A Unified Power Management Framework for Wireless Sensor Networks

1
A Unified Power Management Framework for Wireless
Sensor Networks
Guoliang Xing Department of Computer Science and
Engineering Washington University in St.
Louis http//www.cse.wustl.edu/xing/
2
Wireless Sensor Network Platform

• Integration of sensing, computation, and
  communication
• Example Mica2 mote
• Radio lt 40 Kbps
• Memory 4KB data, 128 KB program
• Limited power source 2AA batteries
• Several days of lifetime if continuously active

3
Wireless Sensor Network Applications
Healthcare
Structural monitoring
Habitat monitoring
Perimeter security

• Limited power supplies batteries, small solar
  panels
• Long lifetime requirement months to tens of
  years
• Must minimize the total network energy consumption

4
A Unified Power Management Framework

• An analytical model for minimizing total energy
  consumption in all radio states
• A new power management protocol that integrates
  sleep scheduling with power control
• A system architecture for flexible power
  management

5
Understanding Radio Power Cost
Radio power consumption in different states
(unit mW)

• Sleeping consumes much less power than idle
• Reduce idle energy of non-communicating nodes
  through sleeping
• Motivate sleep scheduling Polastre et al. 04, Ye
  et al. 04
• Transmission consumes most power
• Reduce transmission energy of communicating nodes
• 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

6
An Example of Minimizing Total Radio Energy
c

• a sends to c at normalized rate of r Date Rate
  / BandWidth
• Source and relay nodes remain active
• Configuration 1 a ?c, b sleeps
• Configuration 2 a ? b ? c

b
a
node as avg. power
node bs avg. power
node cs avg. power
7
Key Observations
Transmission power dominates short radio range
is preferable
Idle power dominates long radio range is
preferable since more nodes can go to sleep
8
Problem Formulation

• a communicates with c at rate r, b sleeps

ignore sleeping power
group rate related terms
Ca,cPtx(a,c)Prx-2Pidle
edge (a,c) has a cost of Ca,c per unit of data
each active node has a cost of Pidle
Pidle
Ca,c

• Extending to the general case.

c
Pidle
a
b
rate r
9
Minimum Power Configuration (MPC)

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

independent of data rate!
sum of edge cost from si to ti in G

• Sleep scheduling
• Power control

• Sleep scheduling
• Power control

10
Solutions

• Minimum Power Configuration is NP-hard
• Matching Based Algorithm (MBA) can solve the
  one-sink case with approx. ratio O(lgk) Meyerson
  et al. 00
• Distributed implementation is expensive
• New Incremental Shortest-path Tree Heuristic
  (ISTH)
• Distributed and online
• Known approx. ratio is O(k), similar average-case
  performance to MBA

11
Incremental Shortest-path Tree Heuristic (ISTH)

• Initially, all nodes are labeled as asleep
• For each traffic demand (si, ti,ri)
• Find the shortest path from si to ti under edge
  cost H(u,v, ri)
• Label all nodes on the found path as active
• When a node is asleep, H includes both node cost
  Pidle and edge cost riCu,v
• When a node is active (on an existing path), H
  encourages path reuse by removing node cost Pidle

12
Illustration of ISTH

• Edge cost Cu,v2, node cost Pidle1
• Find a new path in each iteration

1
2
cost reduction!
2
1
1
1
0
2
2
New cost
2
1
1
1 2 0.2 2 22.8
2
1
2
2
Source 2 r2 0.2
1
1 0.2 2 21.8
0
1
Source 1 r1 0.2
1 2 0.2 2 22.8
13
Minimum Power Configuration Protocol (MPCP)

• Extends the DSDV protocol with the routing metric
  H(u,v,ri)
• Routing cost is dependent on data rate and state
  of the node (asleep or active)
• Uses different transmission power to different
  neighbors
• Activates a node if on a route, schedules to low
  duty cycle otherwise

14
Simulation Environment

• Prowler simulator extended by Rmase project
• Prowler http//www.isis.vanderbilt.edu/projects/n
  est/prowler/
• Rmase http//www2.parc.com/spl/projects/era/nest/
  Rmase/
• Implemented USC model Zuniga et al. 04 to
  simulate lossy links of Mica2 motes
• Baseline protocols
• MT Extended DSDV that minimizes num of Txs
• MTP Extended DSDV that minimizes Tx Power
• Data rate per flow 0.3 Kbps, 100 nodes

15
Energy Cost
Energy Cost of All Nodes (J)
Energy Cost of Non-Source Nodes (J)
Energy cost of all nodes MPCP saves as much as
30 energy
Energy cost of non-source nodes MPCP saves as
much as 80 energy
16
A Unified Power Management Framework

• An analytical model for minimizing total energy
  consumption in all radio states
• A new power management protocol that integrates
  sleep scheduling with power control
• A system architecture for flexible power
  management

17
Architectural Issues

• Sleep scheduling is coupled with the MAC layer
• Difficult to implement new sleep schedulers
• Sleep scheduling evolution dependent on MAC
• Potential conflicts from different upper-layer
  protocols
• How to coordinate two power management protocols?

