A guide for the selection of routing protocols in … · protocols in WBAN for healthcare...
Transcript of A guide for the selection of routing protocols in … · protocols in WBAN for healthcare...
A guide for the selection of routing protocols in WBAN for healthcare applicationsV. Bhanumathi* and C. P. Sangeetha
IntroductionWireless body area networks (WBANs) include a collection of body sensor nodes that are low power, invasive or non-invasive, light-weight devices which are either worn on the body or implanted inside the body. The architecture of WBAN [1] can be considered as three different tiers, namely: Tier-1 as Intra-BAN, Tier-2 as Inter-BAN and Tier-3 as Extra-BAN as shown in Fig. 1. In Tier-1, the body sensor nodes collect the data and send to the coordinator. In Tier-2, the coordinator processes the received data and sends the
Abstract
In the present scenario, the term wireless body area network (WBAN) is becoming an integral part of human day to day life due its wide variety of applications, especially in the field of healthcare systems. To design such a reliable body area network system, there are a number of factors to be considered both in hardware and software levels. One of such factors still developing is the design and the analysis of routing proto-cols in the network layer. Routing protocols are a set of protocols which can identify and maintain the routes in the network so that the data can be exchanged between the nodes efficiently. Hence, routing protocol plays a vital role in the wireless sensor networks for reliable communication between the sensor nodes. In this paper, differ-ent routing protocols for body area networks are surveyed and observed that they are affected by factors like energy, network topology, various quality of services (QoS) in the nodes, node temperature, transmission range of nodes, human posture, etc. An evocative taxonomy of protocols is presented such as cluster-based, cross-layered, postural movement based, QoS aware and temperature-aware routing protocols. From the survey, it is found that the selection of a routing protocol is application dependent. For example, the energy efficient protocols like reinforcement learning based routing with QoS support or wireless autonomous spanning tree protocol can be used for daily health monitoring systems due to its high packet delivery ratio. If the system is for in vivo networks, routing algorithm for network of homogeneous and Id-less biomedi-cal sensor nodes or mobility-supporting adaptive threshold-based Thermal-aware energy-efficient multi-hop protocols are the suitable ones. For critical and emergency cases, where accuracy with little delay is the major concern, the protocols like critical data routing, reliability aware routing, data-centric multi objective QoS-aware rout-ing protocol, etc. can be rightly chosen. This entire survey paper can be used by the researchers as a reference for studying various WBAN routing protocols, especially in the field of medical health care systems.
Keywords: Quality of service, Routing protocols, Wireless body sensor networks
Open Access
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
REVIEW
Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24 DOI 10.1186/s13673-017-0105-6
*Correspondence: [email protected] Anna University Regional Campus, Coimbatore, Tamil Nadu, India
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information towards the sink node. From the sink node the packets are transmitted to the corresponding health-center through internet or other communication techniques.
According to the survey conducted by the Economic and Social Affairs Department of United Secretariat [2], after 10 years people with age above 65 will be approximately 15% of total world population. As, older people are more exposed to medical health issues, the need for low cost health monitoring devices [3] becomes a major part human life.
WBAN is actually a subset of conventional wireless sensor networks (WSNs), which can be used for early detection of various diseases, for real-time patient monitor-ing of elderly people, etc. The body sensor node is either worn on the body surface or implanted inside the body. The sensed data is sent to the Base Station and is then for-warded to the sink node. The sink node is responsible for sending the information to its corresponding healthcare center.
The reliability and efficiency of WBAN depend on how the system responds quickly and accurately, to send and receive the data between the nodes, which eventually depends on the selected routing protocols or algorithms. The process of sending infor-mation from either an in-body or an on-body sensor node includes the radiation emitted from wireless transceivers which is similar to WSNs.
Although the routing protocols that are used in WSNs have been under study for past few years, these protocols cannot be used for WBANs due to its stringent requirements. For WSNs, the main focus is on minimal routing overhead and maximal throughput than reduced energy consumption [4]. Also, WSNs are mostly homogenous networks, the WBANs are heterogeneous too [5].
In this article, a comprehensive review of the existing recent routing protocols/algo-rithms is discussed. “The required evaluation metrics for healthcare applications” sec-tion discusses the performance metrics that should be considered for WBANs. The classification of the existing routing protocols is explained in “Classification of routing protocols” and “Future challenges and comparative analysis of routing protocols” sec-tions gives the future challenges and a comparative analysis of different selected proto-cols. Finally, “Conclusions” concludes the survey.
EEG Hearing
Glucose
Blood pressure
Coordinator
Sink node
Internet
Physician Hospital
Data Server Emergency
Tier - 1 Tier - 2 Tier - 3
Vision
ECG
Fig. 1 Wireless body area network architecture
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The required evaluation metrics for healthcare applicationsIn order to identify the important metrics that have to be considered in WBANs during the design process, a general overview about the routing challenges in WBANs should be studied. The certain routing issues and challenges include network topology, postural body movements, limited resources, quality of service metrics, radiation and interfer-ence, global network lifetime, heterogeneous environment, etc. By analyzing all these factors we can conclude and list the important performance metrics to be considered while implementing the whole WBAN. The following section defines the metrics:
• Network lifetime defines the total operation time of the network until the last node is dead.
• Path loss is the difference between the transmitted power at the source node and received power at sink node.
• Stability period is the time before first node die. • Residual energy is the difference between initial energy and used energy during the
operation of the network. • End-to-end delay is the average time taken by a data packet to reach the sink from
the source node. • Packet delivery ratio is determined by number packets obtained at the sink divided
by the number of packets send from the source.
Classification of routing protocolsThe classification of routing protocols can be done in different categories that correlate with the routing challenges of WBAN. The following section gives an overview about the existing protocols, which can be categorized as Cluster-based, Cross-layered, Postural movement based, quality of services (QoS) aware and Temperature-aware routing algo-rithms [6] as shown in Fig. 2.
Cluster‑based routing protocols
In both WSNs and WBANs, the limited energy source is the main constraint to be ana-lyzed. Hence, several efficient cluster based schemes are proposed for both networks to minimize the power consumption and maximize the network lifetime [7]. While com-paring hybrid indirect transmission [8] to power-efficient gathering in sensor infor-mation systems [9] and low-energy adaptive clustering hierarchy (LEACH) [10], it consumes less amount of energy if the number of nodes are small. However, AnyBody [11] protocol is better than LEACH, as the numbers of clusters remain constant with an increase in the number of nodes, but LEACH does not. Also, the installation cost is also less with AnyBody. The Table 1 shows an overview of the existing cluster-based protocols.
Cross‑layered routing protocols
These protocols use the concept of cross layering [13] which is already addressed in WSNs, where each layer (adjacent or non-adjacent) in the protocol stack shares their information unlike in the strict layered model. In WBANs, we can utilize the cross layer-ing concept between network and medium access control (MAC) layers for routing and
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thereby can upgrade the overall network performance. Table 2 shows the different cross-layered routing protocols. Considering the detailed analysis of cross layered protocols, Cascading Information retrieval by controlling access with distributed slot assignment (CICADA) [14] and time zone coordinated sleep scheduling [15] have less power con-sumption. Wireless autonomous spanning tree protocol (WASP) [16] has better packet delivery ratio and CICADA has less delay.
