Thesis
Clustering based localization for wireless sensor networks
Washington State University
Master of Science (MS), Washington State University
2006
Handle:
https://hdl.handle.net/2376/521
Abstract
Localization is an important challenge in wireless sensor networks (WSN). Localization usually refers to the process of dynamically determining the position(s) of one or more node(s) in a larger network. The challenge lies in efficiently providing acceptable accuracy while conforming to the many constraints of WSNs and at the same time handling scalability. Scalability is one of the majors concerns than must be addressed, both in terms of very dense and very large networks. We propose a Cluster-based Partial Localization (CPL) to provide efficient localization, where the focus is on providing scalable partial localization suitable to a large and highly-dense network. By partial we mean that CPL will provide localization for subsets of a network in different stages. CPL utilizes both a computationally-intensive localization technique (non-metric MDS) and a less intensive trilateration to achieve balance between complexity and performance. Both trilateration and MDS are well known mathematical techniques. Trilateration, similar to triangulation, has previously been used for localization and similar applications, while variations of MDS have been used in a variety of fields. MDS is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Clustering is utilized to select a subset of nodes to perform the non-metric MDS localization and then extend it to the rest of the network. We show, with simulation results and analysis, that CPL will provide a considerable reduction in both computation and communication, while still yielding acceptable accuracy.
Metrics
2 File views/ downloads
16 Record Views
Details
- Title
- Clustering based localization for wireless sensor networks
- Creators
- Roger Antoniussen. Slaaen
- Contributors
- Murali Medidi (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900525041301842
- Language
- English
- Resource Type
- Thesis