CN109769252A - A kind of disposition optimization method of relay node in wireless sensor network - Google Patents
A kind of disposition optimization method of relay node in wireless sensor network Download PDFInfo
- Publication number
- CN109769252A CN109769252A CN201910077494.6A CN201910077494A CN109769252A CN 109769252 A CN109769252 A CN 109769252A CN 201910077494 A CN201910077494 A CN 201910077494A CN 109769252 A CN109769252 A CN 109769252A
- Authority
- CN
- China
- Prior art keywords
- individual
- node
- relay node
- new
- population
- 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.)
- Pending
Links
Landscapes
- Mobile Radio Communication Systems (AREA)
- Radio Relay Systems (AREA)
Abstract
A kind of disposition optimization method that the invention discloses relay nodes in wireless sensor network, specific steps include: one initial population of relevant parameter and generation of (1) initialization algorithm；(2) N number of new individual is generated using the genetic manipulation in traditional genetic algorithm；(3) operation of insertion relay node is executed to partially more excellent individual；(4) operation for deleting relay node is executed to partially more excellent individual；(5) local displacement operation is executed to partially more excellent individual；(6) new individual that step (2) to step (5) generate is merged with original population, and sorted according to non-dominated ranking and crowding distance to the population after merging, therefrom selected optimal individual and constitute new population；(7) when algorithm meets termination condition, Optimization Steps are terminated, otherwise return step (2).
Description
Technical field
The present invention relates to a kind of deployment of sensor network field more particularly to relay node in wireless sensor network
Optimization method.
Background technique
Wireless sensor network is the product that computing technique is combined with the communication technology, be a kind of completely new acquisition of information and
Processing technique.Wireless sensor network is by a large amount of sensings cheap, with sensing capability and wireless communication ability under normal conditions
Device node is constituted, and be can be used for constituting wireless self-organization network and is monitored physical context information.And in the prior art, sensor is logical
It is often battery powered, and electric energy can not be replenished in time during the work time.Therefore, the limited electric energy of wireless sensor at
One for further developing and optimizing for sensing network is big to be hindered.In order to reduce the consumption of sensor, most of wireless sensor networks
All deploy relay node.Relay node, which is one, to be had sufficient electrical energy and is deployed in detection zone center for collecting biography nearby
One node of sensor information.When a sensor has electric energy and can transfer data to data-collection nodes, then claim
This sensor is effective.Relay node collects data from sensor node and transfers data to data processing centre, thus
The energy consumption for reducing sensor, extends the bulk life time of network.
Traditional relay node deployment method usually only considers the demand of single aspect, such as minimizes the number of relay node
Or maximization network service life etc..However, that network life, construction cost etc. can be influenced is multiple simultaneously for the deployment of relay node
Aspect.Optimal method how is obtained in the case where considering the problems of many-sided interests to consider as more and more scholars.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of relay nodes in wireless sensor network
Disposition optimization method.The present invention simultaneously carries out the number in the service life of network and relay node using multi-objective genetic algorithm excellent
Change, while in order to further optimize, introducing multilayer local optimisation strategies.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of disposition optimization method of relay node in wireless sensor network, specific steps include:
(1) one initial population of the relevant parameter of initialization algorithm and generation；
(2) N number of new individual is generated using the genetic manipulation in traditional genetic algorithm；
(3) operation of insertion relay node is executed to partially more excellent individual；
(4) operation for deleting relay node is executed to partially more excellent individual；
(5) local displacement operation is executed to partially more excellent individual；
(6) by step (2) to step (5) generate new individual merge with original population, and according to non-dominated ranking with
Crowding distance sorts to the population after merging, therefrom selects optimal individual and constitutes new population；
(7) when algorithm meets termination condition, Optimization Steps are terminated, otherwise return step (2).
