Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Localization from Connectivity in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A New Map Stitching Method for Anchor-free Localization in Wireless Sensor Networks
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
The challenges of building mobile underwater wireless networks for aquatic applications
IEEE Network: The Magazine of Global Internetworking
Auto-localization algorithm for local positioning systems
Ad Hoc Networks
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Underwater sensor networks (UWSNs) are considered a costeffective solution to ocean applications, such as the acquisition of natural resources in oceans, protection from underwater disasters, etc. These applications basically require location information of nodes to identify the venue of reported events. To locate more accurately the position of nodes, multidimensional scaling (MDS) is widely used because of its good tolerance to errors in measured distances. MDS requires measured distances between every pair of nodes but in practice, only distances between nodes within a communication range can be measured. Hence, the well-known MDS-MAP(P) [6] calculates unmeasured distances for MDS but these calculations result in large errors. In this paper, we proposed a localization algorithm with merging segmented maps (LaMSM) that constructs many reliable segmented maps composed of only nodes within a communication range, and then merges them together based on their common nodes. The segmented maps are built from only the measured distances and as a result, LaMSM provides more accurate node positions than MDS-MAP(P).