CN107396309B - A kind of wireless sensor network forest localization method - Google Patents
A kind of wireless sensor network forest localization method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 51
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- G—PHYSICS
- 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
- G01S5/14—Determining absolute distances from a plurality of spaced points of known location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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Abstract
The invention discloses a kind of forest wireless sensor network locating methods, including: (1) is divided into m positioning subregion for entire localization region in the coefficient of dispersion of different zones according to RSSI, and is fitted the RSSI path loss model for establishing each positioning subregionForm the RSSI path loss model R of entire localization regionN；(2) RSSI path loss model R is mergedNWith the log path loss model R of entire localization regionD, obtain forest path loss model R；(3) according to forest path loss model R, multiple wireless sensor positioning nodes are determined using subregion trilateration localization method；(4) multiple wireless sensor positioning nodes are clustered using k-means clustering method, determines final wireless sensor positioning node.This method has fully considered influence of the complicated deep woods environment to wireless sensor signal intensity, can accurately obtain the positioning to wireless sensor.
Description
Technical field
The invention belongs to field of forest management, and in particular to a kind of wireless sensor network (Wireless Sensor
Network, WSN) forest localization method.
Background technique
Orientation problem of the wireless sensor network in forest have become wireless sensor network research Important Problems it
One.The problem of WSN network forest Position Research at present is primarily present is localizing environment complexity, and there are multipath effects for signal, cause
Position error is larger.In ranging localization, signal strength is very serious by the interference of barrier, so that dysmetria is true, therefore
Traditional path loss model is not suitable for forest positioning.Due to the complexity of forest environment, lead to received signal strength (Rec
EivedSignalStrengthIndication, RSSI) the larger and current RSSI path loss model of error can not meet
The demand of sensor node localization in forest.
Summary of the invention
In view of above-mentioned, the present invention provides a kind of forest wireless sensor network locating method, this method is fully considered
Influence of the complicated depth woods environment to wireless sensor signal intensity, can accurately obtain the positioning to wireless sensor.
The technical solution of the present invention is as follows:
A kind of forest wireless sensor network locating method, comprising the following steps:
(1) entire localization region is divided into the coefficient of dispersion of different zones by m positioning subregion according to RSSI, and intended
Build the RSSI path loss model for founding each positioning subregion jointlyForm the RSSI path loss model of entire localization region
RN；J is the serial number for positioning subregion, j=1,2,3 ..., m；
(2) the RSSI path loss model R is mergedNWith the log path loss model R of entire localization regionD, obtain gloomy
Woods path loss model R；
(3) according to the forest path loss model R, multiple wireless biographies are determined using subregion trilateration localization method
Sensor positioning node；
(4) the multiple wireless sensor positioning node is clustered using k-means clustering method, determines final nothing
Line sensor positioning node.
Since in forest environment, the environment of different zones is separate very big, as the variation of signal propagation distance is uncertain
Property can be increase accordingly, and signal also can be bigger by the interference of barrier, and the dispersion degree of RSSI can also gradually increase.Therefore, this hair
It is bright to use the method being fitted according to the coefficient of dispersion subregion of RSSI, so that each RSSI path loss model that fitting is established is more
Add and tally with the actual situation, accuracy is some higher.
Specific steps in step (1) are as follows:
(1-1) obtains n discrete RSSI array (RSSI by testing measurementi,di), RSSIiFor i-th of RSSI of acquisition
Value, and i=1,2,3 ..., n, diFor with RSSIiCorresponding distance, the distance are acquisition signaling point away between signal transmitting node
Length；
(1-2) is found using sliding window divides Area Node, calculates the discrete system of discrete RSSI in sliding window in real time
Number, when choosing coefficient of dispersion mutation, the corresponding distance of intermediate value of discrete RSSI is to divide Area Node in sliding window, obtains m
A positioning subregion；
(1-3) is fitted the path RSSI for obtaining RSSI with distance change to the discrete RSSI array in each positioning subregion
Loss model
(1-4) combines m RSSI path loss modelObtain RSSI path loss model RN:
Wherein, for entire localization region [0, a], a1,a2,…,aj,…,anTo divide Area Node.
Transmitting node refers to the sensor node that data frame is actively sent to other nodes.
RSSI is affected by environment larger in forest environment, and log path loss model RDPreferably consider environment shadow
The factor is rung, therefore selects log-distance path loss model model RDA part as forest path loss model R.