Protocol 0
Protocol 1
Protocol 2
Protocol 3
Protocol 4
S-MAC
802.15.4
B-MAC
Sync Sleep Scheduling
TDMA Scheduling
Low Power Listening
PHY
Sleep Scheduling in TinyOS
18
Architectural Requirements of Power Management

• Flexibility
• Power management protocols should be independent
  of other system layers (e.g., app, routing, MAC)
• User can specify desirable power management
  policies
• Composibility
• Combine different power management protocols for
  different applications
• Cross-layer optimization

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Unified Power Management Architecture (UPMA)

• Power management abstraction
• Allow each user to specify desirable power
  management polices
• Power manager
• Aggregate multiple users parameters to a same
  policy
• Coordinate the use of multiple policies
• Interfaces between power management and other
  layers (e.g., MAC, routing)

20
UPMA -- Sleep Scheduling
interfaces of sleep schedulers
Protocol 2
Protocol 1
Protocol 3
Protocol 0

RadioDutyCycling
LowPowerListening
Other Interface

parameters specified by upper-level protocols
OnTime
Mode
Param 0
OffTime
Preamble
Param 1
DutyCycling Table
LPL Table
Other Table
Power Management Abstraction
aggregate parameters in the tables
Power Manager
Aggregator
sleep scheduling protocols

Low Power Listening
Others
Basic Sleep Scheduler
MAC
PreambleLength
ChannelMonitor
On/Off
interfaces with MAC
PHY
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Case Study Duty Cycle Backbone

• Duty cycling application
• turn on radio for 2s to report current
  temperature in every 10s
• PEAS Ye et al. 03
• One active node in any 10-meter range, other
  nodes run in power-saving mode (turn on radio for
  1s in every 25s)
• After aggregation
• In any 10-meter range, only one node reports
  temperature every 10s, other nodes turn on radio
  for 1s in every 25s

application duty cycle

PEAS low duty cycle
PEAS low duty cycle
combined duty cycle
22
Instantiation of UPMA
Duty Cycling App
PEAS
RadioDutyCycling
Aggregator if OnTime 8, combine (2,8) and
(1,24) else, run (1,24)
Power-saving / Active
Duty Cycling
OnTime
1 / 8
2
OffTime
8
24 / 0
Power Management Abstraction
Power Manager
Basic Sleep Scheduler
MAC
ChannelMonitor
On/Off
PHY
23
Implementation

• Implemented UPMA in TinyOS 2.0 for both Mica2 and
  Telosb motes
• Developed interfaces with different types of MAC
• CSMA based S-MAC Ye et al. 04, B-MAC Polastre
  et al. 04
• TDMA based TRAMA Rajendran et al. 05
• Hybrid 802.15.4, Z-MAC Rhee et al. 05
• Separated sleep scheduling modules from B-MAC
• Implemented two new sleep schedulers on top of
  B-MAC

24
Evaluation
PEAS only

• 15 Telosb motes run PEAS, and one of the 6 duty
  cycles
• Active nodes send a packet to base station during
  the on time
• Instrumented the radio stack of CC2420 to account
  the total time the radio stays in each state

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Research Summary

• An unified power management framework TOSN 06,
  MobiHoc 05, tech. report 06
• Spatiotemporal query service for mobile users in
  mostly sleeping sensor networks ICDCS 05, IPSN
  05
• Integrated power management under both sensing
  and communication constraints
• Fundamental relation between connectivity and
  sensing coverage TOSN 1(1), SenSys 03
• Impact of sensing coverage on routing performance
  TPDS 17(4), MobiHoc 04
• Detection-based data fusion IPSN 04
• nORB Light-weight real-time middleware for
  networked embedded systems RTAS 04

26
Conclusions

• A unified power management framework
• MPCP first protocol that jointly optimizes the
  total energy consumed in all radio states
• UPMA first unified power management architecture
  for wireless sensor networks
• Laid foundation for flexible and composable power
  management

27
Future Work

• Integrated power management
• Radio, CPU, flash, sensors
• Tools for configurable power management
• Power management in heterogeneous networks

28
Acknowledgements

• Washington University in St. Louis
• Advisor Chenyang Lu
• Collaborators Robert Pless, Gruia-Catalin Roman,
  Sangeeta Bhattacharya, Octav Chipara, Chien-Liang
  Fok, Kevin Klues
• Palo Alto Research Center (PARC)
• Ying Zhang, Qingfeng Huang

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Overhead

• Most MPCP route updates are local ? overhead
  remains roughly constant as num of flows grows

