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Presentation by: Drew Wichmann Paper by: Samer Hanoun and Saeid Nahavandi 1.

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Presentation on theme: "Presentation by: Drew Wichmann Paper by: Samer Hanoun and Saeid Nahavandi 1."— Presentation transcript:

1 Presentation by: Drew Wichmann Paper by: Samer Hanoun and Saeid Nahavandi 1

2  Set of small nodes  Distributed in space  Monitor conditions  Built with  Transceiver  Microcontroller  Sensor  Energy source 2

3  Biggest Issue  Forwarding  Bottlenecking  Unbalanced distribution  Solution  Mobile Sinks 3

4  Mechanical data carrier  Robot  Unmanned Aerial Vehicle (UAV)  Physically approach sensors  Requires routes  Random  Static  Dynamic 4

5  Assumptions  Known locations  Sensor nodes stationary  Uniformly distributed  Sleep when full  Mobile collector ▪ Sufficient energy ▪ Sufficient memory 5

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7 1. Build a fully connected graph G(V,E) of all sleeping sensors 2. Select a vertex r (center of sensing field) to be a root vertex 3. Compute a minimum spanning tree T for G from root r 4. Let L be the list of vertices visited in a DFS on T 5. Generate the Hamiltonian cycle H that visits the vertices in the order L 6. Follow the Hamiltonian cycle H as the constructed route 7

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10 10 Root r

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12 ABFGCDEABFGCDE 12

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15  Parameters  150 Sensors (uniformly distributed)  100m x 100m area  1 KB buffer  time units 15

16  Events occur at center  R i = i * R 1  R 1 = 10m  sensingrange i = [ baserate * (i-1) + 1, baserate * i ]  baserate = 2 seconds 16

17  20 different independent networks  Compared with closest neighbor  Metrics  Sleeping Time  Number of Sleeping Sensors  Sleeping Time per Request  Distance Travelled per Request 17

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20  No dependency on data generation rates  Minimize sleeping times  Better performance than closest neighbor  Shows effect of speed and number of collectors  Future work  Cooperation between collectors  Real-time requests 20


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