1. 1 Chalermek Intanagonwiwat (USC/ISI) Ramesh Govindan (USC/ISI) Deborah Estrin (USC/ISI and UCLA) DARPA Sponsored SCADDS project Directed Diffusion http://www.isi.edu/scadds

2. 2 The Goal Embed numerous devices to monitor and interact with physical world Network these devices so that they can coordinate to perform higher-level tasks robust distributed systems of tens of thousands of devices Requires robust distributed systems of tens of thousands of devices

3. The Challenge: Dynamics! The physical world is dynamic Dynamic operating conditions Dynamic availability of resources … particularly energy! Devices must adapt automatically to the environment Too many devices for manual configuration Environmental conditions are unpredictable Unattended and un-tethered operation is key to many applications

4. Energy is the bottleneck resource Communication VS Computation Cost [Pottie 2000] E α R 4 10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Avoid communication over long distances Cannot assume global knowledge, cannot pre- configure networks Achieve desired global behavior through localized interactions Empirically adapt to observed environment Can leverage data processing/aggregation inside the network Can leverage data processing/aggregation inside the network

5. Our Approach: Directed Diffusion In-network data processing (e.g., aggregation, caching) Distributed algorithms using localized interactions Application-aware communication primitives expressed in terms of named data (not in terms of the nodes generating or requesting data)

6. 6 Application Example: Remote Surveillance Interrogation: e.g., “Give me periodic reports about animal location in region A every t seconds” e.g., “Give me periodic reports about animal location in region A every t seconds” Interrogation is propagated to sensor nodes in region A Sensor nodes in region A are tasked to collect data Data are sent back to the users every t seconds

7. Basic Directed Diffusion Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested

8. Basic Directed Diffusion Source Sink Sending data and Reinforcing the best path Low rate eventReinforcement = Increased interest

9. Directed Diffusion and Dynamics Recovering from node failure Source Sink Low rate event High rate event Reinforcement

10. Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event

11. Local Behavior Choices For propagating interests In our example, flood In our example, flood More sophisticated behaviors possible: e.g. based on cached information, GPS For data transmission Multi-path delivery with selective quality along different paths Multi-path delivery with selective quality along different paths probabilistic forwarding single-path delivery, etc. For setting up gradients data-rate gradients are set up towards neighbors who send an interest. data-rate gradients are set up towards neighbors who send an interest. Others possible: probabilistic gradients, energy gradients, etc. For reinforcement reinforce paths, or parts thereof, based on observed delays reinforce paths, or parts thereof, based on observed delays, losses, variances etc. other variants: inhibit certain paths because resource levels are low

12. Initial simulation study of diffusion Key metric Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime diffusion Compare diffusion to flooding flooding omniscient multicast centrally computed tree (omniscient multicast)

13. Diffusion Simulation Details ns-2 Simulator: ns-2 Network Size: 50-250 Nodes Transmission Range: 40m Constant Density: 1.95x10 -3 nodes/m 2 (9.8 nodes in radius) MAC: Modified Contention-based MAC Energy Model: Mimic a realistic sensor radio [Pottie 2000] 660 mW in transmission, 395 mW in reception, and 35 mw in idle

14. Diffusion Simulation Surveillance application 5 sources are randomly selected within a 70m x 70m corner in the field 5 sinks are randomly selected across the field High data rate is 2 events/sec Low data rate is 0.02 events/sec Event size: 64 bytes Interest size: 36 bytes All sources send the same location estimate for base experiments All sources send the same location estimate for base experiments

15. Standard 802.11 Average Dissipated Energy (Standard 802.11 energy model) 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Standard 802.11 is dominated by idle energy

16. Sensor radio Average Dissipated Energy (Sensor radio energy model) 0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion Omniscient Multicast Flooding Diffusion can outperform flooding and even omniscient multicast. WHY ?

17. Impact of In-network Processing 0 0.005 0.01 0.015 0.02 0.025 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion With Suppression Diffusion Without Suppression Application-level suppression allows diffusion to reduce traffic and to surpass omniscient multicast.

18. Impact of Negative Reinforcement 0 0.002 0.004 0.006 0.008 0.01 0.012 050100150200250300 Average Dissipated Energy (Joules/Node/Received Event) Network Size Diffusion With Negative Reinforcement Diffusion Without Negative Reinforcement Reducing high-rate paths in steady state is critical

19. Summary of Diffusion Results Under the investigated scenarios, diffusion outperformed omniscient multicast and flooding Application-level data dissemination has the potential to improve energy efficiency significantly Duplicate suppression is only one simple example out of many possible ways. Aggregation (in progress) All layers have to be carefully designed Not only network layer but also MAC and application level Experimentation on our testbed in progress

20. 20 More information SCADDS project http://www.isi.edu/scadds ns-2: network simulator (with diffusion supports) http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz Our testbed and software http://www.isi.edu/scadds/testbeds.html