In the present invention, the detection zone of N number of sensor and a L × W is given, in order to calculate the overlay area of network,
It is w × h lattice by entire detection zone uniform discrete, estimates to cover by counting the number of capped lattice
The ratio in region, calculation formula are as follows:
C=n/ (w × h) (1)
Wherein, n indicates the network covered by effective sensor sum.
The service life of maximization network in order to obtain, it is assumed that the energy of sensor, which mainly consumes, to be received and sending in data.
Therefore, for each sensor, the energy that the place that distance is d needs to consume is sent by the data of 1bit are as follows:
etran=a+bdv (2)
Wherein, a and b is constant related with the medium of transmission；V indicates path loss coefficient and v ∈ [2,4].
Each sensor node receives the energy that the data of 1bit need to consume are as follows:
erec=δ (3)
Wherein, δ is a predefined constant.
The energy that the data of each sensor node induction 1bit need to consume are as follows:
esen=λ (4)
Wherein, λ is a predefined constant.
All nodes in wireless sensor network are as follows:
{z0,z1,z2,...,zn} (5)
Wherein z0Indicate data-collection nodes；z1,…,znIndicate n sensor.
Then sensor ziThe calculation formula in service life are as follows:
Wherein, EiIndicate sensor ziDump energy；WithRespectively indicate sensor ziIn 1 second
The amount for receiving data, sending data and sensed data.Each sensor is determined using traditional minimum transfer energy routing tree
The path of node transmission data.
Therefore, the service life T of wireless sensor network is defined as starting to work from network at least one sensor and consume
The time interval of complete self-energy indicates are as follows:
T=min { ti| i=1,2 ..., n } (7)
Specifically, in the step (1), an initial population P (0) is generated, is indicated are as follows:
Wherein, for each individual Ii, code length DiRandom initializtion is the number of sensor, position vector (xij,
yij) initialization formula are as follows:
Wherein, L and W indicates the length and width of detection zone；Rand (a, b) indicate between a and b equally distributed one it is random
Number.
There is set B to be used to save the N number of feasible solution with minimum relay node found so far in algorithm simultaneously.
N number of initial individuals obtained as above while the also finite element as set B
Specifically, the genetic manipulation in the step (2) includes selection operation, crossover operation and mutation operation.Selection behaviour
Make to select two preferably individuals from current population using algorithm of tournament selection method.Crossover operation by the two individual part
Gene swaps.Mutation operation carries out the variation of small probability to the gene of two new individuals generated after intersection.Wherein make a variation
Process carried out in the way of classical multi-objective Algorithm NSGA-II.To population initial individuals repeat genetic manipulation until
Until generating N number of new individual.
Specifically, the step (3) to it is more excellent individual insertion relay point by way of find with the more long-life can
Row solution, comprising:
(3-1) randomly chooses a non-dominant individual I from current populationx；
(3-2) is from IxThe relay node of one relay node of middle addition, addition will be loaded in key node and its subsequent section
Point on.Wherein, it is the node for exhausting energy earliest that key node, which is and if only if the node,.Assuming that IxKey node be located at
(sx,sy), and its descendant node is (rx,ry), new relay node will be placed on the midpoint for the line segment that this two o'clock is constituted, i.e.,
The coordinate of new relay node are as follows:
(3-3) assesses the target function value of the new individual.
Step (3) is repeated until generating N number of new individual to population initial individuals.
Specifically, the step (4) is found by way of deleting relay point to more excellent individual with less relay node
Feasible solution, comprising:
A feasible solution I is randomly selected in (4-1) from set Bx。
(4-2) is from IxThe middle some relay nodes of random erasure；Wherein, the number of nodes of deletion is 1 to the random number between M, M
For a predefined constant.
The target function value of (4-3) assessment new individual.
Step (4) is repeated until generating N number of new individual to population initial individuals.
Specifically, the step (5) keeps relay node number not by executing local displacement operation to more excellent individual
Extend the service life of network under conditions of change.For each relay node, its service life is compared with its predecessor node.
