Enhancing indoor localization accuracy of sensor-based by advance genetic algorithms
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Computers and Electronics in Agriculture
A GA-based mobile RFID localization scheme for internet of things
Personal and Ubiquitous Computing
Transactions on Computational Science XV
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In most sensor network applications, the information gathered by sensors will be meaningless without the location of the sensor nodes. Node localization has been a topic of active research in recent years. Accurate self-localization capability is highly desirable in wireless sensor network. This paper proposes a genetic algorithm based localization (GAL). The proposed genetic algorithm adopts two new genetic operators: single-vertex-neighborhood mutation and the descend-based arithmetic crossover. Four example problems are used to evaluate the performance of the proposed algorithm. Simulation results show that our algorithm can achieve higher accurate position estimation than semi-definite programming with gradient search localization (SDPL) [11] and simulated annealing based localization (SAL)[13]. Compared to the usual crossover operator: simple arithmetic crossover, whole arithmetic crossover and single-point crossover, the proposed crossover can obtain a lower mean position error.