## Liquan Zhao* and Kexin Zhang*## |

Algorithm | Average relative location error |
---|---|

Shen and Zhang [14] | 0.2106 |

Yang and Zhang [15] | 0.1195 |

Fang et al. [16] | 0.1302 |

Proposed method | 0.0993 |

We also ran simulations using different location methods with different settings for the communication range radius and the number of anchor nodes. The number of anchor nodes ranges from 7 to 15. The maximum communication radiuses are from 30 m to 50 m. The simulation results are shown in Figs. 3–7, respectively. Our proposed location method still has the smallest average relative location error, followed by that for the method specified in literature [15] with different number of anchor nodes. The average relative location error reduces with increasing communication range radius with the condition that the number of anchor nodes is constant. Based on above analysis, our improved sensor node location algorithm has the smallest location error for WSN comparing with these methods [14–16] with different conditions.

An improved DV-Hop p location method is introduced in this paper. We firstly set three different communication powers for all nodes so that each node can operate at different communication ranges.

We can therefore obtain different hop counts for different communication ranges between two nodes. For the purpose of hop count accuracy, we select the minimum value of hop count between wireless sensor nodes as final hop count. Secondly, we compute average value using three different average distances per hop from sensor node that the position is unknown to three wireless anchor nodes that are nearest to the unknown node; for this, the average value is used as the average distance of per hop for the unknown node. Finally, we use average differences between real distances among anchor nodes and estimated distances among anchor nodes in order to modify our estimates of the distance from the wireless anchor nodes to an unknown wireless node. In a comparative study of our proposed algorithm and three other location algorithms, we have shown that the error associated with our proposed algorithm is the smallest among the four methods.

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