Wireless sensor networks (WSNs) consist of a large number of low-cost miniature sensors, which can be applied to battlefield surveillance, environmental monitoring, target tracking, and other applications related to the positions of sensors. The location information of sensors is of great importance for wireless sensor networks. In this paper, we propose a new localization algorithm for the wireless sensor network based on time difference of arrival (TDOA), which is a typical algorithm in the wireless localization field. In order to improve the localization accuracy of a sensor, a new strategy is proposed for a localized sensor being upgraded to an anchor node, which is used to localize the position of the next sensor. Performance analysis and simulation results show that the revised TODA localization algorithm has the higher localization accuracy when compared with the original TDOA location method.

Wireless sensor networks (WSNs) are a technique which has a wide variety of applications, such as target tracking, battlefield surveillance, and environmental monitoring [

The trilateral positioning [

Trilateral positioning method.

The basic assumption for the trilateral positioning method (

The localization method based on TDOA estimates the unknown sensor node

The localization method based on TDOA.

The difference of the distances from the anchors

From (

This paper is organized as follows. In the next section, we give a revised geometric localization algorithm based on TDOA for the sensor network. In Section

It needs at least four anchors (

Since there are errors in the measurement distances and the propagating errors in the process of numerical computation, recalculating the positions of the determined sensors help to reduce the accumulated errors, which are used by generic localized algorithm [

Firstly, search for at least four anchors around the current undetermined sensor node and use their distances with this sensor node and the TDOA method (

Secondly, use the geometric distance measurement error (GDME) to measure the localization accuracy. The GDME is defined as follows:

Thirdly, for those lower precision sensors which are not upgraded to the anchor nodes, in this refinement stage, we use the coordinate information of determined sensors and anchors and their distances recalculate the coordinates of those lower precision sensors.

The simulation model is generated as follows. There are 60 points randomly generated in a one by one square area. Then, we choose the first 10% points as the anchor nodes and compute their all Euclidean distances

We use the positioning accuracy and the successful localization probability to evaluate the performance of an algorithm. The positioning accuracy is defined by GDME (

In order to verify the performance of the revised geometric localization method based on (Algorithm

Localization results of Algorithm

From Figure

In order to evaluate the performance of Algorithm

From Figure

For a real wireless sensor network, the measurement error is not avoided and the localization accuracy depends on the measurement error heavily. Therefore, we evaluate the performances of Algorithm

The successful localization probability with different relative measurement errors.

Noise factor | Improved TDOA | Original TDOA |
---|---|---|

Null (Exact Distance) | 100.00% | 92.78% |

2% | 100.00% | 90.56% |

4% | 100.00% | 91.23% |

6% | 100.00% | 90.56% |

8% | 100.00% | 89.38% |

10% | 100.00% | 88.15% |

12% | 100.00% | 89.38% |

14% | 100.00% | 88.27% |

16% | 100.00% | 87.22% |

18% | 100.00% | 87.72% |

20% | 100.00% | 88.15% |

The localization accuracy with different relative measurement error.

Noise factor | Improved TDOA | Original TDOA | ||

RMSD | GDME | RMSD | GDME | |

Null (Exact Distance) | ||||

2% | ||||

4% | ||||

6% | ||||

8% | ||||

10% | ||||

12% | ||||

14% | ||||

16% | ||||

18% | ||||

20% |

From Tables

Our idea is to use the revised geometric localization method based on TDOA for the wireless sensor network with sparse anchors. For improving the localization accuracy, in the original TDOA localization method, we use the threshold to judge whether the determined sensor is upgraded an anchor node and the iterative refinement idea. Furthermore, for the typical simulation model of wireless sensor network localization problem, this revised strategy is effective, compared with the original TDOA localization method.

This work was supported in part by Grant 2009CB320401 from National Basic Research Program of China, Grant YBWL2011085 from Huawei Technologies Co., Ltd., and Grant BUPT2009RC0118 from the Fundamental Research Funds for the Central Universities.