Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l. 6,
N
o
.
3
,
Ju
n
e
201
6,
p
p
.
9
8
6
~
994
I
S
SN
: 208
8-8
7
0
8
,
D
O
I
:
10.115
91
/ij
ece.v6
i
3.9
032
9
86
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
A Thorough Insight to Techniques for Performance Evaluation
in Biological Sensors
Subh
as Me
ti
1
, V.
G.
S
a
n
g
am
2
1
Dept of
Instrumentation
Technolog
y
,
B V
B Co
llege of
Eng
eenin
g &
Technolog
y
Hubli, Ind
i
a
2
Dept of
Electro
nics and
Instrumentation
,
Day
a
n
a
nd Sagar
College of
Engin
eerin
g Bangaluru, Ind
i
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Sep 15, 2015
Rev
i
sed
Feb 9, 20
16
Accepted
Feb 25, 2016
The bio
l
ogical sensor has play
ed a signi
f
i
cant
and contribu
tor
y
role in
th
e
area of m
e
dical
science and heal
thcar
e industr
y
.
Owing to critical heal
thcar
e
usage, it is ess
e
ntial th
at such
ty
p
e
of sensor
s should be hig
h
ly
robust,
sustainabl
e under the adverse co
ndition and high
l
y
f
a
ult to
leran
t
against
an
y
forms of possible s
y
s
t
em failur
e
in
future. A
massive amount of research
work has been d
one in the area of th
e sensor network. However, works done
in biological sensors are quite less
in number. Hence, this
manuscript
highlights
all the significant
r
e
search work
towar
d
s the line of
dis
c
ussion for
evalu
a
ting
the
effec
tive
in
the
te
chniques
for
perform
anc
e
e
v
alua
tion of
biological sensor. The stud
y
fin
a
lly
e
xplor
es the problems and
discusses it
under resear
ch gap. Finally
, th
e manuscr
ipt gives highlights of the futur
e
direction of
the
work to solve th
e research
gap
explored from th
e proposed
review of
th
e
exi
s
ting s
y
s
t
em
.
Keyword:
B
i
ol
ogi
cal
se
ns
or
Fau
lt to
leran
c
e
Perform
a
nce evaluation
Valid
atio
n
Copyright ©
201
6 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Su
bhas
M
e
t
i
,
Dept
o
f
I
n
st
r
u
m
e
nt
at
i
on Tec
h
n
o
l
o
gy
,
B
V B
C
o
l
l
e
ge
o
f
E
ngee
n
i
n
g
& Tec
h
n
o
l
o
gy
Hu
bl
i
,
In
di
a.
Em
ail: subhas
.
m
e
ti@g
m
ail.com
1.
INTRODUCTION
B
i
ol
ogi
cal
a
n
d
bi
oc
hem
i
cal
proces
ses
pl
ay
a very
i
m
port
a
n
t
rol
e
i
n
t
h
e
fi
el
d o
f
m
e
di
ci
ne, bi
ol
ogi
cal
scien
ces, an
d
b
i
o
t
echno
log
y
. As it is v
e
ry
d
i
fficu
lt to
conv
ert th
e
b
i
o
l
o
g
ical d
a
ta in
to
electrical sig
n
a
ls th
us
biose
n
sors ha
s bee
n
int
r
oduc
e
d
for
ove
rc
oming that
difficulties. In t
h
e
recent yea
r
s
various techni
ques a
n
d
devi
ces i
n
c
r
eas
ed t
h
e usa
g
e o
f
bi
ose
n
s
o
rs
. I
n
t
h
e y
ear of
1
9
6
2
C
l
ark a
nd
Ly
ons
desi
g
n
e
d
t
h
e fi
rst
bi
o-
sens
or
[1
] wh
ere th
ey i
mmo
b
ilize
d
th
e g
l
u
c
o
s
e o
x
i
d
a
se
(GOD) on
an
aero
m
etric o
x
y
g
e
n
electrod
e
su
rface
sem
i
perm
eabl
e
di
al
y
s
i
s
m
e
mbra
ne i
n
or
der
t
o
exam
ine a gluc
ose conc
entrated sam
p
le. The
proces
s of
m
a
ki
ng el
ect
r
o
chem
i
cal
sensors
(
p
H
,
p
o
l
a
r
o
g
r
a
phi
c,
p
o
t
e
nt
i
o
m
e
t
r
i
c
or con
d
u
ct
om
et
ri
c) hav
e
bee
n
di
s
c
usse
d
by
t
h
ese t
w
o a
u
t
h
ors
w
h
ere t
h
ey
ha
ve a
dde
d t
h
at
"E
nz
yme trans
d
ucers a
s
m
e
m
b
rane enclose
d
sa
ndwiches"
.
Acco
r
d
i
n
g t
o
t
h
e
defi
ni
t
i
on
g
i
ven
by
I
U
P
A
C
“A
bi
o
sens
or i
s
a sel
f
-co
n
t
a
i
n
ed
i
n
t
e
gra
t
ed de
vi
ce
whi
c
h i
s
cap
ab
le of prov
id
ing
qu
an
titi
es an
d
an
alytical in
fo
rm
atio
n
"
. Th
e Fig
1
in
b
ackgroun
d
sectio
n
d
i
scu
sses ab
ou
t
vari
ous c
o
m
p
o
n
ent
s
of
a bi
o
sensi
n
g
devi
ce
whi
c
h i
s
m
a
i
n
l
y
a col
l
ect
i
on
of
(a) a
bi
o cat
al
y
s
t
whi
c
h c
o
nve
rt
s
the substrate or analyte to
a product. (b) The trans
duc
er use
d
to
determ
ine the ch
e
m
ical and bi
ological
reactio
n
b
e
tween
lig
an
d
and an
alyte an
d co
nv
erts it in
to
an electrical signal.
The
fi
nal output is
passe
d
thr
o
u
g
h
an (c)
am
plifier (d)
pr
ocess
o
r a
nd
finally
(e) disp
l
a
y
e
d on t
h
e s
c
reen [
2
]
.
It
is found that at
prese
n
t
t
h
e co
nce
p
t
of
bi
o se
ns
ors
o
b
t
a
i
n
ed
hu
ge at
t
e
nt
i
on
o
f
m
a
ny
researc
h
er
s i
n
di
ffe
re
nt
areas
of a
p
pl
i
cat
i
on
whi
c
h
include t
h
e bi
o m
e
dical industries,
bio tec
h
nology,
pha
rm
aceutical and e
nvironm
ental applications
and t
h
e
d
i
agn
o
stic for
h
ealth
related
p
u
rp
o
s
es. Th
e
fun
c
tion
a
lity
o
f
a b
i
o
s
en
sor i
n
clud
es th
at it
is a d
e
v
i
ce
which
is
use
d
fo
r o
b
se
r
v
i
n
g, chec
ki
ng
and kee
p
i
n
g
a cont
i
n
uo
us t
r
ack
of bi
o m
o
l
ecul
a
r i
n
t
e
ra
ct
i
ons i
n
real
t
i
m
e
scenari
o
. In a bio sens
or st
ruct
ur
e one of the com
pone
nts whic
h is k
nown as '
ligand'
or the '
r
eceptor'
im
m
obi
l
i
zed on t
h
e se
nso
r
c
h
i
p
,
on t
h
e o
t
her ha
n
d
t
h
e
com
pone
nt
of
t
h
e sol
u
t
i
o
n
w
h
i
c
h
bi
n
d
s wi
t
h
t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A T
hor
o
u
g
h
I
n
si
ght
t
o
Tec
h
ni
que
s f
o
r
Perf
o
r
ma
nce
Eval
uat
i
on i
n
Bi
ol
o
g
i
c
al
Se
ns
ors
(
S
u
b
h
a
s
Met
i
)
9
87
im
m
obi
l
i
zed l
i
gan
d
or
rece
pt
or i
s
us
ual
l
y
cal
l
e
d as t
h
e
a
n
al
yte. The a
n
alyte pre
s
ents
i
n
the so
lu
ti
o
n
ph
ase. As
th
e fu
n
c
ti
o
n
a
lity o
f
t
h
e an
alyte d
e
fi
n
e
s t
h
at it b
i
nd
s to
th
e i
mmo
b
ilized
lig
an
d and
Bio-recep
tor. Bi
o
-
recep
tor
and t
r
a
n
s
duce
r
are t
h
e
m
o
st
im
port
a
nt
co
m
ponent
s o
f
bi
ose
n
s
o
r (
F
i
g
ure
1). R
e
sea
r
chg m
e
t
hod
ol
ogy
i
s
di
scuss
e
d i
n
S
ect
i
on-
2. T
h
e
R
e
search
h an
d
di
scussi
on
s
i
s
di
scusse
d i
n
Sect
i
o
n
-
3
.
A
n
d Sect
i
o
n-
4 co
ncl
u
des
t
h
e
pa
pe
r wi
t
h
bri
e
f
di
sc
ussi
o
n
of
f
u
t
u
re di
re
ct
i
on of
resea
r
ch.
Fi
gu
re
1.
C
o
m
p
o
n
e
n
t
s
of B
i
o
l
ogi
cal
Se
ns
or
1.
