CN111542069B - Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm - Google Patents

Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm Download PDF

Info

Publication number
CN111542069B
CN111542069B CN202010303266.9A CN202010303266A CN111542069B CN 111542069 B CN111542069 B CN 111542069B CN 202010303266 A CN202010303266 A CN 202010303266A CN 111542069 B CN111542069 B CN 111542069B
Authority
CN
China
Prior art keywords
individuals
wireless
individual
population
probability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010303266.9A
Other languages
Chinese (zh)
Other versions
CN111542069A (en
Inventor
唐震洲
支子聪
沈达
孟欣
郭瑞琪
易家欢
应子怡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wenzhou University
Original Assignee
Wenzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wenzhou University filed Critical Wenzhou University
Priority to CN202010303266.9A priority Critical patent/CN111542069B/en
Publication of CN111542069A publication Critical patent/CN111542069A/en
Application granted granted Critical
Publication of CN111542069B publication Critical patent/CN111542069B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H04W52/143—Downlink power control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/242—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account path loss
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Physiology (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method for realizing wireless AP deployment optimization based on a rapid non-dominant genetic algorithm, which comprises the following steps: determining parameters, binary string codes, setting parameters, performing cross mutation operation, selecting operation, judging conditions and screening an optimal solution; the invention adopts a non-uniform quantized space model design, more accords with the actual demand, and according to the traditional experience and classical theory, the wireless AP power information, signal intensity decay, channel interference and other data, the distribution position and the power of the wireless AP are adjusted through a rapid non-dominant genetic algorithm, so that the purpose of optimizing the network topology is achieved under the condition of meeting the communication requirement, the purposes of reducing the number of the wireless APs and reducing the total power are finally realized, the change condition of the observed signal coincidence rate is greatly reduced, and the resource utilization is effectively improved.

