the tabu search clustering method is as follows:
s301, determining an initial solution by using a clustering method to serve as an initial current optimal solution;
a clustering method was used to obtain a set of optimal solutions (U, V) to the problem, U being a membership matrix of ψ c (ψ is the number of records, c is the number of clusters), V was added to the empty tabu table.
S302, carrying out tabu search along the opposite direction of the last search direction in the neighborhood of the current optimal solution to obtain a sampling solution;
and moving V by a distance of a r in the direction opposite to the last moving direction of V to obtain a sampling solution V 'in the neighborhood and record the solution V'.
S303, judging whether the sampling solution is taboo or not according to a taboo table;
and judging whether the V' is forbidden or not, and sequencing the clustering centers before judging to eliminate the influence of the sequence. Calculating U', comparing with object V in the tabu table, if formula (25) is satisfied, the sampling solution is tabu:
s304, repeating S302 and S303 to obtain a sampling solution set;
the sampling solution set may be a queue to be searched. Specifically, the operations of S302 and S303 are repeated L times to complete neighborhood sampling and tabu determination, and the queue W is defined as { V ═ V {1,V2,…,VLThe length of the queue is L, if all L results are forbidden, W is { V ═ V }1,V2,…,VL}; otherwise W ═ V '| V' is not contraindicated.
S305, carrying out local search on the sampling solutions in the sampling solution set to obtain a candidate solution set corresponding to each sampling solution; respectively determining a candidate solution with the maximum fitness value in a candidate solution set corresponding to each sampling solution according to the fitness function to obtain a local optimal solution corresponding to each sampling solution and obtain a global optimal solution; when the fitness value of the candidate solution is larger than that of the current optimal solution, updating the tabu table, updating the current optimal solution, and reducing the search step length, otherwise, increasing the search step length, wherein the search step length is preset with a minimum value;
fetch (U) from the queue to be searchedα,Vα) As an initial value, a CM algorithm (C-Means, clustering) local search is run to obtain a new solution (U)α*,Vα*) And defining an evaluation index Cα*(the larger the C, the better the solution), to obtain Cα*Maximum value of (C)localAnd corresponding locally optimal solution (U)local,Vlocal) If C islocal＞C*(C*To start the solution (U)*,V*) Corresponding index), then the step length r is r- Δ r, otherwise r is r + Δ r, ClocalWith a known global optimum solution (U)global,Vglobal) Corresponding to CglobalComparison, if (U)local,Vlocal) Larger, the global optimal solution is updated, anyway (U)local,Vlocal) Is marked as (U)*,V*)。
Specifically, the fitness function for evaluating the quality of the solution in this embodiment is shown in equation (26):
where (x, y) is the true coordinate of the node to be located, dqAnd the estimated distance between the node to be positioned and the anchor node. f (x, y) is the maximum value, and the coordinate of the node to be positioned is closest to the real position.
And S306, repeating S302 to S305 until the iteration times reach the preset times, and obtaining a target optimal solution to be used as a positioning result of the node to be positioned.
If the maximum search times is reached, the search is completed, and the global optimum is output, otherwise, the operation returns to the step S302.
It can be seen that, the method for positioning a sensing node of filtering RSSI and tabu search clustering provided by the present embodiment is applied to a wireless sensor network, and based on RSSI measurement processing and a positioning optimization method, in order to further improve the positioning performance, when the RSSI is used to measure the distance between nodes, an NSTF method is used to process a signal value, thereby obtaining a distance value between nodes with higher precision; and the position coordinates of the nodes are optimized by using a tabu search clustering method, so that the positioning error is reduced.
Through a large number of simulation experiments, simulation results show that when the anchor node proportion, the number of sensor nodes and the standard deviation of environmental noise change, the embodiment has higher positioning precision and lower calculation complexity. Therefore, the embodiment can meet the requirements of positioning accuracy and rapid processing, and has certain practical significance.
In the following, a sensor node positioning apparatus for filtering RSSI and tabu search clustering according to an embodiment of the present application is introduced, and a sensor node positioning apparatus for filtering RSSI and tabu search clustering described below and a sensor node positioning method for filtering RSSI and tabu search clustering described above may be referred to correspondingly.
Referring to fig. 4, the sensing node positioning apparatus for filtering RSSI and tabu search clustering according to the present embodiment is applied to a wireless sensor network, and includes:
the filtering module 401: the RSSI measurement values of the nodes to be positioned and the anchor nodes are filtered by utilizing an STF method and an MAF method to obtain an RSSI optimized measurement value;
the distance determination module 402: the distance value between the node to be positioned and the anchor node is determined according to the RSSI optimized measured value;
fitness function determination module 403: the maximum likelihood estimation method is used for generating a constraint function according to the distance value between the node to be positioned and the anchor node, and the constraint function is used as a fitness function of a tabu search clustering method;
the position determination module 404: and the method is used for converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by utilizing a tabu search clustering method based on the fitness function to obtain a positioning result.
The sensor node positioning apparatus for filtered RSSI and tabu search clusters of the present embodiment is used to implement the aforementioned sensor node positioning method for filtered RSSI and tabu search clusters, and therefore the specific implementation manner in the apparatus can be seen in the embodiment parts of the sensor node positioning method for filtered RSSI and tabu search clusters in the foregoing, for example, the filtering module 401, the distance determining module 402, the fitness function determining module 403, and the position determining module 404, which are respectively used to implement the steps S101, S102, S103, and S104 in the sensor node positioning method for filtered RSSI and tabu search clusters. Therefore, specific embodiments thereof may be referred to in the description of the corresponding respective partial embodiments, and will not be described herein.