It is most of to use logarithm apart from loss model R in complex environmentDCalculate the loss situation of RSSI, concrete model
As shown in formula (2):
P (d)=P0(d0)+10vlg(d/d0)+Xσ (2)
In formula (2), d0For reference distance, usual value is 1m；D is actual signal propagation distance, i.e. range transmission is believed
The distance of number node；P0(d0) it is reference distance d0The path loss at place；P (d) is path loss of the signal after distance d；v
Size reflection RSSI for path loss index, v value increases with propagation distance and the rate of variation, with environmental impact factor phase
It closes；XσIt is the Gaussian random variable that mean value is 0, standard deviation is σ.
Range transmission nodal distance is shown in the RSSI such as formula (3) of d:
RSSI=P-P (d) (3)
In formula (3), P is the transmission power of signal source.
Range transmission node d0The reference point path loss P at place0(d0)=P-A, wherein A indicates the reception letter at reference point
Number intensity, and d0=1, by P0(d0)=P-A is substituted into formula (2) and is obtained formula (4), and formula (3) is updated in formula (4)
Obtain formula (5):
P (d)=P-A+10vlg (d)+Xσ (4)
RSSI=A-10vlg (d)-Xσ (5)
Due to XσMean value is 0, thus obtains formula (6):
To reduce experimental error, RSSI more times measurements are simultaneously averagedIt substitutes into formula (6) and obtains formula (7):
RSSI value A and Environmental Factors v in above-mentioned log path loss model at reference point determine method are as follows:
In specific forest environment, using the RSSI value at range transmission node 1m as A；
Since within the scope of different zones, environmental difference is very big, and then the Environmental Factors in each region are also to have greatly
It is different.Therefore, the present invention determines the environment shadow for belonging to j-th of positioning subregion on the basis of determining each positioning subregion
Ring factor vj:
In formula (8), d12To belong to anchor node K in j-th of positioning subregion1And K2The distance between, A is range transmission
RSSI value at node 1m,For anchor node m1、m2Locate the average value of RSSI value.The anchor node is that specific location is sat
Mark determining sensor node.
The log path loss model R of the entire localization region finally determinedDAre as follows:
In step (2), it will be considered that the log path loss model R of Environmental FactorsDWith consideration distance influence factor
RSSI path loss model RN(d) it combines, obtains the forest path loss model R (d) for being more suitable for complicated deep woods environment:
In formula (10), αjAnd βjIt respectively indicatesWithProportion coefficient.
Determine specific gravity factor αjAnd βjMethod are as follows:
Firstly, by many experiments, acquiring multi-group data (RSSI, d) in j-th of subregion and calculating multiple
The average value of RSSI value
Then, according to the multi-group data of acquisition (RSSI, d), model is determined using the above methodWith
Finally, establishing formula (11), and with f (αj,βj) minimum object solving formula (11), and then determine αj,βj,
Due to being influenced in actual environment by external environment, RSSI can have fluctuation, and RSSI value is to influence
The direct factor of positioning accuracy, so, before the specific location for solving unknown node, need effectively to filter RSSI value
Processing.
Preferably, the method for the present invention further includes being filtered using gaussian filtering model to RSSI.Gaussian filtering can
The influence of small probability is effectively reduced, improves the precision of positioning.RSSI obeys (0, σ2) Gaussian Profile, Gaussian probability-density function
As shown in formula (12):
In formula (12),RSSI connects for node
The signal strength indication of receipts.
Rule of thumb value high probability section is (μ-σ, μ+σ), and the probability of happening in the section is 0.6826, therefore excludes the area
Between outer exceptional value, choose the RSSI value in section as experimental data.Gaussian filtering partly solves RSSI actual environment
In vulnerable to interference the problems such as, effectively improve positioning accuracy.
In step (3), for forest path loss model R obtained above, accurately counted according to the signal strength between node
Distance between operator node, and then determine the specific location of unknown node.Preferably, the present invention is fixed using subregion trilateration
Position method determines multiple wireless sensor positioning nodes, specifically: chosen position coordinate is respectively (x1,y1)、(x2,y2)、(x3,
y3) anchor node A, B, C as transmitting signal node, establish respectively the distance between unknown node and three anchor nodes just
Journey forms equation group:
Solution formula (13) determines the position coordinates (x, y) of unknown node (wireless sensor positioning node), dA,dB,dCPoint
Not Wei anchor node A, B, C the distance between to destination node.Using formula (13), pass through multiple bearing in the same area and difference
Resetting in region obtains multiple wireless sensor positioning nodes.