Description

Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm
Technical Field
The invention relates to the technical field of wireless local area networks, in particular to a method for realizing wireless AP deployment optimization based on a rapid non-dominant genetic algorithm.
Background
With rapid development of wireless technology, wireless local area networks are widely used in various fields due to high efficiency, flexibility and low cost of networking;
meanwhile, the topology optimization of the wireless AP in networking is widely studied, which has great significance for improving the performance of the wireless AP network, and in many occasions, the irregularly densely deployed wireless AP can cause the waste of resources, so the invention provides a method for realizing the deployment optimization of the wireless AP based on a rapid non-dominant genetic algorithm to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a method for realizing wireless AP deployment optimization based on a rapid non-dominant genetic algorithm.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a wireless AP deployment optimization method based on a rapid non-dominant genetic algorithm comprises the following steps:
step one: determining parameters
According to the environment in reality, determining a signal attenuation function and signal attenuation caused by obstacles, setting three types of obstacles and signal attenuation values corresponding to each type of obstacle, setting partial parameters including reference distances and path attenuation coefficients, establishing a square simulation map in the simulation, discretizing the square simulation map, and setting the three types of obstacles and the signal attenuation values corresponding to each type of obstacle;
step two: binary string encoding
According to the first step, N APs default to be in a working state are uniformly arranged in an area initially, the position information x, y, power p and state k of each wireless AP are set as calculation variables, and the wireless APs are encoded according to the calculation variables x, y, p and k, and after encoding, each wireless AP exists in the form of a 25-bit binary string, and the encoding mode is as follows: a certain number of the jth individuals rho in the population j Expressed as:
ρ j ＝(B j1 ，B j2 ，…，B jN ) (1)
wherein B is ji ＝[k i ，x i ，y i ，α i ]| j Information indicating an ith wireless AP in the jth individual, the wireless AP including a switch status (k i ) Abscissa (x) i ，y i ) Transmission power (alpha) i ) Information, which is converted into the form of a binary string, is shown as follows:
Figure GDA0004164009700000021
a group of sizes is ranged in [ U ] 1 ，U u ]The data of (2) is encoded as an h-bit binary string in the following manner:
Figure GDA0004164009700000022
wherein λ= (U u -U 1 )/(2 h -1) the decoding process is reversed from the encoding process, assuming the binary string as b h b h-1 …b 1 Then the original value is obtained by decoding:
Figure GDA0004164009700000023
step three: setting parameters
Setting the number and power variation range of wireless APs as [ alpha ] min ，α max ]The iteration number of the population is maxgen, and the crossover probability in each iteration is Pr c The variation probability is Pr m ；
Step four: crossover mutation operation
2M individuals in the father generation are crossed according to the probability specified in the third step, the 2M individuals are randomly mated, the specific operation is that a cross point is selected, binary strings of the two individuals are crossed, new two individuals are generated after crossing, then mutation operation is introduced, the implementation is realized through random reverse binary string form, and finally a new population is formed;
step five: selection operation
Combining the parent population and the new population to form a temporary population containing 4M individuals, performing selection operation on the temporary population, sorting the selection operation in NSGA-II according to the AP quantity and total power of each individual, distinguishing the individuals to different grades, further ranking the individuals of different grades according to the crowding degree, screening out 2M suitable child individuals according to the ranking condition of the selection operation, and taking the 2M child individuals as the parent of the next cycle;
step six: judgment condition
Judging whether iteration times maxgen of the ending cycle condition are met or not according to the step five, if not, using the generated offspring as a parent to continue to cycle, returning to the step four, and if so, returning to the final generation;
step seven: screening optimal solutions
From the last generation of population, selecting the most suitable solution set according to the arranged wireless AP gradient and the actual demand condition of the producer.
The further improvement is that: in the second step, in the process of setting the position information x, y, power p and state k of each wireless AP as the calculation variables, when k is 1, the AP is in a working state; and 0 is the closed state.
The further improvement is that: in the fourth step, the pseudo code of the cross operation is an algorithm:
Figure GDA0004164009700000041
the further improvement is that: in the fourth step, the algorithm of the mutation operation is as follows:
Figure GDA0004164009700000042