Postural movement based routing protocols
The body postural movements affect the network topology of the network, which results in link disconnection. The researchers introduced a cost function that is periodically
ARBA
QPRR
DARE
SIMPLE
CO-LAEEBA
Two-Hop
MLAEEBA
RAR
ZEQoS
TEEN
Multi-hop
CDR
WBAN Routing Protocols
Clustered Cross layered Postural Temperature QoS aware
QPRD
QRP
EAWD
EPR
Gateway
AMR
EBRAR
DMQoS
RACOON
Heuristic
Modified Dijkstras Global
LOCALMOR
EERS
EAR
RLQRP
QoS
M2E2
RE-ATTEMPT
MATTEMPT,
TMQoS
THSR
LTRT
HPR
RAIN
LTR,
TARA
ATTEMPT
CBBAP
AnyBody
HIT
WASP
TICOSS
CICADA
BioComm ETPA
PSR
Opportunistic
PRPLC
DVRPL
OBSFR
Relay Based
LAEEBA
ENSA
Energy Efficient
Fig. 2 Classification of routing protocols
Table 1 clustered routing protocols in body area networks
Sl. no. Protocol [ref. no.] Goal Performance metrics Compared with
1 Cluster based body area protocol (CBBAP) [12]
To enhance the overall energy efficiency of WBAN
Throughput, residual energy, number of live nodes
LEACH
2 AnyBody protocol [11] To divide the network into clusters and to efficiently send packets from source to sink
Number of clusters, average cluster size, number of transmitted messages to setup
LEACH
3 Hybrid indirect transmis-sion (HIT) [8]
To maintain longer network life
Network longevity, network delay, average energy dissipation, average energy dis-sipation × average delay product
LEACH, PEGASIS, direct
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updated for choosing the best route to forward packets to the sink. The protocols listed in Table 3 are the existing postural movement based routing protocols. Among the dis-cussed protocols, on-body store and flood routing (OBSFR) [18] has better performance in reducing the packet delivery delay, but opportunistic postural movement based rout-ing protocol has lower energy consumption than others.
QoS aware routing protocols
Presently, there are a number of diverse QoS aware protocols available in WSNs, which cannot be as such implemented in WBANs, but by considering its unique curbs it can. In WBANs different data types require different QoS [24]. Hence the proposed proto-cols should be aware of different types of QoS metrics for various types of data. The vari-ous QoS aware routing protocols are shortlisted as given in Table 4.
The comparative analysis has shown that energy-aware peering routing protocol (EPR) [45], QoS-aware peering routing protocol for delay sensitive data (QPRD) [41] and QoS aware peering routing protocol for reliability sensitive data (QPRR) [38] have less power consumption when compared to other protocols. Some protocols do not consider energy consumption, while others. One of the most used QoS aware protocols is data-centric multi objective QoS-aware routing protocol (DMQoS) [50] because it can decrease the delay for delay-sensitive information, and similarly, it can provide reliable routing for reliable-sensitive information. The other QoS-aware protocols are used or selected for a particular network, according to the data type and its QoS requirements.
Temperature‑aware routing protocols
The antenna radiation, its absorption and interference are the major challenges to be considered while designing a body sensor network, since the radiated fields cause a temperature rise of node’s electronic circuitry. The field of radiation also has a strong
Table 2 Cross layered protocols in body area networks
Sl. no. Protocol [ref. no.] Goal Performance metrics Compared with
1 Biocomm and Biocomm-D [17]
To optimize the overall performance of the in vivo network
Maximum temperature rise, average energy con-sumption, packet delivery delay, percentage of injected packets dropped, cumulative network throughput
Shortest hop routing, hotspot preventive routing
2 Cascading informa-tion retrieval by controlling access with distributed slot assignment (CICADA) [14]
To introduce less delay and low energy con-sumption
End to end delay –
3 TImezone coordinated sleep scheduling (TICOSS) [15]
To improve 802.15.4 through the division of the network into time zones
Network lifetime IEEE 802.15.4 without TICOSS
4 Wireless autonomous spanning tree proto-col (WASP) [16]
To reduce power consumption, network delay and packet loss rate
End to end delay CSMA with fixed routing
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impact on the human body [57] that may damage the human tissues due to its continu-ous exposure. The goal of all temperature-aware protocols is to decrease the temperature rise of in-body sensor nodes by avoiding routing through hotspots. Table 5 discusses the existing temperature-aware protocols. From the comparison of different protocols, it is seen that LTRT [58] performs much better than others while TARA [59] performs worst. In terms of temperature rise, HPR [60] shows less temperature rise in comparison with others. Finally, the latest M2E2 protocol [61] has proved that it is the best one among all thermal aware protocols, suitable for heterogeneous, multimode, energy efficient body sensor networks.
Future challenges and comparative analysis of routing protocolsThe scope of this article is to open up new research areas in WBAN domain for routing protocol designs. Among all the routing protocols, the cluster based protocol HIT [8] aims at maximizing the network lifetime, but it does not consider the packet delivery ratio which is an important QoS metric. The second one, AnyBody [11] protocol consid-ers the delivery ratio, but does not consider average delay, mobility and energy consump-tion. It leads to poor security measures. In order to optimize the performance of sensor networks for some specific applications, it is necessary to include the aforementioned metrics also in the design considerations.
The cross layered concept is attaining great significance and interest among research-ers due to its flexibility and effectiveness in sensor networks. The future research work aims at improving the reliability of CICADA [14] which performs better in terms of energy efficiency and average delay when compared to other cross layered protocols.
Table 3 Postural routing protocols in body area networks
Sl. no.
Protocol [ref. no.] Goal Performance metrics Compared with
1 Energy efficient thermal and power aware rout-ing (ETPA) [19]
To reduce the node’s temperature and to prevent the hotspot formation
Packet hop count, packet delivery ratio, packet delay, average tem-perature rise of nodes in the network, scalability, depletion time of the first node mobility
PRPLC
2 Prediction based secure and reliable routing framework (PSR) [20]
To provide secure and reliable routing from various data attacks
Packet dropping rate, authentication cost
Backbone protocol
3 Opportunistic routing [21] To increase the network lifetime from the motion of body parts
Average energy consump-tion per bit
Single hop, multi hop
4 Distance vector routing with postural link costs (DVRPLC) [22]
To decrease end to end delay
Average delay, packet delivery ratio, packet hop count
Probabilistic proto-col (PROPHET), opportunistic protocol, UTILITY
5 On-body store and flood routing (OBSFR) [18]
To provide better rout-ing delay and hop count
End-to-end packet delay (PD), number of transmissions per packet (NTPP), packet delivery ratio (PDR)
PROPHET, on-body store and flood routing (OBSFR)
6 Probabilistic routing with postural link costs (PRPLC) [23]
To reduce end to end delay
Average delay, packet delivery ratio
PROPHET, OBSFR
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Tabl
e 4
QoS
aw
are
rout
ing
prot
ocol
s in
bod
y ar
ea n
etw
orks
Sl. n
o.Pr
otoc
ol [r
ef. n
o.]
