Local Positioning for Environmental Monitoring in WSANs


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Local Positioning for Environmental Monitoring in WSANs

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  • 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
  • Local Positioning for Environmental Monitoring in WSANs

    1. 1. Local Positioning for Environmental Monitoring in WSANsMustafa I. Akbas1, Matthias R. Brust2 and Damla Turgut1 1University of Central Florida 2Technological Institute of Aeronautics P2MNET 2010 October 11, 2010
    2. 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. 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. 4. Clustering to build an overlay network
    5. 5. Weight Adaptation Dynamic weight adaptation according to the algorithm Only 1 mobile node k=4
    6. 6. Determining k
    7. 7. Positioning Nodes
    8. 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. 9. 1-hop Clustering 1 sensor node and 25 actors
    10. 10. 1-hop Clustering 1 sensor node and 25 actors
    11. 11. k-hop clustering 25 sensor nodes and 25 actors
    12. 12. k-hop clustering 25 sensor nodes and 25 actors
    13. 13. Error in Positioning k=3  k=1  k=5
    14. 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