The multiple wireless sensor positioning nodes obtained by step (3) are concentrated in a certain range, big many places positioning section
Point compares concentration, and closeness is higher, and a small amount of positioning node deviates from positioning node compact district.It is considered herein that deviating from positioning section
The positioning node of point compact district is the biggish some positioning results of position error.To improve positioning accuracy, the present invention uses K-
Means clustering method excludes error.Specifically:
Multiple wireless sensor positioning nodes are clustered using K-means clustering method, it is most to choose positioning node
Cluster the cluster heart as final wireless sensor positioning node, positioning accuracy can be greatly improved in this way.
Compared with the prior art, advantageous effects of the invention are as follows:
(1) present invention merges path loss model, while according to the complexity of environment, by path loss model
It is segmented, so that the path loss model after segmentation is suitable for the variability of environment.
(2) present invention extracts positioning result using K-means, other interference in position fixing process is eliminated, so that positioning
As a result more acurrate.
Detailed description of the invention
Fig. 1 is the flow chart of forest wireless sensor network locating method of the present invention；
Fig. 2 is the relational graph of RSSI and distance d in the present invention；
Fig. 3 is subregion positioning and subregion trilateration positioning schematic diagram in the present invention；
Fig. 4 is the present invention using the cluster result of K-means clustering method and determines final wireless sensor positioning node
Schematic diagram；
Fig. 5 is the RSSI discrete case schematic diagram under spacious environment in range transmission node 25m；
Fig. 6 is the RSSI discrete case schematic diagram under bamboo grove environment in range transmission node 25m；
Fig. 7 is the positioning result schematic diagram of unknown node in embodiment；
Fig. 8 is the experimental result schematic diagram in embodiment using K-means algorithm to unknown node positioning result.
Specific embodiment
In order to more specifically describe the present invention, with reference to the accompanying drawing and specific embodiment is to technical solution of the present invention
It is described in detail.
Referring to Fig. 1, forest wireless sensor network locating method of the present invention, comprising the following steps:
Entire localization region is divided into m positioning subregion in the coefficient of dispersion of different zones according to RSSI by S01, and
The RSSI path loss model of each positioning subregion is established in fittingForm the RSSI path loss mould of entire localization region
Type RN；J is the serial number for positioning subregion, j=1,2,3 ..., m.
Referring to fig. 2, the specific steps of S01 are as follows:
(1-1) obtains a series of discrete RSSI array (RSSI by testing measurementi,di), RSSIiIt is i-th of acquisition
RSSI value, and i=1,2,3 ..., n, diFor with RSSIiCorresponding distance, the distance are acquisition signaling point away from signal transmitting node
Between length；
(1-2) is found using sliding window divides Area Node, calculates the discrete system of discrete RSSI in sliding window in real time
Number, when choosing coefficient of dispersion mutation, the corresponding distance of intermediate value of discrete RSSI is to divide Area Node in sliding window, obtains m
A positioning subregion；
(1-3) is fitted the path RSSI for obtaining RSSI with distance change to the discrete RSSI array in each positioning subregion
Loss model
(1-4) combines m RSSI path loss modelObtain RSSI path loss model RN(d):
Wherein, for entire localization region [0, a], a1,a2,…,anTo divide Area Node.
S02 merges the RSSI path loss model RNWith the log path loss model R of entire localization regionD, obtain gloomy
Woods path loss model R.
It is most of to use logarithm apart from loss model R in complex environment in this stepDThe loss situation of RSSI is calculated,
Shown in concrete model such as formula (2):
P (d)=P0(d0)+10vlg(d/d0)+Xσ (2)
In formula (2), d0For reference distance, usual value is 1m；D is actual signal propagation distance, i.e. range transmission is believed
The distance of number node；P0(d0) it is reference distance d0The path loss at place；P (d) is path loss of the signal after distance d；v
Size reflection RSSI for path loss index, v value increases with propagation distance and the rate of variation, with environmental impact factor phase
It closes；XσIt is the Gaussian random variable that mean value is 0, standard deviation is σ.
Range transmission nodal distance is shown in the RSSI such as formula (3) of d:
RSSI=P-P (d) (3)
In formula (3), P is the transmission power of signal source.
