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Phero-Trail: A Bio-Inspired Location Service for Mobile Underwater Sensor Networks

Phero-Trail: A Bio-Inspired Location Service for Mobile Underwater Sensor Networks. Luiz Filipe M. Vieira † , Uichin Lee ‡ and Mario Gerla *

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Phero-Trail: A Bio-Inspired Location Service for Mobile Underwater Sensor Networks

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  1. Phero-Trail: A Bio-Inspired Location Service for Mobile Underwater Sensor Networks Luiz Filipe M. Vieira †, Uichin Lee ‡ and Mario Gerla * † Department of Computer Science, UFMG (米納斯州大學) , Brasii‡ Bell Labs, USA* Department of Computer Science, University of California at Los Angeles, USA IEEE Journal on Selected Areas in Communications, vol. 28, no. 4, May 2010

  2. Outline • Introduction • Scenario • Problem & Goal • Phero-Trail • Performance • Conclusion Page 2

  3. acoustic links Underwater Comm. Characteristics • Narrow available bandwidth • Radio is unsuitable under water • Must use acoustic channels • High attenuation • Very slow acoustic signal propagation • 1.5x103 m / sec vs. 3x108m / sec • Causes large propagation delay Sensors can adjust their depths using on-board pressure gauges Page 3

  4. Buoys Example: Submarine Detection • underwater sensor network to detect submarines Radio Data Sink Acoustic Acoustic Mobile sensor Seabed Sensor Page 4

  5. Scenario • Protecting critical installation such as harbor, underwater mining facility, and oil rigs. • Mobile floating sensor nodes • Mobile Sinks • Autonomous Underwater Vehicles (AUV) or Submarines L L D1 R D2 Page 5

  6. Scenario • Sensor Equipped Aquatic (SEA) swarm of mobile sensors: • Enable 4D (space and time) monitoring • Dynamic monitoring coverage • Sensor nodes notify events to corresponding submarines Event Type B Y X Event Type A Event Type B Event Type A Z Page 6

  7. Problem Statements • Mobile sensors report events to submarines • Proactive (OLSR), Reactive Routing (AODV), or Sensor data collection (Directed Diffusion) • All require route discovery (flooding) and/or maintenance • Not suitable for bandwidth constrained underwater mobile sensor networks (collision + energy consumption) • Geographical routing is preferable, but requires geo-location service to know the destination’s location • Goal: design an efficient location service protocol for a SEA swarm Page 7

  8. Related Work – Naïve Flooding • Node periodically floods its current position to the entire network Y X (AUV) (sensors) Z Page 8

  9. Related Work – Quorum Based • Each location update is sent to a subset of nodes (update quorum) • Location query is sent to a subset of nodes (or query quorum) • The query will be resolved when their intersection is non-empty (sensors) (AUV) XYLS Page 9

  10. Related Work – Hierarchical • Location servers are chosen via a set of hash functions • Area recursively divided into a hierarchy of smaller grids. • For each node, one or more nodes in each grid at each level of the hierarchy are chosen as its location servers. Page 10

  11. Hierarchical - Example (AUV) (sensors) Node requesting location Node updating location Server at level 2 Server at level 3 Page 11

  12. Protocol Analysis • M: number of hops to travel a width of a network (L); i.e., L / R (communication range) • Quorum-based must store information in a 2D plane; i.e., O(M2) Page 12

  13. Protocol Analysis • Hierarchical • Must first find a reference point for geographic hashing and propagate this information to every node. • Overhead of “periodical” reference point updates dominates the update/query overhead. Page 13

  14. Reference Point Updates in Hierarchical Schemes • Periodic reference update O/H: O(M3) Page 14

  15. Location Service in 2D • Store location information in 2D; search and update in 2D • But at the cost of vertical routing O(M) to given a location service plane • Put a 2D plane • Upper hull Page 15

  16. Location Service in 2D: Analysis • Naïve flooding • update and query costs are O(M2) • Quorum-based • store information in a 1D line • Update and query costs scale as O(M) • Hierarchical • Reference update, location update and query operations take O(M2) , O(H), and O(M) respectively. • Reference point update is still expensive!!! Page 16

  17. Phero-Trail – Location Update • AUV stores the location updates (pheromone) along its projected trajectory on the upper hull • Periodic updates create a pheromone trail Page 17

  18. Phero-Trail – Location Update • AUV stores the location updates (pheromone) along its projected trajectory on the upper hull • Periodic updates create a pheromone trail D1 DH D2 O(M) Page 18

  19. Phero-Trail – Location Query • A mobile node first routes a query packet vertically upwards to the node on the projected position of the convex hull plane • Node performs an expanding spiral curve search to find a pheromone trail. Page 19

  20. Simulation Results • QualNET 3.9.5 • 1 Km x 1 Km x 1 Km • Submarine 5 m/s • Vary the network size • Compared with flooding • Number of transmitted messages Page 21

  21. Simulation Results Page 22

  22. Simulation Results Page 23

  23. Conclusion • Presented a novel bio-inspired location service (PTLS) • efficient location service protocol for a SEA swarm • maintaining location information in a 2D plane is optimal T h e E N D Thanks for your attention ! Wireless & Mobile Network Laboratory Wireless.cs.tku.edu.tw

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