the further improvement is that: in the fifth step, the sorting operation specifically includes: specifying the time of the individuals ρ 1 Simultaneously lower than another volume p 2 Then ρ 1 Dominance ρ 2 Record the quilt individual ρ i Solution set S of other individuals who are dominant i And govern individual ρ i Number V of other individuals of (E) i The temporary population is subjected to a rapid non-dominant ranking operation, each individual is ranked, and the operation is as shown in an algorithm:
Figure GDA0004164009700000043
/>
Figure GDA0004164009700000051
the further improvement is that: in the fifth step, each individual is rated with a corresponding ranking according to the total power and the number of wireless APs, and is ranked at the bottommost part when a certain individual cannot meet the total coverage in an area, then, the individual in the same ladder is subjected to congestion degree calculation, each individual in the same ladder is respectively ranked according to the number of wireless APs and the total power, the congestion degree of the individual with an extreme value in each queue is set to infinity, and the other individuals calculate the congestion degree according to the distribution of adjacent individuals, wherein the specific calculation method is as follows:
Figure GDA0004164009700000052
/>
Figure GDA0004164009700000061
the further improvement is that: in the fifth step, the selection operation follows the following principle: sequentially selecting proper individuals until the number of the next generation population reaches a specified value; individuals located at different steps, preferably high-step individuals; individuals located on the same stairs are preferred to select individuals with a high degree of congestion.
The beneficial effects of the invention are as follows: the invention adopts a non-uniform quantized space model design, more accords with the actual demand, and according to the traditional experience and classical theory, the wireless AP power information, signal intensity decay, channel interference and other data, the distribution position and the power of the wireless AP are adjusted through a rapid non-dominant genetic algorithm, so that the purpose of optimizing the network topology is achieved under the condition of meeting the communication requirement, the purposes of reducing the number of the wireless APs and reducing the total power are finally realized, the change condition of the observed signal coincidence rate is greatly reduced, and the resource utilization is effectively improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a simulation environment and AP initial distribution diagram of the present invention;
FIG. 3 is a graph of coverage of the present invention as the power of a single wireless AP increases;
FIG. 4 is a plot of total power as a function of iteration number for the present invention;
fig. 5 is a graph of the number of wireless APs as a function of the number of iterations in accordance with the present invention;
FIG. 6 is a graph of signal-to-coincidence rate variation in an iterative process of the present invention;
FIG. 7 is a schematic diagram of the crossover operation of the present invention;
FIG. 8 is a schematic diagram of the mutation operation of the present invention;
FIG. 9 is a schematic diagram of screening of a new population with a parent population after cross mutation according to the present invention.
Detailed Description
The present invention will be further described in detail with reference to the following examples, which are only for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
According to fig. 1, 2, 3, 4, 5, 6, 7, 8, and 9, the present embodiment provides a method for implementing wireless AP deployment optimization based on a fast non-dominant genetic algorithm, including the following steps:
step one: determining parameters
According to the environment in reality, determining a signal attenuation function and signal attenuation caused by obstacles, setting three types of obstacles and signal attenuation values corresponding to each type of obstacle, setting partial parameters including reference distances and path attenuation coefficients, establishing a square simulation map in the simulation, discretizing the square simulation map, and setting the three types of obstacles and the signal attenuation values corresponding to each type of obstacle;
step two: binary string encoding
According to the first step, N APs default to be in a working state are uniformly arranged in an area initially, the position information x, y, power p and state k of each wireless AP are set as calculation variables, and the wireless APs are encoded according to the calculation variables x, y, p and k, and after encoding, each wireless AP exists in the form of a 25-bit binary string, and the encoding mode is as follows: a population has a certain number of individuals, and the jth individual ρ j Expressed as:
ρ j ＝(B j1 ,B j2 ,…,B jN ) (1)
wherein B is ji ＝[k i ，x i ，y i ，α i ]| j Information indicating an ith wireless AP in the jth individual, the wireless AP including a switch status (k i ) Abscissa (x) i ，y i ) Transmission power (alpha) i ) Information, which is converted into the form of a binary string, is shown as follows:
Figure GDA0004164009700000081
a group of sizes is ranged in [ U ] 1 ，U u ]The data of (2) is encoded as an h-bit binary string in the following manner:
Figure GDA0004164009700000082
wherein λ= (U u -U 1 )/(2 h -1) the decoding process is reversed from the encoding process, assuming the binary string as b h b h-1 …b 1 General ruleOver-decoding to obtain an original value:
Figure GDA0004164009700000083
step three: setting parameters
Setting the number and power variation range of wireless APs as [ alpha ] min ，α max ]The iteration number of the population is maxgen, and the crossover probability in each iteration is Pr c The variation probability is Pr m ；
Step four: crossover mutation operation
Performing cross operation on 2M individuals in the father according to the probability specified in the third step, and performing mating on the 2M individuals, wherein the specific operation is to select a cross point, cross binary strings of two individuals, and generate new two individuals after the cross, as shown in fig. 7, the pseudo code of the cross operation is an algorithm:
Figure GDA0004164009700000084
Figure GDA0004164009700000091
then, the mutation operation is realized through a random inverse binary string form, and finally a new population is formed, as shown in fig. 8, wherein the algorithm of the mutation operation is as follows:
Figure GDA0004164009700000092
step five: selection operation
Combining a parent population and a new population to form a temporary population containing 4M individuals, performing selection operation on the temporary population, sorting the selection operation in NSGA-II according to the AP quantity and total power of each individual, distinguishing the individuals to different levels, further ranking the individuals of different levels according to the crowding degree, screening out proper 2M sub-generation individuals according to the ranking condition of the selection operation, taking the proper 2M sub-generation individuals as the parent of the next cycle, arranging the individuals according to the fitness of each individual from high to low in a traditional genetic algorithm, and distributing the offspring of the individuals with higher fitness in the offspring more, thereby causing the situation of being easy to fall into a local optimal solution; in the improved NSGA-II, the offspring generated by the father is directly selected by a genetic algorithm different from the traditional genetic algorithm, in the selection process of NSGA-II, the father and the offspring subjected to crossover and mutation are combined together to form a temporary population, and the selection operation is carried out on the temporary population, as shown in figure 9, so that the condition that the fine personality is filtered out due to the crossover and mutation operation in the early stage can be avoided,
here, it is specified that when the individual ρ is 1 Simultaneously lower than another volume p 2 Then ρ 1 Dominance ρ 2 Record the quilt individual ρ i Solution set S of other individuals who are dominant i And govern individual ρ i Number V of other individuals of (E) i The temporary population is subjected to a rapid non-dominant ranking operation, each individual is ranked, and the operation is as shown in an algorithm:
Figure GDA0004164009700000101
through quick non-dominant ranking, each individual is rated with a corresponding ranking according to the total power and the number of wireless APs, when a certain individual cannot meet the total coverage in an area, the individuals in the same ladder are ranked at the bottommost part, then, the individuals in the same ladder are subjected to congestion degree calculation, each individual in the same ladder is respectively ranked according to the number of the wireless APs and the total power, the congestion degree of each individual with an extreme value in each queue is set as infinity, and the other individuals calculate the congestion degree according to the distribution of adjacent individuals, wherein the specific calculation method is as follows:
Figure GDA0004164009700000111
wherein the selection operation follows the following principle: sequentially selecting proper individuals until the number of the next generation population reaches a specified value; individuals located at different steps, preferably high-step individuals; individuals located on the same stairs, preferably select individuals with high crowding
Step six: judgment condition
Judging whether iteration times maxgen of the ending cycle condition are met or not according to the step five, if not, using the generated offspring as a parent to continue to cycle, returning to the step four, and if so, returning to the final generation;
step seven: screening optimal solutions
From the last generation of population, selecting the most suitable solution set according to the arranged wireless AP gradient and the actual demand condition of the producer.
Verification example:
fig. 2 is a 100m x 100m simulated map, which is discretized into 20 x 20 grids with three types of obstructions. Under the initial condition, 36 wireless APs with power of 10-35 dbm exist in the simulation environment, and each wireless AP defaults to a working state (namely k is 1). The adjustment of the position and power of each wireless AP is adopted, so that the sum of the power is minimized under the condition that the signal coverage rate is 1.
In order to be close to reality, the model is considered from multiple angles such as signal strength, channel interference, coverage, space environment and the like. The following assumptions are now made:
1. when the deployment result meets the requirement that the coverage rate of the target area reaches C0, the area can be considered to be effectively covered;
2. n wireless APs are deployed and operated in the network, and the maximum transmitting power of each wireless AP is alpha max The minimum transmitting power is alpha min 。
