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
An Analysis of Error Inducing Parameters in Multihop Sensor Node Localization
IEEE Transactions on Mobile Computing
Simulated Annealing based Localization in Wireless Sensor Network
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
Efficient weighted multidimensional scaling for wireless sensor network localization
IEEE Transactions on Signal Processing
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Distributed sensor network localization using SOCP relaxation
IEEE Transactions on Wireless Communications - Part 1
Hi-index | 0.25 |
In this paper, we propose a computationally efficient distributed Wireless Sensor Network (WSN) localization method based on Stochastic Proximity Embedding (SPE), which is a dimensionality reduction technique that finds a low dimensional embedding of a high dimensional data by preserving the pair-wise distance data information. Unlike the localization techniques based on classical Multidimensional Scaling (MDS), which is a popular dimensionality reduction technique, SPE method does not require a complete distance information matrix of the network and it scales linearly with the number of nodes in the network. Also the stochastic descent approach adopted in SPE provides an accurate position estimate in reasonable number of iterations. Through extensive simulation study of the proposed method, it is found to provide better results in both uniform and irregular shaped sensor networks.