CN108924755A - DV-HOP indoor orientation method based on immunity particle cluster optimization - Google Patents
DV-HOP indoor orientation method based on immunity particle cluster optimization Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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Abstract
The present invention provides a kind of DV-HOP indoor positioning algorithms based on immunity particle cluster optimization, immunity particle cluster algorithm is introduced into DV-HOP indoor orientation method, particle swarm optimization is improved using immune algorithm, pass through the intersection and mutation operation of population, seek iteration precision maximum and the best situation of fitness, the diversity for improving particle populations maintains ability, expands the search space of solution, convergence speed of the algorithm is improved, is all significantly improved in terms of precision and ability of searching optimum.Accordingly, with respect to traditional DV-HOP indoor orientation method based on standard particle group's algorithm, positioning accuracy is significantly improved.Relative to the indoor orientation method of distance measuring method, external hardware supported is not needed, it is at low cost, expense is small, reduces human input.Under the premise of guaranteeing higher positioning accuracy, and cost overhead is taken into account, is conducive to large-scale promotion and is applicable in.
Description
Technical field
The present invention relates to Indoor Wireless Sensor Networks technical fields, more particularly to one kind is for excellent based on immunity particle cluster
The DV-HOP indoor orientation method of change.
Background technique
Wireless sensor network (wireless sensornetwork, WSNs) is a large amount of cheap by being deployed in detection zone
Microsensor node, the network system of the multi-hop, self-organizing that are formed through wireless communication.Wireless sensor
The most basic function of network first is that in real time know event occur location information or obtain node position.Relative to outdoor ring
Border, indoor environment is more complicated, and wireless sensor network node contrast locating GPS positioning is more accurate.
Location technology is one of main support technology of wireless sensor network, the extensive concern by researcher.It is existing
The positioning of some wireless sensor networks is broadly divided into positioning based on distance and apart from unrelated positioning.Positioning based on distance is calculated
What method utilized the positioning of unknown node is signal strength or angle information between node, needs external hardware device
It supports.And realize and there is no extra demand to network node that there is at low cost, expense is small etc. simple apart from unrelated location algorithm
Advantage, Typical Representative are DV-HOP algorithms.
The position fixing process of existing DV-HOP location algorithm is generally divided into three phases：First stage all anchor node (anchor
Node refers to node known to self-position) data grouping is sent to its neighbor node with the mode to flood, it is all
Node can obtain the relevant information of anchor node；Second stage, after node receives the data grouping that anchor node sends over,
Calculating and broadcast corrections value；Phase III, the minimum hop count and second stage that unknown node is received according to the first stage
The corrected value of calculated Average hop distance obtains its estimated distance between each anchor node, further according to trilateration
Method, Maximum Likelihood Estimation Method and particle group optimizing (particle swarmoptimization, PSO) scheduling algorithm calculate unknown section
The positioning coordinate of point.
Particle swarm optimization algorithm is a kind of global optimization approach based on population search strategy, and each member is called in population
Particle represents a potential feasible solution, and food position then represents globally optimal solution.Particle flies in D dimension search space
Row dynamically adjusts oneself speed according to the experience of experience and entire group, each particle by two extreme values come
Update its speed and position, one is optimal solution pbest that particle itself is found up to now, another is entire group
Up to now the optimal solution gbest found.As soon as the every update time position of particle calculates a fitness value, then according to institute
There are the pbest and gbest of particle to determine new individual extreme value and group's extreme value, and updates position corresponding to respective pbest
Set position corresponding with group gbest.But that there is convergence rates is slow, precision is low, population is easily precocious for standard particle group algorithm
The disadvantages of, so that there are convergence rates slow, the low efficiency, positioning accuracy of the DV-HOP localization method based on standard particle group's algorithm
Difference, the technological deficiencies such as search capability is weak.
Summary of the invention
In order to solve above-mentioned technical problem existing for indoor orientation method in the prior art, the present invention provides one kind and is based on
The DV-HOP indoor orientation method of immunity particle cluster optimization.
The present invention is achieved by the following technical solutions：
A kind of DV-HOP indoor orientation method based on immunity particle cluster optimization, is suitable for Indoor Wireless Sensor Networks,
Include the following steps：
Step 1, several anchor nodes of random placement and unknown node in the target area of wireless sensor network indoors,
Data packet information is sent to the neighbor node of the anchor node in the way of flooding by each anchor node.
The data packet information includes：The mark i of anchor node, anchor node coordinate (xi, xj), hop count hiIt (is initialized as
0)。
Step 2, each unknown node calculates every according to the data packet information sent from corresponding anchor node received
The average jump of a anchor node away from.