Goa
lPe
rfor
man
ce m
etri
csCo
mpa
red
with
1Effi
cien
t nex
t hop
sel
ectio
n al
gorit
hm
(EN
SA-B
AN
) [25
]To
impr
ove
the
over
all Q
oS p
erfo
rman
ce
of th
e ne
twor
k us
ing
link
cost
func
tion
Ener
gy c
onsu
mpt
ion,
pac
kets
forw
arde
d, e
nd-t
o-en
d de
lay,
pac
ket d
eliv
ery
ratio
DM
QoS
2En
ergy
effi
cien
t rou
ting
algo
rithm
[26]
To m
axim
ize
the
wor
king
life
time
of th
e ne
twor
kCo
vera
ge d
ista
nce,
resi
dual
ene
rgy,
co
mm
unic
atio
n co
unt n
ode
criti
calit
y–
3M
ulti-
hop
prot
ocol
usi
ng c
ost f
unct
ion
[27]
To b
oost
the
netw
ork
perf
orm
ance
and
life
time
by
opt
imum
resi
dual
ene
rgy
and
dist
ance
Num
ber o
f dea
d no
des,
resi
dual
ene
rgy,
dat
a pa
cket
s se
nt a
nd re
ceiv
ed to
sin
k, d
elay
Old
ene
rgy
awar
e m
ulti-
hop
4C
ritic
al d
ata
rout
ing
(CD
R) [2
8]To
forw
ard
the
criti
cal d
ata
pack
ets
with
bet
ter
relia
bilit
y al
ong
with
redu
ctio
n in
tem
pera
ture
ris
e of
the
in-b
ody
sens
or n
odes
Pack
et lo
ss ra
tio, p
acke
t suc
cess
ratio
, on-
time
pack
et d
eliv
ery
ratio
, ene
rgy
cons
umpt
ion
TMQ
oS, L
TRT
5Th
resh
old
sens
itive
ene
rgy
effici
ent s
enso
r ne
twor
k pr
otoc
ol (T
EEN
) [29
]To
mon
itor a
nd re
cord
crit
ical
dat
a of
the
pa
tient
’s bo
dy p
aram
eter
sEn
ergy
con
sum
ptio
n, fa
lse
acce
ptan
ce ra
te, f
alse
re
ject
ion
rate
, tim
e se
rved
–
6A
dapt
ive
rout
ing
and
band
wid
th a
lloca
tion
pr
otoc
ol (A
RBA
) [30
]To
enh
ance
ban
dwid
th u
tiliz
atio
n an
d ro
utin
g in
BA
N, b
ette
r net
wor
k lif
etim
eRe
sidu
al e
nerg
y, th
roug
hput
Opt
imal
sol
utio
n
7Re
lay
base
d ro
utin
g pr
otoc
ol [3
1]Fo
r net
wor
k lif
etim
e m
axim
izat
ion
and
end-
to-
end-
dela
y (E
2ED
) min
imiz
atio
nRe
mai
ning
ene
rgy,
no.
of d
ead
node
s, no
. of
dro
pped
pac
kets
, pac
ket a
rriv
al ra
teSi
ngle
-hop
Mul
ti-ho
pC
H-r
otat
e
8Li
nk-a
war
e an
d en
ergy
effi
cien
t sch
eme
for b
ody
area
net
wor
ks (L
AEE
BA) [
32]
To ro
ute
data
with
min
imum
pat
h-lo
ss o
ver t
he
link
in W
BAN
Stab
ility
per
iod,
resi
dual
ene
rgy,
net
wor
k lif
etim
e,
path
-loss
, del
ay s
prea
d, th
roug
hput
SIM
PLE,
M-A
TTEM
PT
9Co
oper
ativ
e lin
k-aw
are
and
ener
gy e
ffici
ent
prot
ocol
for W
BAN
(Co-
LAEE
BA) [
33]
To s
elec
t bet
ter r
outin
g pa
th w
ith m
inim
um
path
-loss
in c
oope
rativ
e lin
ks in
WBA
NSt
abili
ty p
erio
d, re
sidu
al e
nerg
y, p
ath-
loss
, th
roug
hput
LAEE
BA, S
IMPL
E, M
-ATT
EMPT
10Tw
o-ho
p tr
ansm
issi
on s
chem
e [3
4]To
ext
end
netw
ork
lifet
ime
and
to im
prov
e th
e ne
twor
k st
abili
ty o
f WBA
NA
vera
ge re
sidu
al e
nerg
y, n
umbe
r of p
acke
ts p
er
prio
rity
leve
l, to
tal e
nerg
y, n
umbe
r of d
ead
node
s
Dire
ct tr
ansm
issi
on, T
PDS
11M
odifi
ed L
AEE
BA: l
ink
awar
e an
d en
ergy
effi
cien
t sc
hem
e fo
r BA
N (M
LEEB
A) [
35]
To u
pgra
de th
e LE
EBA
pro
toco
l by
incr
easi
ng
thro
ughp
ut a
nd d
ecre
asin
g de
lay
PDR,
end
to e
nd d
elay
, thr
ough
put
LEEB
A
12Re
liabi
lity
awar
e ro
utin
g (R
AR)
[36]
To e
nhan
ce re
liabi
lity
for r
elia
bilit
y co
nstr
aint
da
ta p
acke
tsPa
cket
loss
ratio
, ave
rage
PD
R, a
vera
ge e
nerg
y co
nsum
ptio
nRA
R w
ith re
lay
node
s, TM
QoS
13Za
hoor
ene
rgy
and
QoS
-aw
are
rout
ing
prot
ocol
(Z
EQoS
) [37
]To
pro
vide
bet
ter Q
oS b
y se
lect
ing
the
best
ro
utin
g pa
ths
Ener
gy c
onsu
mpt
ion,
suc
cess
ful t
rans
mis
sion
ra
te, p
acke
ts fo
rwar
ded
and
rece
ived
DM
QoS
, NoR
outin
g
Page 8 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 4
cont
inue
d
Sl. n
o.Pr
otoc
ol [r
ef. n
o.]
