1. Network and Systems Laboratory nslab.ee.ntu.edu.tw Polly@NTU Copyright © 2008 1 Wireless Sensor Networks: Classic Protocols Polly Huang Department of Electrical Engineering National Taiwan University http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw

2. Network and Systems Laboratory nslab.ee.ntu.edu.tw Classic Protocols Designed for outdoor sensor networks Directed diffusion S-MAC Polly@NTU Copyright © 2008 2

3. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Largely based on slides from Chalermek Intanagonwiwat & Deborah Estrin Polly@NTU3 Copyright © 2008

4. Network and Systems Laboratory nslab.ee.ntu.edu.tw In Short A data dissemination mechanism fitting into the data-centric communication paradigm for sensor networks Polly@NTU4 Copyright © 2008

5. Network and Systems Laboratory nslab.ee.ntu.edu.tw Sensor Networks Or another One way Polly@NTU5 Copyright © 2008

6. Network and Systems Laboratory nslab.ee.ntu.edu.tw Applications Scientific: eco-physiology, biocomplexity mapping Infrastructure: contaminant flow monitoring (and modeling) Engineering: monitoring (and modeling) structures www.jamesreserve.edu Polly@NTU6 Copyright © 2008

7. Network and Systems Laboratory nslab.ee.ntu.edu.tw The Real Need Specialized communication in a wild wide space Specialized: application dependent Wild: little or no infrastructure Wide: expensive to build/use communication infrastructure Polly@NTU7 Copyright © 2008

8. Network and Systems Laboratory nslab.ee.ntu.edu.tw Applications: A Longer List Science: monitoring temperature change on a volcanic island Engineering: monitoring power use of industrial district Infrastructure: monitoring passenger traffic at MRT stations Military: tracking enemy migration in a dessert Disaster: emergency relief after Gozzila taking a short tour of Tokyo Polly@NTU8 Copyright © 2008

9. Network and Systems Laboratory nslab.ee.ntu.edu.tw Common Vision Embed numerous distributed devices to monitor and interact with physical world Exploit spatially and temporally dense, in situation, sensing and actuation Network these devices so that they can coordinate to perform higher-level tasks Requires robust distributed systems of hundreds or thousands of devices Polly@NTU9 Copyright © 2008

10. Network and Systems Laboratory nslab.ee.ntu.edu.tw Challenges Tight coupling to the physical world and embedded in unattended systems Different from traditional Internet, PDA, Mobility applications that interface primarily and directly with human users But solutions might be applicable to the Internet, PDA, Mobility applications as well Untethered, small form-factor, nodes present stringent energy constraints Living with small, finite, energy source is different from traditional fixed but reusable resources such as BW, CPU, Storage Communications is primary consumer of energy in this environment R 4 drop off dictates exploiting localized communication and in- network processing whenever possible Polly@NTU10

11. Network and Systems Laboratory nslab.ee.ntu.edu.tw Energy 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 Polly@NTU11 Copyright © 2008

12. Network and Systems Laboratory nslab.ee.ntu.edu.tw In-Network Processing Sensor technology is advancing steadily Situations detected by the sensors can be surprisingly rich For example, all these at once Detecting a speech Inferring the location and identity of the speaker These information can be used to facilitate efficient dissemination of the recorded speech Suppressing speech coming from the same speaker Forwarding towards the likely listeners Polly@NTU12 Copyright © 2008

13. Network and Systems Laboratory nslab.ee.ntu.edu.tw New Design Themes Long-lived systems that can be untethered and unattended Energy efficient communication Self configuring systems that can be deployed ad hoc Polly@NTU13 Copyright © 2008

14. Network and Systems Laboratory nslab.ee.ntu.edu.tw Approach Leverage data processing inside the network Exploit computation near data to reduce communication Achieve desired global behavior with adaptive localized algorithms (i.e., do not rely on global interaction or information) Dynamic, messy (hard to model), environments preclude pre-configured behavior Can ’ t afford to extract dynamic state information needed for centralized control or even Internet-style distributed control Polly@NTU14 Copyright © 2008

15. Network and Systems Laboratory nslab.ee.ntu.edu.tw Why can ’ t we simply adapt Internet protocols and “ end to end ” architecture? Internet routes data using IP Addresses in Packets and Lookup tables in routers Humans get data by “ naming data ” to a search engine Many levels of indirection between name and IP address Works well for the Internet, and for support of Person- to-Person communication Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems can ’ t tolerate communication overhead of indirection Polly@NTU15 Copyright © 2008

16. Network and Systems Laboratory nslab.ee.ntu.edu.tw Therefore, Directed Diffusion Features Operations Evaluations Polly@NTU16 Copyright © 2008

17. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Polly@NTU17 Copyright © 2008

18. Network and Systems Laboratory nslab.ee.ntu.edu.tw IP Communication Organize system based on named nodes Per-node forwarding state Senders need to push data to the node address of sink Bob Alice To Bob My name is Alice. I am a 19-yr old girl… Chris I am Bob Bob there I am Bob Bob there I am Bob To Bob My name is Alice. I am a 19-yr old girl… To Bob My name is Alice. I am a 19-yr old girl… Polly@NTU18 Copyright © 2008

19. Network and Systems Laboratory nslab.ee.ntu.edu.tw Data-Centric Communication Organize system based on named data Per-data diffusion state Sinks need to be specific about what data they’d pull Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Here’s a 19-yr old girl… Polly@NTU19 Copyright © 2008

20. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Polly@NTU20 Copyright © 2008

21. Network and Systems Laboratory nslab.ee.ntu.edu.tw Localized Interaction Diffuse requests/interest across network Set up gradients to guide responses/data Diffuse responses/data based on the gradients (Pretty much the same as in the IP routing) Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Here’s a 19-yr old girl… Polly@NTU21 Copyright © 2008

22. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Polly@NTU22 Copyright © 2008

23. Network and Systems Laboratory nslab.ee.ntu.edu.tw Without In-Network Processing Data are simply passed on Tell me about girls Tell me about girls Girl info goes there Tell me about girls Girl info goes there Tell me about girls Tell me about girls Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’s a 20-yr old girl… Polly@NTU23 Copyright © 2008

24. Network and Systems Laboratory nslab.ee.ntu.edu.tw With In-Network Processing Data are aggregated and then passed on Girl info goes there Here’re two 19+ yr old girls… Girl info goes there Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’re two 19+ yr old girls… Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’re two 19+ yr old girls… Application-specific Aggregation Here! Polly@NTU24 Copyright © 2008

25. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion Paradigm Data-centric communication Supported with distributed algorithms using localized interactions Application-specific in-network processing Polly@NTU25 Copyright © 2008

26. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 Polly@NTU26 Copyright © 2008

27. Network and Systems Laboratory nslab.ee.ntu.edu.tw Basic Directed Diffusion Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested Polly@NTU27 Copyright © 2008

28. Network and Systems Laboratory nslab.ee.ntu.edu.tw Basic Directed Diffusion Source Sink Sending data and Reinforcing the best path Low rate eventReinforcement = Increased interest Polly@NTU28 Copyright © 2008

29. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion and Dynamics Recovering from node failure Source Sink Low rate event High rate event Reinforcement Polly@NTU29 Copyright © 2008

30. Network and Systems Laboratory nslab.ee.ntu.edu.tw Directed Diffusion and Dynamics Source Sink Stable path Low rate event High rate event Polly@NTU30 Copyright © 2008

31. Network and Systems Laboratory nslab.ee.ntu.edu.tw Local Behavior Choices For propagating interests In this example, flood In this 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 Polly@NTU31 Copyright © 2008

32. Network and Systems Laboratory nslab.ee.ntu.edu.tw Simulation Study 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) Polly@NTU32 Copyright © 2008

33. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 Polly@NTU33 Copyright © 2008

34. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 Polly@NTU34 Copyright © 2008

35. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 Diffusion can outperform flooding and even omniscient multicast. WHY ? Polly@NTU35 Copyright © 2008

36. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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. Polly@NTU36 Copyright © 2008

37. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 Polly@NTU37 Copyright © 2008

38. Network and Systems Laboratory nslab.ee.ntu.edu.tw 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 (next) All layers have to be carefully designed Not only network layer but also MAC and application level Polly@NTU38 Copyright © 2008

39. Network and Systems Laboratory nslab.ee.ntu.edu.tw Standard 802.11 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 Polly@NTU39 Copyright © 2008

40. Network and Systems Laboratory nslab.ee.ntu.edu.tw 802.11 Contention-based protocol RTS-CTS-DATA-ACK RTS CTS Sender Receiver DATA ACK [Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11-1999 edition] Polly@NTU40 Copyright © 2008

41. Network and Systems Laboratory nslab.ee.ntu.edu.tw S-MAC Contention-based protocol RTS-CTS-DATA-ACK Listen interval Send packets Receive packets [W. Ye et al., “ An energy-efficient MAC protocol for wireless sensor networks ”, in INFOCOM 2002] Polly@NTU41 Copyright © 2008

42. Network and Systems Laboratory nslab.ee.ntu.edu.tw Schedule synchronization Schedules can differ Neighboring nodes have same schedule Node 1 Node 2 sleep listen sleep listen sleep Schedule 2 Schedule 1 Border nodes: two schedules broadcast twice (Borrowed from S-MAC) Polly@NTU42 Copyright © 2008

43. Network and Systems Laboratory nslab.ee.ntu.edu.tw 13 2 4 Scheduling in S-MAC Unknown neighbors the same schedule 2 3 4 Schedule 2 Schedule 1 Collision 1 Unicast Broadcast Polly@NTU43 Copyright © 2008

44. Network and Systems Laboratory nslab.ee.ntu.edu.tw Questions? Polly@NTU44 Copyright © 2008

45. Network and Systems Laboratory nslab.ee.ntu.edu.tw Polly@NTU Copyright © 2008 45 Questions?