CN104301996B - A kind of wireless sensor network locating method - Google Patents
A kind of wireless sensor network locating method Download PDFInfo
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- CN104301996B CN104301996B CN201410150169.5A CN201410150169A CN104301996B CN 104301996 B CN104301996 B CN 104301996B CN 201410150169 A CN201410150169 A CN 201410150169A CN 104301996 B CN104301996 B CN 104301996B
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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
- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The present invention relates to a kind of wireless sensor network locating method, belong to wireless sensor network technology field.The present invention estimates each beaconing nodes position, and calculation of position errors using artificial fish-swarm location algorithm first；Then it will calculate and obtain measurement distance and corresponding position error between each beaconing nodes and handled, and build positioning expert decision-making table；Yojan is carried out to expertise as front-end system followed by rough set, Attribute Reduction Set is obtained；The location information that obtained yojan finally is concentrated into beaconing nodes broadcasts to unknown node, unknown node determines the estimated position of unknown node according to the beaconing nodes location information received using artificial fish-swarm location algorithm, so as to realize the positioning of the wireless sensor network.The estimation that present invention fusion rough set and artificial fish-swarm location algorithm obtain each node coordinate of wireless senser is accurate, fast convergence rate, and this method computation complexity is low, and positioning performance is good.
Description
Technical field
The present invention relates to a kind of wireless sensor network locating method, belong to wireless sensor network technology field.
Background technology
Wireless sensor network (WSN, wireless sensor network) is a kind of brand-new information acquisition platform,
A wide range of monitoring and the tracking task of complexity can be realized in agriculture field, node locating technique is agriculture wireless sensor network
One of critical support technology of network, realizes the node locating of high efficient and reliable to event observation, target following and improves router efficiency
In terms of it is significant.The location algorithm proposed at present is broadly divided into based on ranging and the algorithm without ranging.Without surveying
Away from location algorithm according to the information realization node locating such as network connectivty, but precision is relatively low.And the location algorithm based on ranging
By the distance between measuring node or angle information calculate node position, conventional distance-finding method has RSSI (Received signal
strength indicator)、TOA(Time of arrival)、AOA(Angle of arrival)、TDOA(Time
Difference of arrival) etc..Wherein, the ranging technology based on RSSI directly utilizes radio transmitting and receiving chip measurement signal
Intensity, without installing extra means additional, cost and energy consumption are relatively low, it is easy to accomplish, it has also become it is main that wireless sensor network is positioned
Method.
In most agriculture application scenarios, influenceed by node energy, power consumption, cost, only beaconing nodes are fixed by GPS
Position system obtains the positional information of itself, and other unknown nodes must be positioned by beaconing nodes.Artificial fish-swarm is calculated
Method (artificial fish swarm algorithm, AFSA) is that one kind of the commune optimizing based on animal behavior is modern
Heuristic random searching algorithm, has the advantages that to select initial value and parameter insensitive, strong robustness, simply, easily realizes, at present
The algorithm has been applied to wireless sensor network RSSI and positioned.The basis that beaconing nodes are positioned as wireless sensor network, by
The influence of the factors such as multipath effect, self-characteristic, geometry distribution, the crucial beaconing nodes of only few of which are to positioning result ratio
It is more sensitive, using the teaching of the invention it is possible to provide complementary information, positioning accuracy is favorably improved, and the beaconing nodes of redundancy can then increase positioning and miss
Difference, how reasonable selection participate in positioning beaconing nodes be a urgent need to resolve major issue.
The content of the invention
It is an object of the invention to provide a kind of wireless sensor network locating method, to solve to produce in current localization method
The beaconing nodes of redundancy cause the problem of position error is big.
