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Wireless Sensor Networks and Laboratories

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Wireless Sensor Networks and Laboratories

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  1. Wireless Sensor Networks and Laboratories Polly Huang EE NTU http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw

  2. Communication Protocols Diffusion Routing Magnetic Diffusion Cross-Layer Performance Analysis

  3. Directed Diffusionlargely based on slides from Chalermek Intanagonwiwat & Deborah Estrin

  4. In Short • A data dissemination mechanism fitting into the data-centric communication paradigm for sensor networks

  5. Sensor network, what? Sensor Networks Common Features Challenges Approach Why not IP based solution?

  6. Sensors • Devices to sense the situation about physical objects or environments • The situations • Location, motion, visual, sound, vital signs, temperature, brightness, etc • The sensors • Could be placed at close proximity of the sensing target • Could be tagged physically on to the sensing target

  7. One way Or another Sensor Networks

  8. Applications Scientific: eco-physiology, biocomplexity mapping Infrastructure: contaminant flow monitoring (and modeling) www.jamesreserve.edu Engineering: monitoring (and modeling) structures

  9. 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

  10. 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

  11. 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

  12. Challenges • Tight coupling to the physical worldand 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 • R4 drop off dictates exploiting localized communication and in-network processing whenever possible

  13. Energy the Bottleneck Resource • Communication VS Computation Cost [Pottie 2000] • E α R4 • 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

  14. 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

  15. New Design Themes • Long-lived systems that can be untetheredand unattended • Energy efficient communication • Self configuring systems that can be deployed ad hoc

  16. 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 interactionor 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

  17. 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

  18. Therefore, Directed Diffusion Features Operations Evaluations

  19. Directed Diffusion Paradigm • Data-centric communication • Supported with distributed algorithms using localized interactions • Application-specific in-network processing

  20. Bob there Bob there IP Communication • Organize system based on named nodes • Per-node forwarding state • Senders need to push data to the node address of sink 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… To Bob My name is Alice. I am a 19-yr old girl… I am Bob I am Bob I am Bob I am Bob Chris Bob Alice

  21. Girl info goes there Girl info goes there 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 Here’s a 19-yr old girl… Tell me about girls Here’s a 19-yr old girl… Tell me about girls Here’s a 19-yr old girl…

  22. Directed Diffusion Paradigm • Data-centric communication • Supported with distributed algorithms using localized interactions • Application-specific in-network processing

  23. Girl info goes there Girl info goes there 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 Here’s a 19-yr old girl… Tell me about girls Here’s a 19-yr old girl… Tell me about girls Here’s a 19-yr old girl…

  24. Directed Diffusion Paradigm • Data-centric communication • Supported with distributed algorithms using localized interactions • Application-specific in-network processing

  25. Tell me about girls Here’s a 19-yr old girl… Here’s a 20-yr old girl… Here’s a 20-yr old girl… Here’s a 19-yr old girl… Here’s a 19-yr old girl… Girl info goes there Tell me about girls Here’s a 20-yr old girl… Girl info goes there Girl info goes there Without In-Network Processing • Data are simply passed on Tell me about girls Tell me about girls Tell me about girls

  26. Here’s a 19-yr old girl… Here’s a 19-yr old girl… Here’re two 19+ yr old girls… Girl info goes there Here’s a 20-yr old girl… Here’s a 20-yr old girl… Girl info goes there Girl info goes there Application-specific Aggregation Here! With In-Network Processing • Data are aggregated and then passed on Here’re two 19+ yr old girls… Here’re two 19+ yr old girls…

  27. Directed Diffusion Paradigm • Data-centric communication • Supported with distributed algorithms using localized interactions • Application-specific in-network processing

  28. Example: Remote Surveillance • Interrogation: • 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

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

  30. Basic Directed Diffusion Sending data and Reinforcing the best path Source Sink Low rate event Reinforcement = Increased interest

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

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

  33. For propagating interests 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 probabilistic forwarding single-path delivery, etc. Local Behavior Choices • For setting up gradients • 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, losses, variances etc. • other variants: inhibit certain paths because resource levels are low

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

  35. Diffusion Simulation Details • Simulator: ns-2 • Network Size: 50-250 Nodes • Transmission Range: 40m • Constant Density: 1.95x10-3 nodes/m2 (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

  36. 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

  37. Average Dissipated Energy (Sensor radio energy model) 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 Diffusion can outperform flooding and even omniscient multicast. WHY ?

  38. 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 Application-level suppression allows diffusion to reduce traffic and to surpass omniscient multicast.

  39. 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

  40. 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

  41. Average Dissipated Energy (Standard 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

  42. Greedy Aggregation • Low-latency tree might be inefficient (late aggregation) • Bias path selection to increase early sharing of paths (early aggregation) • Construct greedy incremental tree (GIT) • establish t shortest path for first source • connect each other source at closest point on existing tree Late Aggregation Source 2 Sink Source 1 Early Aggregation Source 2 Sink Source 1

  43. Mechanisms • Path Establishment • Propagate energy cost with events • On-tree incremental cost message for finding closest point on existing tree • Path selection based on lowest energy cost (events and incremental cost messages) • Path maintenance • Use greedy heuristic of weighted set-covering problem to compute energy cost of an outgoing aggregate E = 2 Incremental cost 2 E = 1 message 2 E = 4 2 E = 0 2 E = 2 E = 3 E = 5 2 2 2 Source 2 E = 1 2 Sink E = 4 2 E = 3 2 E = 2 E = 2 C = 2 2 2 2 Source 1 C = 2 2 C = 2 2 = 2 C 2 Reinforcement Source 2 Sink Source 1

  44. Evaluation opportunistic greedy Greedy aggregation appears to outperform opportunistic aggregation only in very high-density networks

  45. Testbed Experiments

  46. Edge Processing Nested Queries with In-network Processing Proof-of-Concept Experiment:Nested Queries • Edge processing overwhelms power and bandwidth consumption • Nested queries where low-energy sensors trigger high-energy sensors

  47. Nested Queries Experiments @29Palms • Used BAE-Austin’s signal processing • Live, Multiple-target, real-vehicle detections • SITEX’02 validates previous lab experiments • Reduces network traffic/Improves event delivery nested event delivery ratio end-to-end ISI Testbed Data: 2-level are nested queries 29Palms Data

  48. Questions?

  49. Ad Hoc Network Routing

  50. Ad Hoc Network • A collection of wireless mobile nodes • Dynamically forming a temporary network