TY - GEN
T1 - An improved monte carlo localization algorithm for mobile wireless sensor networks
AU - Luan, Jingye
AU - Zhang, Ruida
AU - Zhang, Baihai
AU - Cui, Lingguo
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/3/19
Y1 - 2015/3/19
N2 - Currently localization algorithms for mobile sensor networks are mostly based on Sequential Monte Carlo method. However they appear either low sampling efficiency or demand high beacon density requirement issues to achieve high localization accuracy. Aiming to solve the problems, we proposed an improved algorithm called Genetic and Weighting Monte Carlo Localization (GWMCL) in which we apply the Genetic Algorithm into Sequential Monte Carlo, which indirectly increases the density of beacon nodes via producing virtual beacon nodes. Besides, we also consider the weight of different beacon nodes, which means that the weight of each beacon node is related to the distance between beacon node and unknown node. The simulation results illustrate that the proposed algorithm achieve better performance than Monte Carlo Localization algorithm, especially in the situation with low beacon density. Furthermore, it also exhibits high sampling efficiency and localization accuracy in sparse mobile networks.
AB - Currently localization algorithms for mobile sensor networks are mostly based on Sequential Monte Carlo method. However they appear either low sampling efficiency or demand high beacon density requirement issues to achieve high localization accuracy. Aiming to solve the problems, we proposed an improved algorithm called Genetic and Weighting Monte Carlo Localization (GWMCL) in which we apply the Genetic Algorithm into Sequential Monte Carlo, which indirectly increases the density of beacon nodes via producing virtual beacon nodes. Besides, we also consider the weight of different beacon nodes, which means that the weight of each beacon node is related to the distance between beacon node and unknown node. The simulation results illustrate that the proposed algorithm achieve better performance than Monte Carlo Localization algorithm, especially in the situation with low beacon density. Furthermore, it also exhibits high sampling efficiency and localization accuracy in sparse mobile networks.
KW - Genetic Algorithm
KW - Monte Carlo Localization
KW - sparse mobile sensor networks
KW - weight of beacon node
UR - https://www.scopus.com/pages/publications/84931082664
U2 - 10.1109/ISCID.2014.217
DO - 10.1109/ISCID.2014.217
M3 - Conference contribution
AN - SCOPUS:84931082664
T3 - Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
SP - 477
EP - 480
BT - Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Symposium on Computational Intelligence and Design, ISCID 2014
Y2 - 13 December 2014 through 14 December 2014
ER -