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

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 • Usersspecify the data rate

5. Interest and Event Naming • Query/interest: • Type=four-legged animal • Interval=20ms (event data rate) • Duration=10 seconds (time to cache) • Rect=[-100, 100, 200, 400] • Reply: • Type=four-legged animal • Instance = elephant • Location = [125, 220] • Intensity = 0.6 • Confidence = 0.85 • 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 Gradient Source Interest Sink

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

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

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 Gradient Source Data Reinforcement Sink

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 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. 0.018 0.016 Flooding 0.014 0.012 0.01 0.008 Omniscient Multicast (Joules/Node/Received Event) Average Dissipated Energy 0.006 Diffusion 0.004 0.002 0 0 50 100 150 200 250 300 Network Size

18. Impact ofIn-network Processing 0.025 Diffusion Without Suppression 0.02 0.015 (Joules/Node/Received Event) Average Dissipated Energy 0.01 Diffusion With Suppression 0.005 0 0 50 100 150 200 250 300 Network Size

19. Impact of Negative Reinforcement 0.012 0.01 Diffusion Without Negative Reinforcement 0.008 Average Dissipated Energy (Joules/Node/Received Event) 0.006 0.004 Diffusion With Negative Reinforcement 0.002 0 0 50 100 150 200 250 300 Network Size Reducing high-rate paths in steady state is critical

20. Average Dissipated Energy (802.11 energy model) 0.14 Diffusion 0.12 Flooding Omniscient Multicast 0.1 0.08 Average Dissipated Energy (Joules/Node/Received Event) 0.06 0.04 0.02 0 0 50 100 150 200 250 300 Network Size • 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 thoroughperformance 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?