1. Radio Power Management and Controlled Mobility in Sensor Network Guoliang Xing Department of Computer Science City University of Hong Kong http://www.cs.cityu.edu.hk/~glxing/

2. Agenda Recent work –Holistic radio power management (MSWiM 07, MobiHoc 05, TOSN 07) –Rendezvous scheduling in mobility-assisted sensor networks (RTSS 07) Previous work –Integrated connectivity and coverage configuration (Sensys 03, TOSN 05) –Impact of coverage on greedy geographic routing (MobiHoc 04, TPDS 06)

3. Understanding Radio Power Cost Sleeping consumes much less power than idle listening –Motivate sleep scheduling [Polastre et al. 04, Ye et al. 04] Transmission consumes most power –Motivate transmission power control [Singh et al. 98,Li et al. 01,Li and Hou 03] None of existing schemes minimizes the total energy consumption in all radio states Radio StatesTransmission ( P tx )Reception ( P rx )Idle ( P idle )Sleeping ( P sleep ) Power consumption (mw) 21.2~106.832 0.001 Power consumption of CC1000 Radio in different states

4. An Example of Minimizing Total Radio Energy a sends to c at normalized rate of r = Data Rate / Band Width Source and relay nodes remain active Configuration 1: a → b → c Configuration 2: a →c, b sleeps a c b

5. Average Power Consumption a b c a’s avg. powerc’s avg. powerb’s avg. power b’s activity tx rx idle Configuration 1: a → b → c Configuration 2: a → c, b sleeps time

6. Power Control vs. Sleep Scheduling Transmission power dominates: use low transmission power Idle power dominates: use high transmission power since more nodes can sleep 3P idle 2P idle +P sleep Power Consumption r0r0 1

7. Min-power routing Given traffic demands I={( s i, t i, r i )} and G(V,E), find a sub-graph G´(V´, E´) minimizing Sleep scheduling    Irts ii i iii ts P r ),,( ), ( idle PV|'| PV|'|   Irts ii i iii ts P r ),,( ), ( sum of edge cost from s i to t i in G´ Cost of edge (u,v) c(u,v)=P tx (u,v)+P rx -2P idle independent of data rate! Sleep scheduling Power control Sleep scheduling Power control The problem is NP-Hard node cost

8. Distributed min-power routing algorithms Incremental Shortest-path Tree Heuristic –Known approx. ratio is O(k) Minimum Steiner Tree Heuristic –Approx. ratio is 1.5(P rx +P tx -P idle )/P idle (≈ 5 on Mica2 motes)

9. Dynamic Min-power Data Dissemination Models several realistic properties –Online arrivals of requests –Online data rate changes of existing requests –Total power consumption of all radio states –Broadcast nature of wireless channel –Lossy links Two lightweight tree adaptation heuristics –Path-quality based tree adaptation Monitor the quality of each path, find a new path if necessary –Reference-rate based tree adaptation Monitor the reference of all data rates, find a new tree if necessary

10. Agenda Recent work –Holistic radio power management (MSWiM 07, MobiHoc 05, TOSN 07) –Rendezvous scheduling in mobility-assisted sensor networks (RTSS 07) Previous work –Integrated connectivity and coverage configuration (Sensys 03, TOSN 05) –Impact of coverage on greedy geographic routing (MobiHoc 04, TPDS 06)

11. Mobility in Ad Hoc Networks Used to be treated as a curse –Corruptions to network topologies –Complication of network protocol design Recently exploited as a blessing –Mobile elements (MEs) communicate with sensors and transport data Mechanically –MEs can recharge their power supplies –Reduce network transmission energy cost –Add extra links in partitioned networks

12. Characteristics of ME and Multi- hop Routing Performance Metrics Multi-hop RoutingMobile Elements DelayLowHigh Energy Consumption High0 ~ Low Average Bandwidth Low-mediumMedium-high