The present invention provides a kind of wireless sensor network locating method, the network positions side to solve above-mentioned technical problem
Method comprises the following steps：
1) select a beaconing nodes as aggregation node, calculate the measurement distance between each beaconing nodes and be uploaded to remittance
Poly- node；
2) position and the position error of each beaconing nodes are calculated using artificial fish-swarm location algorithm；
3) measurement distance between beaconing nodes is handled with corresponding position error, builds positioning expert decision-making table；
4) by the use of rough set as front-end system, yojan is carried out to the content in expert decision-making table, Attribute Reduction Set is obtained；
5) location information for the beaconing nodes for concentrating yojan broadcasts to unknown node；
6) unknown node is calculated according to the location information for receiving yojan concentration beaconing nodes by artificial fish-swarm algorithm
Measurement distance between unknown node and respective beacon node, so that it is determined that the estimated position of each unknown node, that is, be somebody's turn to do
The position of each node in wireless sensor network.
The step 1) in the distance between each beaconing nodes be to calculate to obtain by RSSI (received signal strength).
The step 2) in object function in the artificial fish-swarm localization method that uses for：
Wherein y is target function value, represents the food concentration of Artificial Fish current state, xiFor the state of i-th Artificial Fish,
Dimension is 3, represents the three-dimensional space position residing for beaconing nodes to be positioned；N is beaconing nodes sum, (xi1,xi2,xi3) it is to treat
The estimated coordinates of localizer beacon node, i.e. Artificial Fish state xi, (xj,yj,zj) be beaconing nodes actual coordinate, djTo be to be positioned
Measurement distance between beaconing nodes and other beaconing nodes.
Described step 3) in positioning expert decision-making table be by between the node that will be obtained in beaconing nodes position fixing process
Measurement distance carries out sliding-model control using K-means clustering methods with corresponding position error and is used as conditional attribute and decision attribute
Built-up, for S=, (U, A, V, f), S is that wireless sensor network positioning expert knows to the expert knowledge representation after discretization in formula
Know, U={ x1,x2,…,xnIt is domain, correspondence beaconing nodes positioning object set；A=C ∪ D are attribute set,C=
{ck, k=1,2 ..., m } and it is conditional attribute collection, each beaconing nodes of correspondence to other beaconing nodes measurement distance property sets；D=
{ d } is decision kind set, correspondence beaconing nodes position error property value；V is the set of all attribute codomains；F is information function,
Determine value of each object under each attribute in U.
The K-means clustering algorithms are estimated using Euclidean distance as diversity, ask a certain initial cluster center of correspondence to
Measure optimal classification so that clustering criteria function E values are minimum, whereinCjRepresent the class cluster divided, xiRepresent
Cluster CiIn data point, ciRepresent cluster CiAverage, k represents the classification number of cluster.
The step 4) in in expert decision-making table content carry out yojan be by using Skowron differential matrixs and category
What Sexual behavior mode was realized, comprise the following steps：
A. differential matrix M is soughtn×n, list Mn×n=(cij)n×nLower triangular matrix, wherein i, j=1,2 ..., n；
B. the relatively core CORE of decision table is calculatedD(C) B=CORE, is madeD(C)；
C. to any cij, i, j=1,2 ..., n, ifThen
D. to any cij, i, j=1,2 ..., n, if hadStep F is then gone to, step E is otherwise gone to；
E. current matrix M is countedn×nIn the number of times that occurs of each attribute, it is a to choose the most element of occurrence numberm, make B
=B ∪ { am, go to step C；
F. output B is required yojan, and its mathematical sense is most simple shape of the decision attribute to conditional attribute set dependencies
Formula.
The beneficial effects of the invention are as follows：The present invention estimates each beaconing nodes position using artificial fish-swarm location algorithm first,
And calculation of position errors；Then it will calculate and obtain measurement distance and corresponding position error between each beaconing nodes and handled, structure
Build positioning expert decision-making table；Yojan is carried out to expertise as front-end system followed by rough set, Attribute Reduction Set is obtained；
The location information that obtained yojan finally is concentrated into beaconing nodes broadcasts to unknown node, and unknown node is according to receiving
Beaconing nodes location information determines the estimated position of unknown node using artificial fish-swarm location algorithm, so as to realize the wireless sensing
The positioning of device network.Present invention fusion rough set and artificial fish-swarm location algorithm obtain the estimation of each node coordinate of wireless senser
Accurately, fast convergence rate, and this method computation complexity is low, and positioning performance is good.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of wireless sensor network locating method of the present invention；
Fig. 2 is beaconing nodes deployment model schematic diagram in the embodiment of the present invention；
Fig. 3 is position error comparison schematic diagram of the localization method with existing localization method of the present invention.