Range transmission node d0The reference point path loss P at place0(d0)=P-A, wherein A indicates the reception letter at reference point
Number intensity, and d0=1, by P0(d0)=P-A is substituted into formula (2) and is obtained formula (4), and formula (3) is updated in formula (4)
Obtain formula (5):
P (d)=P-A+10vlg (d)+Xσ (4)
RSSI=A-10vlg (d)-Xσ (5)
Due to XσMean value is 0, thus obtains formula (6):
To reduce experimental error, RSSI more times measurements are simultaneously averagedIt substitutes into formula (6) and obtains formula (7):
RSSI value A and Environmental Factors v in above-mentioned log path loss model at reference point determine method are as follows:
In specific forest environment, using the RSSI value at range transmission node 1m as A；
Since within the scope of different zones, environmental difference is very big, and then the Environmental Factors in each region are also to have greatly
It is different.Therefore, the present invention determines the environment shadow for belonging to j-th of positioning subregion on the basis of determining each positioning subregion
Ring factor vj:
In formula (8), d12To belong to anchor node K in j-th of positioning subregion1And K2The distance between, A is range transmission
RSSI value at node 1m,For anchor node m1、m2Locate the average value of RSSI value.
The log path loss model R of the entire localization region finally determinedDAre as follows:
Based on above-mentioned, in S02, it will be considered that the log path loss model R of Environmental FactorsDWith consider distance influence because
The RSSI path loss model R of sonN(d) it combines, obtains the forest path loss model R (d) for being more suitable for complicated deep woods environment:
In formula (10), αjAnd βjIt respectively indicatesWithProportion coefficient.
Data, which are acquired, by many experiments determines specific gravity factor αjAnd βj, detailed process are as follows:
Firstly, by many experiments, acquiring multi-group data (RSSI, d) in j-th of subregion and calculating multiple
The average value of RSSI value
Then, according to the multi-group data of acquisition (RSSI, d), model is determined using the above methodWith
Finally, establishing formula (11), and with f (αj,βj) minimum object solving formula (11), and then determine αj,βj,
S03 is filtered RSSI using gaussian filtering model.
Gaussian filtering can be effectively reduced the influence of small probability, improve the precision of positioning.RSSI obeys (0, σ2) Gauss point
Cloth, shown in Gaussian probability-density function such as formula (12):
In formula (12),RSSI connects for node
The signal strength indication of receipts.
Rule of thumb value high probability section is (μ-σ, μ+σ), and the probability of happening in the section is 0.6826, therefore excludes the area
Between outer exceptional value, choose the RSSI value in section as experimental data.Gaussian filtering partly solves RSSI actual environment
In vulnerable to interference the problems such as, effectively improve positioning accuracy.
S04 is determined multiple wireless according to the forest path loss model R using subregion trilateration localization method
Sensor positioning node.
In this step, the specific location of unknown node is determined using subregion trilateration localization method, specifically: referring to
Fig. 3, chosen position coordinate are respectively (x1,y1)、(x2,y2)、(x3,y3) anchor node A, B, C as transmitting signal node,
The distance between unknown node O and three anchor nodes equation are established respectively, form equation group:
Solution formula (13) determines the position coordinates (x, y) of unknown node (wireless sensor positioning node), dA,dB,dCPoint
Not Wei anchor node A, B, C the distance between to destination node.Using formula (13), pass through multiple bearing in the same area and difference
Resetting in region obtains multiple wireless sensor positioning nodes.
S05 clusters the multiple wireless sensor positioning node using k-means clustering method, determines final
Wireless sensor positioning node.
In this step, referring to fig. 4, multiple wireless sensor positioning nodes are clustered using K-means clustering method,
The cluster heart of the most cluster of positioning node is chosen as final wireless sensor positioning node, positioning accurate can be greatly improved in this way
Degree.
Embodiment
It is tested using Telosb sensor node, 15 anchor sections is uniformly disposed in the bamboo grove of one piece of 30m × 30m
Point, 5 unknown nodes of random placement.Telosb sensor node will send information to computer end and carry out positioning experiment.Field experiment
Scape is the bamboo grove of Density inhomogeneity, and sensor node is placed on the shelf of 1.2m high in experiment, it is ensured that sensor node deployment
On sustained height level.
