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Radio Power Management and Controlled Mobility in Sensor Network

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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 ... – PowerPoint PPT presentation

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Title: Radio Power Management and Controlled Mobility in Sensor Network


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
Radio States Transmission (Ptx) Reception (Prx) Idle (Pidle) Sleeping (Psleep)
Power consumption (mw) 21.2106.8 32 32 0.001
Power consumption of CC1000 Radio in different
states
  • 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

4
An Example of Minimizing Total Radio Energy
c
  • 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

b
a
5
Average Power Consumption
c
  • Configuration 1 a ? b ? c

as avg. power
cs avg. power
bs avg. power
b
rx
a
idle
bs activity
time
tx
  • Configuration 2 a ? c, b sleeps

6
Power Control vs. Sleep Scheduling
Transmission power dominates use low
transmission power
Power Consumption
3Pidle
2PidlePsleep
1
r0
Idle power dominates use high transmission power
since more nodes can sleep
7
Min-power routing
  • Given traffic demands I( si , ti , ri ) and
    G(V,E), find a sub-graph G(V, E) minimizing


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!
node cost
  • Sleep scheduling
  • Sleep scheduling
  • Power control
  • Sleep scheduling
  • Power control
  • The problem is NP-Hard

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(PrxPtx-Pidle)/Pidle ( 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 Routing Mobile Elements
Delay Low High
Energy Consumption High 0 Low
Average Bandwidth Low-medium Medium-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 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 gt 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
base station
  • 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

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 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 lt 2
  • CCP SPAN chen et al. 01

19
Greedy Forwarding with Coverage
  • Always forward to the neighbor closest to
    destination
  • Simple, local decision based on neighbor
    locations
  • Fail when a node cant find a neighbor better
    than itself
  • Always succeed with coverage when Rc/Rs gt 2
  • Hop count from u and v is

shortest Euclidean distance to destination
Rc
A
destination
B
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

x and y are candidates
Rc
x
y
u
z
v
not a candidate
21
Relevant Publications
  • ACM/IEEE Transaction Papers
  • 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
  • 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
  • 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
  • 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
    41/16124.8.
  • 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.
  • 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 40/28114
  • 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 24/2759
  • 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
    24/13517.8
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