Embodiment
The embodiment to the present invention is further described below in conjunction with the accompanying drawings.
Wireless sensor node S={ Si| i=1,2 ..., m } random placement is in three-dimensional rectangular parallelepiped space region, each section
Point isomorphism, and with identical computing capability, beaconing nodes deployment model is as shown in Figure 1.All nodes are divided into beacon by function
Node and unknown node.Preceding n node S1(x1,y1)、S2(x2,y2)、…、Sn(xn,yn) can by external equipments such as GPS or
The actual arrangement known obtains self-position in advance, is used as beaconing nodes；Node Si(xi,yi)(n<I≤m) position in a network
It is unknown and itself can obtain self information without special hardware device, be used as unknown node.Network section in the present invention
Point takes hypothesis below：
1) unknown node can arbitrarily be moved in region；
2) the radio signal propagation mode of beaconing nodes is preferable spheroid；
3) all the sensors node time stringent synchronization, and can direct communication.
Rough set is one of powerful of current uniform data acess, from Polish scholar Pawlak in nineteen eighty-two
Since proposing the theory, it has turned into the powerful for describing imperfect inaccurate and Noise Data.Rough set theory is at place
Managing in the constitutive relations between uncertain knowledge, elimination redundancy, discovery data attribute has prominent advantage, and it is disobeyed
Rely the priori of model there is provided the reduction of condition attributes of complete set and value reduction method, so as to find description system
The minimum prediction rule collection of system normal model, is to complete location feature Attributions selection to provide new way with positioning precision is improved
Footpath.The present invention is by artificial fish-swarm algorithm and rough set theory, and the position that selected distance error is minimized is used as estimated coordinates, tool
Have that positioning precision is high, anti-interference strong, computation complexity is low, be difficult it is affected by environment, adapt to agricultural environment fusion rough set and
The wireless sensor network locating method of artificial fish-swarm algorithm, the specific implementation process of this method is as follows：
1. the distance between each beaconing nodes of collection
Radio signal propagation path loss in greenhouse is described using simplified Lognormal shadowing model, its expression formula is：
Wherein d0For near-earth reference distance, unit is m, and d is the distance between receiving terminal and transmitting terminal, and unit is m, Pr
(d0) it is that distance is d0When the signal intensity that receives, unit is dBm；Pr(d) it is apart from the signal intensity received when being d, list
Position is dBm；β is the path loss index relevant with the environment such as obstacle, and scope is between 2~6.Randomly choosed first during positioning
One beaconing nodes is as aggregation node, and each beaconing nodes send the packet for including self-position to aggregation node, secondly each
Beaconing nodes are in communication with each other, the measurement distance between being calculated by RSSI value, and are uploaded to aggregation node.
2. localizer beacon node
A beaconing nodes position, and calculation of position errors are estimated using artificial fish-swarm location algorithm.Artificial fish-swarm algorithm is
A kind of stochastic search optimization algorithm for simulating fish school behavior, main looking for food and behavior of bunching using fish, passes through each in the shoal of fish
The local optimal searching of body realizes global optimizing.
1)xiFor the state of i-th Artificial Fish, dimension is 3, represents the three-dimensional space position residing for beaconing nodes to be positioned；
dij=| | xi-xj| | represent the distance between Artificial Fish individual；Visual represents the perceived distance of Artificial Fish；δ represents Artificial Fish
The crowding factor, try number represent the number of times of repeated attempt in foraging behavior.