Goa
lPe
rfor
man
ce m
etri
csCo
mpa
red
with
14Q
oS a
war
e pe
erin
g ro
utin
g pr
otoc
ol fo
r rel
iabi
lity
sens
itive
dat
a (Q
PRR)
[38]
To e
nhan
ce th
e re
liabl
e de
liver
y of
em
erge
ncy
BAN
dat
a fo
r ind
oor h
ospi
tal c
omm
unic
atio
nSu
cces
sful
tran
smis
sion
rate
, net
wor
k tr
affic
load
, ov
eral
l ene
rgy
cons
umpt
ion,
late
ncy
DM
Qos
, NoR
outin
g
15D
ista
nce
awar
e re
layi
ng e
nerg
y effi
cien
t pro
toco
l (D
ARE
) [39
]To
ach
ieve
bet
ter n
etw
ork
lifet
ime
for m
onito
ring
patie
nts
in m
ulti-
hop
body
are
a ne
twor
ksRe
sidu
al e
nerg
y, P
DR,
num
ber o
f pac
kets
se
nt to
sin
kM
-ATT
EMPT
16St
able
incr
ease
d-th
roug
hput
mul
ti-ho
p pr
otoc
ol
for l
ink
effici
ency
(SIM
PLE)
[40]
To b
oost
the
netw
ork
stab
ility
per
iod
and
pack
et
deliv
ered
to s
ink
Net
wor
k lif
etim
e, s
tabi
lity
perio
d, th
roug
hput
, re
sidu
al e
nerg
y, p
ath
loss
ATTE
MPT
17Q
oS-a
war
e pe
erin
g ro
utin
g pr
otoc
ol fo
r del
ay
sens
itive
dat
a (Q
PRD
) [41
]To
less
en th
e en
d to
end
del
ayTr
affic
load
, suc
cess
ful t
rans
mis
sion
rate
, num
ber
of p
acke
ts ti
meo
utD
MQ
oS
18Q
-lear
ning
bas
ed ro
utin
g pr
otoc
ol (Q
RP) [
42]
To d
esig
n a
pow
er e
ffici
ent a
nd re
duce
d ho
p co
unt b
ody
sens
or n
etw
orks
Resi
dual
ene
rgy,
ave
rage
hop
cou
ntPS
R, E
BRA
R
19A
dapt
ive
mul
tihop
tree
-bas
ed ro
utin
g (A
MR)
pr
otoc
ol [4
3]To
ass
ess
seve
ral n
ode
and
netw
ork
para
met
ers
in
ord
er to
enh
ance
net
wor
k pe
rfor
man
ce
usin
g fu
zzy
logi
c
Net
wor
k lif
etim
e, P
DR,
nor
mal
ized
resi
dual
en
ergy
Shor
test
pat
h tr
ee, r
ecei
ved
sig-
nal s
tren
gth
indi
cato
r, ba
tter
y
20G
atew
ay s
elec
tion
algo
rithm
[44]
To a
dapt
ivel
y se
lect
the
gate
way
nod
e fo
r bal
anc-
ing
the
load
am
ong
the
node
sN
etw
ork
lifet
ime
No
ener
gy a
nd in
depe
nden
t en
ergy
har
vest
ing
devi
ce
21En
ergy
-aw
are
peer
ing
rout
ing
prot
ocol
(EPR
) [45
]To
enh
ance
BA
N re
liabi
lity
and
to re
duce
net
wor
k tr
affic
and
pow
er c
onsu
mpt
ion
Traffi
c lo
ad, e
nerg
y co
nsum
ed a
nd s
aved
, buff
er
over
flow
, pac
kets
forw
arde
d an
d re
ceiv
edD
MQ
oS
22En
ergy
-aw
are
topo
logy
des
ign
(EAW
D) [
46]
To re
duce
the
tota
l ene
rgy
cons
umpt
ion
and
inst
alla
tion
cost
by
wire
less
sen
sors
and
rela
ysTo
tal e
nerg
y co
nsum
ed, i
nsta
llatio
n co
st–
23En
ergy
-bal
ance
d ra
te a
ssig
nmen
t and
ro
utin
g pr
otoc
ol (E
BRA
R) [4
7]To
less
en th
e to
tal e
nerg
y co
nsum
ed in
the
netw
ork
at th
e ex
pens
e of
hig
h ne
twor
k ut
ility
, ad
aptiv
e re
sour
ce a
lloca
tion
Nor
mal
ized
resi
dual
ene
rgy,
rout
ing
tree
siz
eEB
RAR-
SP, E
BRA
R-PD
, EBR
AR-
PE
24En
ergy
-effi
cien
t rou
ting
sche
me
(EER
S) [4
8]To
pro
vide
ada
ptiv
e tr
ansm
issi
on p
ower
for s
enso
r no
des,
esta
blis
h an
ene
rgy-
effici
ent p
ath
Pack
et re
cept
ion
ratio
, ave
rage
hop
cou
nt, c
ol-
lect
ion
dela
y, a
vera
ge n
umbe
r of t
rans
mis
sion
s pe
r pac
ket,
ener
gy c
onsu
mpt
ion
per p
acke
t, pe
r ho
p, o
verh
ead
Colle
ctio
n tr
ee p
roto
col (
CTP
)
25M
odifi
ed D
ijkst
ra’s
glob
al ro
utin
g al
gorit
hm [4
9]To
yie
ld b
ette
r net
wor
k lif
etim
e in
WBA
NN
etw
ork
lifet
ime
ratio
, ene
rgy
per b
it ra
tioO
ppor
tuni
stic
rout
ing,
tran
smit
pow
er a
dapt
atio
n, m
in.
ener
gy p
acke
t for
war
ding
, use
of
ded
icat
ed re
lays
Page 9 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 4
cont
inue
d
Sl. n
o.Pr
otoc
ol [r
ef. n
o.]
Goa
lPe
rfor
man
ce m
etri
csCo
mpa
red
with
26D
ata-
cent
ric m
ulti
obje
ctiv
e Q
oS-a
war
e
rout
ing
prot
ocol
(DM
QoS
) [50
]To
ach
ieve
bes
t QoS
ser
vice
s fo
r diff
eren
t dat
a ty
pes
Ave
rage
end
-to-
end
dela
y, o
n-PD
R, a
vera
ge
ener
gy c
onsu
mpt
ion
per p
acke
t, op
erat
ion
ener
gy o
verh
ead
MM
SPEE
DLO
CA
LMO
RD
ARA
27Ra
ndom
con
tent
ion-
base
d re
sour
ce
allo
catio
n pr
otoc
ol (R
ACO
ON
) [51
]To
pro
vide
bet
ter t
he q
ualit
y of
ser
vice
for m
ulti-
user
mob
ile w
irele
ss b
ody
area
net
wor
ksPa
cket
late
ncy,
pow
er c
onsu
mpt
ion,
pa
cket
col
lisio
n, u
ser c
apac
ityBo
dyQ
oS
28H
euris
tic a
dapt
ive
rout
ing
algo
rithm
[52]
To m
ake
mul
ti-ho
p W
BAN
ene
rgy
effici
ent
Life
time,
sta
ndar
d de
viat
ion
of re
mai
ning
pow
er,
aver
age
end
to e
nd d
elay
, pac
ket l
oss
Opt
imal
sch
eme
29En
viro
nmen
t-ad
aptiv
e ro
utin
g al
gorit
hm (E
AR)
[5
3]To
ach
ieve
bet
ter n
etw
ork
lifet
ime
and
relia
ble
com
mun
icat
ion
for h
eter
ogen
eous
net
wor
ksN
umbe
r of a
live
node
s, am
ount
of c
olle
cted
dat
a in
the
coor
dina
tor
Hop
-cou
nt b
ased
met
hod,
en
ergy
-bas
e m
etho
d
30Lo
caliz
ed m
ulti-
obje
ctiv
e ro
utin
g pr
otoc
ol
(LO
CA
LMO
R) [5
4]To
con
side
r the
traffi
c di
vers
ity ty
pica
l for
bio
med
i-ca
l app
licat
ions
and
to p
rovi
de a
diff
eren
tiatio
n ro
utin
g fo
r diff
eren
t qua
lity
of s
ervi
ce (Q
oS)
met
rics
Pack
et re
cept
ion
ratio
, end
to e
nd d
elay
, pac
kets
re
ceiv
ing
with
in d
eadl
ine
SPEE
D, M
MSP
EED
, GFW
, EA
GFS
31Re
info
rcem
ent l
earn
ing
base
d ro
utin
g w
ith Q
oS
supp
ort (
RL-Q
RP) p
roto
col [
55]
To a
ttai
n de
sira
ble
QoS
in re
spec
t of t
hrou
ghpu
t an
d en
d to
end
del
ayA
vera
ge e
nd to
end
del
ay, a
vera
ge P
DR,
nod
e m
obili
ty, n
etw
ork
traffi
c lo
adQ
oS-A
OD
V
32Q
oS a
war
e ro
utin
g se
rvic
eTo
pro
vide
ser
vice
with
prio
ritiz
ed ro
utin
g, u
ser
spec
ific
QoS
End
to e
nd d
elay
, pac
ket d
eliv
ery
ratio
–
Page 10 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 5
Tem
pera
ture
aw
are
rout
ing
prot
ocol
s in
bod
y ar
ea n
etw
orks
Sl. n
o.Pr
otoc
ol [r
ef. n
o.]