3. The propagation loss beta is divided into indoor propagation loss beta 1 Attenuation beta caused by obstacles 2 。
4. The lowest power capable of ensuring the normal communication quality of the terminal is alpha 0 。
5. The area to be covered is a two-dimensional plane S, the area is m x m, and the area is discretized into m 2 The grids to be coveredThe geometric center position of each grid point is a coverage target position, and the set is P (x, y). The wireless AP is deployed within a plane S, denoted g= { AP 1 ，AP 2 ，…，AP N }。
If the j-th overlay target point P j (x j ，y j )，j＝1，2，3…m 2 The signal received by any wireless AP is more than or equal to alpha 0 The grid is considered to be covered by the network.
Path loss beta of indoor wireless propagation model 1 The formula is as follows:
Figure GDA0004164009700000121
in formula (1), γ is a path loss index, which represents the rate of increase of path loss with distance, depending on the surrounding environment and the type of building; x-shaped articles σ Is a normal random variable of standard deviation sigma; d, d 0 Is the reference distance; d, d i，j Is the distance between the wireless AP and the test point.
Ith AP node to coverage target point P j Distance of (2)
Figure GDA0004164009700000131
Wherein, parameters of the specific initialization of the wireless AP are shown in table 1
TABLE 1
Figure GDA0004164009700000132
Wherein, considering that there are factors that hinder propagation within the floor, typical attenuation values for a portion of the obstacles are introduced, as shown in table 2 below:
TABLE 2
Figure GDA0004164009700000133
If an obstacle is encountered in the test point and the linear transmission path of the wireless AP, the attenuation is calculated from the attenuation value of the obstacle. The attenuation β during transmission is divided into two parts: attenuation beta caused by obstacles 2 And attenuation beta of indoor radio propagation itself 1 。
β＝β 1 +β 2 (3)
In summary, the ith AP pair target point P j The coverage probability of (j ε M) is expressed in Boolean type as shown in the following equation (4):
Figure GDA0004164009700000141
any target position P j (j ε M) can be covered simultaneously by multiple wireless APs, and the joint coverage probability for the target point is expressed as the following formula (5):
Figure GDA0004164009700000142
the coverage for this region is COV (M, G) as shown in the following formula (6):
Figure GDA0004164009700000143
to investigate its redundancy, the coincidence ratio is introduced here. Defining a certain position P j (j.epsilon.M) if covered simultaneously by two or more wireless APs, it is a coincident point. Target point P j The boolean expression of whether or not to overlap is:
Figure GDA0004164009700000144
calculating the coincidence ratio, the calculation is shown in the following formula (8):
Figure GDA0004164009700000145
traditionally, non-optimized wireless APs are always evenly distributed. In this case, power is allocated to the wireless APs, as shown in fig. 3, 36 wireless APs in the map are uniformly arranged according to the conventional situation, and only when the power allocated to each wireless AP reaches 23.1dbm, the area can reach full coverage, and at this time, the signal superposition rate reaches 76.8%. We consider this as the optimal case when the APs are uniformly arranged.
After the virtual model is built, NSGA-II substitution and calculation are applied.
As shown in fig. 4, the sum of the transmission powers of the first-step individuals decreases with the iterative process. After 2600 iterations, it tends to mature. The signal overlap ratio of the most optimal batch of individuals was changed as shown in fig. 6, and the signal overlap ratio was reduced from 80.8% in the initial state to 32.0% in the final state. The utilization rate of the resources is effectively improved.
According to fig. 5, the initial number of wireless APs is 36, all in the on state. Wherein the red line represents the number of active APs for each individual (feasible solution) in a population during the optimization process, and the blue line represents the number of active APs at the first step (both averages). After 3400 iterations, the number of wireless APs in the first step remains at 26, indicating that 26 wireless APs are the minimum number of APs that can be used to maintain full coverage of the area, well below the initial 36 wireless APs.
At this point, the top few feasible solutions are analyzed, and 7 feasible solutions are located at 3 steps, respectively, as shown in table 3. All 2 individuals at the first ladder were pareto optimal solutions. The feasible solution a can be interpreted as the pareto optimal solution with the least number of sink layer nodes, and the feasible solution b is the pareto optimal solution with the least total power.
TABLE 3 partial feasible solution
Figure GDA0004164009700000151
Figure GDA0004164009700000161
In the simulation, the power sum of all wireless APs is obviously reduced after NSGA-II is used, and the aim of reducing energy consumption is fulfilled. In real life, the optimization of the wireless AP network topology can be achieved through a genetic algorithm by only acquiring the power information and the environmental condition of the wireless AP and substituting the power information and the environmental condition into the model with the above.