2) y is target function value, represents the food concentration of Artificial Fish current state
In formula, n is beaconing nodes sum, is the estimated coordinates of beaconing nodes to be positioned, i.e. Artificial Fish state xi, (xj,yj,
zj) be beaconing nodes actual coordinate, djFor the measurement distance between beaconing nodes to be positioned and other beaconing nodes.Due to
There is error in RSSI rangings, orientation problem essence is to minimize error, then target function value is bigger, and obtained positioning result is got over
It is excellent.
Typical behavior during beaconing nodes positioning summary based on artificial fish-swarm algorithm has 4：Look for food, bunch, knocking into the back and with
Machine behavior.
1) foraging behavior
The current state for setting Artificial Fish is xi, in its (d within sweep of the eyeij≤ visual) one state of random selection
xjIf, yj>yi, then taken a step forward to the direction, conversely, then reselecting state, judge whether to meet forward travel state, repeatedly
Try number, if being still unsatisfactory for advance condition, the step of random movement one, i.e.,
Wherein rand (N (xi, visual)) represent in xiVisual fields in randomly select a new state.
2) bunch behavior
If Artificial Fish current state is to explore its number of partners nf within the vision and its center, when, and, table
There is more food at bright partner center and less crowded, then the direction towards partner takes a step forward, and otherwise performs foraging behavior.
3) knock into the back behavior
If Artificial Fish current state is that to explore in its partner within the vision be maximum partner, if showing partner
State to have higher be thing concentration, and surrounding is less crowded, then the direction towards partner takes a step forward, and otherwise performs and looks for food
Behavior.
4) random behavior
The behavior refers to randomly choosing a state within sweep of the eye, then thinks that the direction is moved, belong to row of looking for food
For a default behavior.
The property of extreme value is asked for according to orientation problem, using heuristic perform bunch and knock into the back wait behavior, then evaluate go
Value after dynamic, selects the maximum therein actually to perform, and default behavior is foraging behavior.Position and calculate in artificial fish-swarm
A bulletin board is set up in method, the food concentration to record optimal Artificial Fish individual state and the Artificial Fish position.Every people
The food concentration of itself current state and bulletin board are just compared by work fish after action once, if better than bulletin board,
With oneself state substitution bulletin board status.
3. position expert decision-making table
Measurement distance is with after corresponding position error sliding-model control, dividing between the node that will be obtained in beaconing nodes position fixing process
Positioning expert decision-making table is not built as conditional attribute and decision attribute, on the premise of information is not lost, to expert decision-making table
Yojan is carried out, minimum form of the decision attribute to conditional attribute set associativity is obtained, i.e., letter is had a significant impact to positioning precision
Mark the set of node.
Discretization for Continuous Attribute
Rough set theory can only handle discrete type attribute, therefore, it is necessary to enter to location data before attribute reduction processing
Row discrete processes, the present invention is using K-means clustering methods to Discretization for Continuous Attribute.Specific method is the condition by decision table
Attribute carries out clustering one by one, and the cluster result under each attribute is sorted in ascending order, will cluster accordingly classification as its from
Value is dissipated, wherein each beaconing nodes to the measurement distance of itself is zero, without participating in cluster, minimum class label is directly assigned to.K-
Means clustering algorithms are estimated using Euclidean distance as diversity, a certain initial cluster center vector optimal classification of correspondence so that poly-
Class criterion function E values are minimum.WhereinCjRepresent the class cluster divided, xiRepresent cluster CiIn data point, ci
Represent cluster CiAverage, k represents the classification number of cluster.The algorithm flow of K-means clustering algorithms：
Input：Object X={ x to be sorted1,x2,…,xn, clusters number k
Output：K class cluster Cj, j=1,2 ..., k
Step1 is randomly assigned k Ge Cu center { m1,m2,…,mk}；
Step2 is for each data point xi, the cluster center nearest from it is found, and assign it to such；
Step3 recalculates Ge Cu centers
Step4 calculates clustering criteria function
If Step5 E values restrain, { m is returned1,m2,…,mk, algorithm is terminated；Otherwise Step2 is turned.
