1. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks

2. Motivation Properties of Sensor Networks –Data centric –No central authority –Resource constrained –Nodes are tied to physical locations –Nodes may not know the topology –Nodes are generally stationary How can we get data from the sensors?

3. Directed Diffusion Data centric –Individual nodes are unimportant Request driven –Sinks place requests as interests –Sources satisfying the interest can be found –Intermediate nodes route data toward sinks Localized repair and reinforcement Multi-path delivery for multiple sources, sinks, and queries

4. Motivating Example Sensor nodes are monitoring animals Users are interested in receiving data for all 4-legged creatures seen in a rectangle Users specify the data rate

5. Interest and Event Naming Query/interest: 1.Type=four-legged animal 2.Interval=20ms (event data rate) 3.Duration=10 seconds (time to cache) 4.Rect=[-100, 100, 200, 400] Reply: 1.Type=four-legged animal 2.Instance = elephant 3.Location = [125, 220] 4.Intensity = 0.6 5.Confidence = 0.85 6.Timestamp = 01:20:40 Attribute-Value pairs, no advanced naming scheme

6. Directed Diffusion Sinks broadcast interest to neighbors –Initially specify a low data rate just to find sources for minimal energy consumptions Interests are cached by neighbors Gradients are set up pointing back to where interests came from Once a source receives an interest, it routes measurements along gradients

7. Interest Propagation Flood interest Constrained or Directional flooding based on location is possible Directional propagation based on previously cached data Source Sink Interest Gradient

8. Data Propagation Multipath routing –Consider each gradient’s link quality Source Sink Gradient Data

9. Reinforcement Reinforce one of the neighbor after receiving initial data. –Neighbor who consistently performs better than others –Neighbor from whom most events received Source Sink Gradient Data Reinforcement

10. Negative Reinforcement Explicitly degrade the path by re-sending interest with lower data rate. Time out: Without periodic reinforcement, a gradient will be torn down Source Sink Gradient Data Reinforcement

11. Summary of the protocol

12. Sampling & forwarding Sensors match signature waveforms from codebook against observations Sensors match data against interest cache, compute highest event rate request from all gradients, and (re) sample events at this rate Receiving node: –Find matching entry in interest cache If no match, silently drop –Check and update data cache (loop prevention, aggregation) –Resend message along all the active gradients, adjusting the frequency if necessary

13. Design Considerations

14. Evaluation ns2 simulation Modified 802.11 MAC for energy use calculation –Idle time: 35mW –Receive: 395mw –Transmit: 660mw Baselines –Flooding –Omniscient multicast: A source multicast its event to all sources using the shortest path multicast tree –Do not consider the tree construction cost

15. Simulate node failures No overload Random node placement –50 to 250 nodes (increment by 50) –50 nodes are deployed in 160m * 160m Increase the sensor field size to keep the density constant for a larger number of nodes –40m radio range

16. Metrics Average dissipated energy –Ratio of total energy expended per node to number of distinct events received at sink –Measures average work budget Average delay –Average one-way latency between event transmission and reception at sink –Measures temporal accuracy of location estimates Both measured as functions of network size

17. Average Dissipated Energy 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 They claim diffusion can outperform omniscient multicast due to in-network processing & suppression. For example, multiple sources can detect a four-legged animal in one area.

18. 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

19. 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

20. 802.11 Average Dissipated Energy (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

21. Average energy and delay Average delay is misleading Directed Diffusion is better than Omniscient Multicast? –Why don’t they suppress messages in Omniscient Multicast as done in Directed Diffusion? –Topology has little path diversity

22. Failures Dynamic failures –10-20% failure at any time Each source sends different signals <20% delay increase, fairly robust Energy efficiency improves: –Reinforcement maintains adequate number of high quality paths –Shouldn’t it be done in the first place?

23. Analysis Energy gains are dependent on 802.11 energy assumptions Can the network always deliver at the interest’s requested rate? Can diffusion handle overloads? Does reinforcement actually work?

24. Conclusions Data-centric communication between sources and sinks Aggregation and duplicate suppression More thorough performance evaluation is required

25. Extensions One-phase pull –Propagate interest –A receiving node pick the link that delivered the interest first –Assumes the link bidirectionality

26. Push diffusion –Sink does not flood interest –Source detecting events disseminate exploratory data across the network –Sink having corresponding interest reinforces one of the paths

27. TEEN (Threshold-sensitive Energy Efficient sensor Network protocol) [IPDPS01] Push-based data centric protocol Nodes immediately transmit a sensed value exceeding the threshold to its cluster head that forwards the data to the sink

28. LEACH [HICSS00] Proposed for continuous data gathering protocol Divide the network into clusters Cluster head periodically collect & aggregate/compress the data in the cluster using TDMA Periodically rotate cluster heads for load balancing

29. Discussions Criteria to evaluate data-centric routing protocols? –Or, what do we need to try to optimize? Energy consumption? Data timeliness? Resilience? Confidence of event detection? Too many objectives already? Can we pick just one or two?

30. Questions?