1.
Back
ground
Th
ere are two
d
i
fferen
t
typ
e
s o
f
step
s associated
with
th
e b
i
o-sensor activ
ities wh
ich
are d
i
scu
ssed
as fo
llo
ws.
1.
2.
Recogni
t
ion Step
In t
h
e rec
o
gni
t
i
on st
e
p
, t
h
e i
m
m
obi
l
i
zed bi
ol
o
g
i
cal
ele
m
e
n
t that is re
ferred as '
L
igand'
can rec
o
gniz
e
or detect the Analyte, which
can be
presen
t eith
er in
th
e so
lu
tion
or th
e at
m
o
sp
h
e
re. Immo
b
ilized
el
e
m
en
ts
can be
proteins, antibodies, receptors
, a
nd
enzym
e
s, etc.
The a
n
alytes whic
h bi
nd t
o
these ligands
can be
antigens, dra
g
m
o
lecules,
protein substrates, etc.
1.
3.
Transd
ucing Step
Analyte-rec
e
pt
or binding on
the biose
n
s
o
r
c
h
ip
s
u
rface
ge
nerates a
signa
l
that can
be measure
d
a
nd
analyzed. The
rece
ptor is
designe
d
whe
r
e it has
a cl
ose c
o
ntact with the t
r
ans
d
ucing elem
ent. T
h
is
transduci
ng el
e
m
ent convert
s
the an
alyte-receptor bindi
n
g eve
n
t into a
qua
ntitative optical or elec
trical
si
gnal
.
Th
e
ge
nerat
e
d si
gnal
can
be ei
t
h
e
r
a
.
a c
h
a
nge
i
n
t
h
e
reso
na
nce
uni
t
,
b.
A
c
h
a
nge
i
n
t
h
e
U
V
o
r
IR
abs
o
rption c. C
h
ange i
n
Mass
d. C
h
a
nge
in el
ectrical propert
i
es.
Th
e in
tensity t
h
at is p
r
esen
t in
th
e b
i
o
s
ensor g
e
n
e
rated
sign
al foun
d
to
be in
v
e
rsely p
r
op
ortion
a
l to
t
h
e co
nce
n
t
r
at
i
o
n
o
f
t
h
e a
n
al
y
t
e. The m
a
i
n
com
pone
nt
s f
o
r
desi
gni
ng a
n
d
de
vel
o
pi
n
g
bi
ose
n
s
o
r
s
ar
e a
n
electrochem
ica
l
transdu
cer.
Low cost
s
,
sim
p
l
e
design
or sm
all dim
e
nsi
ons
can be achi
e
ved
by
electrochem
ica
l
transducers
.
The desi
gn c
o
ncept
o
f
t
h
e bi
ose
n
so
rs al
so
ba
se
d on gra
v
i
m
etric, calorimetric or
optical detecti
o
n techniques. It is
found t
h
at the
biose
n
sors
are
cla
ssified acc
ording to the t
r
ansducing
ele
m
ents as well as electroc
h
em
ical, optical, piez
oelect
ric and t
h
erm
a
l sens
ors
.
Electrochem
ical bios
ens
o
rs
al
so can
be c
a
t
e
go
ri
zed
pot
ent
i
o
m
e
t
r
i
c
, am
perom
e
t
r
i
c
and c
o
nd
uct
o
m
e
t
r
i
c
sens
ors
.
There a
r
e
var
i
ous
application areas of
biosensors t
h
at are
a clinic,
di
ag
nost
i
c
, m
e
di
cal
appl
i
cat
i
ons
,
pr
ocess c
o
nt
r
o
l
,
bi
o
reactors, quality
cont
rols,
de
fense researc
h
, and
de
velopm
e
n
t,
etc. A few adva
ntage
s
of biose
n
sors
are listed
bel
o
w:
They ca
n be
a
pplicable
for
measur
em
ent of
nonpolar m
o
lecules t
h
at do
not
repl
y
t
o
m
o
st
m
easure
m
ent
devi
ces
.
Biosens
o
rs are
specific as
a
va
rious imm
ob
ilized system
are use
d
for
designing t
h
em
.
Qui
c
k a
n
d
en
o
r
m
ous co
nt
r
o
l
i
s
p
o
ssi
bl
e
wi
t
h
bi
o
s
ens
o
rs.
Resp
on
se ti
m
e
is sho
r
t
(typ
ically less th
an
a
min
u
t
e) as
wel
l
as
Pract
i
cal
im
pl
em
ent
a
t
i
on ca
n
be
do
ne.
Vari
ous a
r
eas
of a
ppl
i
cat
i
o
ns t
h
at
i
n
cl
u
d
e t
h
e
bi
o
-
l
o
gi
cal
sens
or
depl
oy
m
e
nt
s
are m
e
di
cal
appl
i
cat
i
o
ns, r
a
pi
d an
d
acc
u
r
at
e
det
e
rm
i
n
at
i
on of
chem
i
cal and
biologi
cal age
n
ts
t
h
at
co
ul
d
be
us
ef
ul
f
o
r
n
a
tio
n
a
l secu
ri
ty, etc. In
b
o
t
h
ch
em
ical an
d
n
a
tion
a
l secu
rities, v
a
riou
s ap
p
licatio
n
s
are u
s
ed
fo
r
d
e
tecting
vi
r
u
ses an
d
pat
h
o
g
e
n
s i
n
di
l
u
t
e
conce
n
t
r
at
i
o
ns as t
h
e
pat
h
o
g
en
s co
ul
d
be
m
o
re l
i
f
e-t
h
rea
t
eni
ng i
n
co
nt
r
a
st
t
o
secu
rity app
licatio
n
s
rath
er than
m
e
dical applications. T
h
e identification
of
exotic and lethal diseases m
o
stly
associated with
the biologi
cal weapon progra
m
s
[3].
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
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088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
98
6 – 9
9
4
98
8
1.
4.
The Problem
Thi
s
sect
i
o
n di
scusses
vari
ou
s i
ssues rel
a
t
e
d
t
o
wi
rel
e
ss c
o
m
m
uni
cat
i
on o
f
bi
ol
o
g
i
cal
senso
r
s
whi
c
h
are u
n
i
q
uel
y
and
f
u
n
d
am
ent
a
l
l
y
di
ssim
i
l
a
r f
r
om
ot
her
sens
or
net
w
or
ks a
ppl
i
cat
i
o
n
s
. The
r
e a
r
e
vari
o
u
s
chal
l
e
ng
es a
r
i
s
es i
n
t
h
e
fi
el
d
o
f
bi
ol
o
g
i
cal
sens
or
net
w
o
r
k
whi
c
h m
a
kes wi
rel
e
ss
n
e
t
w
o
r
ki
n
g
si
g
n
i
f
i
cant
l
y
di
ffe
re
nt
f
r
om
ot
he
r
net
w
or
ks
. T
h
e f
o
l
l
o
wi
n
g
a
r
e t
h
e
resea
r
ch i
s
s
u
es
of
B
i
ose
n
so
r
Net
w
o
r
ks
.
Lo
w Po
wer:
As i
t
has bee
n
o
b
ser
v
e
d
i
n
t
h
e st
udy
of
[4]
t
h
at
wi
rel
e
ss sens
or net
w
o
r
k
whet
her
i
t
i
s
bi
om
edi
cal
or
ot
he
rwi
s
e
hav
e
ener
gy
co
ns
um
pt
i
on i
ssue
s
. As t
h
e bi
o
m
edi
cal
senso
r
s are al
s
o
b
a
t
t
e
ry
dri
v
en
de
vices, thus they also have lim
ite
d
power
sou
r
ce. Th
is issu
e requ
ires seri
o
u
s
atten
tio
n
for
enha
nci
n
g su
p
p
l
y
i
n
wi
rel
e
ss bi
ol
o
g
i
cal
no
d
e
s.
Li
mi
t
e
d C
o
mp
ut
at
i
o
n:
C
o
m
put
at
i
on ca
paci
t
y
i
n
bi
o sens
o
r
s has b
ecom
e
very
l
i
m
i
t
e
d and l
e
ss ef
fect
i
v
e
due t
o
ve
ry
l
e
ss am
ount
of
p
o
we
r s
u
p
p
l
y
. I
t
i
s
sai
d
by
[5]
t
h
e am
ount
o
f
com
put
at
i
on
whi
c
h i
s
p
o
ssi
bl
e
with
th
e use
o
f
b
i
osen
sors is sig
n
i
fi
can
tly less th
an
trad
itio
nal sen
s
o
r
s.
Mat
e
ri
al
C
o
ns
t
r
ai
nt
s:
As
biomedical sensors are im
plante
d in
th
e
hu
m
a
n
bod
y so
th
e size, sh
ap
e and
p
r
op
erties o
f
a material
sh
ou
ld
b
e
ex
am
in
ed
p
r
op
erly
[6
].