Goa
lPe
rfor
man
ce m
etri
csCo
mpa
red
with
1M
2 E2 pro
toco
l [61
]To
redu
ce e
nerg
y co
nsum
ptio
n an
d in
crea
se li
fe ti
me,
to
redu
ce li
nk h
otsp
ot n
etw
ork
in h
eter
ogen
eous
W
BSN
s
Pack
ets
sent
to B
S, n
umbe
r of d
ead
node
s, to
tal e
nerg
y of
net
wor
k, n
umbe
r of c
lust
er h
ead
M-A
TTEM
PT
2Re
liabi
lity
enha
nced
-ada
ptiv
e th
resh
old
base
d th
erm
al
unaw
are
ener
gy-e
ffici
ent m
ulti-
hop
prot
ocol
(RE-
ATTE
MPT
) [62
]
To m
axim
ize
the
netw
ork
lifet
ime
and
to re
mov
e th
e de
ficie
ncie
s of
ATT
EMPT
pro
toco
lA
vera
ge ra
te o
f dea
d no
des,
num
ber o
f pac
kets
sen
t to
sink
, pac
kets
dro
pped
and
thro
ughp
utAT
TEM
PT
3Th
erm
al-a
war
e m
ulti
cons
trai
ned
intr
a bo
dy Q
oS ro
ut-
ing
prot
ocol
(TM
QoS
) [63
]To
ens
ure
the
requ
ired
QoS
dem
ands
alo
ng w
ith
mai
ntai
ning
the
tem
pera
ture
of e
ach
node
to a
des
ir-ab
le le
vel
Ener
gy e
ffici
ency
, ave
rage
tem
pera
ture
rise
, del
ay re
li-ab
ility
, max
imum
tem
pera
ture
rise
, pac
kets
mee
ting
dead
line
LTRT
, TA
RA
4M
obili
ty-s
uppo
rtin
g ad
aptiv
e th
resh
old-
base
d th
erm
al-a
war
e en
ergy
-effi
cien
t mul
ti-ho
p pr
otoc
ol
(M-A
TTEM
PT),
ATTE
MPT
[64]
To s
ense
the
link
hot-
spot
, to
forw
ard
the
data
aw
ay
from
suc
h lin
ksN
umbe
r of d
ead
node
s, th
roug
hput
, num
ber o
f clu
ster
he
ads
per r
ound
, tot
al e
nerg
y of
net
wor
kM
ulti-
hop
5Th
erm
al-a
war
e sh
orte
st h
op ro
utin
g al
gorit
hm (T
HSR
) [6
5]To
redu
ce th
e no
de’s
tem
pera
ture
and
to p
reve
nt th
e fo
rmat
ion
of h
otsp
otM
axim
um te
mpe
ratu
re ri
se, a
vera
ge d
elay
, pac
kets
dr
oppe
dLT
R, S
HR,
HPR
, TA
RA
6H
otsp
ot p
reve
ntin
g ro
utin
g (H
PR) a
lgor
ithm
[60]
To p
reve
nt th
e fo
rmat
ion
of h
otsp
ots
and
to re
duce
th
e av
erag
e pa
cket
del
iver
y de
lay
Max
imum
tem
pera
ture
rise
of a
ny n
ode,
ave
rage
pa
cket
del
iver
y de
lay,
tota
l num
ber o
f pac
kets
dr
oppe
d
TARA
, SH
R
7Ro
utin
g al
gorit
hm fo
r net
wor
k of
hom
ogen
eous
and
Id
-less
bio
med
ical
sen
sor n
odes
(RA
IN) [
66]
To b
e us
ed in
an
in v
ivo
netw
ork
of h
omog
eneo
us a
nd
id-le
ss b
iom
edic
al s
enso
r nod
esM
axim
um te
mpe
ratu
re ri
se o
f nod
es, a
vera
ge e
nerg
y co
nsum
ptio
n pe
r nod
e, p
erce
ntag
e pa
cket
del
iver
y,
aver
age
pack
et d
eliv
ery
dela
y
Cont
rolle
d-FL
OO
D
8Le
ast t
otal
-rou
te te
mpe
ratu
re ro
utin
g pr
otoc
ol (L
TRT)
[5
8]To
redu
ce te
mpe
ratu
re c
ause
d by
bio
med
ical
sen
sors
im
plan
ted
in h
uman
bod
ies
Ave
rage
tem
pera
ture
rise
and
hop
cou
nt p
er a
rriv
al
pack
etLT
R, A
LTR
9Le
ast t
empe
ratu
re ro
utin
g pr
otoc
ol (L
TR),
adap
tive
leas
t tem
pera
ture
rout
ing
prot
ocol
(ALT
R) [6
7]To
redu
ce th
e am
ount
of h
eat p
rodu
ced
in th
e ne
twor
kA
vera
ge te
mpe
ratu
re ri
se, d
elay
, pow
er c
onsu
mpt
ion,
pa
cket
s dr
oppe
d, li
fetim
e of
net
wor
ksTA
RA, S
HR
10Th
erm
al-a
war
e ro
utin
g al
gorit
hm (T
ARA
) [59
]To
est
imat
e th
e te
mpe
ratu
re v
aria
tion
of n
eigh
bor
node
s an
d di
rect
pac
kets
nea
rby
the
hots
pot r
egio
nM
axim
um te
mpe
ratu
re ri
se, a
vera
ge te
mpe
ratu
re ri
se,
dela
y pe
rfor
man
ceSh
orte
st h
op
Page 11 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
When IEEE 802.15.4 standard networks are used, it will be good if the TICOSS [15] pro-tocol is redesigned for reducing the average delay which is not considered in the existing one. If the entire network performance is to be optimized, the choice will be the Bio-comm protocols along with new techniques to reduce the node temperature. Hence, the scope for research in this area is very much wide enough to work with. The comparative analysis of postural movement protocols has shown that, none of them considered the thermal effects of nodes and QoS issues together. Therefore, the future protocols could be proposed in such a way that, it could achieve better QoS parameters along with tech-niques to reduce the node temperature rise and methods to counter security attacks. The survey on QoS aware protocols unveils various research areas for future work because of its importance. Every new protocol, which has been designed, is meant for addressing the limitation of the previous one. For example, in routing service framework [56] and reinforcement learning based routing with QoS support [55] the energy consumption is not considered, but it is taken into account in the remaining protocols.