30
Minimum Steiner Tree Heuristic (MSTH)

• The algorithm
• Assign the cost of each edge to be Pidle
• Run a distributed minimum Steiner tree
  approximate algorithm
• Properties
• The approximate ratio is about
  ( 5 on mica2 motes)
• Good performance when Pidle is high

31
Greedy Prefetching
Uninvolved nodes
Collector node
Active nodes
Alerted nodes

• Forward a prefetch msg ASAP
• Many query areas are set up simultaneously
• High network contention storage cost
• Prediction to pickup points far away is likely
  wrong

32
Just-in-time (JIT) Prefetching
Uninvolved nodes
Collector node
Active nodes
Alerted nodes

• Forward a prefetch msg at the right time
• Only a few query areas are set up simultaneously
• Reduced network contention storage cost
• More robust to user motion changes
• Implemented on Mica2 motes, demoed at SenSys 04

33
Performance of Centralized Algorithms

• TMST Min spanning tree Li et al. 2003
• TSPT Shortest path tree singh et al. 1998
• MBA the Matching Based Algorithm Meyerson et
  al. 00
• MBA-opt MBA with our optimization

• 200 nodes distributed in a 500m X 500m region
• Each data flow has a rate of 0.2 Kbits/s
• Radio uses Mica2 mote setting

34
Evaluation IIAggregation Performance
Up to 6 duty cycles On time200ms Off
time0.2s, 0.6s, 1.4s, 3s, 6s, 12.6s Each slave
node runs one of the duty cycles, the master node
runs the aggregate duty cycle
Up to 6 duty cycles On time200ms Off
time0.2s, 0.6s, 1.4s, 3s, 6s, 12.6s Each slave
node runs one of the duty cycles, the master node
runs the aggregate duty cycle
35
Properties of ISTH

• Distributed implementation is easy
• Known approx. ratio is k, num of sources
• Performance for special cases
• Approx. ratio is 2 when r 0
• Suggest good performance with low data rates
• Optimal when Pidle0

36
Delivery Rate and Delay

• MPCP/MASP cause slightly higher network
  contention due to more path reuse

37
Use Case I Duty Cycle Aggregation

• Applications specify different duty cycles
  through the abstraction component
• Power manager aggregates different duty cycles
• Power manager runs the Basic Sleep Scheduler to
  turn on/off radios

Duty Cycle 0
Duty Cycle 1
Aggregate Duty Cycle
38
nORB Critical Path
39
Critical Path Issue Discovered
GIOP Header
A
A
B
A
B
A
GIOP Payload
A
A
A
A
B
B
A
A
A
40
Critical Path Issue Discovered Contd.
41
nORB Critical Path Optimizations

• Client ORB
• Use gather-write pattern to send header payload
  at the same time
• Server ORB
• Issue one non-blocking read with large buffer
  size(2K)
• Check the integrity of request after read returns
• If the request is complete, dispatch it to the
  servant.
• If the request is not complete, return to the
  reactor
• If gets multiple requests, split and queue them
  for later dispatching

42
nORB Critical Path Optimization - Effect
Average latency reduction is 50
43
Existing Approaches of Radio Power Management

• Sleep scheduling
• Duty cycling
• Nodes run in duty cycles (interleaving active and
  sleeping intervals)
• Backbone maintenance
• Keep a subset of nodes active and schedule others
  to sleep
• Transmission power control
• Topology control reduce per node power
• Power aware routing reduce per packet power
• Sleep scheduling in TinyOS
• MAC protocols S-MAC Ye et al. 04, B-MAC
  Polastre et al. 04

44
Problems with Existing Solutions

• Only reduce partial radio energy
• Sleep scheduling only reduces idle energy
• Power control only reduces TX energy
• Lack of flexibility and composibility
• Tight coupling with other system functions
• Sleep scheduling is often part of MAC
• Power control is often part of routing
• Difficult to replace existing or implement new
  power management protocols
• Different protocols cannot work together
  efficiently

45
Greedy Geographic Routing in Sensing-covered
Networks

• Greedy forwarding
• Chooses as the next hop the neighbor closest to
  destination
• Always succeeds in sensing-covered networks
• Bounded Voronoi Greedy Forwarding (BVGF)
• Combines greedy forwarding with Voronoi diagram
• Always finds routing paths with bounded lengths

destination
Rc
B
A
Closest to destination
46
Topology Control for Wireless Sensor Networks

• Wireless links are inherently lossy
• Excessive packet loss and energy waste
• 50-80 transmission energy was wasted by packet
  retransmissions zhao et al. 03
• When network workload is low
• Link failures are mostly due to path fading and
  environmental noise
• Higher transmission power leads to higher link
  quality
• Existing topology control algorithms do not
  account for lossy links
• Achieve required topology quality using minimum
  transmission power