Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Three-dimensional wireless sensor network coverage optimization method

A wireless sensor and network coverage technology, applied in the field of three-dimensional wireless sensor network coverage optimization, to achieve the effects of easy implementation, expanding the exploration range, and avoiding invalid searches

Pending Publication Date: 2022-01-11
JIANGXI UNIV OF SCI & TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide two improved multi-objective algorithms, namely the MOSSA-I algorithm and the MOSSA-II algorithm. In addition to deploying wireless sensor nodes on a relatively gentle saddle surface, it also involves complex environments. Peak terrain, while considering the multi-objective coverage control optimization problem of WSN, including three objectives of coverage, data transmission quantity and network life

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Three-dimensional wireless sensor network coverage optimization method
  • Three-dimensional wireless sensor network coverage optimization method
  • Three-dimensional wireless sensor network coverage optimization method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0093] Example 1: MOSSA-I algorithm, input: T: maximum number of iterations, N: population size, Dim: number of sensors, M: number of targets, ∝: proportion of producers, SD: number of sparrows aware of danger, output : non-dominated solution set IP={I 1 , I 2 , I 3 ,..., I N}.

[0094] MOSSA-I algorithm, which comprises the following steps,

[0095] Step 1. Initialize the population;

[0096] Step 2, use the reverse learning strategy (the following formula) to initialize the population;

[0097]

[0098] In N-dimensional space, let is the individual x i =x i，1 , x i，2 ,...,x i，n The inverse solution of , then da j and db j respectively represent the minimum and maximum values ​​on the jth dimension in the current search space with population;

[0099] Step 3. Update the non-dominated solution set NDS;

[0100] Step 4, start iteration;

[0101] Step 5. Use the following formula to update the location of the producer,

[0102]

[0103] Among them, t rep...

Embodiment 2

[0122] Example 2: MOSSA-II, input: T: maximum number of iterations, N: population size, Dim: number of sensors, M: number of targets, ∝: proportion of producers, SD: number of sparrows aware of danger, output: Non-dominated solution set IP={I 1 , I 2 , I 3 ,..., I N}.

[0123] MOSSA-II algorithm, which includes the following steps,

[0124] Step 1. Initialize the population;

[0125] Step 2. Update the non-dominated solution set NDS;

[0126] Step 3, start iteration;

[0127] Step 4. Use the following formula to update the location of the producer,

[0128]

[0129] Step 5. Use the following formula to update the follower's position,

[0130]

[0131] Step 6. Use the following formula to update the dangerous sparrow position,

[0132]

[0133] The sparrow individual learning rate of change is as follows:

[0134]

[0135] Among them, ε is the minimum constant to prevent the denominator from being 0, is the position information of the i-th sparrow in the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a three-dimensional wireless sensor network coverage optimization method, which comprises the following steps of: deploying in two different simulation environments, and modeling a coverage control problem of a WSN (Wireless Sensor Network) into a multi-target planning problem by considering three targets, namely maximization of coverage rate, maximization of network service life and maximization of data transmission quantity, two multi-target sparrow search algorithms, namely, an MOSSA-I algorithm and an MOSSA-II algorithm, are provided on the basis of an SSA algorithm, a reverse learning strategy and improved crowdedness degree calculation are mixed in the MOSSA-I algorithm, a learning rate and an external solution set strategy are added in the MOSSA-II algorithm, and a non-dominated sorting method based on a reference point is fused in the MOSSA-II algorithm. The method has the advantages that the MOSSA-I algorithm improves the algorithm diversity and prevents the algorithm from falling into local optimum. According to the MOSSA-II algorithm, the convergence precision is improved through the learning rate, an external solution set strategy is introduced into MOSSA-II, individuals in a population can guide flight of themselves through individuals in an external solution set, and the exploration ability of the algorithm is enriched.

Description

technical field [0001] The invention relates to a three-dimensional wireless sensor network coverage optimization method, which belongs to the technical field of wireless sensor networks. Background technique [0002] Wireless sensor network is a traditional wireless ad hoc network that can collect, integrate and transmit data autonomously. It is a rapidly developing information acquisition technology that can integrate the latest technological achievements, including microelectronics, networking, and communication, making it an important element in fields such as healthcare, environmental monitoring, industrial manufacturing, and smart cities. [0003] Coverage control is one of the crucial issues in WSNs, which mainly involves how sensor networks can monitor target areas through proper node deployment. Usually sensor nodes are deployed in complex areas where the environment is harsh and people cannot reach. Moreover, the initial position of the sensor nodes is not predet...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/38H04W16/18H04W16/22H04W84/18
CPCH04W4/38H04W16/18H04W16/22H04W84/18
Inventor 王振东汪嘉宝杨书新王俊岭李大海
Owner JIANGXI UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com