C
ont
i
n
u
ous O
p
er
at
i
o
n
:
As
bio
s
en
sors are
d
e
sign
ed
to
h
a
v
e
th
e po
ten
tial to
o
p
e
rate o
n
lim
i
t
ed
b
a
ttery
po
we
r, s
o
m
a
ny
resea
r
c
h
ers
p
u
t their
f
u
ll
eff
o
rt
fo
r e
x
t
e
ndi
ng
t
h
e
bat
t
ery
l
i
f
e
of
a
b
i
osens
o
r f
o
r t
h
ei
r
continuous ope
r
ation
on the
hum
a
n body.
As
it is a ve
ry low
power de
vic
e
so t
h
at
the freque
nt placement
and
ad
j
u
st
m
e
nt o
f
t
h
i
s
de
vi
ce
can ca
use s
o
m
e
ri
sk
an
d
f
unc
t
i
onal
di
s
o
r
d
e
r
s i
n
h
u
m
a
n or
g
a
ns
[7]
.
Rob
u
s
t
n
ess and
Fau
lt To
leran
ce:
Th
e study o
f
[
8
] says th
at th
e b
i
o
l
o
g
i
cal sen
s
or
s ar
e ex
p
ected
to
be
ro
b
u
st
i
n
nat
u
r
e
as i
t
i
s
not
possi
bl
e t
o
su
rg
i
cal
l
y
adju
st
a
bi
ose
n
s
o
r eve
r
y
week. O
n
t
h
e ot
her ha
n
d
fa
ul
t
,
tolerance ca
pa
bility should
be prese
n
t in every node
of the
biose
n
sor net
w
ork t
hus m
a
lfunctioning
of
one
in
d
i
v
i
d
u
a
l
node shou
ld
no
t d
i
stu
r
b
th
e who
l
e syste
m
o
r
n
e
t
w
or
k.
S
c
a
l
a
b
ility:
It i
s
no
t clear th
at
a sen
s
o
r
or b
i
o
sen
s
or
n
e
twork
will b
e
ab
le to
h
a
nd
le a gro
w
i
n
g
am
o
u
n
t
o
f
wo
rk i
n
a di
st
r
i
but
ed m
a
nner
or n
o
t
as i
t
i
s
al
so not
f
o
un
d aft
e
r s
o
m
a
ny
i
nvest
i
g
at
i
o
n
s
t
h
at
ho
w m
a
n
y
sens
ors
are
nee
d
ed
t
o
be
pl
ace
d t
o
m
a
ke a w
h
ol
e sy
st
em
fun
c
t
i
onal
[
9
]
.
Security and Interference
:
Security and i
n
terfe
rence
issue
s
are c
o
ns
id
ered
as m
o
st im
p
o
rtan
t issu
es of the
sens
or
net
w
or
k
as sens
or
net
w
o
r
k i
s
i
n
f
r
ast
r
uct
u
re
l
e
ss a
n
d us
e A
d
-
h
oc
m
echani
s
m
for
com
m
uni
cat
i
on so
t
h
at
cur
r
ent
l
y
m
a
ny
researc
h
ers are i
nvest
i
g
at
i
ng w
h
i
c
h t
o
pol
ogy
m
i
ght
be sui
t
a
bl
e an
d
m
o
st
effect
i
v
e
fo
r
Bio
s
en
so
r N
e
t
w
or
ks [10
]
.
Regulatory
Re
quire
me
nts:
D
e
si
gn a
n
d sa
f
e
t
y
m
u
st
be
fu
n
d
am
ent
a
l
feat
ures
of
bi
o
s
ens
o
r
net
w
o
r
k
devel
opm
ent
.
As
bi
o se
ns
or
pr
ot
ot
y
p
e
de
vi
ces are i
m
pl
ant
e
d i
n
a
n
o
r
ga
n cel
l
of t
h
e
h
u
m
a
n bo
dy
so
t
h
at
wi
rel
e
ss t
r
a
n
s
m
i
ssi
on o
f
dat
a
sh
oul
d
n
o
t
ha
r
m
t
h
e su
rr
o
u
n
d
i
ng t
i
s
s
u
es
[1
1]
.
The
st
u
d
y
o
f
[
12]
hi
g
h
l
i
ght
s vari
ous n
o
i
s
e pr
ocesses
a
n
d l
i
m
i
t
s
on
t
h
e per
f
o
r
m
a
nce
o
f
bi
osen
so
r
net
w
or
ks.
A
com
p
rehe
nsi
v
e
st
ochast
i
c
m
odel
has bee
n
p
r
o
p
o
se
d whi
c
h desc
ri
b
e
s t
h
e m
easurem
ent
unce
r
t
a
i
n
t
y
,
ou
t
put
si
gnal
,
an
d l
i
m
i
t
a
t
i
ons associ
at
ed
wi
t
h
d
e
t
ect
i
on t
ech
ni
que
t
h
at
i
s
bas
e
d
on
em
pat
h
y
base
d
bi
ose
n
s
o
rs
. It
i
s
al
so fo
un
d t
h
at
t
h
e bi
oc
he
m
i
cal event
s
wi
t
h
i
n
t
h
e bi
o sens
or pl
at
f
o
r
m
have been
d
e
si
gne
d
with
a Marko
v
sto
c
h
a
stic
p
r
ocess to
o
p
tim
iz
e th
e no
isy
sign
al tr
ansdu
c
tion
s
. Th
is ap
proach
has b
e
en
used
for
eval
uat
i
n
g t
h
e
out
put
si
g
n
al
and t
h
e
SNR
(Si
g
nal
t
o
N
o
i
s
e rat
i
o
),
noi
s
e
fi
gu
re, a
nd
det
ect
i
on
of
d
y
n
am
i
c
ran
g
e
fo
r a
ffi
ni
t
y
-based
bi
ose
n
so
rs
ha
ve
bee
n
c
r
eat
ed m
e
t
hodi
cal
l
y
.
Gene
rat
i
o
n of
t
a
rget
speci
f
i
c si
gnal
:
T
o
produce the
target specifi
c
sig
n
a
l, th
e
targ
et syste
m
a
tic
co
m
p
u
t
atio
n
a
l
an
alysis o
f
data o
r
statistic
s affect
s th
e reco
gn
itio
n
layer b
y
in
teractin
g
with
th
ro
ug
h
in
v
e
stig
ation
in
to
th
e crim
e
an
d
also
p
a
rt
icip
ates
in
tran
sdu
c
tion
pro
c
ess. Vari
o
u
s prob
ab
ilistic
mass
t
r
ans
f
er
p
r
oce
sses h
a
ve
bee
n
rai
s
ed t
o
d
o
m
i
nat
e
t
h
e anal
y
t
e m
o
t
i
on. It
has
bee
n
obs
er
ved
t
h
at
t
h
e
co
llisio
n
s
b
e
t
w
een
an
alyte
m
o
lecu
les an
d
p
r
ob
es ar
e
v
e
ry
m
u
ch
p
r
ob
ab
ilistic an
d
d
e
als with
n
u
m
erou
s
unce
r
t
a
i
n
t
i
e
s
t
o
t
h
e bi
o sensi
n
g pr
oce
d
u
r
es [
13]
.
Tra
n
s
ducer
an
d rea
d
o
u
t
C
i
rcui
t
r
y:
After an
alyzin
g
v
a
riou
s prob
ab
ilisti
c
m
a
ss tran
sfer p
r
o
cesses, it is
foun
d
th
at th
e tran
sdu
c
er an
d
r
eadou
t circu
itry ad
d
m
o
re no
ise in
th
e Prob
ab
ilistic
an
d
sto
c
h
a
sti
c
pr
ocesses
[
1
4]
.
Co
n
c
en
tra
tion
o
f
th
e non
sp
ecific An
a
l
yte:
If the c
o
ncent
r
ation
of the
no
n-specific a
n
alyte bec
o
m
e
s
m
u
c
h
higher tha
n
the target analyte,
no
n
-
speci
fi
c bi
ndi
ng
s m
a
y dom
i
n
at
e t
h
e
measured si
gnal and cause s
o
m
e
effects
wh
ich
l
i
m
i
t th
e m
i
n
i
mu
m
d
e
tectab
le li
m
i
ts (MDL)
of th
e b
i
o
s
ensor
p
l
atform
[1
5
]
.
Analyte Motion:
Mo
lecu
les
cells
m
a
n
y
o
t
h
e
r th
ing
s
th
at
are
conside
r
ed as analytes
can be
dipped
and
subm
erge
d i
n
t
h
e aq
ueo
u
s
m
e
di
um
s of b
i
o sens
ory
pl
at
form
s cause t
h
erm
a
l
fl
uct
u
at
i
ons.
