A top-down positioning scheme for underwater wireless sensor networks

• 作 者: ZHANG SenLin ZHANG Qiang LIU MeiQin FAN Zhen
• 机 构: College of Electrical Engineering,Zhejiang University,
• 出 处: Science China(Information Sciences)
• 发表日期: 2014年03期
• 关键词: underwater Euclidean localization reasonably confidence coverage anchor infrastr

In order to increase the localization coverage while keeping the localization error small in a unique network architecture in which there are not evenly distributed anchor nodes with great ability of communication or additional infrastructure,a Top-down Positioning Scheme(TPS)for underwater acoustic sensor networks is proposed.By defining node’s confidence reasonably,TPS insures the quality of the new reference nodes.TPS also refines the nodes which have just been positioned via the gradient method and helps non-localized nodes search for more reference nodes via the new scheme for 3D Euclidean distance estimation.By comparing the new scheme for 3D Euclidean distance estimation with the existing scheme,the new scheme is shown to have greater ability to estimate two-hop Euclidean distance in 3D space.Simulation results show that TPS which integrates node’s confidence defined reasonably,the gradient method,and the new scheme for 3D Euclidean distance estimation can increase the localization coverage ratio,while keeping the localization error small.

In order to increase the localization coverage while keeping the localization error small in a unique network architecture in which there are not even distributed anchor nodes with great ability of communication or additional infrastructure, a Top-down Positioning Scheme (TPS) for underwater acoustic sensor networks is proposed. By defining node’s confidence reasonably, TPS insures the quality of the new reference nodes. TPS also ref refines the nodes which have just been positioned via the gradient method and helps non-localized nodes search for more reference nodes via the new scheme for 3D Euclidean distance estimation.By comparing the new scheme for 3D Euclidean distance estimation with the existing scheme, the new scheme is shown to have greater ability to estimate two-hop Euclidean distance in 3D space. Simulation results show that TPS which integrates node’s confidence defined reasonably , the gradient method, and the new scheme for 3D Euclidean distance estimation can increase the local ization coverage ratio, while keeping the localization error small.