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Routing and Data Dissemination

Routing and Data Dissemination. Outline . Motivation and Challenges Basic Idea of Three Routing and Data Dissemination schemes in Sensor Networks Some Thoughts on Comparison of the Data dissemination schemes. Differences with Current Networks.

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Routing and Data Dissemination

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  1. Routing and Data Dissemination

  2. Outline • Motivation and Challenges • Basic Idea of Three Routing and Data Dissemination schemes in Sensor Networks • Some Thoughts on Comparison of the Data dissemination schemes

  3. Differences with Current Networks • Difficult to pay special attention to any individual node: • Collecting information within the specified region • Collaboration between neighbors • Sensors may be inaccessible: • embedded in physical structures. • thrown into inhospitable terrain.

  4. Differences with Current Networks • Sensor networks deployed in very large ad hoc manner • No static infrastructure • They will suffer substantial changes as nodes fail: • battery exhaustion • accidents • new nodes are added.

  5. Overall Design of Sensor Networks • Internet technology coupled with ad-hoc routing mechanism • Each node has one IP address • Each node can run applications and services • Nodes establish an ad-hoc network amongst themselves when deployed • Application instances running on each node can communicate with each other

  6. Why Different and Difficult? • Content based and data centric • Where are nodes whose temperatures will exceed more than 10 degrees for next 10 minutes? • Tell me the location of the object ( with interest specification) every 100ms for 2 minutes.

  7. Why Different and Difficult? • Multiple sensors collaborate to achieve one goal. • Intermediate nodes can perform data aggregation and caching in addition to routing. • where, when, how?

  8. Why Different and Difficult? • Not node-to-node packet switching, but node-to-node data propagation. • High level tasks are needed: • At what speed and in what direction was that elephant traveling? • Is it the time to order more inventory?

  9. Challenges • Energy-limited nodes • Computation • Aggregate data • Suppress redundant routing information • Communication • Bandwidth-limited • Energy-intensive Goal: Minimize energy dissipation

  10. Challenges • Scalability: ad-hoc deployment in large scale • Fully distributed w/o global knowledge • Large numbers of sources and sinks • Robustness: unexpected sensor node failures • Dynamically Change: no a-priori knowledge • sink mobility • target moving

  11. Challenges • Topology or geographically issue • Time : out-of-date data is not valuable • Value of data is a function of time, location, and its real sensor data. • Is there a need for some general techniques for different sensor applications? • Small-chip based sensor nodes • Large sensors, e.g., radar • Moving sensors, e.g., robotics

  12. Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat R. Govindan D. Estrin

  13. Application Example: Remote Surveillance • e.g., “Give me periodic reports about animal location in region A every t seconds” • Tell me in what direction that vehicle in region Y is moving?

  14. Basic Idea • In-network data processing (e.g., aggregation, caching) • Distributed algorithms using localized interactions • Application-aware communication primitives • expressed in terms of named data

  15. Elements of Directed Diffusion Source and Sink Data • Data is named using attribute-value pairs Interests • Record indicating desire of certain types of information is called interest Gradients • Gradients is set up within the network designed to data matching the interest.

  16. Reply Node data Type =four-legged animal Instance = elephant Location = [125, 220] Confidence = 0.85 Time = 02:10:35 Naming • Content based naming • Tasks are named by a list of attribute – value pairs • Task description specifies an interest for data matching the attributes • Animal tracking: Request Interest ( Task ) Description Type = four-legged animal Interval = 20 ms Duration = 1 minute Location = [-100, -100; 200, 400]

  17. Interest • The sink periodically broadcasts interest messages to each of its neighbors • Every node maintains an interest cache • Each item corresponds to a distinct interest • No information about the sink • Interest aggregation : identical type, completely overlap rectangle attributes • Each entry in the cache has several fields • Timestamp: last received matching interest • Several gradients: data rate, duration, direction

  18. Setting Up Gradient Source Neighbor’s choices : 1. Flooding 2. Geographic routing 3. Cache data to direct interests Sink Interest = Interrogation Gradient = Who is interested (data rate , duration, direction)

  19. Data Propagation • Sensor node computes the highest requested event rate among all its outgoing gradients • When a node receives a data: • Find a matching interest entry in its cache • Examine the gradient list, send out data by rate • Cache keeps track of recent seen data items (loop prevention) • Data message is unicast individually to the relevant neighbors

  20. Reinforcing the Best Path Source The neighbor reinforces a path: 1. At least one neighbor 2. Choose the one from whom it first received the latest event (low delay) 3. Choose all neighbors from which new events were recently received Sink Low rate event Reinforcement = Increased interest

  21. For propagating interests In the example, flood More sophisticated behaviors possible: e.g. based on cached information, GPS 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.

  22. Local Behavior Choices • For data transmission • Multi-path delivery with selective quality along different paths • probabilistic forwarding • single-path delivery, etc. • For reinforcement • reinforce paths based on observed delays • losses, variances etc.

  23. 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)

  24. 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

  25. 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

  26. Average Dissipated Energy 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.

  27. Comments • Primary concern is energy • Simulations only • Only use five sources and five sinks • How to exam scalability?

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