In addition, since the sensor node positioning apparatus for filtered RSSI and tabu search clustering according to this embodiment is used to implement the aforementioned sensor node positioning method for filtered RSSI and tabu search clustering, its effect corresponds to that of the above method, and is not described herein again.
In addition, the present application further provides a sensor node positioning device for filtering RSSI and tabu search clusters, as shown in fig. 5, including:
the memory 100: for storing a computer program;
the processor 200: for executing the computer program for implementing the steps of the method for sensor node location of filtered RSSI and tabu search clusters as described above.
Finally, the present application provides a readable storage medium having stored thereon a computer program for implementing the steps of the method for sensor node location of filtered RSSI and tabu search clustering as described above when executed by a processor.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above detailed descriptions of the solutions provided in the present application, and the specific examples applied herein are set forth to explain the principles and implementations of the present application, and the above descriptions of the examples are only used to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A sensing node positioning method for filtering RSSI and tabu search clustering is characterized by being applied to a wireless sensor network and comprising the following steps:
filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method and an MAF method to obtain an RSSI optimized measured value;
determining a distance value between the node to be positioned and the anchor node according to the RSSI optimized measured value;
generating a constraint function by utilizing a maximum likelihood estimation method according to the distance value between the node to be positioned and the anchor node, wherein the constraint function is used as a fitness function of a tabu search clustering method;
and converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by using a tabu search clustering method based on the fitness function to obtain a positioning result.
2. The method of claim 1, wherein filtering the RSSI measurements of the node to be located and the anchor node using an STF method and a MAF method to obtain an RSSI-optimized measurement comprises:
determining a strong tracking filtering weakening factor of the STF method by using the MAF method;
and filtering the RSSI measured values of the nodes to be positioned and the anchor nodes by utilizing an STF method according to the strong tracking filtering weakening factor to obtain the RSSI optimized measured value.
3. The method of claim 1, wherein the optimizing the position coordinates of the node to be positioned by tabu search clustering based on the fitness function to obtain a positioning result comprises:
s11, determining an initial solution by using a clustering method according to the distance value between the node to be positioned and the anchor node, and taking the initial solution as an initial current optimal solution;
s12, carrying out tabu search in the neighborhood of the current optimal solution according to a tabu table to obtain a candidate solution set which is not tabu;
s13, determining a candidate solution with the maximum fitness value in the candidate solution set according to the fitness function; when the fitness value of the candidate solution is larger than that of the current optimal solution, updating the tabu table and updating the current optimal solution;
and S14, repeating S12 and S13 until the iteration times reach the preset times, and obtaining a target optimal solution as a positioning result.
4. The method of claim 3, wherein performing a tabu search in a neighborhood of a current optimal solution according to a tabu table to obtain a set of candidate solutions that are not tabu includes:
s21, carrying out tabu search in the neighborhood of the current optimal solution according to a tabu table to obtain a sampling solution which is not tabu;
s22, repeating S21 to obtain a sampling solution set;
and S23, carrying out local search on the sampling solutions in the sampling solution set to obtain a candidate solution set corresponding to each sampling solution.
5. The method of claim 4, wherein determining the solution candidate with the highest fitness value in the set of solution candidates according to a fitness function comprises:
and respectively determining the candidate solution with the maximum fitness value in the candidate solution set corresponding to each sampling solution according to the fitness function to obtain the local optimal solution corresponding to each sampling solution and obtain the global optimal solution.
6. The method of claim 5, wherein after determining the solution candidate with the highest fitness value in the set of solution candidates according to the fitness function, further comprising:
and when the adaptability value of the local optimal solution is greater than the adaptability value of the global optimal solution, reducing the search step length, otherwise, increasing the search step length, wherein the search step length is preset with a minimum value.
7. The method of claim 4, wherein performing a tabu search in a neighborhood of a current optimal solution according to a tabu table to obtain a sampling solution that is not tabu includes:
and according to a tabu table, carrying out tabu search along the reverse direction of the last search direction in the neighborhood of the current optimal solution to obtain a sampling solution which is not tabu.
8. The utility model provides a sensing node positioner of filtering RSSI and taboo search cluster which characterized in that is applied to wireless sensor network, includes:
a filtering module: the RSSI measurement values of the nodes to be positioned and the anchor nodes are filtered by utilizing an STF method and an MAF method to obtain an RSSI optimized measurement value;
a distance determination module: the distance value between the node to be positioned and the anchor node is determined according to the RSSI optimized measured value;
a fitness function determination module: the maximum likelihood estimation method is used for generating a constraint function according to the distance value between the node to be positioned and the anchor node, and the constraint function is used as a fitness function of a tabu search clustering method;
a position determination module: and the method is used for converting the positioning problem into an optimization problem, and optimizing the position coordinates of the node to be positioned by utilizing a tabu search clustering method based on the fitness function to obtain a positioning result.
9. A sensing node locating device for filtering RSSI and tabu search clusters, comprising:
a memory: for storing a computer program;
a processor: for executing the computer program for implementing the steps of the method for sensor node localization of filtered RSSI and tabu search clusters as claimed in any of the claims 1-7.
10. A readable storage medium, having stored thereon a computer program for implementing the steps of the method for sensor node location of filtered RSSI and tabu search clustering according to any one of claims 1-7, when being executed by a processor.
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