Node placement optimization for distributed sensor network using adaptive genetic algorithm

Sensor placement is an important issue for distributed sensor network design, especially when it is used for collaborative tasks (e.g., source localization and tracking). In this paper, the averaged Cramer-Rao Lower Bound (CRLB) of TDOA-based sensor network localization system is derived at first in...

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Vydáno v:2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) s. 1 - 4
Hlavní autoři: Yan Qingli, Chen Jianfeng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2016
Témata:
adaptive genetic algorithm
Convergence
CRLB
Genetic algorithms
Optimization
sensor placement
Signal processing
Simulation
Sociology
source localization
Statistics
Time of Arrival
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Shrnutí:Sensor placement is an important issue for distributed sensor network design, especially when it is used for collaborative tasks (e.g., source localization and tracking). In this paper, the averaged Cramer-Rao Lower Bound (CRLB) of TDOA-based sensor network localization system is derived at first in a more practical scenario where the variance of the Time Of Arrival (TOA) estimate error of each sensor node is proportional to the square of its distance from the source. An adaptive genetic algorithm is then designed and applied to the optimal sensor placement process. A series of simulations are carried out to verify and demonstrate the performance of the proposed method when there is a sound source and 5, 10, 18 and 45 sensor nodes in a square test region, respectively. The simulation results show that the proposed adaptive genetic algorithm works successfully and the obtained optimal sensor placement emerges as an even distribution.
DOI:10.1109/ICSPCC.2016.7753692