Step 3, each unknown node is according to average jump calculated in the minimum hop count and step 2 received in step 1
Away from calculating each unknown node to the revised estimated distance of each anchor node.
Step 4, population is initialized, determines speed and the position of each particle；Initiation parameter includes：Particle populations rule
Mould N, Studying factors C1、C2, reproductive order of generation M, inertia weight ω, search space dimension D.
Step 5, the fitness value that contemporary particle is calculated according to preset objective function, determines the history optimal value of population
And global optimum；The termination condition for checking iterative calculation judges whether to reach preset maximum number of iterations or optimal suitable
Answer angle value condition：If reached, terminate to calculate, exports result；Otherwise, step 6 is continued to execute.
Step 6, immunological memory cell is generated：It is ranked up according to the sequence of affinity from big to small, affinity is larger
M antibody deposit data base in be used as immunocyte.
Step 7, immune vaccine is generated：Two maximum antibody of affinity are selected to be handed over from the immunological memory cell
Fork operation, is stored in vaccine library obtained common subset part as vaccine.
Step 8, according to formula (12), formula (13) and dynamic inertia weight formula (14) update each particle speed and
Position；N new particles will be obtained after update, and randomly select q immunocyte from the data base, and group is n+ on a large scale
Q particle populations.
vi(t+1)=ω vi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)=xi(t)+vi(t+1) (13)
Wherein, i=1,2 ..., N, ω are inertia weight, ωmax、ωminRespectively minimum and maximum inertia weight, t are to work as
Preceding the number of iterations, tmaxFor maximum number of iterations, r1、r2It is that equally distributed random number, pbest are obeyed in [0,1]iFor particle i
History optimal location, gbestiFor the global history optimal location of particle i.
Step 9, the promotion or inhibition of antibody：Each particle in the particle populations that step 8 obtains is calculated according to formula (15)
Select probability：
Wherein, i=1,2 ..., N+M, xiIndicate i-th of particle, G (xi) indicate i-th of particle affinity functional value；p
(xi) indicate i-th of particle select probability.
According to the size of the select probability, n particle for selecting maximum probability forms new antibody population.
Step 10, vaccine inoculation obtains：The maximum antibody of two affinity is selected to carry out from the antibody population that step 9 obtains
Intersect operation, using obtained common subset as vaccine inoculation.
Step 11, Immune Selection：The fitness value of the vaccine inoculation is calculated, if the fitness value of the vaccine inoculation is small
Fitness value in step 5 then receives the vaccine inoculation and carries out inoculation operation, otherwise abandons the vaccine inoculation, retains
Initial value；Revolution executes step 5.
The beneficial effect of the present invention compared with the existing technology is：
(1) the DV-HOP indoor orientation method provided by the invention based on immunity particle cluster optimization, immunity particle cluster is calculated
Method introduces DV-HOP indoor orientation method, improves particle swarm optimization using immune algorithm, is grasped by the intersection and variation of population
Make, seek iteration precision maximum and the best situation of fitness, the diversity for improving particle populations maintains ability, expands solution
Search space improves convergence speed of the algorithm, is all significantly improved in terms of precision and ability of searching optimum.Therefore, phase
For traditional DV-HOP indoor orientation method based on standard particle group's algorithm, positioning accuracy is significantly improved.
(2) the DV-HOP indoor orientation method provided by the invention based on immunity particle cluster optimization belongs to non-ranging mode
Indoor orientation method does not need external hardware supported relative to the indoor orientation method of distance measuring method, it is at low cost, expense is small,
Reduce human input.Under the premise of guaranteeing higher positioning accuracy, and cost overhead is taken into account, is conducive to large-scale promotion and is applicable in.
Detailed description of the invention
Fig. 1 is the nodal plane layout drawing of the DV-HOP indoor orientation method optimized based on immunity particle cluster.
Fig. 2 is the general flow chart of the DV-HOP indoor orientation method optimized based on immunity particle cluster.
Fig. 3 is the analysis of simulation experiment result figure of the DV-HOP indoor orientation method optimized based on immunity particle cluster.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are only used to explain the present invention,
It is not intended to limit the present invention.
Embodiment 1：
Based on the DV-Hop indoor positioning algorithms of immunity particle cluster optimization, the immunoevolution being introduced primarily into immune algorithm
Mechanism is combined with particle swarm algorithm.Experiment of the present invention using the region of 100m × 100m as Indoor Wireless Sensor Networks is imitative
True environment, as shown in Figure 1.Normal, communication radius is communicated in the Indoor Wireless Sensor Networks region of emulation between all nodes
For 30m, population scale N=30, maximum number of iterations gmax=50, maximum speed vmax=10, maximum inertia weight ωmax
=0.8, minimum inertia weight ωmin=0.2, Studying factors c1=c2=1.4962, search space dimension D=3, all emulation are real
It tests and carries out 200 times, judge algorithm positioning accuracy using average localization error：
Wherein,For the mean place of unknown node estimation, x is unknown node physical location, and Q is unknown node in network
Number, R are communication radius.