According to the table 3, taking the feasible solution a as an example, it is compared with the optimal case when the APs are uniformly arranged, as shown in table 4: the total power is reduced from 7350.3mW before optimization to 4230.3mW, and the energy consumption is saved by 42.4%; the number of the nodes is reduced from 36 to 25, and the number is reduced by 30.6%; the coverage overlap is also reduced from the original 76.75% to 32.0%. The optimization effect is more obvious.
Table 4 comparison of parameters before and after optimization
Figure GDA0004164009700000162
FIG. 6 is a graph of signal-to-coincidence rate variation of an individual in a first step of an iterative process. From the above, it can be seen that the signal superposition rate is reduced and the resource utilization rate is improved by NSGA-II algorithm.
The invention adopts a non-uniform quantized space model design, more accords with the actual demand, and according to the traditional experience and classical theory, the wireless AP power information, signal intensity decay, channel interference and other data, the distribution position and the power of the wireless AP are adjusted through a rapid non-dominant genetic algorithm, so that the purpose of optimizing the network topology is achieved under the condition of meeting the communication requirement, the purposes of reducing the number of the wireless APs and reducing the total power are finally realized, the change condition of the observed signal coincidence rate is greatly reduced, and the resource utilization is effectively improved.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A method for realizing wireless AP deployment optimization based on a rapid non-dominant genetic algorithm is characterized in that: the method comprises the following steps:
step one: determining parameters
According to the actual environment, determining a signal attenuation function and signal attenuation caused by obstacles, setting three types of obstacles and signal attenuation values corresponding to each type of obstacle, setting partial parameters including reference distance and path attenuation coefficient, establishing a square simulation map in simulation, and discretizing the square simulation map;
step two: binary string encoding
According to the first step, N APs default to be in a working state are uniformly arranged in an area initially, the position information x, y, power p and state k of each wireless AP are set as calculation variables, and the wireless APs are encoded according to the calculation variables x, y, p and k, and after encoding, each wireless AP exists in the form of a 25-bit binary string, and the encoding mode is as follows: a population has a certain number of individuals, and the jth individual ρ j Expressed as:
ρ j ＝(B j1 ,B j2 ,…,B jN ) (1)
wherein B is ji ＝[k i ,x i ,y i ,α i ]| j Information indicating an ith wireless AP in the jth individual, the wireless AP including a switch status (k i ) Abscissa (x) i ,y i ) Transmission power (alpha) i ) Information, which is converted into the form of a binary string, is shown as follows:
Figure FDA0004164009690000011
a group of sizes is ranged in [ U ] 1 ,U u ]The data of (2) is encoded as an h-bit binary string in the following manner:
Figure FDA0004164009690000012
wherein λ= (U u -U 1 )/(2 h -1) the decoding process is reversed from the encoding process, assuming the binary string as b h b h-1 …b 1 Then the original value is obtained by decoding:
Figure FDA0004164009690000021
step three: setting parameters
Setting the number and power variation range of wireless APs as [ alpha ] min ,α max ]The iteration number of the population is maxgen, and the crossover probability in each iteration is Pr c The variation probability is Pr m ；
Step four: crossover mutation operation
2M individuals in the father generation are crossed according to the probability specified in the third step, the 2M individuals are randomly mated, the specific operation is that a cross point is selected, binary strings of the two individuals are crossed, new two individuals are generated after crossing, then mutation operation is introduced, the implementation is realized through random reverse binary string form, and finally a new population is formed;
step five: selection operation
Combining the parent population and the new population to form a temporary population containing 4M individuals, performing selection operation on the temporary population, sorting the selection operation in NSGA-II according to the AP quantity and total power of each individual, distinguishing the individuals to different grades, further ranking the individuals of different grades according to the crowding degree, screening out 2M suitable child individuals according to the ranking condition of the selection operation, and taking the 2M child individuals as the parent of the next cycle;
the steps ofIn the fifth step, the sorting operation specifically includes: specifying the time of the individuals ρ 1 Simultaneously lower than another volume p 2 Then ρ 1 Dominance ρ 2 Record the quilt individual ρ i Solution set S of other individuals who are dominant i And govern individual ρ i Number V of other individuals of (E) i The temporary population is subjected to a rapid non-dominant ranking operation, each individual is ranked, and the operation is as shown in an algorithm:
s101, inputting temporary population P temp For temporary population P temp Each individual ρ of (1) i Obtain the set S of all the individuals it dominates i Dominant ρ i Total number V of other individuals of (E) i ；
S102, V is taken i Put individuals of 0 into set U 1 This is the individual of the first echelon;