In
ge
ne
ral
sens
ory platform
s are consi
d
ere
d
as elect
rom
a
gnetic
or
m
echanical forces
. It is observe
d
unde
r
the
micro
s
cop
e
th
at th
erm
a
l flu
c
tu
atio
n
o
f
a p
a
rticle lead
s to
fo
llo
w th
e ch
aracteristics o
f
typ
i
cal ran
d
o
m
walk
pr
ocesses
,
as
an e
x
am
pl
e, i
t
can
be
sai
d
t
h
at
B
r
ow
ni
a
n
m
o
ti
on i
s
t
h
e
effect
of
s
ubs
eque
nt
di
f
f
usi
v
e
sprea
d
ing
phe
nom
enon or occurrence i
n
a
m
i
croscopic
syste
m
. Statistical analys
is associated with t
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A T
hor
o
u
g
h
I
n
si
ght
t
o
Tec
h
ni
que
s f
o
r
Perf
o
r
ma
nce
Eval
uat
i
on i
n
Bi
ol
o
g
i
c
al
Se
ns
ors
(
S
u
b
h
a
s
Met
i
)
9
89
m
o
ti
on o
f
i
ndi
vi
d
u
al
anal
y
t
e
m
o
l
ecul
e
s subject
e
d
t
o
m
a
ny
chal
l
e
n
g
i
n
g
si
t
u
at
i
ons apa
r
t
fr
om
usi
ng t
h
e
cont
i
n
ui
t
y
eq
u
a
t
i
on
fo
rm
ul
at
ion
[
1
6]
.
1.
5.
The Proposed So
lut
i
on
Th
is section
talk
s abou
t th
e d
a
ta p
r
ocessing
of b
i
o
l
o
g
i
cal
senso
r
s i
n
a
desc
ri
pt
i
v
e m
a
nner.
B
i
o sen
s
o
r
d
a
ta can
b
e
affected
b
y
th
e
p
o
s
ition
o
n
the reso
n
a
n
ce
un
it scale, no
ise, non
sp
eci
fic respon
ses and
o
t
h
e
r
objects t
h
at com
p
licate the further pro
cessi
n
g
of
t
h
e dat
a
.
The
st
udy
o
f
[
17]
hi
g
h
l
i
g
ht
ed
t
h
at
ra
w dat
a
nee
d
t
o
b
e
pro
cessed
to
en
sure th
e cap
a
b
ility o
f
b
e
in
g
co
m
p
ared
. It h
a
s b
e
en
also fou
n
d
t
h
at m
a
n
y
SPR -
b
a
sed
d
a
t
a
pr
ocessi
ng
t
echni
que
s
have
bee
n
i
n
t
r
o
d
u
ced.
The
st
u
d
y
o
f
[1
8]
d
i
scusse
d t
h
at
t
h
e m
o
st
com
m
on
com
m
e
rci
a
l
l
y
avai
l
a
bl
e SPR
based
bi
o se
nso
r
s a
r
e bi
a-
core
20
0
0
an
d
30
0
0
sy
st
em
s. The S
P
R
ba
sed bi
o
sen
s
o
r
s are fortified
with
a
fou
r
flow-
cell fl
uidic syste
m
where one
flow c
e
ll
is used as a
refe
rence t
o
s
u
btract
pos
si
bl
e
no
ns
p
eci
fi
c si
gnal
.
It
i
s
al
so
fo
u
n
d
t
h
at
seve
ral s
o
ftware
pac
k
a
g
es are a
v
ailabl
e for sim
p
lifying the
dat
a
set
s
f
o
r
bi
acore e
x
peri
m
e
nt
s e.
g.
Scr
u
b
b
er et
c.
R
a
w
d
a
t
a
whi
c
h are
obt
ai
ne
d
by
t
h
e B
i
acore e
x
pe
ri
m
e
nt
s
are sp
read
fo
r di
ffe
re
nt
uni
t
s
of fl
ow cel
l
t
h
i
s
proce
ss f
o
l
l
o
ws t
h
e m
echani
s
m
of R
U
(R
espo
nse
Uni
t
s). T
h
e
Dat
a
-
p
r
o
cessi
n
g
of
B
i
ol
o
g
i
cal
sens
or
s ca
n
be
cl
assi
fi
ed i
n
t
o
t
w
o m
e
t
hod
s.
1.
5.
1.
Da
ta
A
g
gre
g
a
t
ion
In t
h
e rece
nt
t
i
m
e
s, t
h
e usa
g
es o
f
bi
o se
ns
ors
com
b
i
n
e p
r
oces
si
n
g
o
f
d
i
ffere
nt
ki
nd
o
f
dat
a
.
Dat
a
aggre
g
ation is
a process of extr
acting va
luable inform
a
tion or accu
m
u
lating information from
the raw
d
a
tab
a
ses th
at
are ob
tain
ed
by th
e b
i
osens
o
r
s
. Dat
a
agg
r
e
g
at
i
on p
r
oces
s o
f
B
i
ol
ogi
cal
se
nso
r
net
w
o
r
k
s
whe
r
e
m
i
croor
ga
ni
sm
i
d
ent
i
f
i
cat
i
on
has b
een
do
ne
wi
t
h
t
h
e t
ech
ni
que
o
f
sp
ot
col
o
r a
n
al
y
s
i
s
. Th
e expe
ri
m
e
nt
al ra
w
dat
a
set
s
hav
e
b
een eval
uat
e
d
wi
t
h
t
h
e deci
si
on
pr
oces
s
o
r t
o
prepa
r
e a com
b
ined
m
i
cro
o
rg
an
ism
id
en
tificatio
n
d
a
tasets [19
]
.
1.
5.
2.
Existin
g
S
t
udi
es of
Data Aggregation
in B
i
o Sens
or
Th
is sub
section
h
i
gh
ligh
t
s v
a
riou
s ex
isting
stu
d
i
es
toward
s
Data Aggreg
at
io
n
of b
i
o
s
en
sors till d
a
te.
The st
udy
of
Hal
i
r
et
al
.
[2
0
]
prese
n
t
e
d
a c
ohe
re
nt
det
ect
i
o
n
schem
e
bas
e
d
on
i
n
t
e
g
r
at
e
d
opt
i
c
s t
h
at
c
oul
d
be
u
s
ed
to
en
ab
l
e
th
e u
n
a
m
b
ig
u
o
u
s
reado
u
t
o
f
th
e op
tical p
h
a
se with
a co
n
s
tan
t
sen
s
itiv
ity. Ex
p
e
rimen
t
al
out
c
o
m
e
s sho
w
t
h
e ef
fect
i
v
e
n
ess
of t
h
e
pr
op
ose
d
sy
st
em
and Phase s
h
i
f
ts are consi
d
e
r
ed as a
performance
param
e
t
e
rs. A sy
st
em
ati
c
anal
y
t
i
c
and n
u
m
e
ri
cal
st
udy
has
been p
r
op
ose
d
by
Wu et
al
. [2
1]
whi
c
h i
n
cl
ude
s
t
h
e det
ect
i
on l
i
m
i
t
of a refr
act
i
v
e i
nde
x s
e
ns
or. T
h
e p
r
op
ose
d
st
u
d
y
appl
i
e
d c
o
upl
e
d
m
ode t
h
eor
y
and
i
nvest
i
g
at
e
d
i
m
pl
em
ent
a
t
i
on res
u
l
t
s
w
h
ere
wa
vel
e
n
g
t
h
(
n
m
)
, Ti
m
e
(T)
have
bee
n
u
s
ed as
pe
rf
o
r
m
a
nce
param
e
t
e
rs. H
o
rem
a
n et
al
. [2
2]
de
vel
o
pe
d t
w
o dy
nam
i
c bi
m
a
nual
po
si
t
i
oni
n
g
t
a
sks
t
h
at
req
u
i
r
e a
d
eq
uat
e
m
o
ti
on c
ont
r
o
l
as wel
l
as f
o
r
ce cont
rol
.
Th
e aim
of t
h
e
pr
op
ose
d
st
u
d
y
i
s
t
o
i
n
vest
i
g
at
e
t
h
e ad
de
d val
u
es
of
fo
rce
param
e
ters
with
resp
e
c
t to tim
e param
e
ters wh
er
e 10
o
f
th
e
13 p
a
r
a
m
e
ter
s
sh
ow
ed
a
sign
if
ican
t
di
ffe
re
nce bet
w
een
gr
o
ups
. I
t
i
s
foun
d i
n
t
h
e st
u
d
y
of M
e
ht
a and Za
g
h
l
oul
[
2
3]
t
h
at
a t
uni
n
g
o
f
t
h
e opt
i
c
a
l
nan
o
a
n
t
e
n
n
a i
n
t
h
e
vi
si
bl
e s
p
ect
r
u
m
usi
n
g
gra
p
hene
has
been i
n
t
r
od
uce
d
w
h
er
e a di
p
o
l
e
st
ruct
ure
f
o
r t
h
e
n
a
no
an
tenn
a i
s
con
s
id
ered
with
th
e
reason
i
n
g wav
e
le
n
g
t
h
. T
h
e F
D
T
D
si
m
u
l
a
t
i
on res
u
l
t
s ha
ve
bee
n
ve
ri
fi
ed
wi
t
h
t
h
e
ex
pe
ri
m
e
nt
al
out
c
o
m
e
s. Im
peda
nce
and
wa
vel
e
n
g
t
h
have
bee
n
t
a
ken
as
per
f
o
rm
ance
param
e
t
e
rs.
1.
5.
3.
