Chun Zhang * , Jim Kurose + , Yong Liu ~ , Don Towsley + , Michael Zink +

1. A Distributed Algorithm for Joint Sensing and Routing in Wireless Networks with Non-Steerable Directional Antennas Chun Zhang*, Jim Kurose+, Yong Liu~, Don Towsley+, Michael Zink+ * IBM T.J. Watson Research Center + Dept of Computer Science, University of Massachusetts at Amherst ~ Dept of Electrical & Computer Engineering, Polytechnic University Nov 14, 2006 ICNP

2. Outline • motivation • problem formulation • distributed algorithm • result • summary

3. Multi-hop wireless sensor networks applications: weather monitoring sink A • sensor nodes • directional-antenna links • link capacity constraints • 802.11 protocol: 2/5.5/11Mbps • energy constraints • energy supplied by solar panel sink B • performance metric • amount of information delivered to sinks

4. application layer radio layer sensing energy communication energy sensing rate (information) link capacities routing solution ? Interesting problem ? limited energy capacity generator demand generator or more capacity? more demand ? network layer

5. Our contribution • joint optimization problem formulation for energy allocation (between sensing, data transmission, and data reception), and routing • distributed algorithm to solve the joint optimization problem, with its convergence proved • simulation to demonstrate the energy balance achieved in a network of X-band radars, connected via point-to-point 802.11 links with non-steerable directional antennas

6. Related work • [Lin,Shroff@CDC04] [Eryilmaz,Srikant@ISC06] • joint rate control, resource allocation, and routing in wireless networks • our work further considers energy consumption for • data sensing • data reception

7. Outline • motivation • problem formulation • distributed algorithm • result • summary

8. Resource model • power resource • three power usages: data sensing, data transmitting, data reception • power is a convex and increasing function of data rate • constraint: consumption rate ≤ harvest rate • link capacity resource • constraint: link data rate ≤ link capacity • resource constraints satisfied by penalty functions

9. Goal : information maximization informationmodeled by utility function • : node i sensed and delivered data rate • node i collected information assumption: is a concave and increasing function

10. Optimization problem formulation Joint sensing and routing problem s: sensing rates; X: data routes routes X deliver sensing rates s to data sink

11. difference link sensing link i’ Transforming joint sensing/routing problem to routing problem with fixed demands sensing power -> reception power i wireless sensor network idea: treat data sensing as data reception through sensing link

12. Transformed problem Routing problem with fixed traffic demand fixed demand: maximum sensing rates; X: data routes routes X deliver maximum sensing rates to data sink

13. Outline • motivation • problem formulation • distributed algorithm • result • summary

14. Distributed algorithm: generalize [Gallager77] wired network algorithm • wired network • link-level resource constraint • wireless network • node-level resource constraint How to generalize from link-level to node-level?

15. Generalized distributed algorithm • generalize algorithm from wired network (link-level) to wireless network (node-level) repeat, until all traffic loaded on optimal path • each link locally compute gradient information • gradient information propagated from downstream to upstream in accumulative manner • routing fractions adjustment from non-optimal path to optimal path • for generalized gradient-based algorithm: • prove convergence • provide step-size for routing fraction adjustment

16. Outline • motivation • problem formulation • distributed algorithm • result • summary

17. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Simulation scenario goodput rate From CASA student testbed • energy harvest rate: 7-13W • X-band radar-on power: 34W • radar-on rate 1.5Mbps • link-on trans power: 1.98W • link-on receive power: 1.39W • link-on goodput rate: as shown • Utility function 1Mb 2Mb 5.5Mb 2Mb 1Mb

18. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Optimization results for different energy harvest rates • As power budget increases • utility and sensing power increase • communication power first increases, then decreases and flats out

19. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Node level energy balance for different energy harvest rates • power rich network: max-min fair (single-sink) : sensing rates not affected by choice of utility functions power budget = 13W power budget = 9W • power constrained network: close to sink nodes spend less energy on sensing

20. Summary: a distributed algorithm for joint sensing and routing in wireless networks Goal : a distributed algorithm for joint sensing and routing Approach : • mapping joint problem to routing problem • proposed a distributed algorithm with convergence proof and step size Simulation to demonstrate energy balance for different energy harvest rates: • energy rich: proven max-min fairness (for single sink) • energy constrained: close-to-sink nodes spend more energy on communication, and thus less energy on sensing

21. Thanks !Questions ?