Localization region divides:
Since the density of trees of different zones in woods environment has very big difference, if do not distinguished in positioning experiment pair
To then have a great impact to positioning accuracy.Localization region is drawn in the discrete case of different zones using RSSI herein
Point, the density of trees is very close in the same area.Experiment tests the RSSI discrete case in range transmission node 25m.By
In the density that the principal element for influencing RSSI is trees, so compared spacious environment and bamboo grove ring by taking bamboo grove as an example in experiment
Border, experiment handle bamboo grove environment, arrange the bamboo grove of four kinds of different densities, and experimental result is as shown in Figure 5, Figure 6, figure
5 be spacious environmental consequences, and Fig. 6 is bamboo grove environmental consequences, and abscissa is euclidean distance between node pair, and ordinate is RSSI value.
RSSI coefficient of dispersion is calculated using sliding window technique in experiment, coefficient of dispersion relatively connects in spacious field environment
Closely, value is fluctuated about -0.018, and apparent variation three times, range transmission node occurs in coefficient of dispersion in bamboo grove environment
Respectively at 5.5m, 12.5m and 19.5m.Bamboo grove is divided into four regions, corresponding RSSI coefficient of dispersion difference at three by this
It is -0.051, -0.037, -0.026, -0.008.The experimental results showed that being carried out using RSSI coefficient of dispersion to woods localization region
Division is reliable.
Subregion positioning:
It compared positioning accuracy of the log path loss model under subregion and regardless of areas case.Pass through region division
Experiment has determined the division of localization region in bamboo grove, the Environmental Factors v in each region is respectively 2.96,1.96,2.61,
2.71.Environmental Factors v is 2.42 in the case where no division region, and reference value A is -56.59.Not dividing regions in experiment
The case where domain, randomly selects three anchor nodes and carries out 30 positioning to 5 unknown nodes, in the case where dividing region according to S04
Localization method 5 unknown nodes are positioned.Experimental result is as shown in fig. 7, abscissa is experiment number (Experiment
Number), ordinate is position error (Deviation).
In experiment, in the case where not divided to localization region: K-means algorithm not being used to improve precision (Non-
Subregion-Kmeans), the error of positioning is in 3.8m or so；Using K-means algorithm (Non-subregion), can will determine
Position precision improves 0.3m or so.
In the case where being divided to localization region: calculate separately the Environmental Factors of different zones, and by
Anchor node is chosen in different zones to be positioned.If not using K-means algorithm () Subregion), position error exists
2.5m left and right；If can be improved positioning accuracy using K-means algorithm (Subregion-Kmeans) and improve 0.2m or so,
And not subregion the case where positioning accuracy fluctuation it is larger, position it is more unstable.
The experimental results showed that dividing to localization region according to RSSI coefficient of dispersion, positioning accuracy can be greatly improved, because
It is different for the bamboo density of different zones, cause Environmental Factors different, and utilize the pass of log path loss model ranging
Key be Environmental Factors accurately whether, and experiment show K-means algorithm can effectively improve positioning accuracy.
Fusion Model:
Establish model of fit (Fusion Model) using the data of actual measurement, then with log path loss model
(Path Loss Model) is blended, so that the model established more adapts in complex environment.It is calculated according to the method for S02
The α and β value in each region be respectively (5.79, -5.05), (- 9.42,10.01), (- 3.85,4.95), (10.88, -
9.99) path blend loss model, is then established, subregion positioning is carried out to 5 unknown nodes in localization region, in experiment
The Environmental Factors v in each region is respectively 2.96,1.96,2.61,2.71, and reference value A is -56.59, every time the result is that fixed
Position 30 times as a result, be repeated 10 times positioning experiment, improve positioning accuracy, experimental result such as Fig. 8 using K-means algorithm in experiment
It is shown.Abscissa is positioning experiment number (Experiment number), and ordinate is position error (Deviation) (5
The average localization error of unknown node), unit is rice (m).