As shown in Fig. 2, being applicable in the present embodiment provides a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization
In Indoor Wireless Sensor Networks, include the following steps：
Step 1, several anchor nodes of random placement and unknown node in the target area of wireless sensor network indoors,
When disposing anchor node, hop count data information corresponding between each anchor node is subjected to initialization return-to-zero.Using each
Anchor node sends data packet information to the neighbor node of the anchor node by way of flooding.In the present embodiment, in conjunction with such as
20 anchor nodes and 5 unknown nodes arranged in Fig. 1 experiment are illustrated.
The data packet information includes：The mark i of anchor node, anchor node coordinate (xi, xj), hop count hiIt (is initialized as
0)。
Step 2, each unknown node calculates every according to the data packet information sent from corresponding anchor node received
The average jump of a anchor node away from.
Specifically, according to formula (1) calculate the average jump of each anchor node away from：
Wherein, CiFor the Average hop distance of anchor node i, hijIndicate the minimum hop count (i between anchor node i and anchor node j
≠ j), (xi, yi)、(xj,yj) indicate anchor node i and anchor node j coordinate.
Step 3, according to the minimum hop count received in step 1, (minimum hop count refers to unknown node to each unknown node
Receive the minimum value of hop count in the data grouping of the same anchor node) and step 2 in calculated average jump away from calculating every
A unknown node each revised estimated distance d of anchor node into oneself data groupingi(distance diIt is unknown node to anchor section
The measurement distance of point).
The process for calculating each unknown node to the revised estimated distance of each anchor node specifically includes：
Step 301, the actual range d between anchor node i and j is calculated according to formula (3)rij；
Step 302, the measurement distance d between anchor node i and j is calculated according to formula (4)eij；
deij=ci×hij (4)
Step 303, the error ε of anchor node i Average hop distance is calculated according to formula (5)i；
Wherein, L is anchor node number in network；
Step 304, the weighted value λ of i-th of anchor node Average hop distance is calculated according to formula (6)i；
Step 305, the every hop distance correction value C of unknown node is calculated according to formula (7), formula (8)；
C=λk×Ck+λs×Cs+λt×Ct (7)
λk+λs+λt=1 (8)
Wherein, k, s, t are three anchor nodes nearest from unknown node.
Step 306, the distance value of unknown node anchor node i into oneself data grouping is calculated according to formula (9)；
di=C × hi (9)
Wherein, C is the every hop distance correction value of unknown node, hiThe minimum hop count of anchor node i is received for unknown node.
Step 4, population is initialized, determines speed and the position of each particle；Initiation parameter includes：Particle populations rule
Mould N, Studying factors C1、C2, reproductive order of generation M, inertia weight ω, search space dimension D.
Step 5, the fitness value that contemporary particle is calculated according to preset objective function, determines the history optimal value of population
And global optimum；The termination condition for checking iterative calculation judges whether to reach preset maximum number of iterations or optimal suitable
Answer angle value condition：If reached, terminate to calculate, exports result；Otherwise, step 6 is continued to execute.
Wherein the method for calculating particle fitness value is specially：
The fitness value of current each particle is calculated according to formula (10),
Wherein, L is anchor node number, (xi, yi) it is unknown node position coordinates, (xj, yj) it is anchor node position coordinates.
Step 6, immunological memory cell is generated：It is ranked up according to the sequence of affinity from big to small, affinity is larger
M antibody deposit data base in be used as immunocyte.Affinity indicates the degree of closeness between antibody and antigen, and particle is expressed as
Antibody, the solution of objective function are expressed as antigen, the affinity affinity (x in the present inventioni) be
In formula (11), F (xi) be particle i fitness value.
Step 7, immune vaccine is generated：The maximum antibody of two affinity is selected to carry out phase from the immunological memory cell
Mutual crossover operation is stored in vaccine library obtained common subset part as vaccine.