In almost, all the existing QoS aware protocols, only the QoS metrics are exam-ined, without concentrating on the human body movements and temperature rise of implanted devices. The proposed temperature aware protocols perform better by reduc-ing the temperature rise due to radiation from antenna and other node circuitry. Along with the thermal issue and power consumption, it will be better if these protocols can also address the routing issues like shortest path as in QoS aware.
Table 6 summarizes the comparison between some of the routing protocols used in WBAN. From the analysis, it is seen that almost all the protocols have considered dif-ferent QoS metrics for their performance analysis. Hence, choosing the protocol for a WBAN system depends on the particular application of the system; whether it should be energy efficient, good reliable one or it should reduce the temperature of the node cir-cuitry. Table 7 lists the pros and cons and the application domain of each protocols used in body area networks. This table helps to choose a particular protocol based on the QoS requirements. For example, if the application of the proposed system is patient moni-toring in hospitals, then the protocols like WASP [16] or TICOSS [15] can be selected due to its high packet delivery ratio and low average delay. If the sensors are implanted within the body, then the protocols like Co-LEEBA [33], TARA [59], or routing algo-rithm for network of homogeneous and Id-less biomedical sensor nodes (RAIN) [66] can be chosen. If the network is heterogeneous one, then M-ATTEMPT or M2E2 can be used.
ConclusionsWireless body area network is a part of wireless sensor network, with a number of nodes deployed within and on the surface of human body to measure different bio-logical parameters for a particular application. In this survey article, various existing routing protocols that are used in WBANs are categorized and briefly analyzed from the available articles between the years 2002–2016. It is seen that the routing proto-col plays a vital role in the design process of every efficient, reliable, low cost wireless
Page 12 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
body sensor networks. Based on the structure and nature of networks, the routing protocols for WBANs are categorized as cluster-based, cross-layered, postural move-ment based QoS aware and temperature-aware protocols. It is observed that there is no strict classification of protocols is possible since most of them aims or results in achieving the challenges of sensor networks. It is also concluded that each protocol is application dependent, i.e., the protocols used for daily monitoring and the criti-cal medical cases are different. The future directions for each group of protocols are also presented which helps the researchers to focus on their interested area. Also, a comparative study of different protocols has been examined so that an appropriate protocol can be selected according to the targeted application. This survey will ben-efit the researchers to study the existing routing protocols for WBANs in the field of healthcare systems.
The Future work includes the design and implementation of a body sensor prototype with a newly designed routing protocol, which will be highly energy efficient and reliable one for rehabilitation of old age people using a microcontroller based system with suit-able sensors.
Table 6 Comparative analysis of routing protocols
* Not applicable
Sl. no. Routing protocol
Type of protocol
Average delay
Energy consumption
Packet delivery ratio
Average temperature rise
1 HIT [8] Clustered Very Low Low * *
2 AnyBody [11] Clustered * * Very high *
3 WASP [16] Cross-layered Low Low Very high *
4 CICADA [14] Cross-layered Low Low * *
5 TICOSS [15] Cross-layered * Low High *
6 Routing service framework [56]
QoS-aware * * Moderate *
7 RL-QRP [55] QoS-aware High * High *
8 ZEQoS [37] QoS-aware * High High *
9 RL-QRP [55] QoS-aware Low Low High *
10 ENSA-BAN [25] QoS-aware Very low Low Very high *
11 Co-LEEBA [33] QoS-aware * Low High *
12 DMQoS [50] QoS-aware Low Moderate High *
13 LOCALMOR [54] QoS-aware Low Low High *
14 TARA [59] Temperature-aware Very High High Low Low
15 LTRT [58] Temperature-aware * * Very high Low
16 RAIN [66] Temperature-aware Moderate Low High Low
17 M-ATTEMPT [64] Temperature-aware Low Low High Low
18 M2E2 [61] Temperature-aware Low Very low Very high Low
19 TMQoS [63] Temperature-aware Very low Very low High Moderate
20 ETPA [19] Postural High Low High Low
21 PSR [20] Postural Low High Moderate *
Page 13 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 7
Pros
and
con
s an
d th
e ap
plic
atio
n do
mai
n of
rout
ing
prot
ocol
s
Sl. n
o.Ro
utin
g pr
otoc
olPr
osCo
nsA
pplic
atio
n do
mai
n
1H
IT [8
]Re
quire
s on
ly 2
5% o
f the
tim
e re
quire
d by
PE
GA
SIS
and
LEA
CH
for d
ata
colle
ctio
n, n
etw
ork
long
evity
is 1
.05
and
1.44
tim
es th
at o
f PEG
ASI
S an
d LE
AC
H re
spec
tivel
y
Not
men
tione
d fo
r a s
peci
fic m
edic
al a
pplic
atio
n,
the
term
s se
curit
y, fa
ult-
tole
ranc
e an
d re
liabi
lity
of th
e ne
twor
k ne
ed to
be
addr
esse
d
Mic
ro s
enso
r net
wor
ks, b
io m
edic
al s
ensi
ng li
ke
EEG
and
EM
G s
igna
ls, b
io e
lect
ric c
ompu
ter
inte
rfac
es
2A
nyBo
dy [1
1]A
sel
f-or
gani
zing
pro
toco
l, m
aint
ains
con
stan
t nu
mbe
r of c
lust
ers
with
incr
easi
ng n
umbe
r of
node
s. PD
R is
app
roxi
mat
ely
100%
The
met
rics
like
netw
ork
dela
y an
d en
ergy
co
nsum
ptio
n ar
e no
t ana
lyze
d. H
ence
can
not
cons
ider
ed fo
r crit
ical
med
ical
app
licat
ions
Perio
dic
patie
nt m
onito
ring
in h
ospi
tals
3W
ASP
[16]
Min
imiz
es th
e co
ordi
natio
n ov
erhe
ad, t
hrou
gh-
put o
btai
ned
is 9
4%. I
t can
min
imiz
e th
e de
lay
by re
duci
ng th
e nu
mbe
r of l
evel
s in
the
span
-ni
ng tr
ee a
nd a
lso
the
ener
gy c
onsu
mpt
ion
Mob
ility
is n
ot s
uppo
rted
, hen
ce c
anno
t be
used
fo
r dyn
amic
sen
sor n
etw
ork
appl
icat
ions
For i
ndoo
r hos
pita
l pat
ient
mon
itorin
g
4C
ICA
DA
[14]
Enha
nced
mob
ility
is s
uppo
rted
, gen
erat
ion
of
the
sche
me
is e
asie
r, en
d to
end
del
ay is
abo
ut
110
ms,
node
s w
ake
up o
nly
to tr
ansm
it an
d re
ceiv
e da
ta, h
ence
dis
sipa
tion
of e
nerg
y is
m
inim
ized
It do
es n
ot s
uppo
rt tr
affic
from
the
sink
to
the
node
sFo
r sen
sors
whe
re c
ompu
tatio
nal r
esou
rces
are
sc
arce
5TI
COSS
[15]
Dou
bles
the
netw
ork
lifet
ime
for h
igh
traffi
c sc
e-na
rios,
PDR
is h
ighe
r tha
n 92
%. L
ifetim
e of
4 m
in
per J
oule
for T
ICO
SS w
ith 8
02.1
5.4
and
2 m
in
per J
oule
with
802
.15.
