Localization in Self-Healing Autonomous Sensor Networks (SASNet): Studies on Cooperative Localization of Sensor Nodes using Distributed Maps

Abstract

The Self-healing Autonomous Sensing Network (SASNet) presents an advanced Wireless Sensor Network (WSN) that aims to enhance the effectiveness of mission operation in the contemporary military environment, by providing relevant and accurate situational awareness information. In order to achieve this objective, precise location information is required in SASNet. We present the studies on cooperative localization algorithms for wireless sensor nodes. We have taken the cooperative localization approach which can often produce accurate results using a very small number of anchor nodes or even no anchor nodes. The cooperative localization scheme adopted in this study computes a local map for each sensor node using all the available link metric constraints, and then merges the local maps into a global map where each node acquires its location coordinates. We examined advanced techniques of non-linear data mapping for computing local maps from the large data set of link constraints. We selected the Curvilinear Component Analysis (CCA) from a class of highly efficient neural networks and applied it to WSN localization, proposing a cooperative localization scheme based on CCA. We studied CCA localization in comparison with the MDS (Multi-Dimensional Scaling) map method. We first review related work on WSN localization and re-examine the pros and cons of the selected cooperative approach vs. other approaches. We then describe the CCA algorithm for data non-linear mapping, and extend it to solve the problem of sensor node position estimation. The performance simulations of CCA-MAP are conducted using SASNet scenarios and their results compared with the MDS-MAP algorithm. Advantages and shortcomings of the CCA-MAP algorithm are analyzed. Further, we discuss the design considerations of the discussed cooperative localization algorithms to compare and examine their implementation feasibility. Finally, conclusions and recommendations from this study are presented.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2008
Accession Number
ADA479356

Entities

People

  • Li Li

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Communication Systems
  • Computer Networks
  • Coordinate Systems
  • Data Sets
  • Dead Reckoning
  • Detectors
  • Dimensionality Reduction
  • Global Positioning Systems
  • Mesh Networks
  • Neural Networks
  • Sensor Networks
  • Signal Processing
  • Simulations
  • Three Dimensional
  • Two Dimensional
  • Wireless Sensor Networks

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Distributed Systems and Data Platform Development
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms