Energy Efficient Data Collection In Distributed Sensor Environments

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Energy Efficient Data Collection In Distributed
Sensor Environments

• Qi Han, Sharad Mehrotra, Nalini
  Venkatasubramanian
• qhan, sharad, nalini _at_ics.uci.edu

QUASAR Project University of California,
Irvine School. of Information Computer Science
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Ubiquitous Sensor Environments

• Generational advances to computing infrastructure
• sensors will be everywhere
• Continuous monitoring and recording of physical
  world and its phenomena
• limitless possibilities
• New challenges
• limited bandwidth energy
• highly dynamic systems
• System architectures are due for an overhaul
• at all levels of the system networks, OS,
  middleware, databases, applications

Battlefield Monitoring
Sensor Networks
Earthquake Monitoring
Medical Condition Monitoring
Oceanographic current monitoring
Video Surveillance
Traffic Congestion Detection
Target Tracking
Intrusion Detection
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Quasar (Quality Aware Sensing Architecture)

• Hierarchical architecture
• data flows from producers to server to clients
  periodically
• queries flow the other way
• if client cache does not suffice
• query routed to appropriate server
• if server cache does not suffice
• access current data at producer
• this is a logical architecture
• producers could also be clients
• a server may be a base station or a (more)
  powerful sensor node
• servers might themselves be hierarchically
  organized
• the hierarchy might evolve over time

DATA FLOW
QUERY FLOW
client cache
server
server cache and archive
producer its cache
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Quasar Observations Approach

• Applications can tolerate errors in sensor data
• applications may not require exact answers
• small errors in location during tracking or error
  in answer to query result may be OK
• data cannot be precise due to measurement errors,
  transmission delays, etc.
• Communication is the dominant cost
• limited wireless bandwidth, source of major
  energy drain
• Quasar Approach
• exploit application error tolerance to reduce
  communication between producer and server and/or
  to conserve energy
• two approaches
• Minimize resource usage given quality constraints
  
• Maximize quality given resource constraints

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This Paper

• Explore data collection protocols for sensor
  environments that exploits the natural tradeoff
  between application quality and energy
  consumption at the sensors
• Consider a series of sensor models that
  progressively expose increasing number of power
  saving states
• For each of the sensor models considered, develop
  quality-aware data collection mechanisms that
  ensure quality requirements of the queries while
  minimizing the resource consumption

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Data Collection Framework
query Qm (Am,D)
query Q1 (A1,D)

source-initiated update
consumer-initiated request
?ili,ui
sensor si
consumer-initiated update
Imprecise data representation

• If query quality tolerance satisfied at server
• Answer query at the server
• Else
• Probe the sensor
• Sensor guaranteed to respond within a bounded
  time D

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Abstract Sensor States
radio mode radio mode sensor state
1-radio node 2-radio node
Tx on, Rx off Tx on, Rx on active (a)
Tx off, Rx on Tx off, Rx on listening (l)
Tx off, Rx off Tx off, Rx off sleeping (s)
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Problem Statement

• Objective minimize sensor energy consumption in
  the process of answering all queries
• Given user queries with varying accuracy
  constraints and latency bound
• Formally stated
• Issues
• How to maintain the precision range r for each
  sensor
• Larger r increases possibility of expensive
  probes
• Small r wastes communication due to
  source-initiated updates
• When to transition between sensor states
• Powering down might not be optimal if we have to
  power up immediately
• Powering down may increases query response time

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Our Approaches

• We solve the energy optimization problem by
  solving two sub-problems
• Optimize energy consumption by adjusting range
  size under the assumption that the state
  transition is fixed
• Optimize energy consumption by adapting sensor
  states while assuming that the precision range
  for sensor is fixed
• Progressively expose increasing number of sensor
  power saving states
• AA Always Active
• AL Active-Listening
• AS Active-Listening
• ALS Active-Listening-Sleeping

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The AL(Active-Listening) model
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Analysis of the AL Model
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Range Size Adjustment for the AA/AL Model

• Optimal range can be realized by maintaining the
  probability ratio
• Can be done at the sensor
• Assuming that ? is the ratio of
  consumer-initiated update probability to
  source-initiated update probability
• for source-initiated update
• with probability min?,1, set r r(1?)
• for consumer-initiated update
• with probability min1/?,1, set rr/(1
  ?)

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The AS Model (Active-Sleeping)
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The ALS Model (Active-Listening-Sleeping)
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Range Size Adjustment for the AS/ALS Model

• Not possible to express the ratio ? in terms of
  other parameters
• Need to monitor parameters such as K1, K2 etc.
• Sensor side
• Keep track of the number of state transitions of
  the last k updates
• Piggyback the probability of state transitions
  with the Kth update
• Server side
• Keep track of the number of sensor-initiated
  updates and probes of the last k updates
• Upon receiving the Kth update from the sensor
• Compute the optimal precision range r
• Inform the sensor about the new r

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Adaptive Sensor State Management

• Consider the AS model for derivation of optimal
  Ta to minimize energy consumption
• Assuming ?(t) is the probability of receiving a
  request at time instant t, the expected energy
  consumption for a single silent period is
• E is minimized when Ta0 if requests are
  uniformly distributed in interval 0, TaTs.
• In practice, learn ?(t) at runtime and select Ta
  adaptively
• Choose a window size w in advance
• Keep track of the last w silent period lengths
  and summarizes this information in a histogram
• Periodically use the histogram to generate a new
  Ta

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Adaptive State Management (Cont.)

• ci the number of silent periods for bin i among
  the last w silent periods
• estimate ? by the distribution which generates a
  silent period of length ti with probability ci/w
• Ta is chosen to be the value tm that minimizes
  the energy consumption as follows

c1
cn-1
c0
bin 1
bin n-1
c2
bin 0
bin 2
t0 t1 t2 t3
tn-1 tnTaTs
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Performance Study

• Modeling sensor
• Sensor values
• uniformly from the range -150, 150
• perform a random walk in one dimension every
  second, the values either increases or decreases
  by an amount sampled uniformly from 0.5,1.5.
• Modeling queries
• query arrival times at the server are Poisson
  distributed
• mean inter-arrival time 2 seconds.
• each query is accompanied by an accuracy
  constraint A
• Auniform( Aavg(1- Avar ), Aavg(1 Avar ))
• Aavg 20 (average accuracy constraint)
• Avar1 (accuracy constraint variation)

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System Performance Comparison of Proposed Sensor
Models
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Impact of Ta adaptation on System Performance
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Impact of Range Size Adaptation on System
Performance
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Conclusions

• Explored the tradeoff between sensor data
  accuracy and energy consumption for sensor data
  collection in distributed sensor environments
• Both theoretical analysis and experimental
  results validated the effectiveness of our
  approaches
• The AS model consumes the least amount of sensor
  energy
• Our proposed strategies of adaptive sensor state
  transition reduce energy consumption to a great
  extent
• Optimized range size adjustment works effectively
  with corresponding sensor models and saves more
  energy than using static range or instantaneous
  values