Da
ta
Fusi
on
Data fusion
rep
r
esen
ted
t
h
e
p
r
o
cess
o
f
en
han
c
ing
m
u
ltip
l
e
d
a
ta and
kn
owledg
e abou
t a real o
b
j
e
ct
and
m
a
pped t
h
em
i
n
t
o
a c
o
nsi
s
t
e
nt
, acc
urat
e
and
u
s
ef
ul
re
pr
esent
a
t
i
o
n
.
T
h
e
Fi
nal
pr
ocesse
d
dat
a
i
s
se
nt
t
o
t
h
e
base station.
Exi
s
t
i
ng St
udi
e
s
of
D
a
t
a
F
u
si
on i
n
Bi
ose
n
s
o
r:
Th
is section
g
i
v
e
s a
b
e
tter ov
erv
i
ew ab
ou
t
th
e ex
isting
studies
associated wit
h
the
Data Fusi
on
of
Bio
s
en
so
rs. I
t
h
a
s b
e
en
f
oun
d
in
t
h
e st
udy
of Blasch et
al.
[24] t
h
at
ext
r
act
i
o
n o
f
f
eat
ures f
r
om
inf
o
rm
at
i
on has
been p
e
r
f
o
r
m
e
d wi
t
h
t
h
e
us
e of p
u
l
s
e co
u
p
l
e
d ne
u
r
al
ne
t
w
o
r
k
.
The
pr
op
ose
d
m
e
t
hod e
x
t
r
act
s a feat
u
r
e f
r
o
m
im
ages. Di
f
f
ere
n
t
t
y
pes o
f
m
a
t
h
em
at
i
c
al
m
odel
s
ha
ve
been
explaine
d t
o
ac
hieve a
n
e
ffici
ent feature
bas
e
d se
ns
or
fu
si
on
. Sim
u
latio
n
resu
lts show targ
et in
an
im
ag
e and
the effectivene
ss of Puls
e C
o
u
p
l
e
Neu
r
al
Net
w
or
k
(PC
NN M
o
del
)
.
R
a
hm
an and
M
a
ri
n [
2
5]
p
r
esent
e
d a
p
a
rticle swarm o
p
tim
izat
io
n
(PDO)
b
a
sed
al
g
o
rith
m
fo
r
fin
d
i
n
g
t
h
e
o
p
t
i
m
al sin
k
po
sitio
n. Relay n
odes are
i
n
t
r
o
d
u
ced
fo
r opt
i
m
i
z
i
ng t
h
e geom
et
ri
c net
w
o
r
k
defi
ci
e
n
c
i
es. The experi
mental outcomes show the Li
fetim
e
co
m
p
ariso
n
o
f
relay n
o
d
e
s on size o
f
th
e area an
d
o
p
tim
al
sin
k
lo
cation
.
Th
e im
p
r
o
v
e
m
e
n
t
o
f
d
a
ta u
tility b
y
t
h
e opt
i
m
al
sched
u
l
i
n
g
al
go
r
i
t
h
m
has bee
n
p
r
o
p
o
sed
by
Hu
et
al
. [
26]
whe
r
e Si
m
u
l
a
ti
on res
u
l
t
s
s
h
o
w
t
h
e
per
f
o
r
m
a
nce anal
y
s
i
s
of
vi
rt
ual
se
nso
r
sc
h
e
dul
i
n
g al
go
ri
t
h
m
s
. Perf
orm
a
nce a
n
al
y
s
i
s
al
so s
h
ows
t
h
e
n
e
t
w
o
r
k
u
tilities v
e
rsus
th
e nu
m
b
er
o
f
t
i
m
e
slo
t
s in
a l
a
rg
e scale network.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
98
6 – 9
9
4
99
0
2.
R
E
SEARC
H M
ETHOD
In ge
ne
ral
,
t
h
e bi
osen
so
r sh
oul
d be vi
e
w
e
d
as a bi
op
hy
si
cal
i
n
st
rum
e
nt
and
,
as wi
t
h
any
hi
g
h
-
reso
l
u
tio
n
tech
n
i
q
u
e
, th
e
better th
e reagen
ts, th
e
b
e
tter and
th
e
resu
lts. At
p
r
es
ent, the
perform
a
nce
val
i
d
at
i
o
ns
of
t
h
e
bi
ol
o
g
i
cal
s
e
ns
ors
are
f
o
u
n
d
t
o
be
as
fol
l
o
wi
n
g
:
2.
1.
Datamining
Dat
a
m
i
ni
ng i
s
t
h
e pr
ocess
of
ext
r
act
i
n
g a t
ypi
cal
and u
n
i
q
ue set
of i
n
fo
r
m
at
i
on cal
l
e
d as kn
owl
e
d
g
e
fr
om
t
h
e gi
ven
set
of dat
a
. It
con
s
i
s
t
s
of va
r
i
ous co
n
v
e
n
t
i
o
nal
al
go
ri
t
h
m
s
whi
c
h i
s
resp
o
n
si
bl
e f
o
r d
o
i
n
g so.
Usual
l
y
, t
h
e
a
m
ount
s o
f
t
h
e
i
n
f
o
rm
at
i
on e
x
t
r
act
ed
f
r
om
t
h
e bi
ol
o
g
i
cal
sens
or a
r
e
qui
t
e
m
a
ssi
ve an
d i
t
i
s
ex
trem
ely
i
m
p
o
rtan
t to
retain th
e reliab
ility
wh
en
pro
cessi
n
g
th
e
d
a
ta. Th
e stud
y co
ndu
cted
b
y
th
e au
tho
r
in
[2
7]
em
phasi
zed a
b
o
u
t
dat
a
m
i
ni
ng a
p
p
r
oa
ch o
n
bi
oi
nf
or
m
a
t
i
c
s as
m
i
crobi
ol
o
g
y
.
T
h
e
st
udy
c
o
n
d
u
ct
ed by
t
h
e
aut
h
or i
n
[
28]
has al
so
si
g
n
i
f
i
e
d t
h
e i
m
port
a
nce o
f
dat
a
m
i
ni
ng t
ech
ni
ques
.
The
pri
m
e reason
be
hi
n
d
i
t
i
s
t
h
at
a
set
of st
at
i
s
t
i
c
al
operat
i
o
ns ca
n be pe
rf
o
r
m
e
d o
n
t
h
e da
t
a
g
e
nerat
e
d fr
om
t
h
e bi
ol
o
g
i
cal
sens
ors a
nd a
hi
g
h
e
r
d
e
gr
ee
o
f
know
ledg
e co
u
l
d
be ex
tr
acted
f
r
om
su
ch
d
a
ta
f
o
r
m
at
io
n
.
2.
2.
Mac
h
ine Le
ar
ning
As
d
i
scu
s
sed
earlier th
at m
a
ch
in
e learn
i
ng
ap
pro
ach
es can
tack
le th
e
h
i
g
h
e
r d
e
gree of co
m
p
lex
ities
i
n
t
h
e
dat
a
be
i
ng
gene
rat
e
d
fr
om
t
h
e bo
dy
sens
or
. Th
e s
t
udy
co
n
d
u
c
t
e
d by
t
h
e aut
h
or i
n
[
2
9]
has
al
so
di
scuss
e
d
t
h
e s
i
gni
fi
ca
nce
of
machine learning techniques.
It is fo
und
th
at ad
ap
tiv
e th
resh
o
l
d
s
are
u
s
ed
for d
e
tecting
failures and eli
m
in
ate n
o
i
se fro
m
th
e
bi
ose
n
s
o
r t
r
a
n
s
duce
r
ge
ner
a
t
e
d si
g
n
al
s. A fa
i
l
u
re eve
n
t is u
s
ually d
e
tecte
d
b
y
co
m
p
aring
th
e pred
icted
and
m
easured
pe
rf
orm
a
nce of t
h
e sens
or.
Wh
e
r
e t
h
e c
o
m
p
ari
s
on
o
f
t
h
e m
e
asure
d
a
nd
pr
edi
c
t
e
d pe
rf
o
r
m
a
nce
lead
s to
th
e
bu
ild
ing
of a resid
u
a
l. Th
e
resid
u
a
l ev
alu
a
t
i
o
n
is don
e by th
resho
l
ds an
d
t
h
en
i
d
en
ti
fies th
e
sym
p
tom
s
that
are the
n
a
n
alyzed
fo
r finding
t
h
e
ass
o
ciated fault.
Id
eally, th
e resid
u
a
l is zero
wh
en
th
er
e are no faults and not zero whe
n
a fau
lt is p
r
esen
t. It is also
foun
d th
at so
meti
m
e
s th
e resid
u
a
ls resu
lt greater th
an
zero
ev
en wh
en
n
o
fau
lt is presen
t.
Th
is m
a
y b
e
du
e to
Noisy m
easurements
U
n
kn
ow
n D
i
stu
r
b
a
n
ces
Un
certain
ties i
n
th
e m
o
d
e
ls.
Fau
lt d
e
tectio
n
m
ech
an
is
m
is
u
s
ed
to d
e
tect fau
lts p
r
esen
t
in
th
e b
i
o
s
en
so
rs.
