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Wireless sensor network node combined movement algorithm based on genetic fuzzy tree

A wireless sensor network and fuzzy tree technology, applied in genetic rules, gene models, wireless communication, etc., can solve problems such as increased computational complexity, achieve the effects of reduced robust performance, strong adaptability, and improved positioning and tracking performance

Active Publication Date: 2017-08-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with single node, multi-node joint mobile path planning can better optimize the task allocation of wireless sensor network, reduce data delay and improve the timeliness of wireless sensor network, but because multi-node mobile algorithm adds one more dimension than single node , the computational complexity increases significantly

Method used

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  • Wireless sensor network node combined movement algorithm based on genetic fuzzy tree
  • Wireless sensor network node combined movement algorithm based on genetic fuzzy tree
  • Wireless sensor network node combined movement algorithm based on genetic fuzzy tree

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0079] Ordinary fuzzy logic tree and the present invention adopt the comparison of the number of rules in the fuzzy logic tree: the number of the performance index membership function set that the system inputs is K=3, and 3 performance index membership function sets, distance S membership function set, The number of functions in the distance F membership function set is 3; the system outputs the comprehensive score membership function set F a and moving distance value membership function set F b Two output membership function sets; 3 performance index membership function sets and comprehensive score membership function set F a Correlation, distance S membership function set and distance F membership function set and moving distance value membership function set F b relevant;

[0080] Ordinary fuzzy logic tree utilizes a rule base for the above five input membership function sets to output two membership function sets, and the number of rules adopted is: G=3×3×3×3×3×2=486 (1...

Embodiment 2

[0085] The operation process of the fuzzy logic system A that the present invention adopts is as follows:

[0086] The fuzzy logic system includes a fuzzer, an inference mechanism, a rule and a defuzzifier. The input quantity is input into the fuzzy logic system, which is firstly fuzzy, and then the output fuzzy set is obtained through the inference mechanism by using the fuzzy rules and membership functions, and then the fuzzy value is transformed by the defuzzifier. output for a specific value.

[0087] 1, the present invention adopts single-point fuzzer, suppose the performance index number K=3 of sensor, with 3 performance indexes as input vector, then input vector is The three performance indicators of the sensor are converted into a single-point fuzzy set by using a single-point fuzzer, and the formula adopted is as follows:

[0088]

[0089] where x k Indicates the kth performance index, x' k Indicates the input value of the kth performance index, Represents th...

Embodiment 3

[0106] 100 nodes are deployed in the wireless sensor network, one sensor corresponds to one node, assuming that two of the nodes move, the mobile sensor set is {T 1 , T 2}.

[0107] 1. Initialize rule base A of fuzzy logic system A a (as in Table 1)

[0108] mobile performance Detection performance remaining battery output score Difference Difference Low Low Difference Difference medium Low Difference Difference high Low Difference medium Low Low Difference medium medium Low Difference medium high medium Difference it is good Low Low Difference it is good medium medium Difference it is good high high medium Difference Low Low medium Difference medium medium medium Difference high medium medium medium Low medium medium medium medium medium medium medium high high medium it is good Low med...

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Abstract

The invention belongs to the field of wireless sensor network node movement, and discloses a wireless sensor network node combined movement algorithm based on a genetic fuzzy tree. The algorithm comprises the steps of firstly, performing sensor network task allocation: inputting performance indexes of sensors Si, performing comprehensive scoring on the performance indexes by using a fuzzy logic system A, moving the front N sensors Tn having the highest comprehensive scores, and keeping the remaining sensors still; secondly, performing sensor combined route planning: solving the distance between the sensor Tn and a target and the distance between the sensor Tn and the sensor Tj, processing the distance values as an input of a fuzzy logic system B, and outputting the obtained movement distance value of the sensor Tn, wherein the movement direction of the Tn is determined by the Coulomb's law; and finally, optimizing the fuzzy logic system A and the fuzzy logic system B in the fuzzy logic tree by using a genetic algorithm, so that a rule library and a database are adaptively changed, and the combined route planning time is shortest. By adopting the algorithm, the positioning and tracking performance of the network on the target can be effectively improved.

Description

technical field [0001] The invention belongs to the field of wireless sensor network node movement, and in particular relates to a wireless sensor network node joint movement algorithm based on a genetic fuzzy tree. Background technique [0002] In the early wireless sensor network research, because the sensor has no charging capability, the sensor is designed to be stationary in order to save energy consumption. With the practical application of large-scale wireless sensor networks, it has been found that a significant weakness of static wireless sensor networks is that some sensors become hot spots due to the heavy load in the network, causing the battery to run out rapidly, causing the entire wireless sensor The death of the web. Luo et al. from the Swiss Federal Institute of Technology in Lausanne proposed a joint mobility and routing algorithm in 2005, and Shi et al. from Tianjin University proposed a mobile-assisted data collection model (MADG) in 2007. Both demonstra...

Claims

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Application Information

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IPC IPC(8): H04W4/00H04W84/18G06N5/04G06N3/12
CPCG06N3/126G06N5/048H04W4/70H04W84/18
Inventor 梁菁余萧峰刘晓旭张健段珍珍张洋任杰
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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