Build expert decision-making table
For S=, (U, A, V, f), S is that agriculture wireless sensor network positioning is special to expert knowledge representation after discretization in formula
Family's knowledge, U={ x1,x2,…,xnIt is domain, correspondence beaconing nodes positioning object set；A=C ∪ D are attribute set,C={ ck, k=1,2 ..., m } and it is conditional attribute collection, each beaconing nodes of correspondence to other beaconing nodes measurement distances
Property set；D={ d } is decision kind set, correspondence beaconing nodes position error property value, and V is the set of all attribute codomains；f
For information function, that is, determine value of each object under each attribute in U.4. Attribute Reduction Set
Attribute reduction is under conditions of keeping expert knowledge library classification capacity constant, to delete wherein inessential or uncorrelated
Attribute, the yojan of decision table is realized using the Algorithm for Reduction based on Skowron differential matrixs and Attributions selection.Detailed process
It is as follows：
1) differential matrix M is soughtn×n, the lower triangular matrix M listedn×n=(cij)n×n, wherein i, j=1,2 ..., n；
2) the relatively core CORE of decision table is calculatedD(C) B=CORE, is madeD(C)；
3) to any cij, i, j=1,2 ..., n, ifThen
4) to any cij, i, j=1,2 ..., n, if hadThen go to step 6), otherwise go to step 5)；
5) statistics current matrix Mn×nIn the number of times that occurs of each attribute, it is a to choose the most element of occurrence numberm, make B
=B ∪ { am, go to step 3)；
6) output B is required yojan, and its mathematical sense is most simple shape of the decision attribute to conditional attribute set dependencies
Formula.
5. the complete positioning properties of yojan are broadcast into unknown node, unknown node is broadcasted according to beaconing nodes are received
Information calculates the measurement distance between unknown node and respective beacon node, and its calculating process is realized using formula (1), and is uploaded
To aggregation node, the estimated position of unknown node is determined using artificial fish-swarm location algorithm again.
6. export artificial fish-swarm location algorithm in colony's optimal location as mobile node estimated coordinatesMove
Dynamic node the whole network broadcasts this positioning end signal, and aggregation node receives rear steering step 1.
Simulating scenes setting is as follows：1) experiment 3D region is 100m × 50m × 20m；2) node total number amount is 40, its
In 20 be beaconing nodes；3) the transmission signal power P of beaconing nodestFor 30dBm, reference distance d0For 20m, transmitting antenna increases
Beneficial Gt, receiving antenna gain GrIt is 1dBi, path loss index β is 2；4) Artificial Fish number R=50, crowding factor delta=
0.618, repeated attempt number of times try_number=50, the classification number k=4 of cluster；5) positioning result is emulation under identical parameters
The average value of result obtained by 100 times；6) influence for simulation actual environment to RSSI rangings, is calculated according to Node distribution position
Corresponding received signal strength, increases zero-mean gaussian stochastic variable λ as environmental disturbances, then receives this on this basis
Signal intensity obtains measurement distance as RSSI value.
Compare under identical simulated conditions set forth herein location algorithm and artificial fish-swarm algorithm locating effect, take Gauss
Stochastic variable λ=N (0,25), positioning result is as shown in Figure 3, it can be seen that when the larger feelings of error occurs in artificial fish-swarm location algorithm
During condition, positioning precision, error compensation effect can be effectively improved using the localization method of fusion rough set and artificial fish-swarm algorithm
Significantly.The average localization error of two kinds of location algorithms is respectively 1.6052m, 2.0688m, shows to be used as preposition system by the use of rough set
System, can simplify knowledge of orientation expression of space dimension, obtain the main beaconing nodes information of influence positioning result, effectively improve
Positioning precision.