s103, for set U 1 All individuals p within e Corresponding V e Subtracting 1;
s104, after operation S103, V is again performed i Individual input set U of 0 2 This set is the individuals of the second echelon;
s105, repeating the operations S102, S103 and S104 in sequence until all individuals are distinguished to the corresponding echelons, and finishing the sequencing;
s106, outputting the ranking R (ρ) of each individual;
in the fifth step, each individual is rated with a corresponding ranking according to the total power and the number of wireless APs, and is ranked at the bottommost part when a certain individual cannot meet the total coverage in an area, then, the individual in the same ladder is subjected to congestion degree calculation, each individual in the same ladder is respectively ranked according to the number of wireless APs and the total power, the congestion degree of the individual with an extreme value in each queue is set to infinity, and the other individuals calculate the congestion degree according to the distribution of adjacent individuals, wherein the specific calculation method is as follows:
s201, inputting a population P consisting of individuals at the same level l The crowding degree ρ of each individual is calculated i The_distance is initially 0;
s202, taking the number of wireless sensors as a standard, and grouping P l The individuals in the system are arranged from small to large, and the crowding degree of the individuals with the smallest and largest number of the sensors is set to be positive infinity;
s203, calculating the intermediate individual rho k Is at an initial ρ k On the basis of the distance
Figure FDA0004164009690000031
Wherein (ρ) k+1 .n-ρ k-1 N) is ρ k Adjacent to the difference of two individual sensors, +.>
Figure FDA0004164009690000032
Setting the number of the wireless sensors as a standard maximum value and a standard minimum value;
s204, changing the standard in S202 into the total power, and repeating S502 and S503;
s205, outputting the crowding degree D (ρ) of each individual;
step six: judgment condition
Judging whether iteration times maxgen of the ending cycle condition are met or not according to the step five, if not, using the generated offspring as a parent to continue to cycle, returning to the step four, and if so, returning to the final generation;
step seven: screening optimal solutions
From the last generation of population, selecting the most suitable solution set according to the arranged wireless AP gradient and the actual demand condition of the producer.
2. The method for implementing wireless AP deployment optimization based on the rapid non-dominant genetic algorithm according to claim 1, wherein the method comprises the following steps: in the second step, in the process of setting the position information x, y, power p and state k of each wireless AP as the calculation variables, when k is 1, the AP is in a working state; and 0 is the closed state.
3. The method for implementing wireless AP deployment optimization based on the rapid non-dominant genetic algorithm according to claim 1, wherein the method comprises the following steps: in the fourth step, the pseudo code of the cross operation is an algorithm:
parent individuals to input binary strings
Figure FDA0004164009690000041
And->
Figure FDA0004164009690000042
Cross probability Pr c ；
Randomly acquiring a number r between 0 and 1, judging, and if r is smaller than the crossover probability Pr c The parent individuals of the binary string randomly select the intersection points for intersection;
if r is greater than the crossover probability Pr c The cross operation is not performed;
outputting child individuals in the form of binary strings
Figure FDA0004164009690000043
And->
Figure FDA0004164009690000044
4. The method for implementing wireless AP deployment optimization based on the rapid non-dominant genetic algorithm according to claim 1, wherein the method comprises the following steps: in the fourth step, the algorithm of the mutation operation is as follows:
inputting individuals
Figure FDA0004164009690000045
Probability of variation Pr m Probability of variation Pr m Much less than 1;
for individuals
Figure FDA0004164009690000046
Is +.>
Figure FDA0004164009690000047
Carrying out random judgment;
randomly acquiring a number r between 0 and 1, if r is smaller than the variation probability Pr m The point location
Figure FDA0004164009690000048
The number of (2) is inverted and becomes 0;
if r is greater than the variation probability Pr m No operation is performed;
outputting new individuals
Figure FDA0004164009690000051
5. The method for implementing wireless AP deployment optimization based on the rapid non-dominant genetic algorithm according to claim 1, wherein the method comprises the following steps: in the fifth step, the selection operation follows the following principle: sequentially selecting proper individuals until the number of the next generation population reaches a specified value; individuals located at different steps, preferably high-step individuals; individuals located on the same stairs are preferred to select individuals with a high degree of congestion.
CN202010303266.9A 2020-04-17 2020-04-17 Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm Active CN111542069B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010303266.9A CN111542069B (en) 2020-04-17 2020-04-17 Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010303266.9A CN111542069B (en) 2020-04-17 2020-04-17 Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm

Publications (2)

Publication Number Publication Date
CN111542069A CN111542069A (en) 2020-08-14
CN111542069B true CN111542069B (en) 2023-05-16

Family

ID=71975006

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010303266.9A Active CN111542069B (en) 2020-04-17 2020-04-17 Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm

Country Status (1)

Country Link
CN (1) CN111542069B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115866807B (en) * 2022-11-17 2023-10-27 华东交通大学 Wireless sensor network node deployment method based on topographic information

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017225118A (en) * 2016-06-13 2017-12-21 富士通株式会社 Method and device for arranging radio network
CN110430579A (en) * 2019-08-17 2019-11-08 温州大学 The wireless aps disposition optimization method of non-homogeneous environment based on drosophila optimization
CN110505634A (en) * 2019-08-17 2019-11-26 温州大学 One kind realizing wireless aps disposition optimization method based on genetic algorithm

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017225118A (en) * 2016-06-13 2017-12-21 富士通株式会社 Method and device for arranging radio network
CN110430579A (en) * 2019-08-17 2019-11-08 温州大学 The wireless aps disposition optimization method of non-homogeneous environment based on drosophila optimization
CN110505634A (en) * 2019-08-17 2019-11-26 温州大学 One kind realizing wireless aps disposition optimization method based on genetic algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Zicong Zhi, etc..AP deployment optimization in non-uniform service areas: a genetic algorithm approach.《2019 IEEE 90th Vehicular Technology Conference(VTC2019-Fall)》.2019,全文. *

Also Published As

Publication number Publication date
CN111542069A (en) 2020-08-14

Similar Documents

Publication Publication Date Title
Wang et al. Wireless sensor network deployment optimization based on two flower pollination algorithms
CN106651628B (en) Regional cooling, heating and power comprehensive energy optimal allocation method and device based on graph theory
Tam et al. A decomposition-based multi-objective optimization approach for balancing the energy consumption of wireless sensor networks
CN108184241B (en) Heterogeneous directed sensor network node scheduling method for targets with different priorities
CN109242150A (en) A kind of electric network reliability prediction technique
CN106067074B (en) A method of network system robustness is promoted by optimizing the switch state of link
CN111542069B (en) Method for realizing wireless AP deployment optimization based on rapid non-dominant genetic algorithm
Sett et al. Effect of optimal cluster head placement in MANET through multi objective GA
Sathe et al. Optimizing location and relocation of response units in guarding critical facilities
CN108734349A (en) Distributed generation resource addressing constant volume optimization method based on improved adaptive GA-IAGA and system
CN114037363B (en) Multi-platform task allocation method based on collaborative intelligent optimization algorithm
CN103945388B (en) A kind of user access network method based on genetic algorithm in heterogeneous network
Gao et al. Relay selection scheme based on quantum differential evolution algorithm in relay networks
Reddy et al. Elitist-mutated multi-objective particle swarm optimization for engineering design
CN110505634B (en) Method for realizing wireless AP deployment optimization based on genetic algorithm
Ghasemzadeh et al. Improving LEACH protocol using SFLA algorithm to reduce the energy consumption of wireless sensor networks
Feng et al. Spectrum Allocation for Cognitive Radio Networks Using the Fireworks Algorithm.
CN116132403A (en) Route distribution method and device of computing power network, electronic equipment and storage medium
CN112183843B (en) Load optimization distribution method for thermal power plant based on hybrid intelligent algorithm
Robbi et al. A modified genetic algorithm for resource allocation in cognitive radio networks
CN110365006B (en) Power grid partitioning method based on nwbbo algorithm
Shakya et al. Investigating binary EAs for Passive In-Building Distributed Antenna Systems
CN109862574B (en) Dynamic collaborative topology planning method and system for wireless backhaul problem
Sharma et al. Evolutionary Multi-Objective Optimization Algorithm for Resource Allocation using Deep Neural Network in Ultra-Dense Networks
CN115396905A (en) Wireless sensor network coverage optimization method based on improved genetic algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant