Lightweight Routing with QoS Support in Wireless Sensor and Actor Networks (L...
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
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
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
Notas do Editor
An example of a sensor node’s weight adaptation according to the algorithm described in this slide. All the other nodes in this example are assumed to be stationary and k value for the network is set to four. The mobile node in the figure is initially not affiliated to any actors, so its weight iszero. Then the node becomes directly connected to an actor and its weight becomes k −1. As the node moves away from the actor node and affiliates with another actor node, its weight changes according to “the highest neighbor weight – 1” rule. Finally, it ends up not being affiliated with any actor node, its weight is decreased until it becomes zero and stays there as stated in the last step.
Selection of k is criticalDefines coverage range of an actor
-First observation, tracking error increases with the increasing number of intruders. There are essentially two pairs here: DD-25 --- TAB-0.001 (bad) AND DD-10 --- TAB-0.003 (good). More often to transmit, better accuracyEven though, DD-25 is a bit better than TAB-0.001, in terms of average stealth values, TAB-0.001 is at least 2-3 times better