It is v
e
ry
essen
tial
t
o
id
en
tify th
e fau
lts in
th
e b
i
o
s
en
so
rs as it is i
m
p
l
an
ted
in
th
e h
u
m
an
b
o
d
y
an
d
in
teracts with
v
a
rio
u
s
ch
emic
al
and
bi
ol
ogi
cal
react
i
ons fo
r p
r
od
uci
ng
rel
i
a
ble and
cruci
a
l in
form
a
tio
n
ab
ou
t v
a
riou
s
p
a
ramet
e
rs p
r
esen
t
in
th
e
hum
an body
.
Sect
i
on VI
di
s
c
usses t
h
e va
ri
ous
val
i
d
at
i
on
t
echni
ques
us
ed t
o
det
ect
t
h
e faul
t
i
n
bi
os
ensor
net
w
or
ks [
30]
.
2.
3.
Fuz
z
y
logic b
a
sed Techniq
u
e
s
It can
b
e
seen th
at q
u
a
n
titativ
e an
alysis of d
o
p
a
m
i
n
e
in
th
e sam
p
les o
f
u
r
i
n
e and
p
l
asm
a
is v
e
ry
essent
i
a
l
as i
t
i
s
very
use
f
ul
fo
r c
u
ri
ng
va
ri
ous
heal
t
h
di
s
eases l
i
k
e
ga
n
g
l
i
one
u
r
om
a, s
c
hi
zo
ph
re
ni
a,
m
a
ni
c-
dep
r
essi
ve psy
c
ho
si
s, b
u
r
n
o
u
t
sym
p
t
o
m
s
et
c.As t
h
e p
r
ese
n
ce o
f
d
opam
i
ne i
n
a sol
u
t
i
o
n re
qui
re
s an e
n
zy
m
e
react
i
on t
h
us t
h
e ex
peri
m
e
nt
i
nduce
d
t
e
m
p
erat
ure al
s
o
af
fect
s t
h
e o
u
t
p
u
t
si
gnal
of
bi
o
s
ens
o
r. T
h
e pa
per
of
R
a
ngl
ova et
a
l
. [3
1]
pr
esent
s
a fuzzy
bas
e
d ap
p
r
oac
h
of
bi
ose
n
s
o
r i
n
p
u
t
/
o
ut
p
u
t
ne
cessi
t
y
Sim
u
l
a
t
i
ons
per
f
o
r
m
e
d on
vari
ous s
o
ft
wa
re'
s
confi
r
m
s
the hi
gh acc
ura
c
y
of f
u
zzy
l
o
gi
c m
e
t
hod
. R
e
l
a
t
i
v
e erro
rs
whi
c
h
h
a
v
e
b
e
en
come o
u
t
with
t
h
e calcu
lation o
f
ex
p
e
rim
e
n
t
al d
a
ta, u
s
ed
for th
e leg
itimacy o
f
th
e
p
r
op
o
s
ed
t
echni
q
u
e.
It
i
s
f
o
u
n
d
i
n
t
h
e
st
u
d
y
o
f
Si
ng
h et
al
[3
2]
p
r
esent
s
a
n
opt
i
m
i
zed Sel
f
or
gani
ze
d F
u
zzy
Lo
gi
c
cont
rol
l
e
r
fo
r
pH c
o
nt
rol
wi
t
h
t
h
e
use o
f
pe
rf
orm
a
nce cor
r
ect
i
on t
a
bl
e.
It
t
h
e co
nt
rol
l
e
r
per
f
o
r
m
a
nce i
s
po
o
r
t
h
en t
h
i
s
pr
o
p
o
s
ed m
echani
s
m
i
nvol
ve
s a p
e
nal
t
y
for t
h
e
out
put
m
e
m
b
ershi
p
f
u
nct
i
ons
. A
gene
ral
i
zed f
u
zz
y
l
ogi
c ba
sed a
p
pr
oac
h
f
o
r e
n
e
r
gy
a
w
are
ro
ut
i
ng i
n
sens
o
r
n
e
t
w
o
r
k
has
bee
n
de
vel
o
pe
d
b
y
Hai
d
er et
al
[3
3]
i
n
or
der
t
o
o
p
t
i
m
ize t
h
e e
n
er
gy
c
ons
um
pt
i
on i
s
s
u
es
of
se
nso
r
n
e
t
w
o
r
ks
. T
h
i
s
app
r
oach
i
s
s
o
f
t
and
t
u
na
bl
e a
n
d
i
t
can a
d
j
u
st
wi
t
h
di
f
f
ere
n
t
t
y
pes
o
f
se
nso
r
n
ode
s ha
vi
n
g
di
f
f
er
ent
ene
r
gy
i
ssu
es.
2.
4.
Game
the
o
r
y
based
Techni
ques
Vari
ous
gam
e
t
h
eory
ba
sed
val
i
d
at
i
on t
e
c
hni
que
s al
so f
o
u
n
d
aft
e
r a
n
al
y
z
i
ng so m
a
ny
researc
h
pape
rs, t
h
i
s
se
ct
i
on hi
g
h
l
i
g
ht
s som
e
of t
h
e si
gni
fi
ca
nt
ga
m
e
t
h
eory
-ba
s
ed ap
pr
oach
es
for se
nso
r
net
w
o
r
k
s
.
Kol
t
s
i
d
as
et
al
[3
4]
de
si
g
n
ed
a gam
e
t
h
eoret
i
cal
m
odel
l
i
ng
of
cl
ust
e
ri
ng
f
o
r se
ns
or
net
w
o
r
ks
. T
h
e a
n
al
y
s
i
s
has
been
per
f
o
r
m
e
d i
n
t
h
e basi
s o
f
no
n
-
co
o
p
erat
i
v
e gam
e
approach
whe
r
e se
nso
r
be
ha
ves sel
f
i
s
hl
y
for
pre
s
ervi
n
g
i
t
s
energy
. T
h
e pr
op
ose
d
C
l
ust
e
re
d R
o
ut
i
n
g f
o
r Sel
f
i
s
h S
e
ns
ors -C
R
O
S
S
) has
bee
n
co
m
p
ared wi
t
h
v
a
ri
o
u
s
p
opu
lar clu
s
terin
g
techn
i
qu
es
th
u
s
th
e sim
u
latio
n
resu
lt sh
ows the efficiency of the techniques
with res
p
ect to
p
e
rform
a
n
ce param
e
ters su
ch as Nu
m
b
er o
f
Players (Nodes), Pro
b
a
b
ility, Netwo
r
k
Li
feti
m
e
, Parameter
?
,
No
of n
o
d
es al
i
v
e and
num
ber of n
o
d
es ar
o
u
n
d
. T
h
e p
r
o
p
o
se
d st
udy
o
f
Aga
h
et
al
[35
]
pro
pose
d
a p
r
ot
ocol
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
A T
hor
o
u
g
h
I
n
si
ght
t
o
Tec
h
ni
que
s f
o
r
Perf
o
r
ma
nce
Eval
uat
i
on i
n
Bi
ol
o
g
i
c
al
Se
ns
ors
(
S
u
b
h
a
s
Met
i
)
9
91
whi
c
h i
s
based
o
n
gam
e
t
h
eo
r
e
t
i
cal
appr
oac
h
. The
m
a
i
n
ob
j
ect
i
v
e of
t
h
e
p
r
o
p
o
sed
st
u
d
y
t
o
desi
gn
a p
r
o
t
ocol
whi
c
h rec
o
g
n
i
ze t
h
e pre
s
ence
of
no
des
that
agree a
n
d fails to forward the
da
t
a
pac
k
et
s o
v
er a se
ns
or
ne
t
w
o
r
k
.
A
v
er
ag
e
n
u
m
b
e
r of
ho
p
s
, p
e
r
cen
tag
e
of
m
a
lic
io
u
s
no
d
e
s,
Thr
ough
pu
t and
Ti
me ar
e co
n
s
i
d
er
ed
as
per
f
o
r
m
a
nce p
a
ram
e
t
e
rs t
o
ev
al
uat
e
t
h
e
rep
u
t
at
i
on o
f
eac
h
no
de.
2.
5.
Genetic
Algor
i
thm b
a
sed
Te
chnique
Qi
n et
al
. [
3
6]
p
r
esent
e
d a
n
ovel
c
o
m
m
uni
cat
i
on
pr
ot
oc
o
l
fo
r st
udy
i
n
g
t
h
e
u
ppe
r
bo
un
ds
o
n
t
h
e
l
i
f
et
im
e of vi
d
e
o se
nso
r
net
w
o
r
k
.
T
h
e c
o
s
t
effect
i
v
e
n
ess
m
a
k
e
s th
e network typ
i
cally
sm
a
ll. Th
e pro
p
o
s
ed
al
go
ri
t
h
m
m
a
xim
i
zes
t
h
e net
w
o
r
k l
i
f
et
i
m
e
rat
h
er t
h
an m
i
ni
m
i
zi
ng t
h
e ener
gy
de
pl
et
i
on o
f
sens
o
r
s.
Som
e
clusters, the
num
b
er of
network lifetim
e
are taken as
perform
a
nce pa
ram
e
ters for presenting a graphical
sim
u
l
a
t
i
on w
h
i
c
h s
h
o
w
s
N-
of
-N
net
w
or
k l
i
f
et
im
e generat
e
d by
t
h
e
p
r
o
p
o
se
d ap
pr
oac
h
.
