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

2. 1-2 Announcements r Reading assignment 1 is up.

3. 1-3 Notes on Directed Diffusion r Multiple paths can be used to forward data back to the sink. r Is it the same as multicast?

4. 1-4 Data MULEs

5. 1-5 Target deployments. r Sparse networks. r Multi-tiered deployments. m Sensors. m Wired access points. m Mules.

6. 1-6 Approach r Mobile agents. r MULEs: mobile ubiquitous LAN extensions. m Mobility. m Communication (short range). UWB radios? [low power and ability to handle bursts]. m Buffering.

7. 1-7 Pros and cons

8. 1-8 Pros and cons r Pros: m Energy efficiency ? Listen for the mule. m Intermittent connectivity. r Cons: m Increased latency.

9. 1-9 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. 1-10 3-tier architecture r Wired APs. r Mules. r Sensors.

11. 1-11 Considerations r APs have no limitations. r Mules: m Storage, mobility, ability to communicate with sensors and APs. m Unpredictable movement patterns. m Can talk to other mules. Benefits? r Robustness. r Reliability.

12. 1-12 More considerations… r No routing overhead. r Mules can transport data for multiple applications. r High latency. m Delay bounds? r Mobility limitations.

13. 1-13 System model r Simple, discrete. r Lots of assumptions. m Realistic? r Performance metrics: m Reliability. m Buffer size. m Delay?

14. 1-14 Main results r Buffer requirements at sensors inversely proportional to ratio of number of mules to grid size. r Buffer requirement at mule inversely proportional to ratio of number of mules to grid size and ratio of APs to grid size. r Relationship between buffer capacity, number of mules, and reliability.

15. 1-15 Energy-efficient routing

16. 1-16 [Schurgers et al.] r Two approaches: m Efficient data collection using aggregation. m Load balancing: spread traffic uniformly.

17. 1-17 Observations r Energy-optimal routing needs to consider future traffic. m Energy limitations. B D F A C E T 0 A and E send 50 pkts to B. T 1 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.

18. 1-18 Energy-efficient versus energy-optimal r Statistically optimal and only considers causal information. r Lifetime:worst-case time until node fails.

19. 1-19 Traffic spreading r Make sure that nodes are used uniformly by routing. r Gradient-based routing (GBR): m Directed-diffusion variant. m Use shortest path (in number of hops) to sink to forward data. r Performance metric: E RMS. m Root mean square of the PDF of energy used by nodes.

20. 1-20 Traffic spreading approaches r Stochastic: node picks next-hop randomly (chosen from neighbors with equal gradient). r Energy-based: node increases its “height” when its energy falls below a certain threshold. All nodes need to adjust their height accordingly. r Stream-based: divert streams from nodes that are part of paths used b other streams.

21. 1-21 Results r Target tracking scenario. r Stream-based spreading performs the best. r Stochastic spreading does better than energy-based and pure GBR.

22. 1-22 [Krishnamachari et al.]

23. 1-23 Energy-robustness tradeoff in multipath routing r Multipath for robustness. m Fault-tolerance through redundancy. r Alternatively, reduce number of intermediate nodes. m Single paths. m Nodes use higher transmit power.

24. 1-24 Considerations r Energy metric: number of transmissions * transmit energy. m Independent of number of receivers. r Robustness metric: m Probability message reaches sink in the face of node failures. m Assume that nodes fail with probability p independently from other nodes. r Pareto optimality criteria: m Routing scheme dominates another iff more robust with strictly less energy, or m Iff it uses equal or less energy with strictly higher robustness.

25. 1-25 Results r For the simple scenario chosen (with path loss exponent equal to 2), the Pareto optimal schemes only include single-path routing. r For higher path loss exponent, some multipath schemes are dominated by single path routing.