4 al
one,
sav
es e
nerg
y du
e to
tim
ezon
e co
ordi
nate
d sl
eepi
ng m
echa
nism
Not
sui
tabl
e fo
r del
ay to
lera
nt n
etw
orks
and
als
o it
is n
ot a
n ap
plic
atio
n sp
ecifi
c pr
otoc
olCo
ntin
uous
vita
l sig
n m
onito
ring,
for a
mbi
ent
sens
or n
odes
pla
ced
thro
ugho
ut a
site
6Ro
utin
g se
rvic
e FR
AM
EWO
RK
[56]
Prov
ides
prio
ritiz
ed ro
utin
g se
rvic
e, u
ser s
peci
fic
QoS
sup
port
for s
mal
l sca
le n
etw
orks
Not
con
side
red
ener
gy c
onsu
mpt
ion,
whi
ch is
on
e of
the
maj
or c
onst
rain
ts o
f sen
sor n
etw
orks
Dyn
amic
, sm
all s
cale
wire
less
bod
y ar
ea n
etw
orks
7RL
-QRP
[55]
Use
s in
depe
nden
t dis
trib
uted
rein
forc
emen
t le
arni
ng a
ppro
ach
for Q
oS ro
ute
calc
ulat
ion,
PD
R ab
ove
90%
Ave
rage
del
ay is
hig
her (
abov
e 20
0 m
s), e
nerg
y co
nsum
ptio
n is
not
con
side
red,
not
suffi
cien
t fo
r glo
bal o
ptim
izat
ion
in la
rge
scal
e ne
twor
ks
Dyn
amic
, sm
all s
cale
wire
less
bod
y ar
ea n
etw
orks
8ZE
QoS
[37]
Suita
ble
and
effec
tive
for a
ll da
ta ty
pes
like
ordi
-na
ry, d
elay
and
relia
bilit
y se
nsiti
ve p
acke
ts, 8
4%
cons
iste
nt th
roug
hput
No
cons
ider
able
impr
ovem
ent i
n te
rms
of e
nerg
y co
nsum
ptio
nH
ospi
tal B
AN
com
mun
icat
ion
9RL
-QRP
[55]
Fits
wel
l in
dyna
mic
env
ironm
ents
usi
ng o
ptim
al
rout
ing
polic
y. G
ood
perf
orm
ance
dur
ing
heav
y tr
affic
cond
ition
s, av
erag
e de
lay
is le
ss th
an
200
ms
Not
sui
tabl
e fo
r lar
ge s
cale
net
wor
ks li
ke m
ulti
agen
t sys
tem
sD
ynam
ic b
io-m
edic
al s
enso
r net
wor
ks
Page 14 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 7
cont
inue
d
Sl. n
o.Ro
utin
g pr
otoc
olPr
osCo
nsA
pplic
atio
n do
mai
n
10EN
SA-B
AN
[25]
Alo
ng w
ith a
ll Q
oS re
quire
men
ts, i
t con
side
rs
the
ener
gy c
onsu
mpt
ion
of n
odes
to im
prov
e th
e ne
twor
k pe
rfor
man
ce. A
ppro
x. 9
6% P
DR
com
pare
d to
DM
QoS
can
be
achi
eved
, ave
rage
de
lay
is le
ss th
an 1
6 m
s
Alth
ough
it is
a Q
oS a
war
e ro
utin
g pr
otoc
ol, t
he
body
mov
emen
t is
not c
onsi
dere
dCo
ntin
uous
pat
ient
mon
itorin
g se
nsor
net
wor
ks
11Co
-LEE
BA [3
3]It
is a
link
aw
are
rout
ing
prot
ocol
. Pat
h lo
ss is
re
duce
d du
e to
the
use
of d
iffer
ent p
ath
loss
m
odel
s. W
ith th
e di
scon
tinuo
us d
ata
tran
smis
-si
on, i
t pro
vide
s be
tter
life
tim
e. It
max
imiz
es
the
thro
ughp
ut to
36
Mbp
s co
mpa
red
to o
ther
pr
otoc
ols
with
a th
roug
hput
of a
vera
ge 2
Mbp
s
Max
imiz
es th
e th
roug
hput
at t
he c
ost o
f in
crea
sed
dela
yIm
plan
ted
sens
ors,
mon
itorin
g of
age
d pe
ople
12D
MQ
oS [5
0]U
ses
mod
ular
arc
hite
ctur
e fo
r del
ay c
ritic
al a
nd
relia
bilit
y cr
itica
l pac
kets
, end
to e
nd d
elay
is
less
than
120
ms
whe
n co
mpa
red
to o
ther
QoS
aw
are
prot
ocol
s of
260
ms.