Som
e
C
l
ust
e
rs an
d
Me
m
o
ry use
d
(Bytes), Network lifetim
e ar
e pres
ente
d as
p
e
rform
a
n
ce param
e
ters to
o
.
It is also
fou
nd in
th
e
p
a
p
e
r of Ch
akrab
o
rty et al. [3
7
]
, g
e
n
e
tic algo
rith
m
in
sp
ired
rou
ting
alg
o
rith
m
th
at
is ter
m
ed
as GROUP th
at
in
creases th
e
network p
e
rfo
r
man
ce b
y
m
iti
g
a
tin
g th
e en
erg
y
d
i
ssi
p
a
tio
n
issu
es. A lin
ear
q
u
a
n
titativ
e stru
cture
activ
ity relat
i
o
n
s
h
i
p
(QSAR
)
m
o
d
e
l h
a
s b
een
in
trodu
ced
in
t
h
e paper o
f
u
s
es t
h
e conce
p
t
of ge
net
i
c
al
gori
t
h
m
and
va
ri
abl
e
se
l
ect
i
on t
o
ol
s. P
e
rf
orm
a
nce an
al
y
s
i
s
of t
h
e
pr
op
ose
d
m
e
t
hod o
b
t
a
i
n
t
h
e e
f
fi
ci
ent
res
u
l
t
s
whi
c
h
sho
w
s t
h
at
GA
-M
LR
m
odel
is supe
ri
o
r
t
h
at
S
W-M
LR
m
odel
.
The com
p
ar
i
s
on
has bee
n
do
ne wi
t
h
res
p
ect
t
o
st
anda
rdi
z
e
d
re
si
dual
s
a
n
d l
e
v
e
rage
.
2.
6.
Neur
al Ne
tworks Base
d
vali
d
ati
o
n Tec
hni
ques
Very
fe
w t
ech
ni
q
u
es ha
ve be
en fo
u
nd
fo
r v
a
l
i
d
at
i
on o
f
bi
ose
n
so
rs wi
t
h
Neu
r
al
Net
w
or
k C
once
p
t
s
.
Keller et al. [3
8
]
in
t
r
odu
ced th
ree
p
r
o
t
o
t
y
p
e sen
s
ing
syste
m
s th
at are u
s
ed
to
d
e
tect th
e co
m
p
o
s
itio
n
of
chem
ical dyes
in liqui
d and
identify
the
ra
dioactive is
otopes re
spectivel
y. The
performance param
e
ters are
use
d
t
o
di
scu
ss t
h
e effect
i
v
eness o
f
t
h
e pr
o
pose
d
sy
st
em
. The st
ud
y
of Si
ng
h et
al
. [39]
m
odel
e
d a
ph
ot
om
et
ri
c bi
ose
n
so
r w
h
ere
t
h
e perf
orm
a
nce m
e
t
r
i
c
s of t
h
e pro
p
o
sed
senso
r
has b
een eval
uat
e
d
usi
n
g
Artificial Neural Network
s
. Graph
i
cal
re
prese
n
tation
using No
of Epochs Vs Mean
squa
re error
as
p
e
rf
or
m
a
n
ce par
a
m
e
ter
s
sh
ow
s
9
3
%
acc
ura
c
y of the
proposed system
.
3.
RESEA
R
C
H AN
D DIS
C
US
SION
Th
e ex
isting
st
u
d
i
es
d
i
scu
s
sed
till th
e p
r
ev
i
o
u
s
secti
o
n
h
i
gh
lig
h
t
s th
e effectiv
en
ess as
well as v
a
riou
s
fo
rm
s of t
h
e l
a
t
e
nt
l
i
m
i
t
a
t
i
ons.
The
r
e a
r
e
vari
ous
f
o
rm
s of t
h
e t
ech
ni
q
u
es
di
scus
sed
i
n
t
h
i
s
m
a
nusc
r
i
p
t
t
h
at
was
fo
u
n
d
t
o
be
use
d
f
o
r e
v
al
uat
i
n
g
t
h
e
pe
rf
orm
a
nce o
f
t
h
e
bi
ol
o
g
i
cal
s
e
ns
ors.
H
o
we
v
e
r, t
o
un
der
s
t
a
nd
t
h
e
effectiv
en
ess
of th
e ex
isting
stu
d
i
es, it is i
m
p
o
rtan
t to
re
vi
ew the tra
d
eoff and
narrow
it d
o
wn
to
th
e research
g
a
p. Exp
l
o
r
ati
o
n of th
e research
g
a
p
will furth
e
r en
rich
th
e qu
ality o
f
research
wo
rk in
th
e d
i
rectio
n of
expl
ori
ng t
h
e b
e
st
t
echni
q
u
e o
f
eval
u
a
t
i
ng t
h
e per
f
o
r
m
a
nce of
bi
ol
o
g
i
cal
s
e
ns
ors.
The e
x
pl
o
r
ed
researc
h
ga
ps
are as
follows:
3.
1.
Iterative
Nature of
Algorithm
The m
a
jori
t
y
of t
h
e
st
u
d
i
e
s were e
xpl
ore
d
t
h
at
uses a ge
net
i
c
al
go
ri
t
h
m
,
neural
net
w
o
r
k
,
o
r
g
a
m
e
th
eory for p
e
rfo
r
m
a
n
ce ev
aluatio
n
of
th
e sen
s
ors.
A clo
s
er lo
ok
at all
th
ese alg
o
rith
m
t
y
p
e
s will show th
at
th
ey carry
ou
t
an
iterativ
e
fo
rm
o
f
p
r
o
cessi
ng
to g
e
t
th
e elite ou
tco
m
es. Ho
wev
e
r th
e ou
t
c
o
m
es are no
t
foun
d
reliab
l
e as adop
tio
n
of su
ch
alg
o
rith
m
are foun
d
with
certain
flaws e.g.
i) in
ab
ility o
r
n
o
assured
to find
g
l
ob
al op
tim
u
m
[3
1
]
,[32
], i
i
) in
ab
ility to
en
su
re un
ifo
r
m
o
p
t
i
m
izatio
n
d
u
ring
respo
n
s
e in
p
e
rfo
r
man
c
e
v
a
lid
ation
o
f
sen
s
o
r
s [33
]
, and
iii) p
r
o
cessing
ti
m
e
o
f
alg
o
r
ith
m
in
creases with
in
crease in
n
e
twork
size [38
]
-
[3
9]
.
3.
2.
L
e
ss
w
o
rk on
B
i
ol
ogi
c
a
l
Sen
s
or
B
i
ol
ogi
cal
sen
s
ors
(o
r t
h
e we
arabl
e
sens
o
r
s)
are t
h
e very
n
e
w fo
rm
of t
e
chn
o
l
o
gy
t
h
at
h
a
s evol
ve
d i
n
last 3-4 years
.
Hence
,
a less am
ount
of re
se
arch
work
has been witnesse
d
in
the
literature a
r
chi
v
al towards
bi
ol
o
g
i
cal
sens
ors a
nd m
a
jori
t
y
of t
h
e wor
k
i
s
carri
ed out
for co
n
v
ent
i
o
n
al
sens
ors (
n
on
-
b
i
o
l
o
gi
cal
-s
m
oke
sens
or,
m
o
t
i
on sens
or
,
hum
i
d
i
t
y
sensor
, et
c.
).
Al
t
h
o
u
g
h
t
h
ere are
n
o
t
m
u
ch
di
ffe
re
nce i
n
bi
ol
o
g
i
cal
an
d
no
n-
b
i
o
l
og
ical in
th
e form
o
f
th
e wo
rk
ab
ility, t
h
ere is a s
lig
h
t
d
i
fferen
ce b
e
t
w
een
th
em
. Bi
o
l
og
ical sen
s
ors (e.g.
heart
beat
sen
s
or
or
gl
uc
ose
sens
or
) f
r
eq
u
e
nt
l
y
keeps
on
capt
u
ri
n
g
t
h
e
si
gnal
s
a
nd
h
e
nce ha
ve t
h
e
hi
g
h
e
r
p
o
s
sib
ility o
f
d
r
ai
n
i
ng
en
ergy
m
o
re th
an
t
h
e con
v
e
n
tion
a
l
sen
s
o
r
s. Th
erefore, t
h
e
fau
l
t
to
leran
ces
o
n
en
erg
y
fact
or
o
f
t
h
e se
nso
r
s
we
re
nev
e
r e
xpl
ore
d
i
n
t
h
i
s
area
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
98
6 – 9
9
4
99
2
3.
3.
Less Foc
u
s
on
Compu
t
ation
a
l Model
Evi
d
e
n
ce
of m
a
t
h
em
ati
cal
or em
pi
ri
cal
m
odel
l
i
ng o
f
pe
rf
or
m
a
nce eval
uat
i
on
of
bi
ol
o
g
i
cal
senso
r
s i
s
missin
g
fro
m
t
h
e literatu
re.