Step 8, according to formula (12), formula (13) and dynamic inertia weight formula (14) update each particle speed and
Position；N new particles will be obtained after update, and randomly select q immunocyte from the data base, and group is n+ on a large scale
Q particle populations；
vi(t+1)=ω vi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)=xi(t)+vi(t+1) (13)
Wherein, i=1,2 ..., N, ω are inertia weight, ωmax、ωminRespectively minimum and maximum inertia weight, t are to work as
Preceding the number of iterations, tmaxFor maximum number of iterations, r1、r2It is that equally distributed random number, pbest are obeyed in [0,1]iFor particle i
History optimal location, gbestiFor the global history optimal location of particle i.
Step 9, the promotion or inhibition of antibody：Each particle in the particle populations that step 8 obtains is calculated according to formula (15)
Select probability：
In formula (15), i=1,2 ..., N+M, xiIndicate i-th of particle, G (xi) indicate i-th of particle affinity letter
Numerical value；p(xi) indicate i-th of particle select probability；
According to the size of the select probability, n particle for selecting maximum probability forms new antibody population.
Step 10, vaccine inoculation obtains：The maximum antibody of two affinity is selected to carry out from the antibody population that step 9 obtains
Intersect operation, using obtained common subset as vaccine inoculation.
Step 11, Immune Selection：The fitness value of the vaccine inoculation is calculated, if the fitness value of the vaccine inoculation is small
Fitness value in step 5 then receives the vaccine inoculation and carries out inoculation operation, otherwise abandons the vaccine inoculation, retains
Initial value；Revolution executes step 5.
In the present invention positioning of unknown node be just to solve for unknown node in objective function i.e. fitness function coordinate (x,
y)。
The fitness function of particle is：
Wherein, (xi, yi) be anchor node i coordinate position, diIt is between the unknown node obtained by measurement and anchor node
Distance, and the minimum point of fitness function F (x, y) then be require positioning coordinate.
Below with specific example, experimental demonstration is carried out for the method for the present invention, particular content is as follows：
1, experimental situation
To verify the locating effect of DV-HOP indoor orientation method optimized the present invention is based on immunity particle cluster,
Immune grain is based on to the DV-HOP indoor orientation method based on standard particle group algorithm (PSO algorithm) under MATLABR2011b environment
The DV-HOP indoor orientation method that subgroup optimizes (IA-PSO algorithm) carries out emulation experiment.
Emulation experiment environment is the plane room area of 100m × 100m, in this region at random known to 20 positions of arrangement
Anchor node, 5 unknown nodes.
2, evaluation index
The present embodiment uses placement error value as experimental evaluation index, and placement error value is smaller, that is, thinks positioning accuracy
It is higher.
3, experimental result
In order to verify the validity of method proposed by the invention, the DV- of standard particle group algorithm (PSO algorithm) will be based on
The position error of HOP indoor orientation method and the DV-HOP indoor orientation method based on immunity particle cluster optimization (IA-PSO algorithm)
Value compares, and comparing result is as shown in Figure 3.As can be seen from FIG. 3, the DV- based on immunity particle cluster optimization (IA-PSO algorithm)
The positioning accuracy of HOP indoor orientation method is apparently higher than the DV-HOP indoor positioning based on standard particle group algorithm (PSO algorithm)
Method illustrates that the DV-HOP indoor orientation method provided by the invention based on immunity particle cluster optimization (IA-PSO algorithm) can have
Effect improves the positioning accuracy of wireless sensor network.
As it will be easily appreciated by one skilled in the art that the above is merely preferred embodiments of the present invention, not to limit
The present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in this
Within the protection scope of invention.