It is just that the node is small toward the direction of corresponding descendant node mobile one if the service life of the node is more shorter than corresponding predecessor node
Section distance.Otherwise the node just will be moved into a small distance toward the corresponding predecessor node direction with minimum life.To kind
Group's initial individuals repeat step (5) until generating N number of new individual.
By step (2)~(5), produces 4 × N number of new individual, new individual is used for Population Regeneration and set B:
Specifically, the step (6), comprising:
(6-1) merges current population and all 4 × N number of new individuals.
(6-2) is ranked up the individual collections after merging using quick non-dominated ranking method and crowding distance operation.
Best individual is selected as new population by (6-3).
(6-4) merges set B and all 4 × N number of new individuals.Then according to value (the i.e. relay node of formula (1)
Number) it is ranked up and selects optimal individual as new set B.
The present invention compared to the prior art, have it is below the utility model has the advantages that
The present invention, can also while maximization network bulk life time by being modeled using the genetic algorithm of multiple target
Enough numbers for further considering to minimize relay node.And by introducing relevant stratification local optimization operations, Neng Gougao
Effect ground carries out disposition optimization to relay node.
Detailed description of the invention
Fig. 1 is a kind of the specific of the disposition optimization method of relay node in wireless sensor network in the embodiment of the present invention
Flow chart.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are unlimited
In this.
Embodiment
The flow chart of disposition optimization method of a kind of relay node in wireless sensor network as shown in Figure 1, it is specific to walk
Suddenly include:
(1) one initial population of the relevant parameter of initialization algorithm and generation；
(2) N number of new individual is generated using the genetic manipulation in traditional genetic algorithm；
(3) operation of insertion relay node is executed to partially more excellent individual；
(4) operation for deleting relay node is executed to partially more excellent individual；
(5) local displacement operation is executed to partially more excellent individual；
(6) by step (2) to step (5) generate new individual merge with original population, and according to non-dominated ranking with
Crowding distance sorts to the population after merging, therefrom selects optimal individual and constitutes new population；
(7) when algorithm meets termination condition, Optimization Steps are terminated, otherwise return step (2).
In the present embodiment, in order to test and assess the performance of algorithm of the invention, there is different sensors number with 20
It is tested for mesh and the network of deployment strategy.The parameter setting of algorithm of the invention is as shown in the table:
| Parameter | Value | Explanation |
| N | 100 | Population scale |
| px | 0.8 | Crossing-over rate |
| pm | 0.01 | Aberration rate |
| T | 2 | Championship scale |
| M | 2 | The maximum number of relay node can be deleted in delete operation |
| ε | 0.02 | Fine tuning step-length in local optimum |
According to it is obtaining as a result, the average effect of optimization of algorithm of the invention be better than traditional two-phase deployment method and
Current famous multi-objective Evolutionary Algorithm NSGA-II.This illustrates the present invention in wireless sensor network relay node disposition optimization
In be highly effective.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (7)
1. a kind of disposition optimization method of relay node in wireless sensor network, which is characterized in that specific steps include:
(1) one initial population of the relevant parameter of initialization algorithm and generation；
(2) N number of new individual is generated using the genetic manipulation in traditional genetic algorithm；
(3) operation of insertion relay node is executed to partially more excellent individual；
(4) operation for deleting relay node is executed to partially more excellent individual；
(5) local displacement operation is executed to partially more excellent individual；
(6) new individual that step (2) to step (5) generate is merged with original population, and according to non-dominated ranking and crowded
Distance sorts to the population after merging, therefrom selects optimal individual and constitutes new population；
(7) when algorithm meets termination condition, Optimization Steps are terminated, otherwise return step (2).
2. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, in the step (1), generates an initial population P (0), indicates are as follows:
Wherein, for each individual Ii, code length DiRandom initializtion is the number of sensor, position vector (xij,yij)
Initialize formula are as follows:
Wherein, L and W indicates the length and width of detection zone；Rand (a, b) indicates an equally distributed random number between a and b；
There is set B to be used to save the N number of feasible solution with minimum relay node found so far in algorithm simultaneously；More than
The N number of initial individuals of gained while the also finite element as set B.
3. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, the genetic manipulation in the step (2) includes selection operation, crossover operation and mutation operation；Selection operation uses prize
Match back-and-forth method selects two preferably individuals from current population；Crossover operation hands over the portion gene of the two individuals
It changes；Mutation operation carries out the variation of small probability to the gene of two new individuals generated after intersection；The process wherein to make a variation according to
The mode of classical multi-objective Algorithm NSGA-II carries out；Genetic manipulation is repeated until generating N number of new to population initial individuals
Until body.
4. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, the step (3) finds the feasible solution with the more long-life by way of to more excellent individual insertion relay point, comprising:
(3-1) randomly chooses a non-dominant individual I from current populationx；
(3-2) is from IxThe relay node of one relay node of middle addition, addition will load on key node and its descendant node；
Wherein, it is the node for exhausting energy earliest that key node, which is and if only if the node,；Assuming that IxKey node be located at (sx,sy),
And its descendant node is (rx,ry), new relay node will be placed on the midpoint for the line segment that this two o'clock is constituted, i.e., new relaying
The coordinate of node are as follows:
(3-3) assesses the target function value of the new individual；
Step (3) is repeated until generating N number of new individual to population initial individuals.
5. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, the step (4) finds the feasible solution with less relay node by way of deleting relay point to more excellent individual, wraps
It includes:
A feasible solution I is randomly selected in (4-1) from set Bx；
(4-2) is from IxThe middle some relay nodes of random erasure；Wherein, the number of nodes of deletion is 1 to the random number between M, M mono-
A predefined constant；
The target function value of (4-3) assessment new individual；
Step (4) is repeated until generating N number of new individual to population initial individuals.
6. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, the step (5) is kept by executing local displacement operation to more excellent individual under conditions of relay node invariable number
Extend the service life of network；For each relay node, its service life is compared with its predecessor node；If the node
Service life it is more shorter than corresponding predecessor node, just by the mobile a small distance in the direction of the node toward corresponding descendant node；It is no
The node just then will be moved into a small distance toward the corresponding predecessor node direction with minimum life；To population initial individuals
Step (5) is repeated until generating N number of new individual.
7. a kind of disposition optimization method of the relay node according to claim 1 in wireless sensor network, feature
It is, the step (6), comprising:
(6-1) merges by current population and by the 4 × N number of new individual that step (2)~(5) generate；
(6-2) is ranked up the individual collections after merging using quick non-dominated ranking method and crowding distance operation；
Best individual is selected as new population by (6-3)；
(6-4) merges set B and all 4 × N number of new individuals；Then it is ranked up and is selected most according to relay node number
Excellent individual is as new set B.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910077494.6A CN109769252A (en) | 2019-01-28 | 2019-01-28 | A kind of disposition optimization method of relay node in wireless sensor network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910077494.6A CN109769252A (en) | 2019-01-28 | 2019-01-28 | A kind of disposition optimization method of relay node in wireless sensor network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109769252A true CN109769252A (en) | 2019-05-17 |
Family
ID=66455423