The experimental results showed that Fusion Model can be retouched preferably than logarithm path loss model in the case where subregion positions
State loss of signal situation.Using Fusion Model, more accurate positioning result can be obtained according to signal strength.Because of model of fit
It is more in line with localizing environment, but model of fit lacks applicability, changing a kind of localizing environment positioning accuracy just can decrease, and need
Re-establish model of fit.In order to increase the applicability of Fusion Model, log path loss model and fitting mould are utilized herein
Type blends, and establishes all reliable RSSI path loss Fusion Model of positioning accuracy and applicability.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of forest wireless sensor network locating method, comprising the following steps:
(1) entire localization region is divided into the coefficient of dispersion of different zones by m positioning subregion according to RSSI, and is fitted and builds
Found the RSSI path loss model of each positioning subregionForm the RSSI path loss model R of entire localization regionN；J is
Position the serial number of subregion, j=1,2,3 ..., m；
(2) the RSSI path loss model R is mergedNWith the log path loss model R of entire localization regionD, obtain forest track
Diameter loss model R；
(3) according to the forest path loss model R, multiple wireless sensors are determined using subregion trilateration localization method
Positioning node；
(4) the multiple wireless sensor positioning node is clustered using k-means clustering method, determines final wireless biography
Sensor positioning node；
Specific steps in the step (1) are as follows:
(1-1) obtains n discrete RSSI array (RSSI by testing measurementi, di), RSSIiFor i-th of RSSI value of acquisition, and
I=1,2,3 ..., n, diFor with RSSIiCorresponding distance, the distance are acquisition signaling point away from the length between signal transmitting node
Degree；
(1-2) is found using sliding window divides Area Node, calculates the coefficient of dispersion of discrete RSSI in sliding window, choosing in real time
When coefficient of dispersion being taken to be mutated, the corresponding distance of the intermediate value of discrete RSSI is to divide Area Node in sliding window, obtains m positioning
Subregion；
(1-3) is fitted the RSSI path loss for obtaining RSSI with distance change to the discrete RSSI array in each positioning subregion
Model
(1-4) combines m RSSI path loss modelObtain RSSI path loss model RN:
Wherein, for entire localization region [0, a], a1, a2..., aj..., anTo divide Area Node.
2. forest wireless sensor network locating method as described in claim 1, which is characterized in that the entire positioning area
The log path loss model R in domainDAre as follows:
Wherein,For the log path loss model of j-th of subregion, A indicates the received signal strength at reference point, vj
The Environmental Factors of subregion are positioned for j-th.
3. forest wireless sensor network locating method as claimed in claim 2, which is characterized in that the A and vjDetermination
Method are as follows:
Using the RSSI value at range transmission node 1m as A；
Belong to the Environmental Factors v of j-th of positioning subregionj:
In formula (3), d12To belong to anchor node K in j-th of positioning subregion1And K2The distance between, A is range transmission node
RSSI value at 1m,For anchor node m1、m2Locate the average value of RSSI value.
4. forest wireless sensor network locating method as claimed in claim 2, which is characterized in that the forest path damage
Consume model R are as follows:
In formula (4), αjAnd βjIt respectively indicatesWithProportion coefficient.
5. forest wireless sensor network locating method as claimed in claim 4, which is characterized in that determine specific gravity factor αjWith
βjMethod are as follows:
Firstly, by many experiments, acquiring multi-group data (RSSI, d) in j-th of subregion and calculating multiple RSSI value
Average value
Then, according to the multi-group data of acquisition (RSSI, d), model is determined using the above methodWith
Finally, establishing formula (5), and with f (αj, βj) minimum object solving formula (5), and then determine αj, βj,
6. forest wireless sensor network locating method as described in claim 1, which is characterized in that the method for the present invention further includes
RSSI is filtered using gaussian filtering model.
7. forest wireless sensor network locating method as described in claim 1, which is characterized in that described uses subregion
Trilateration localization method determines multiple wireless sensor positioning nodes, specifically:
Chosen position coordinate is respectively (x1, y1)、(x2, y2)、(x3, y3) anchor node A, B, C as transmitting signal node,
The distance between unknown node and three anchor nodes equation are established respectively, form equation group:
Solution formula (6) determines the position coordinates (x, y) of unknown node, dA, dB, dCRespectively anchor node A, B, C is to destination node
The distance between；
Multiple wireless sensings are obtained by the resetting in multiple bearing and different zones in the same area using formula (6)
Device positioning node.
8. forest wireless sensor network locating method as described in claim 1, which is characterized in that in step (4), utilize K-
Means clustering method clusters multiple wireless sensor positioning nodes, and the cluster heart for choosing the most cluster of positioning node is made
For final wireless sensor positioning node.
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| CN102761913A (en) * | 2011-04-26 | 2012-10-31 | 航天信息股份有限公司 | Positioning method of wireless signal transmission parameter determination based on area division |
| CN103889057A (en) * | 2014-04-18 | 2014-06-25 | 上海海事大学 | Wireless sensor network search-and-rescue target location method based on maritime environment self-adaptation RSST distance measurement |
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| CN102761913A (en) * | 2011-04-26 | 2012-10-31 | 航天信息股份有限公司 | Positioning method of wireless signal transmission parameter determination based on area division |
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