A robu
st m
a
th
e
m
atical
m
o
d
e
l will assist
to
d
e
v
e
l
o
p
th
e
furth
e
r po
ten
tial sk
eleton
o
f
th
e arch
itectu
r
e th
at can
fu
rn
ish
b
e
tter reliab
ility
facto
r
in
th
e ou
tcomes. Mo
reov
er, alth
oug
h
t
h
ere are
vari
ous st
u
d
i
e
s
t
o
war
d
s eval
u
a
t
i
ng t
h
e pe
rf
o
r
m
a
nce of bi
ol
ogi
cal
sens
o
r
s,
but
t
h
e w
o
r
k
s
t
o
war
d
s val
i
d
at
i
on
tech
n
i
qu
es fo
rm
u
l
a
tio
n
are
qu
ite a few.
Less wo
rk
on
reli
ab
ility an
alysis
on
th
e
ou
tcomes are also
so
m
e
o
f
th
e sign
ifican
t
g
a
ps ex
p
l
o
r
ed
i
n
th
e literatu
res.
3.
4.
Need of More
Realistic Perform
a
nce
P
a
r
a
meters
Very less em
p
h
a
sis is laid
to
ward
s th
e realistic
assu
m
p
t
i
on
of t
h
e
per
f
o
r
m
a
nce para
m
e
t
e
rs. The
ex
istin
g
p
a
ram
e
ter d
o
esn’t see
m
to
co
n
s
id
er critical
p
e
rform
a
n
ce ab
ou
t fau
lt to
lera
nce
e.g. M
A
PE
(Mean
Ab
so
lu
te
Percen
tag
e
Error), fau
lt d
e
tectio
n
t
i
m
e
, alar
m
typ
e
, etc. Hen
ce,
u
n
til and
u
n
l
ess su
ch
p
a
ram
e
t
e
rs are
not
c
o
nsi
d
e
r
ed
fo
r t
h
e
st
u
d
y
,
t
h
e e
x
t
e
nt
of
fa
ul
t
t
o
l
e
ra
nce ca
nn
ot
be st
udi
e
d
rel
i
a
bl
y
.
4.
CO
NCL
USI
O
N
B
i
ol
ogi
cal
sen
s
ors a
r
e di
f
f
er
ent
fr
om
t
h
e con
v
e
n
t
i
onal
se
nso
r
s t
h
at
are
use
d
i
n
ha
bi
t
a
t
m
oni
t
o
ri
n
g
,
b
a
ttlefield
etc. Alth
oug
h
su
ch typ
e
s o
f
co
nven
tio
n
a
l sen
s
o
r
s are also
used in
a h
ealth
care syste
m
, b
i
o
l
o
g
i
cal
sens
ors
are
m
u
ch
di
f
f
ere
n
t
f
r
o
m
t
h
em
. B
i
ol
ogi
cal
se
ns
or
s
are eithe
r
c
o
nnected to th
e sup
e
rficial p
a
rt of the
bo
dy
,
or i
t
m
a
y
be eve
n
fi
xe
d i
n
t
h
e i
n
t
e
rn
al
orga
ns
of t
h
e bo
dy
e.
g. m
oder
n
-
d
ay
pace
m
a
kers ha
ve s
e
ns
ors
.
Hence
,
t
h
e
dat
a
bei
n
g
col
l
ect
ed
by
t
h
em
sh
oul
d e
n
s
u
re
t
h
e hi
ghest
de
gr
ee o
f
faul
t
t
o
l
e
rance
.
The
r
e
f
ore
,
t
h
i
s
pape
r
has
di
sc
usse
d t
h
e
si
g
n
i
fi
cance
of t
h
e
dat
a
pr
ocessi
ng
an
d
per
f
o
r
m
ance o
f
t
h
e
bi
ol
o
g
i
cal
se
ns
ors
o
n
vari
ous t
e
c
hni
que
s. Th
e pri
m
e
m
o
t
i
v
e of t
h
i
s
pape
r i
s
t
o
revi
e
w
t
h
e
exi
s
t
i
ng t
ech
ni
que
s of
per
f
o
r
m
ance
eval
uat
i
o
n
fo
r t
h
e
bi
ol
o
g
i
cal
s
e
ns
or a
n
d e
x
t
r
a
c
t
t
h
ei
r r
e
searc
h
gap
.
Our fu
ture work
will b
e
in
th
e d
i
rectio
n
o
f
reso
lv
i
n
g
th
e ex
istin
g
prob
lem
s
an
d
will ai
m
t
o
evo
l
v
e
up
with a novel fra
m
ework that can ens
u
re t
h
e
preci
se eval
ua
t
i
on of t
h
e p
e
r
f
o
r
m
a
nce of t
h
e bi
ol
o
g
i
cal
senso
r
s
.
The sec
o
n
d
a
r
y
aim
of t
h
e pr
o
pos
ed st
udy
i
s
t
o
i
n
co
r
p
o
r
at
e dat
a
fu
si
o
n
t
echni
que a
n
d aut
o
-ass
oci
a
t
i
v
e
n
e
ural
net
w
or
k
fo
r e
v
al
uat
i
ng a
s
we
l
l
as en
hanci
n
g t
h
e
pe
rf
o
r
m
a
nce
of
bi
ol
o
g
i
cal
sens
ors
.
T
h
e resea
r
ch
o
b
j
ect
i
v
es
set
for t
h
i
s
p
u
r
pos
e are as f
o
l
l
ows e.
g. i
)
t
o
desi
g
n
a t
ech
n
i
que f
o
r en
ha
n
c
i
ng t
h
e
per
f
o
r
m
ance of
bi
ol
ogi
cal
sens
ors
usi
n
g
dat
a
fusi
on t
e
c
hni
que
, i
i
)
t
o
devel
op a t
ech
ni
q
u
e t
h
at
can
perf
o
r
m
an effect
i
v
e val
i
d
at
i
on
of
b
i
o
l
og
ical sen
s
o
r
s u
s
i
n
g enh
a
n
ced au
t
o
-associativ
e n
e
ural
n
e
two
r
k
,
iii) t
o
furth
e
r enh
a
n
ce th
e p
e
rforman
ce
eval
uat
i
o
n t
e
c
hni
que
o
f
bi
ol
ogi
cal
se
ns
ors
usi
n
g
n
o
v
e
l
opt
i
m
i
zati
on a
l
go
ri
t
h
m
based
on
aut
o
-as
s
o
ci
at
i
v
e
neural
network, iii) t
o
perform
com
p
arative perform
a
n
ce analysis
of the
outcom
e
accom
p
lished from
pr
o
pose
d
sy
st
e
m
wi
t
h
t
h
e exi
s
t
i
ng
one
.
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0
8
A T
hor
o
u
g
h
I
n
si
ght
t
o
Tec
h
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que
s f
o
r
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o
r
ma
nce
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uat
i
on i
n
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ol
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g
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al
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ns
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I
S
SN
:
2
088
-87
08
IJEC
E
V
o
l
.
6,
No
. 3,
J
u
ne 2
0
1
6
:
98
6 – 9
9
4
99
4
BIOGRAP
HI
ES OF
AUTH
ORS
Sub
has A.
M
e
t
i
rec
e
iv
ed th
e B
.
E. d
e
gre
e
in
E
l
e
c
tr
onics and
Co
mmunication En
gineer
ing from
the Karn
atak
a u
n
iversity
Dh
arwad, Karn
ataka
in
2000,
the M
.
Tech degr
ee in Ins
t
rumentation
Engineering for
m
Swami Rama
nand Teer
th Marath
wada Univers
i
ty
of Nand
ed,
Maharashtra in
2007. Presently pursuing the P
h
.D. degr
ee in
El
ectrical Engin
eering from th
e Visvesvaray
a
Techno
logic
a
l
Univers
i
t
y
Be
lg
aum
,
Karnat
aka
.
Current
l
y
, He
is
working as
an as
s
i
s
t
ant
Professor of Instrumentation En
gineer
ing at
B
V B college of
Engineering
and
Technolog
y
,
Hubli. His teach
ing and resear
ch
areas includ
e
i
n
strum
e
ntation
,
Multisensor data
fusion, S
o
ft
computing.
V G Sangam
r
eceived th
e B.E. degr
ee
in In
strumentation
Engineer
ing from the M
y
sor
e
university
,
Karn
atak
a in 1989, the M.E degree
in Instrumentation Engineer
ing form My
sore
university
,
K
a
rn
atak
a in 2000,
and the Ph.D.
degree
in Instrumentation Eng
i
neering from
SwamiRamanand Teerth Mar
a
thwada Univ
er
sity
of Nan
d
ed, Mahar
a
shtra in 2007,
res
p
ect
ivel
y.
. C
u
rrentl
y
,
He is
working as
P
r
ofes
s
o
r of Elec
tric
al s
c
i
e
nce
at Ad
am
a s
c
ienc
e &
Techno
log
y
Uni
v
ers
i
t
y
,
Eth
i
opi
a. His
te
aching
and r
e
s
ear
ch
a
r
eas
in
clud
e ins
t
rum
e
ntat
ion,
Biosensor, pro
c
ess instrumentatio
n and Con
t
rol s
y
stem.
Evaluation Warning : The document was created with Spire.PDF for Python.