LEAP: Localized Energy

Aware
Prediction for Data Collection in
Wireless Sensor Network
Hongbo Jiang
Shudong Jin
Mobile Ad Hoc and Sensor Systems(MASS), 2008
Outline
Introduction
Model selection
Prediction model
Energy

efficient algorithm
Algorithm design
Experiment results
Conclusion
Introduction
Nowadays, sensors’ processing power and compacity have been
increased. Sophisticated algorithm on sensors like
predictor
become possible.
A predictor use past input values from sensors to perform
prediction, implys sensors do not need to transmit the data if
they differ from a predicted value by less than a threshold
Simple prediction way is that all sensor nodes send data to base
station and only
base station
do the predictor training and
prediction, despite their increasing computing capacity. But it
cost high transmission energy, wireless bandwidth and potential
high latency.
Introduction

localized prediction
Clustering

based localized prediction divide the network into
clusters and use a cluster

head(also a sensor node) to maintain
each cluster member’s history data. Localized prediction may
highly energy

efficient due to the reduction of routing path.
There is a trade off between communication and computation
since prediction computation cost is not negligible in localized
prediction.
Base
station
Localized Energy

Aware Prediction
(LEAP)
Localized Energy

Aware Prediction (LEAP)
Cluster head
–
receive data that selective reported by all
cluster members and perform local prediction
Cluster member
–
also perform prediction and transmit
to cluster head only if they are not within a specified
error bound
Prediction model selection
Autoregressive(AR) model
can be calculated by Yule Walker equation or least
square method
p
i
i
t
t
t
x
w
c
x
1
ˆ
Prediction model selection
Lemma
:
Given a linear predictor P, a m

step prediction error is less than
m times a one

step prediction error .
m

step prediction error is .
Given an error bound , the predictor can provide confidence level of
is cummulative distribution of Gaussion white noise
SDV
is the standard deviation
2
2
)]
(
[
m
m
e
VAR
1
)]
(
[
2
m
e
SDV
m
Energy

Efficient Algorithm Selection
Tradeoff
Communication cost: Without local prediction, all sensor
nodes will send original data values to the cluster head.
Computation cost: With local prediction.
Energy

Efficient Algorithm Selection
Theorem
:
If the error bound satisfies the scheme
with local prediction is more energy

efficient
The variance is unknown, use correlation coefficient to
represent m

step prediction error
Eliminate , we have
condition (1)
, if error tolerence and
correlation coefficient satisfy
the scheme with local prediction is more energy

efficient
k
k
m
2
1
j
x
2
2
1
2
2
)
)
(
1
(
)]
(
[
m
j
m
m
e
VAR
p
j
x
j
x
k
k
m
j
x
p
j
x
j
2
1
)
(
1
1
1
Algorithm Design

cluster head
Pseudo

code description
Cluster head will continuously receive data from cluster
members to update the set of history data, or when no data
values are received, will use the predicted value instead of
update.
Algorithm Design

cluster head
Periodic process to determine algorithm selection, with or without
local prediction.
Algorithm Design

cluster member
Each cluster member maintains a set of history data of its own.
Local prediction disable: transmit the data values
Local prediction enable: perform prediction on each data value, if
not within the error bound, still have to send the value to the cluster
head
Algorithm Design
–
sleep/wake
scheduling
For some applications may tolerate a few missing value not
within error bound. If confidence level(or having data values
within the error bound) is very high. There is no need for the
nodes to stay awake to obtain data values.
Algorithm Design
–
sleep/wake
scheduling
Disable local prediction as default. When cluster members is
awake, the cluster head checks if the member
'
s data values are
within the error bound with high probability. If yes, send a
message to power off the member.
Algorithm Design
–
sleep/wake
scheduling
The condition (2) should be the condition is higher
than ,i.e.
m
threshold
threshold
p
j
x
j
x
j
m
1
1
2
1
Algorithm Design
–
sleep/wake
scheduling
To remain accurate prediction, periodic but infrequent collection
from cluster member is still necessary.
Here use a heuristic solution: let be the time interval between
two consecutive report. We set a sleep duration of , when
a member wakes up, it will continuously perform data reading
(and possibly reporting) for the next time.
is initially set to , increased if condition (2) holds, or
decreased if it does not.
*
f
m
*
m
f
m
m
Experiment result
Data

set: Intel Berkeley Lab Data
54 nodes spread around the lab
Temperature data within one week
Nodes close to sink set as cluster heads
Nodes report data every 30 seconds
10mJ/Byte, 80mJ per message(assume the value is 8 bytes
double precision number)
13 cluster heads and 40 cluster members, only measure the
consumption of cluster members and LEAP with control of sleep,
set confidence level
9
.
0
threshold
Experiment result
k represents the ratio between transmission/prediction energy
consumption.
(i.e. k=10 and 100, prediction energy consumption is 8 and 0.8
mJ per value)
Conclusion
LEAP is a energy

aware data collection
approach
clustering

based: sensor nodes form clusters
and cluster head collect and maintain data
values
prediction

based: energy

aware prediction is
used to find the subtle tradeoff between
communication and prediction cost.
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