radio power management and controlled mobility in sensor network guoliang xing department of...

Radio Power Management and Controlled Mobility in Sensor

Network

Guoliang XingDepartment of Computer Science

City University of Hong Konghttp://www.cs.cityu.edu.hk/~glxing/

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)

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 States Transmission (Ptx) Reception (Prx) Idle (Pidle) Sleeping (Psleep)

Power consumption (mw)

21.2~106.8 32 32 0.001

Power consumption of CC1000 Radio in different states

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

idlerxsleepidletx PrrPPPrcarPcaP )1()1(),()(

Average Power Consumption

idlerxidlerxtxidletx PrrPPrrPcbrPPrbarPcbaP )1()21(),()1(),()(

a

b

c

a’s avg. power c’s avg. powerb’s avg. power

b’s activitytx

rx

idle

• Configuration 1: a → b → c

• Configuration 2: a → c, b sleeps

time

Power Control vs. Sleep SchedulingTransmission power dominates: use low transmission power

Idle power dominates:use high transmission power since more nodes can sleep

)( caP

)( cbaP 3Pidle

2Pidle+Psleep

Pow

er C

onsu

mpt

ion

widthband

ratedata r0 1

Min-power routing

• Given traffic demands I={( si , ti , ri )} and G(V,E), find a sub-graph G´(V´, E´) minimizing

• Sleep scheduling

Irts

iii

iii

tsPr),,(

),(idlePV |'| idlePV |'| Irts

iii

iii

tsPr),,(

),(

sum of edge cost from si to ti in G´

Cost of edge (u,v) c(u,v)=Ptx(u,v)+Prx-2Pidle

independent of data rate!

• Sleep scheduling • Power control

• Sleep scheduling • Power control• The problem is NP-Hard

node cost

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(Prx+Ptx-Pidle)/Pidle (≈ 5 on

Mica2 motes)

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

Agenda







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

Characteristics of ME and Multi-hop Routing

Performance Metrics

Multi-hop Routing Mobile Elements

Delay Low High

Energy Consumption

High 0 ~ Low

AverageBandwidth

Low-medium Medium-high

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

Illustration

• Sensing field is 500 × 500 m2.• 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

20 minutes tovisit all RPs

ME path

Rendezvouspoints

Wireless links

Base station

Sources

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

Agenda







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

http://www.kawartha.net/~jleonard/tank.JPG

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 Rc/Rs 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 Rc/Rs < 2– CCP + SPAN [chen et al. 01]

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 Rc/Rs > 2

– Hop count from u and v is sc RR

uv

2

||

Rc

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

x y

z

Rc

Relevant PublicationsACM/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%

radio power management and controlled mobility in sensor network guoliang xing department of...

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