Claims (5)
1. a kind of wireless sensor network locating method, it is characterised in that the network locating method comprises the following steps：
1) a beaconing nodes are selected as aggregation node, the measurement distance between each beaconing nodes is calculated and is uploaded to convergence section
Point；
2) position and the position error of each beaconing nodes are calculated using artificial fish-swarm location algorithm；
3) measurement distance between beaconing nodes is handled with corresponding position error, builds positioning expert decision-making table；The positioning is special
Family's decision table is by the way that measurement distance between the node obtained in beaconing nodes position fixing process and corresponding position error are used into K-
Means clustering methods progress sliding-model control is built-up as conditional attribute and decision attribute, the expertise after discretization
Being expressed as S=, (U, A, V, f), S is that wireless sensor network positions expertise, U={ x in formula1,x2,…,xnIt is domain, it is right
Beaconing nodes are answered to position object set；A=C ∪ D are attribute set,C={ ck, k=1,2 ..., m } it is conditional attribute
Collection, each beaconing nodes of correspondence to other beaconing nodes measurement distance property sets；D={ d } is decision kind set, correspondence beaconing nodes
Position error property value；V is the set of all attribute codomains；F is information function, that is, determines that each object belongs at each in U
Value under property;
4) by the use of rough set as front-end system, yojan is carried out to the content in expert decision-making table, Attribute Reduction Set is obtained；
5) location information for the beaconing nodes for concentrating yojan broadcasts to unknown node；
6) unknown node is calculated by artificial fish-swarm algorithm according to the location information for receiving yojan concentration beaconing nodes and obtained not
The measurement distance between node and respective beacon node is known, so that it is determined that the estimated position of each unknown node, that is, obtain this wireless
The position of each node in sensor network.
2. wireless sensor network locating method according to claim 1, it is characterised in that the step 1) in each beacon
The distance between node is to calculate to obtain by received signal strength value.
3. wireless sensor network locating method according to claim 2, it is characterised in that the step 2) middle use
Object function in artificial fish-swarm localization method is：
Wherein y is target function value, represents the food concentration of Artificial Fish current state, xiFor the state of i-th Artificial Fish, dimension
For 3, the three-dimensional space position residing for expression beaconing nodes to be positioned；N is beaconing nodes sum, (xi1,xi2,xi3) it is to be positioned
The estimated coordinates of beaconing nodes, i.e. Artificial Fish state xi, (xj,yj,zj) be beaconing nodes actual coordinate, djFor beacon to be positioned
Measurement distance between node and other beaconing nodes.
4. wireless sensor network locating method according to claim 1, it is characterised in that the K-means clusters are calculated
Method is estimated using Euclidean distance as diversity, seeks a certain initial cluster center vector optimal classification of correspondence so that clustering criteria letter
Number E values are minimum, whereinCjRepresent the class cluster divided, xiRepresent cluster CiIn data point, ciRepresent cluster Ci
Average, k represents the classification number of cluster.
5. wireless sensor network locating method according to claim 1, it is characterised in that the step 4) in expert
It is, by using Skowron differential matrixs and Attributions selection realization, to specifically include following that content in decision table, which carries out yojan,
Step：
A. differential matrix M is soughtn×n, list Mn×n=(cij)n×nLower triangular matrix, wherein i, j=1,2 ..., n；
B. the relatively core CORE of decision table is calculatedD(C) B=CORE, is madeD(C)；
C. to any cij, i, j=1,2 ..., n, ifThen
D. to any cij, i, j=1,2 ..., n, if hadStep F is then gone to, step E is otherwise gone to；
E. current matrix M is countedn×nIn the number of times that occurs of each attribute, it is a to choose the most element of occurrence numberm, make B=B ∪
{am, go to step C；
F. output B is required yojan, and its mathematical sense is minimum form of the decision attribute to conditional attribute set dependencies.
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| CN108089148B (en) * | 2017-12-14 | 2019-08-30 | 电子科技大学 | A kind of passive track-corelation direction cross positioning method based on time difference information |
| CN108846434A (en) * | 2018-06-11 | 2018-11-20 | 广东工业大学 | A kind of missing data fill method based on improvement K-means clustering algorithm |
| CN112055303A (en) * | 2020-08-28 | 2020-12-08 | 太原理工大学 | Artificial fish swarm optimization positioning method for unknown sensor nodes of wireless sensor network |
| CN114727251B (en) * | 2022-03-17 | 2023-04-21 | 石河子大学 | Data transmission optimization method and system |
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