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CMPE 259

CMPE 259. Sensor Networks Katia Obraczka Winter 2005 Routing Protocols II. Announcements. Reading assignment 1 is up. Notes on Directed Diffusion. Multiple paths can be used to forward data back to the sink. Is it the same as multicast?. Data MULEs. Target deployments. Sparse networks.

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CMPE 259

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  1. CMPE 259 Sensor Networks Katia Obraczka Winter 2005 Routing Protocols II

  2. Announcements • Reading assignment 1 is up.

  3. Notes on Directed Diffusion • Multiple paths can be used to forward data back to the sink. • Is it the same as multicast?

  4. Data MULEs

  5. Target deployments. • Sparse networks. • Multi-tiered deployments. • Sensors. • Wired access points. • Mules.

  6. Approach • Mobile agents. • MULEs: mobile ubiquitous LAN extensions. • Mobility. • Communication (short range). • UWB radios? [low power and ability to handle bursts]. • Buffering.

  7. Pros and cons

  8. Pros and cons • Pros: • Energy efficiency ? • Listen for the mule. • Intermittent connectivity. • Cons: • Increased latency.

  9. Alternatives Approaches Latency Power Reliability Infrastructure cost Base stations Low High High High Ad-hoc Medium M/L Medium M/H MULE High Low Medium Low

  10. 3-tier architecture • Wired APs. • Mules. • Sensors.

  11. Considerations • APs have no limitations. • Mules: • Storage, mobility, ability to communicate with sensors and APs. • Unpredictable movement patterns. • Can talk to other mules. • Benefits? • Robustness. • Reliability.

  12. More considerations… • No routing overhead. • Mules can transport data for multiple applications. • High latency. • Delay bounds? • Mobility limitations.

  13. System model • Simple, discrete. • Lots of assumptions. • Realistic? • Performance metrics: • Reliability. • Buffer size. • Delay?

  14. Main results • Buffer requirements at sensors inversely proportional to ratio of number of mules to grid size. • Buffer requirement at mule inversely proportional to ratio of number of mules to grid size and ratio of APs to grid size. • Relationship between buffer capacity, number of mules, and reliability.

  15. Energy-efficient routing

  16. [Schurgers et al.] • Two approaches: • Efficient data collection using aggregation. • Load balancing: spread traffic uniformly.

  17. Observations • Energy-optimal routing needs to consider future traffic. • Energy limitations. T0 A and E send 50 pkts to B. T1 F sends 100 pkts to B. Load balancing: ADB, ECB, FDB. But, if nodes can only send 100 pkts, D would no be able to deliver all of F’s pkts to B. In this case, ACB, ECB, FDB. B C D F A E

  18. Energy-efficient versus energy-optimal • Statistically optimal and only considers causal information. • Lifetime:worst-case time until node fails.

  19. Traffic spreading • Make sure that nodes are used uniformly by routing. • Gradient-based routing (GBR): • Directed-diffusion variant. • Use shortest path (in number of hops) to sink to forward data. • Performance metric: ERMS. • Root mean square of the PDF of energy used by nodes.

  20. Traffic spreading approaches • Stochastic: node picks next-hop randomly (chosen from neighbors with equal gradient). • Energy-based: node increases its “height” when its energy falls below a certain threshold. All nodes need to adjust their height accordingly. • Stream-based: divert streams from nodes that are part of paths used b other streams.

  21. Results • Target tracking scenario. • Stream-based spreading performs the best. • Stochastic spreading does better than energy-based and pure GBR.

  22. [Krishnamachari et al.]

  23. Energy-robustness tradeoff in multipath routing • Multipath for robustness. • Fault-tolerance through redundancy. • Alternatively, reduce number of intermediate nodes. • Single paths. • Nodes use higher transmit power.

  24. Considerations • Energy metric: number of transmissions * transmit energy. • Independent of number of receivers. • Robustness metric: • Probability message reaches sink in the face of node failures. • Assume that nodes fail with probability p independently from other nodes. • Pareto optimality criteria: • Routing scheme dominates another iff more robust with strictly less energy, or • Iff it uses equal or less energy with strictly higher robustness.

  25. Results • For the simple scenario chosen (with path loss exponent equal to 2), the Pareto optimal schemes only include single-path routing. • For higher path loss exponent, some multipath schemes are dominated by single path routing.

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