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910077494.6A Pending CN109769252A (en) | 2019-01-28 | 2019-01-28 | A kind of disposition optimization method of relay node in wireless sensor network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109769252A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111683378A (en) * | 2020-06-05 | 2020-09-18 | 国网河南省电力公司经济技术研究院 | Reconfigurable wireless sensor network relay deployment method facing power distribution network |
| CN114584991A (en) * | 2022-03-10 | 2022-06-03 | 南京邮电大学 | Group intelligent optimization method for solving power distribution network sensor node coverage |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103249179A (en) * | 2013-04-25 | 2013-08-14 | 中山大学 | Multi-objective mother foraging algorithm based optimization method for relay node deployment in wireless sensor network |
-
2019
- 2019-01-28 CN CN201910077494.6A patent/CN109769252A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103249179A (en) * | 2013-04-25 | 2013-08-14 | 中山大学 | Multi-objective mother foraging algorithm based optimization method for relay node deployment in wireless sensor network |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111683378A (en) * | 2020-06-05 | 2020-09-18 | 国网河南省电力公司经济技术研究院 | Reconfigurable wireless sensor network relay deployment method facing power distribution network |
| CN111683378B (en) * | 2020-06-05 | 2023-05-30 | 国网河南省电力公司经济技术研究院 | Reconfigurable wireless sensor network relay deployment method for power distribution network |
| CN114584991A (en) * | 2022-03-10 | 2022-06-03 | 南京邮电大学 | Group intelligent optimization method for solving power distribution network sensor node coverage |
| CN114584991B (en) * | 2022-03-10 | 2023-07-25 | 南京邮电大学 | Intelligent group optimization method for solving coverage of sensor nodes of power distribution network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Özdemir et al. | Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks | |
| Al-Otaibi et al. | Hybridization of metaheuristic algorithm for dynamic cluster-based routing protocol in wireless sensor Networksx | |
| Rawat et al. | Energy-efficient clusterhead selection scheme in heterogeneous wireless sensor network | |
| Rezaeipanah et al. | A Hybrid Approach for Prolonging Lifetime of Wireless Sensor Networks Using Genetic Algorithm and Online Clustering. | |
| CN103249179A (en) | Multi-objective mother foraging algorithm based optimization method for relay node deployment in wireless sensor network | |
| CN107277889A (en) | A kind of network clustering method of wireless sensor based on k means | |
| Zhong et al. | Ant colony optimization algorithm for lifetime maximization in wireless sensor network with mobile sink | |
| Konstantinidis et al. | An evolutionary algorithm to a multi-objective deployment and power assignment problem in wireless sensor networks | |
| Ali et al. | A tree based data aggregation scheme for wireless sensor networks using GA | |
| CN109769252A (en) | A kind of disposition optimization method of relay node in wireless sensor network | |
| Su et al. | Efficient skyline query processing in wireless sensor networks | |
| Chen et al. | A Lifetime Optimization Algorithm Limited by Data Transmission Delay and Hops for Mobile Sink‐Based Wireless Sensor Networks | |
| Qaim et al. | DRAW: Data replication for enhanced data availability in IoT-based sensor systems | |
| CN107612806B (en) | Wireless sensor network energy perception and path architecture data collection method based on mobile sink node | |
| Wang et al. | An improved routing algorithm based on social link awareness in delay tolerant networks | |
| CN115915327A (en) | Double-cluster-head WSNs self-adaptive relay routing method based on optimized clustering | |
| CN114727353A (en) | Self-adaptive information transmission method of mobile opportunistic sensor network based on genetic algorithm | |
| Singh et al. | Designing an energy efficient network using integration of KSOM, ANN and data fusion techniques | |
| Alansari et al. | FCERP: a novel WSNs fuzzy clustering and energy efficient routing protocol | |
| CN112987789A (en) | Unmanned aerial vehicle cluster network topology design method for improving Leach protocol | |
| CN112188518B (en) | Sensor node communication optimization method and device and readable storage medium | |
| CN115396905A (en) | Wireless sensor network coverage optimization method based on improved genetic algorithm | |
| Ghasemzadeh et al. | Improving LEACH protocol using SFLA algorithm to reduce the energy consumption of wireless sensor networks | |
| CN114827933A (en) | Multipath routing method for wireless sensor network | |
| Hajian et al. | Increasing WSN lifetime by using learning automata for optimal route selection |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190517 |
|
| WD01 | Invention patent application deemed withdrawn after publication |