CN113316084A - RSSI-based gray wolf optimization differential correction centroid positioning algorithm - Google Patents
RSSI-based gray wolf optimization differential correction centroid positioning algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of wireless sensor network positioning. In particular to an RSSI-based gray wolf optimization differential correction centroid positioning algorithm. Mainly aiming at the problems of low speed and low precision of a traditional wireless sensor network positioning method, the grey wolf optimization differential correction centroid positioning algorithm based on RSSI is provided, the algorithm calculates the distance between the algorithm and a beacon node through the received signal strength and determines the centroid, a differential correction factor is calculated according to the position relation between the centroid and the beacon node to correct the centroid coordinate, the grey wolf optimization algorithm is adopted to obtain the unknown node position based on the centroid coordinate, and the positioning precision is effectively improved.
Description
Technical Field
The invention relates to the technical field of wireless sensor network positioning. In particular to an RSSI-based gray wolf optimization differential correction centroid positioning algorithm. The distance between the beacon nodes is calculated according to the received signal strength, the mass center is determined, the difference correction factor is calculated according to the position relation between the mass center and the beacon nodes to correct the mass center coordinate, the unknown node position is obtained by adopting a wolf optimization algorithm based on the mass center coordinate, and the positioning precision is effectively improved.
Background
Methods for sensor network node location fall broadly into two categories, one based on ranging and the other without ranging. Positioning algorithms that do not require ranging have the advantage of less power consumption, but lower accuracy. The node positioning algorithm based on the distance measurement has high precision, but has the defect of high computational complexity. The RSSI-based positioning algorithm is one of the widely applied wireless sensor network positioning algorithms, is based on the connectivity of nodes and does not need to measure the distance between the nodes. Is also one of the hot spots of research.
Compared with the traditional mathematical method, the intelligent optimization algorithm provides another idea for solving the unknown node coordinates through the idea of group optimization on the basis of centroid calculation, and can reduce the positioning error to a certain extent. Common colony intelligent optimization algorithms include genetic algorithm, particle swarm algorithm and ant colony algorithm. The group intelligent Optimization algorithms can improve the positioning precision, and compared with the gray Wolf intelligent Optimization algorithm (GHO, Grey Wolf Optimization), the method has better Optimization stability and accuracy.
Disclosure of Invention
The invention aims to solve the problems of low speed and low precision of the traditional positioning method of the wireless sensor network, provides a gray wolf optimization differential correction centroid positioning algorithm based on RSSI, introduces a gray wolf intelligent optimization algorithm (GWO) to optimize a centroid to realize the positioning of an unknown node, and improves the positioning precision of the positioning method based on RSSI.
The technical scheme of the invention is as follows: the RSSI-based gray wolf optimization differential correction centroid positioning algorithm calculates the distance between the RSSI and a beacon node and determines the centroid through the strength of received signals, calculates a differential correction factor according to the position relation between the centroid and the beacon node to correct the centroid coordinate, and adopts the gray wolf optimization algorithm to obtain the position of an unknown node based on the centroid coordinate, so that the positioning precision is effectively improved. The method comprises the following specific steps.
Step 1: initializing a wireless sensor network: the sensor nodes are randomly arranged in a target area, and the beacon nodes periodically broadcast signals which comprise information such as ID and coordinates of the beacon nodes.
Step 2: and the unknown node receives a plurality of beacon node signals and preprocesses each group of RSSI values. And then calculating the distance to the beacon nodes through a propagation loss model, sequencing the distances from small to large, and establishing a mapping between the distances and the beacon nodes. And sequentially and successively taking the three distance values and the corresponding beacon nodes to construct a coincidence region, and calculating the intersection point of the coincidence region and the centroid of the intersection point.
Step 3, passing through the center of mass M of the first three distances1And calculating a differential correction factor, and then performing differential correction on other centroids by using the differential correction factor.
And 4, step 4: and taking the obtained k (k = n-2) centroids as an initial value of the gray wolf optimization algorithm, and assigning coordinates of the k (k = n-2) centroids to gray wolf individuals. And 5: and obtaining the position of the unknown node through a wolf optimization algorithm, and iterating to a preset time threshold value. Further, in step 2, the unknown node receives a plurality of beacon signals, and preprocesses each set of RSSI values. The specific contents are as follows: a set of RSSI values for each beacon node is preprocessed. The formula is as follows:
wherein the RSSIiAs a result of the RSSI preprocessing of the ith beacon,
is the mean value of the RSSI of the ith beacon,
for the median of the RSSI of the ith beacon, the calculation formula is as follows:
where m is the number of RSSI value samples each beacon node is received by the unknown node.
Further, the centroid M passes through the first three distances in step 31And calculating a difference correction factor, and then calculating the difference correction factor by taking the first centroid as a reference node to correct the centroid in the process of performing difference correction on other centroids by using the difference correction factor. Further, k (k = N-2) centroids obtained in step 4 are used as initial values of the gray wolf optimization algorithm, coordinates of the k centroids are assigned to gray wolf individuals, and the sizes of k and N are compared, so that the algorithm can be initialized normally.
Furthermore, the gray wolf algorithm is used for optimizing the mass center to realize accurate positioning of the unknown node in step 5, and therefore the positioning accuracy is improved. The invention has the beneficial effects that: based on the RSSI technology, the distance between the beacon nodes and the received signal strength is calculated, the mass center is determined, the difference correction factor is calculated according to the position relation between the mass center and the beacon nodes to correct the mass center coordinate, the unknown node position is obtained by adopting a wolf optimization algorithm based on the mass center coordinate, and the positioning precision is effectively improved.
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FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a graph comparing average errors for different beacon node densities;
fig. 3 is a graph comparing average errors in the case of different communication radii of nodes.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings: as shown in figure 1, the invention is a gray wolf optimization differential correction centroid positioning algorithm based on RSSI, the distance between the received signal strength and a beacon node is calculated, the centroid is determined, a differential correction factor is calculated according to the position relation between the centroid and the beacon node to correct the centroid coordinate, an unknown node position is obtained by adopting the gray wolf optimization algorithm based on the centroid coordinate, and the positioning precision is effectively improved. The method comprises the following specific steps.
Step 1: initializing a wireless sensor network: the sensor nodes are randomly arranged in a target area, and the beacon nodes periodically broadcast signals which comprise information such as ID and coordinates of the beacon nodes.
Step 2: and the unknown node receives a plurality of beacon node signals and preprocesses each group of RSSI values. And then calculating the distance to the beacon nodes through a propagation loss model, sequencing the distances from small to large, and establishing a mapping between the distances and the beacon nodes. And sequentially and successively taking the three distance values and the corresponding beacon nodes to construct a coincidence region, and calculating the intersection point of the coincidence region and the centroid of the intersection point.
And step 3: center of mass M through the first three distances1And calculating a differential correction factor, and then performing differential correction on other centroids by using the differential correction factor.
And 4, step 4: and taking the obtained k (k = n-2) centroids as an initial value of the gray wolf optimization algorithm, and assigning coordinates of the k (k = n-2) centroids to gray wolf individuals.
And 5: and obtaining the position of the unknown node through a wolf optimization algorithm, and iterating to a preset time threshold value. Further, in step 2, the unknown node receives a plurality of beacon signals, and preprocesses each set of RSSI values. The specific contents are as follows: a set of RSSI values for each beacon node is preprocessed. The formula is as follows:
wherein the RSSIiAs a result of the RSSI preprocessing of the ith beacon,
is the mean value of the RSSI of the ith beacon,
for the median of the RSSI of the ith beacon node, the calculation formula is:
further, the centroid M of the first three distances is passed in step 31Calculating a difference correction factor, and then performing difference correction on other centroids by using the difference correction factor, wherein the difference correction factor is as follows:
the centroid is corrected by:
wherein,
is the corrected coordinates of the center of mass. And the deviation of the centroid and the unknown node is reduced by performing differential correction on the centroid coordinates.
Further, in step 4, the obtained k (k = n-2) centroids are used as initial values of the gray wolf optimization algorithm, and coordinates of the k (k = n-2) centroids are assigned to gray wolf individuals, and the method specifically comprises the following steps: setting the initial iteration times t =0, setting the population size N, if k is larger than or equal to N, taking the first N as the initial values of the wolf population according to the sorting of the triangles corresponding to the centroids, and taking M1(xm1,ym1)、M2(xm2,ym2)，……，Mi(xmi,ymi)，……，MN(xmN,ymN) Assigning to the wolf individual (x)m1(t),ym1(t))、(xm2(t),ym2(t))，……，(xmi(t),ymi(t))，……，(xmN(t),ymN(t)) I is [1, N ]](ii) a If k is<N, then the centroid M closest to the unknown node needs to be utilized1(xm1,ym1) Generating N-k centroid coordinates and assigning them to (x)m1(t),ym1(t)) I is [ k +1, N ]]，(xmi(t),ymi(t)) Is the coordinate value of the ith grey wolf at the t-th iteration.
In step 5, the unknown node position is obtained through a wolf optimization algorithm, and the specific steps are as follows:
(1) calculating the fitness function value of each individual wolf:
wherein (x)mj(t),ymj(t)) Is the coordinate of the jth individual wolf, dijIs the distance between grey wolfs i and j. The fitness values of the wolf individuals are sorted according to ascending order, the wolf with the first rank is defined as alpha wolf, the wolf with the second rank is defined as beta wolf, and the wolf with the third rank is defined as delta wolf. (2) And updating the gray wolf position. In the gray wolf attack stage, the position of the gray wolf is updated through a formula. (3) Recalculating the fitness function of each wolf individual, updating the positions of alpha, beta and delta according to the fitness function in sequence, and enabling the iteration times t = t + 1. (4) t is tmaxIs an iteration number threshold value, if t is less than or equal to tmaxAnd then jumping to (1); if t is>tmaxThen the search is stopped.
In the following, the RSSI-based gray wolf optimization differential correction centroid location algorithm of the present invention is compared with GWO location algorithm and weighted centroid location algorithm under the condition of different beacon node densities and different node communication radii.
In order to verify the performance of the algorithm, 100 sensor nodes were randomly distributed in a region of 50m by 50m, including 30% of unknown nodes. The population size of the wolf algorithm is 30, and the maximum iteration number of the algorithm is 300. And carrying out comparative analysis on the algorithm of the current chapter, the trilateral centroid location algorithm and the weighted centroid location algorithm from two aspects of the node communication radius and the beacon node density.
Fig. 2 is a graph showing the relationship between the beacon density and the average positioning accuracy, and it can be seen from the graph that as the beacon density increases, more beacons participate in positioning, and therefore the average positioning error gradually decreases. The average positioning error of the algorithm is always lower than that of the other two algorithms, and the effectiveness of the method for improving the differential correction centroid through the gray wolf optimization is shown. When the density of the beacon nodes is more than 35%, the variation of the average positioning error is small, and when the density of the beacon nodes is 50%, the average positioning error is 0.096.
Fig. 3 shows the effect of the change in communication radius on the average positioning error at a beacon density of 35%. As can be seen from fig. 3, when the communication radius is small, there are fewer unknown nodes in the coverage area of the beacon node, the average positioning error is large, as the communication radius increases, the average positioning error of the three positioning algorithms gradually decreases, and the algorithm is significantly better than the other two positioning algorithms, when the communication radius is 30, the average positioning accuracy of the algorithm is 0.092, the positioning algorithm GWO is 0.124, and the weighted centroid positioning algorithm is 0.16.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (5)
1. An RSSI-based gray wolf optimization differential correction centroid positioning algorithm is characterized in that the distance between the RSSI-based gray wolf optimization differential correction centroid positioning algorithm and a beacon node is calculated through the strength of received signals, the centroid is determined, a differential correction factor is calculated according to the position relation between the centroid and the beacon node to correct a centroid coordinate, an unknown node position is obtained through the gray wolf optimization algorithm based on the centroid coordinate, the positioning accuracy is effectively improved,
the method comprises the following specific steps:
step 1, initializing a wireless sensor network: the method comprises the following steps that sensor nodes are randomly arranged in a target area, and beacon nodes periodically broadcast signals which comprise information such as ID and coordinates of the beacon nodes;
step 2, the unknown node receives a plurality of beacon node signals, each group of RSSI values are preprocessed, then the distance to the beacon nodes is calculated through a propagation loss model, the distances are sorted from small to large, the mapping between the distances and the beacon nodes is established, three distance values and the corresponding beacon nodes are sequentially taken, an overlapping area is constructed, and the intersection point of the overlapping area and the mass center of the intersection point are calculated;
step 3, passing the center of mass M of the first three distances1Calculating a difference correction factor, and then performing difference correction on other centroids by using the difference correction factor;
step 4, taking the obtained k (k = n-2) centroids as initial values of a gray wolf optimization algorithm, and assigning coordinates of the k (k = n-2) centroids to gray wolf individuals;
and 5, obtaining the position of the unknown node through a wolf optimization algorithm, and iterating to a preset time threshold value.
2. The RSSI-based grayish wolf optimized differential correction centroid localization algorithm as claimed in claim 1, wherein in step 2, the unknown node receives a plurality of beacon node signals and preprocesses each set of RSSI values, the specific steps are as follows:
preprocessing a set of RSSI values for each beacon, as follows:
wherein RSSIi is the result of RSSI preprocessing of the ith beacon node,
is the mean value of the RSSI of the ith beacon,
for the median of the RSSI of the ith beacon, the calculation formula is as follows:
where m is the number of RSSI value samples each beacon node is received by the unknown node.
3. The RSSI-based grayish wolf optimized differential modified centroid localization algorithm as claimed in claim 1, wherein: centroid M through the first three distances in step 31And calculating a difference correction factor, and then performing difference correction on other centroids by using the difference correction factor, namely calculating the difference correction factor by using the first centroid as a reference node to correct the centroids.
4. The RSSI-based grayling optimization differential correction centroid localization algorithm as claimed in claim 1, wherein k (k = n-2) centroids obtained in step 4 are used as initial values of the grayling optimization algorithm, and coordinates thereof are assigned to grayling individuals, specifically comprising the following steps:
setting initial iteration times t =0, setting a population size N, if k is larger than or equal to N, taking the first N as initial values of the gray wolf population according to the sorting of triangles corresponding to centroids, and assigning M1(xm1, ym1), M2(xm2, ym2), … …, Mi (xmi, ymi), … …, MN (xmN, ymN) to gray wolf individuals (xm1(t), ym1(t)), (xm2(t), ym2(t)), … …, (xmi (t), ymi (t)), … …, (xmn t), ymN (t)), and i takes the value of [1, N ]; if k < N, then N-k centroid coordinates need to be generated using centroid M1(xm1, ym1) nearest to the unknown node and assigned to (xm1(t), ym1(t)), i takes the value of [ k +1, N ], (xmi (t), ymi (t)) is the coordinate value of the ith grey wolf in the t-th iteration.
5. The RSSI-based grayling optimization differential correction centroid localization algorithm as claimed in claim 1, wherein in step 5, the unknown node location is obtained by grayling optimization algorithm, and the fitness function value formula of each individual grayling is calculated as follows:
wherein (x)mj(t),ymj(t)) Is the coordinate of the jth individual wolf, dijFor the distance between the gray wolves i and j, the positioning error formula is calculated as follows:
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114245334A (en) * | 2021-12-16 | 2022-03-25 | 西南交通大学 | Ultra-wideband indoor positioning algorithm integrating error-calculable map and gray wolf optimization |
| CN114727373A (en) * | 2022-03-08 | 2022-07-08 | 中国科学院上海微系统与信息技术研究所 | Fault-tolerant-based WSN target tracking dormancy scheduling method |
| CN115499916A (en) * | 2022-11-15 | 2022-12-20 | 中国人民解放军海军工程大学 | Wireless sensor network node positioning method based on improved whale optimization algorithm |
| CN115550837A (en) * | 2022-09-22 | 2022-12-30 | 合肥工业大学 | DV-Hop positioning method based on chaos mapping and Husky algorithm optimization |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114245334A (en) * | 2021-12-16 | 2022-03-25 | 西南交通大学 | Ultra-wideband indoor positioning algorithm integrating error-calculable map and gray wolf optimization |
| CN114245334B (en) * | 2021-12-16 | 2023-01-24 | 西南交通大学 | Ultra-wideband indoor positioning algorithm integrating error-calculable map and gray wolf optimization |
| CN114727373A (en) * | 2022-03-08 | 2022-07-08 | 中国科学院上海微系统与信息技术研究所 | Fault-tolerant-based WSN target tracking dormancy scheduling method |
| CN114727373B (en) * | 2022-03-08 | 2024-04-23 | 中国科学院上海微系统与信息技术研究所 | WSN target tracking dormancy scheduling method based on fault tolerance |
| CN115550837A (en) * | 2022-09-22 | 2022-12-30 | 合肥工业大学 | DV-Hop positioning method based on chaos mapping and Husky algorithm optimization |
| CN115550837B (en) * | 2022-09-22 | 2024-04-02 | 合肥工业大学 | DV-Hop positioning method based on chaotic mapping and gray wolf algorithm optimization |
| CN115499916A (en) * | 2022-11-15 | 2022-12-20 | 中国人民解放军海军工程大学 | Wireless sensor network node positioning method based on improved whale optimization algorithm |
| CN115499916B (en) * | 2022-11-15 | 2023-01-20 | 中国人民解放军海军工程大学 | Wireless sensor network node positioning method based on improved whale optimization algorithm |
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