comparison of data-driven link estimation methods in low-power wireless networks
DESCRIPTION
Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks. Hongwei Zhang Lifeng Sang Anish Arora. From sensor networks to cyber-physical systems (CPS). Sensing, networking, and computing tightly coupled with the physical world Automotive - PowerPoint PPT PresentationTRANSCRIPT
Comparison of Data-driven Link Estimation
Methods in Low-power Wireless Networks
Hongwei Zhang Lifeng Sang Anish Arora
From sensor networks to cyber-physical systems (CPS)
Sensing, networking, and computing
tightly coupled with the physical world
Automotive
Alternative energy grid
Industrial monitoring and control
Wireless networks as carriers of
mission-critical sensing and control
information
Stringent requirements on predictable
QoS such as reliability and latency
Dynamic wireless links
Link estimation becomes a basic element of routing in wireless networks.
0 2 4 6 8 10 12 140
20
40
60
80
100
distance (meter)
pack
et d
eliv
ery
rate
(%
)
0 50 100 150 200 250 30075
80
85
90
95
100
time seriespa
cket
del
iver
y ra
te (
%)
5.5 meters
(2 secs)
transitional region (unstable & unreliable)
Why not beacon-based link estimation?
Sampling error due to traffic-induced interference
Unicast ETX in different traffic/interference scenarios
Sampling error due to temporal link correlation
2 4 6 8 10 12
-80
-60
-40
-20
0
distance (meter)
diffe
renc
e in
del
iver
y ra
te (
%)
d = 0d = 0.01d = 0.04d = 0.07d = 0.1d = 0.4d = 0.7d = 1
mean reliability of each unicast-
physical-transmission minus that
of broadcast
Errors in estimating unicast ETX via broadcast
reliability: estimated unicast ETX minus actual
unicast ETX and then divided by actual
unicast ETX
Data-driven link estimation
Unicast MAC feedback
{NTi}: # of physical transmissions for the i-th unicast
As a simple, low cost mechanism to address the
sampling errors of beacon-based link estimation
Two representative methods for estimating ETX
L-NT uses aggregate unicast feedback {NTi}
represents SPEED, LOF, CARP
L-ETX uses derived information for individual unicast-physical-
transmission
represents four-bit-estimation, EAR, NADV, MintRoute
EWMA{NTi} ETX
PDRcalculation
{NTi} {PDRj}EWMA
PDR1/PDR
ETX
Won’t L-NT and L-ETX behave the same?
Accuracy of EWMA estimators
Given {xi: i = 1, 2, …} where xi is a random variable with
mean and variance 2, the EWMA estimator for is
Degree of estimation error (DEk) for using estimator
,...3,2 ,10 ,)1(1
11
kxyy
xy
kkk
ky
COV[xi
]
DEk is approximately proportional to COV[xi].
1
12])[( 122
,...,1
k
kxx
k
yEDE k
Relative accuracy in L-NT and L-ETX
where P0 is the failure probability of a unicast-physical-
transmission, and W is the window size for calculating PDRj;
COV[NTi] > COV[PDRj] if (which generally holds), thus
DEk(L-NT) > DEk(PDR)
)1(
PDRCOV ,NTCOV0
0
j0i PW
PP
L-ETX tends to be more accurate than L-NT in estimating link ETX.
DEk(L-ETX)
01
1
PW
Can we experimentally verify the analytical
results?
Testbed based link-level experimentation
We use Mica2 motes that are deployed in a 147 grid
Focus on links of the middle row
Interferers randomly distributed in the rest 6 rows, with 7 motes on each row
on average; interfering traffic is controlled by the probability d of generating
a packet at an arbitrary time
L-NT vs. L-ETX: when d = 0.1
Estimated ETX values in L-NT and L-ETX for a link 9.15 meters (i.e., 30
feet) long
2 4 6 8 10 120
0.5
1
1.5
distance (meter)
CO
V
L-NTPDR
COV[NTi] vs. COV[PDRj]
Variants of L-NT and L-ETX
Variant/stabilized L-NT: L-WNT
L-NADV (variant of L-ETX): estimate PER instead of PDR
L-NT vs. L-ETX: forwarders used
Method Forwarder Percentage(%)
Cost ratio
L-NT
5
6
7
8
10
0.1
4.14
7.17
21.26
67.33
2.3
1.3
1.5
1.3
1
L-ETX
6
7
8
10
5.91
0.2
5.1
88.79
1.3
1.5
1.3
1
Implications for routing behaviors?
Testbed based routing experiments
Convergecast routing in a 77 grid A node at one corner as the sink
Other 48 nodes as sources generating packets based on the event traffic
trace from “A Line in the Sand”
sink
L-NT vs. L-ETX: routing performance
Event reliabilityNumber of transmissions
per packet received
L-NT L-WNT L-ETX L-NADV0
5
10
15
20
Num
Tx
Seemingly similar methods may differ significantly in routing
behaviors (e.g., stability, optimality, and energy efficiency)
L-NT vs. L-ETX: routing stability
Two consecutiveroutes (%)
L-NT L-WNT L-ETX L-NADV
Same 36.55 42 99.94 99.97
Diff. routes but
samehop count
17.08 11.18 0.03 0.03
Increased hop
count
23.96 24.19 0.03 0
Decreased hop
count
22.41 22.63 0 0
Other experimental results
Related data-driven protocols
L-ETX-geo, L-ETX
Periodic traffic, other event traffic load
Sparser network
Random network
Network throughput
Concluding remarks
Two seemingly methods L-ETX and L-NT differ
significantly in routing performance
Variability of parameters being estimated significantly
affects the reliability, stability, latency, and energy
efficiency of data-driven link estimation and routing
Future work
Other metrics (e.g., RT oriented)
Opportunistic routing and biased-link-sampling
Backup slides
Traffic pattern affects temporal link correlation
Autocorrelation tends to decrease, especially for smaller lags, as interference load
increases, partly due to increased randomization as a result of random traffic
0 20 40 60 80
0
0.2
0.4
0.6
0.8
h
(h)
d = 0d = 0.01d = 0.04d = 0.07d = 0.1d = 0.4d = 0.7d = 1
2 4 6 8 10 12
0
0.5
1
1.5
Link length (meter) (
4)
d = 0d = 0.01d = 0.04d = 0.07d = 0.1d = 0.4d = 0.7d = 1
Autocorrelation coefficient for a
link of length 9.15 meters (i.e.,
30 feet)
Autocorrelation coefficient for
lag 4
Beacon-based vs. data-driven routing
Event reliability Number of transmissions per packet received
ETX RNP L-ETX0
1
2
3
4
5
6
Num
Tx