Local Positioning for Environmental Monitoring in WSANs

1. Local Positioning for Environmental
Monitoring in WSANs
Mustafa I. Akbas1, Matthias R. Brust2 and Damla Turgut1
1University of Central Florida
2Technological Institute of Aeronautics
P2MNET 2010
October 11, 2010

2. Localization in Sensor Networks:
Amazon Scenario
 Goal
 Gather data about the
river
 Sensor nodes
 Thrown in the river
 Local communication
 No geographical
information
 Actors
 At rare accessible points
 Actor backbone network
 Have geographical
information

3. The Problem Definition
 Characteristics
 Rapid changes in neighborhood and actor associations
 No self-awareness at sensor nodes
 Unknown sensor node positions
 Our objective
 Develop an algorithm to enrich the collected data with
localization information in Amazon scenario

4. Clustering to build an overlay
network

5. Weight Adaptation
 Dynamic weight adaptation
according to the algorithm
 Only 1 mobile node
 k=4

6. Determining k

7. Positioning Nodes

8. Simulation Study
 Network
 Interest area: 200x300 m
 Number of sensor nodes : Variable (1…25)
 Number of actors: Variable (1…25)
 Sensor transmission range : 40 m
 Metrics
 Single hop clustering (no affect of k)
 Multi hop clustering (affect of k) with different k values
 Error distribution

9. 1-hop Clustering
 1 sensor node and 25 actors

10. 1-hop Clustering
 1 sensor node and 25 actors

11. k-hop clustering
 25 sensor nodes and 25 actors

12. k-hop clustering
 25 sensor nodes and 25 actors

13. Error in Positioning
 k=3  k=1
 k=5

14. Conclusions
 The proposed algorithm improves the on-site monitoring
of Amazon river
 Communication in the system is locality preserving
 The collected data enriched with localization information
 Future work:
 Use localization information for data aggregation and
dissemination
 Test the algorithm in a real life scenario