Sweeps Over Sensor Networks. Primoz Skraba, An Nguyen, Qing Fang, Leonidas Guibas AHPCRC Stanford University August 3, 2007

Transcription

1 Sweeps Over Sensor Networks Primoz Skraba, An Nguyen, Qing Fang, Leonidas Guibas AHPCRC Stanford University August 3, 2007

2 Data aggregation over entire network - Exact aggregate - Only a few nodes need answer Motivation

3 Data aggregation over entire network - Exact aggregate - Only a few nodes need answer Disseminate query Collect information Motivation

4 Data aggregation over entire network - Exact aggregate - Only a few nodes need answer Disseminate query Collect information Motivation

5 Data aggregation over entire network - Exact aggregate - Only a few nodes need answer Disseminate query Collect information Motivation

6 Data aggregation over entire network Lightweight protocol Robust - Exact aggregate - Only a few nodes need answer Disseminate query Collect information No location information Motivation

7 Overall Approach Topological sweep - Aggregation occurs along a sweep front - Small band of nodes active

8 Overall Approach Topological sweep - Aggregation occurs along a sweep front - Small band of nodes active Preprocessing phase - Compute a potential field over the sensor network: - A helper function to guide the sweep

9 Overall Approach Topological sweep - Aggregation occurs along a sweep front - Small band of nodes active Preprocessing phase - Compute a potential field over the sensor network: - A helper function to guide the sweep Sweep - Lightweight distributed algorithm - Use the potential field many times

10 Potential Fields on Sensor Networks Use Laplace s equation with Dirichlet boundary conditions 2 φ(i) = 0

11 Potential Fields on Sensor Networks Use Laplace s equation with Dirichlet boundary conditions 2 φ(i) = 0 Solution is a harmonic function

12 Potential Fields on Sensor Networks Use Laplace s equation with Dirichlet boundary conditions 2 φ(i) = 0 Solution is a harmonic function Solve discretized version on communication graph φ(i) 1 N(i) φ(j) j N(i)

13 Potential Fields on Sensor Networks Use Laplace s equation with Dirichlet boundary conditions 2 φ(i) = 0 Solution is a harmonic function Solve discretized version on communication graph φ(i) 1 N(i) φ(j) Boundary conditions: Two sets of nodes - Source and sink j N(i)

14 Potential Fields on Sensor Networks Use Laplace s equation with Dirichlet boundary conditions 2 φ(i) = 0 Solution is a harmonic function Solve discretized version on communication graph φ(i) 1 N(i) φ(j) Boundary conditions: Two sets of nodes - Source and sink j N(i)

15 Smoothing Effect Potential field samples underlying smooth continuum Diffusion potential Hop distance to center

16 Smoothing Effect Potential field samples underlying smooth continuum Robustness

17 Sweep Intuition Potential values give local ordering Gradient gives a sense of direction Locally last node propagates the sweep forward - Invite nodes into the sweep - Forward data - Leave sweep Sweep Direction

18 Sweep Propagation Begin Unswept

19 Sweep Propagation Begin Unswept

20 Sweep Propagation Begin Unswept

21 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep

22 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep

23 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep

24 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep

25 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep

26 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep - Issue new invitations

27 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep - Issue new invitations

28 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep - Issue new invitations - Forward data to downstream neighbor

29 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep - Issue new invitations - Forward data to downstream neighbor

30 Sweep Propagation Begin Unswept Receive invitation - Enter Sweep If all upstream neighbors have left the sweep - Issue new invitations - Forward data to downstream neighbor Leave Sweep

31 Starting/Stopping the Sweep To start the sweep, a node in the source must be notified

32 Starting/Stopping the Sweep To start the sweep, a node in the source must be notified

33 Starting/Stopping the Sweep To start the sweep, a node in the source must be notified To sweep terminates when all information reaches sink - Collection done through an aggregation tree or another lower-dimensional sweep - Sent to the query node

34 Starting/Stopping the Sweep To start the sweep, a node in the source must be notified To sweep terminates when all information reaches sink - Collection done through an aggregation tree or another lower-dimensional sweep - Sent to the query node

35 Comparison with TAG Aggregation Tree Sweep Sweep more robust than aggregation tree

36 Summary Class of global operations on a WSN Two-part solution:

37 Summary Class of global operations on a WSN Two-part solution: Potential field - Robust to low-level network volatility - Gives a consistent sense of direction

38 Summary Class of global operations on a WSN Two-part solution: Potential field - Robust to low-level network volatility - Gives a consistent sense of direction Sweep - Lightweight and local - Online forwarding decision through implicitly encoded aggregation trees

39 Related Work Graph-based methods Geometry-based Sweeps TAG - [Madden, Franklin, Hellerstein, Hong, 2002] Synopsis Diffusion - [Nath, Gibbons, Seshan, Anderson, 2004] Based on geographic location WaveScheduling - [Trigoni, Yao, Demers, Gehrke, Rajaraman, 2005 WaveSensing - [Ren, Li, Wang, Zhang, 2005]

40 Related Work Graph-based methods Geometry-based Sweeps TAG - [Madden, Franklin, Hellerstein, Hong, 2002] Synopsis Diffusion - [Nath, Gibbons, Seshan, Anderson, 2004] Based on geographic location WaveScheduling - [Trigoni, Yao, Demers, Gehrke, Rajaraman, 2005 WaveSensing - [Ren, Li, Wang, Zhang, 2005] Our Method: Topological Sweep