13. High-bandwidth Data Collection Tight delay requirements –“Report the temperature every 20 minute, data are sampled every 10 seconds” –Traveling to each sensor is not feasible Rendezvous-based data collection –Some nodes serve as rendezvous points (RPs) –Sources send data to RPs via multiple hops –MEs visit RPs within the deadline –Minimize the network energy cost

14. Illustration Sensing field is 500 × 500 m 2. The ME moves at 0.5 m/s. It takes ME ~ 20 minutes to visit all RPs located about 100 m from the BS. It takes ME > 2 hours to visit 100 randomly distributed sources

15. Solutions An optimal algorithm when ME moves along the routing tree A constant approx-ratio algorithm when data can be aggregated in the network Two heuristics when there is no data aggregation

16. Agenda Recent work –Holistic radio power management (MSWiM 07, MobiHoc 05, TOSN 07) –Rendezvous scheduling in mobility-assisted sensor networks (RTSS 07) Previous work –Integrated connectivity and coverage configuration (Sensys 03, TOSN 05) –Impact of coverage on greedy geographic routing (MobiHoc 04, TPDS 06)

17. Power Management under Performance Constraints Performance constraints –“Any target within the region must be detected”  K-coverage: every point is monitored by at least K active sensors –“Report the target to the base station within 30 sec”  N-connectivity: network is still connected if N-1 active nodes fail Routing performance: route length can be predicted Focus on fundamental relations between the constraints base station

18. Connectivity vs. Coverage: Analytical Results Network connectivity does not guarantee coverage –Connectivity only concerns with node locations –Coverage concerns with all locations in a region If R c / R s  2 –K-coverage  K-connectivity –Implication: given requirements of K-coverage and N- connectivity, only needs to satisfy max(K, N)-coverage –Solution: Coverage Configuration Protocol (CCP) If R c / R s < 2 –CCP + SPAN [chen et al. 01]

19. Greedy Forwarding with Coverage A destination shortest Euclidean distance to destination B Always forward to the neighbor closest to destination –Simple, local decision based on neighbor locations Fail when a node can’t find a neighbor better than itself Always succeed with coverage when R c /R s > 2 –Hop count from u and v is RcRc

20. Bounded Voronoi Greedy Forwarding (BVGF) A neighbor is a candidate only if the line joining source and destination intersects its Voronoi region Greedy: choose the candidate closest to destination u v x and y are candidates not a candidate xy z RcRc

21. Relevant Publications ACM/IEEE Transaction Papers: 1.Minimum Power Configuration for Wireless Communication in Sensor Networks, G. Xing C. Lu, Y. Zhang, Q. Huang, R. Pless, ACM Transactions on Sensor Networks, Vol 3(2), 2007 2.Integrated Coverage and Connectivity Configuration for Energy Conservation in Sensor Networks, G. Xing; X. Wang; Y. Zhang; C. Lu; R. Pless; C. D. Gill, ACM Transactions on Sensor Networks, Vol. 1 (1), 2005 3.Impact of Sensing Coverage on Greedy Geographic Routing Algorithms, G. Xing; C. Lu; R. Pless; Q. Huang. IEEE Transactions on Parallel and Distributed Systems (TPDS),17(4), 2006 Conference Papers: 1.Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks, H. Luo, G. Xing, M. Li, X. Jia, 10th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2007, Greece, acceptance ratio 41/161=24.8%. 2.Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia, The 28th IEEE Real-Time Systems Symposium (RTSS), December 3-6, 2007, Tucson, Arizona, USA. 3.Minimum Power Configuration in Wireless Sensor Networks, G. Xing; C. Lu; Y. Zhang; Q. Huang; R. Pless, The Sixth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2005,acceptance ratio: 40/281=14% 4.On Greedy Geographic Routing Algorithms in Sensing-Covered Networks, G. Xing; C. Lu; R. Pless; Q. Huang. The Fifth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May, 2004, Tokyo, Japan, acceptance ratio: 24/275=9% 5.Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks, X. Wang; G. Xing; Y. Zhang; C. Lu; R. Pless; C. D. Gill, First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003, acceptance ratio: 24/135=17.8%