Node Localization with AoA Assistance in Multi-Hop Underwater Sensor Networks
This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods.
H. Huang and Y. R. Zheng, "Node Localization with AoA Assistance in Multi-Hop Underwater Sensor Networks," Ad Hoc Networks, vol. 65, pp. 32-41, Elsevier, Sep 2018.
The definitive version is available at https://doi.org/10.1016/j.adhoc.2018.05.005
Electrical and Computer Engineering
Keywords and Phrases
Direction of arrival; Estimation; Least squares approximations; Location; Angle of Arrival (AoA); Local coordinate system; Localization; Multi-hop localizations; Multihop; Underwater sensor networks; Underwater wireless sensor networks; Weighted least squares; Sensor nodes
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Elsevier, All rights reserved.
01 Sep 2018