Claims (6)
1. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization, is suitable for Indoor Wireless Sensor Networks,
It is characterized in that, includes the following steps：
Step 1, several anchor nodes of random placement and unknown node in the target area of wireless sensor network indoors utilize
Each anchor node sends data packet information to the neighbor node of the anchor node by way of flooding；
The data packet information includes：The mark i of anchor node, anchor node coordinate (xi, xj), hop count hi(being initialized as 0)；
Step 2, each unknown node calculates each anchor according to the data packet information sent from corresponding anchor node received
The average jump of node away from；
Step 3, each unknown node is according to average jump calculated in the minimum hop count and step 2 received in step 1 away from meter
Each unknown node is calculated to the revised estimated distance of each anchor node；
Step 4, population is initialized, determines speed and the position of each particle；Initiation parameter includes：Particle populations scale N,
Studying factors C1、C2, reproductive order of generation M, inertia weight ω, search space dimension D；
Step 5, the fitness value that contemporary particle is calculated according to preset objective function determines the history optimal value of population and complete
Office's optimal value；The termination condition for checking iterative calculation, judges whether to reach preset maximum number of iterations or optimal fitness
Value condition：If reached, terminate to calculate, exports result；Otherwise, step 6 is continued to execute；
Step 6, immunological memory cell is generated：It is ranked up according to the sequence of affinity from big to small, by affinity biggish M
Antibody, which is stored in data base, is used as immunocyte；
Step 7, immune vaccine is generated：Two maximum antibody of affinity are selected to carry out intersection behaviour from the immunological memory cell
Make, obtained common subset part is stored in vaccine library as vaccine；
Step 8, speed and the position of each particle are updated according to formula (12), formula (13) and dynamic inertia weight formula (14)
It sets；N new particles will be obtained after update, and randomly select q immunocyte from the data base, and group is n+q on a large scale
A particle populations；
vi(t+1)=ω vi(t)+c1r1(pbesti-xi(t))+c2r2(gbesti-xi(t)) (12)
xi(t+1)=xi(t)+vi(t+1) (13)
Wherein, i=1,2 ..., N, ω are inertia weight, ωmax、ωminRespectively minimum and maximum inertia weight, t are current change
Generation number, tmaxFor maximum number of iterations, r1、r2It is that equally distributed random number, pbest are obeyed in [0,1]iFor going through for particle i
History optimal location, gbestiFor the global history optimal location of particle i；
Step 9, the promotion or inhibition of antibody：The choosing of each particle in the particle populations that step 8 obtains is calculated according to formula (15)
Select probability：
Wherein, i=1,2 ..., N+M, xiIndicate i-th of particle, G (xi) indicate i-th of particle affinity functional value；p(xi)
Indicate the select probability of i-th of particle；
According to the size of the select probability, n particle for selecting maximum probability forms new antibody population；
Step 10, vaccine inoculation obtains：The maximum antibody of two affinity is selected to carry out from the antibody population that step 9 obtains mutual
Crossover operation, using obtained common subset as vaccine inoculation；
Step 11, Immune Selection：The fitness value of the vaccine inoculation is calculated, if the fitness value of the vaccine inoculation is less than step
Fitness value in rapid 5 then receives the vaccine inoculation and carries out inoculation operation, otherwise abandons the vaccine inoculation, retains initial value；
Revolution executes step 5.
2. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization according to claim 1, feature exist
In the step 1 further comprises：When disposing anchor node, hop count data information corresponding between each anchor node is carried out
Initialize return-to-zero.
3. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization according to claim 1, feature exist
In the step 2 further comprises：
According to formula (2) calculate the average jump of each anchor node away from：
Wherein, CiFor the Average hop distance of anchor node i, hijIndicate anchor node i and anchor node j between minimum hop count (i ≠
J), (xi, xj)、(yi,yj) indicate anchor node i and anchor node j coordinate.
4. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization according to claim 1, feature exist
In the step 3 further comprises：
Step 301, the actual range d between anchor node i and j is calculated according to formula (3)rij；
Step 302, the measurement distance d between anchor node i and j is calculated according to formula (4)eij；
deij=ci×hij (4)
Step 303, the error ε of anchor node i Average hop distance is calculated according to formula (5)i；
Wherein, M is anchor node number in network；
Step 304, the weighted value λ of i-th of anchor node Average hop distance is calculated according to formula (6)i；
Step 305, the every hop distance correction value C of unknown node is calculated according to formula (7), formula (8)；
C=λk×Ck+λs×Cs+λt×Ct (7)
λk+λs+λt=1 (8)
Wherein, k, s, t are three anchor nodes nearest from unknown node；
Step 306, the distance value of unknown node anchor node i into oneself data grouping is calculated according to formula (9)；
di=C × hi (9)
Wherein, C is the every hop distance correction value of unknown node, hiThe minimum hop count of anchor node i is received for unknown node.
5. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization according to claim 1, feature exist
In the method for calculating particle fitness value in the step 5 is specially：
The fitness value of current each particle is calculated according to formula (10),
6. a kind of DV-HOP indoor orientation method based on immunity particle cluster optimization according to claim 1, feature exist
In affinity affinity (x in the step 6i) calculation formula such as formula (11) shown in：
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| CN110346517A (en) * | 2019-07-25 | 2019-10-18 | 中南大学 | A kind of smart city industrial air pollution visualization method for early warning and its system |
| CN110545552A (en) * | 2019-09-02 | 2019-12-06 | 重庆三峡学院 | Multipath transmission routing method based on immune particle swarm |
| CN108924755B (en) * | 2018-06-29 | 2020-05-22 | 合肥工业大学 | DV-HOP indoor positioning method based on immune particle swarm optimization |
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| CN110545552B (en) * | 2019-09-02 | 2023-04-14 | 重庆三峡学院 | Multipath transmission routing method based on immune particle swarm |
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