PDR
is a
bove
92%
fo
r var
ying
traffi
c flo
ws
The
estim
atio
n of
sev
eral
tuni
ng p
aram
eter
s is
no
t ana
lytic
al. T
hey
are
fixed
thro
ugh
diffe
rent
si
mul
atio
n ex
perim
ents
Reso
urce
-con
stra
ined
bod
y ar
ea n
etw
orks
13LO
CA
LMO
R [5
4]Th
is ro
utin
g al
gorit
hm c
an b
e us
ed a
long
with
an
y M
AC
pro
toco
l with
AC
K m
echa
nism
, con
-si
dere
d th
e di
vers
ity o
f dat
a tr
affic
like
regu
lar,
dela
y- s
ensi
tive,
relia
bilit
y-se
nsiti
ve a
nd c
ritic
al
traffi
c, e
nd to
end
del
ay is
less
than
200
ms,
Pack
et re
cept
ion
ratio
n is
abo
ve 8
5%
Scal
abili
ty o
f the
pro
toco
l with
hig
her n
umbe
r of
sens
or n
odes
sho
uld
be in
vest
igat
edD
iver
se tr
affic
biom
edic
al a
pplic
atio
ns
14TA
RA [5
9]H
andl
es d
ata
tran
smis
sion
in th
e pr
esen
ce o
f te
mpe
ratu
re h
ot s
pots
, rou
tes
pack
ets
thro
ugh
low
tem
pera
ture
are
a, h
as lo
ad b
alan
cing
cap
a-bi
lity,
sm
alle
r ave
rage
tem
pera
ture
rise
Hig
her p
acke
t los
s du
e to
larg
er d
elay
(gre
ater
th
an 4
00 m
s), u
niqu
e ha
rdw
are
ids
for n
odes
he
nce
this
alg
orith
m fa
ils to
ope
rate
in id
-less
se
nsor
nod
es, h
omog
eneo
us a
nd n
ot e
mer
-ge
ncy
supp
orte
d
Impl
ante
d se
nsor
net
wor
ks a
nd a
pplic
atio
ns li
ke
retin
al p
rost
hesi
s an
d ca
ncer
det
ectio
n
15LT
RT [5
8]O
ptim
izat
ion
of ro
utin
g is
acc
ompl
ishe
d, v
ery
high
pac
ket d
eliv
ery
ratio
, whi
ch is
clo
se to
10
0%, S
mal
ler a
vera
ge te
mpe
ratu
re ri
se
The
anal
ysis
is d
one
only
on
the
aver
age
tem
-pe
ratu
re ri
se a
nd p
acke
t los
s ra
teIm
plan
ted
bio
-med
ical
net
wor
ks, c
ardi
ac p
atie
nt
mon
itorin
g ap
plic
atio
ns
Page 15 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
Tabl
e 7
cont
inue
d
Sl. n
o.Ro
utin
g pr
otoc
olPr
osCo
nsA
pplic
atio
n do
mai
n
16RA
IN [6
6]Ro
utes
the
data
effi
cien
tly to
war
ds th
e si
nk in
an
id-le
ss b
iom
edic
al s
enso
r net
wor
ks, p
reve
nts
the
form
atio
n of
hig
h te
mpe
ratu
re z
ones
in th
e ne
twor
k, m
axim
um te
mpe
ratu
re ri
se in
crea
ses
slow
ly th
an C
FLO
OD
pro
toco
l, PD
R is
gre
ater
th
an 9
0%, t
he a
vera
ge e
nerg
y co
nsum
ptio
n is
le
ss th
an 1
000
ener
gy u
nits
com
pare
d to
300
0 un
its o
f CFL
OO
D
Ave
rage
pac
ket d
eliv
ery
dela
y is
slig
htly
hig
her
than
CFL
OO
D p
roto
col,
PDR
is s
light
ly lo
wer
th
an C
FLO
OD
pro
toco
l
In-v
ivo
netw
ork
of h
omog
eneo
us a
nd id
-less
bi
omed
ical
sen
sor n
odes
17M
-ATT
EMPT
[64]
Mob
ility
sup
port
ed, g
reat
er n
etw
ork
lifet
ime
(29.
5%),
bett
er s
tabi
lity
perio
d (g
reat
er th
an
20%
) and
29%
bet
ter r
esul
ts fo
r suc
cess
fully
re
ceiv
ed p
acke
ts w
hen
com
pare
d to
mul
tihop
co
mm
unic
atio
n, e
nerg
y effi
cien
t and
em
er-
genc
y su
ppor
ted
A m
ovin
g no
de n
eeds
a n
ew p
aren
t and
the
new
pa
rent
may
refu
se th
is re
ques
t, an
alys
is o
f ave
r-ag
e/m
axim
um te
mpe
ratu
re ri
se is
not
incl
uded
Het
erog
eneo
us a
nd h
omog
eneo
us w
irele
ss b
ody
area
net
wor
ks
18M
2 E2 [61]
Mob
ility
and
mul
ti-m
ode
supp
orte
d, e
nerg
y effi
-ci
ent a
nd e
mer
genc
y su
ppor
ted,
thro
ughp
ut is
ab
ove
100M
bps
whe
n co
mpa
red
to 5
0 M
bps
of
M-A
TTEM
PT
Requ
ires
mor
e ha
rdw
are
than
the
othe
r pro
toco
lsH
eter
ogen
eous
wire
less
bod
y se
nsor
net
wor
ks
19TM
QoS
[63]
Tabl
e-dr
iven
pro
toco
l with
hig
h ne
twor
k lif
etim
e,
low
end
to e
nd d
elay
whi
ch is
less
than
130
ms,
abov
e 85
% re
liabi
lity,
can
mee
t the
QoS
de
man
ds a
long
with
mai
ntai
ning
the
tem
pera
-tu
re o
f the
nod
es to
an
acce
ptab
le le
vel,
uses
a
hots
pot a
void
ance
mec
hani
sm
Ave
rage
tem
pera
ture
rise
is h
ighe
r in
orde
r to
mee
t the
des
ired
QoS
dem
ands
In-v
ivo
wire
less
bod
y ar
ea n
etw
orks
20ET
PA [1
9]M
obili
ty s
uppo
rted
, It s
olve
s th
e lin
k di
scon
nec-
tion
prob
lem
due
to b
ody
mov
emen
ts a
long
w
ith a
redu
ctio
n in
tem
pera
ture
rise
, PD
R is
up
to 9
5%
The
aver
age
dela
y is
slig
htly
hig
her t
han
PRPL
C
in o
rder
to b
alan
ce th
e te
mpe
ratu
re ri
se in
the
netw
ork
Wire
less
bod
y ar
ea n
etw
ork
with
long
last
ing
com
-m
unic
atio
n an
d sc
arce
reso
urce
s
21PS
R [2
0]It
prov
ides
relia
ble
and
secu
re c
omm
unic
atio
n ag
ains
t dat
a in
ject
ion
atta
cks,
PDR
up to
80%
, sh
orte
r rou
ting
dela
y
Use
s A
CK
tech
niqu
es fo
r mea
surin
g lin
k qu
ality
an
d if
the
num
ber o
f AC
Ks is
larg
e, it
may
co
nsum
e a
lot o
f net
wor
k re
sour
ces,
as a
who
le,
netw
ork
lifet
ime
is le
ss
Relia
ble
and
secu
re w
irele
ss b
ody
area
net
wor
ks
Page 16 of 19Bhanumathi and Sangeetha Hum. Cent. Comput. Inf. Sci. (2017) 7:24
AbbreviationsWBAN: wireless body area network; QoS: quality of services; RL-QRP: reinforcement learning based routing with Qos support; WASP: wireless autonomous spanning tree protocol; RAIN: routing algorithm for network of homogeneous and Id-less biomedical sensor nodes; M-ATTEMPT: mobility-supporting adaptive threshold-based thermal-aware energy-effi-cient multi-hop protocols; CDR: critical data routing; RAR: reliability aware routing; DMQoS: data-centric multi objective QoS-aware routing protocol; LEACH: low-energy adaptive clustering hierarchy; CBBAP: cluster based body area protocol; HIT: hybrid indirect transmission; PEGASIS: power-efficient gathering in sensor information systems; MAC: medium access control; CICADA: cascading information retrieval by controlling access with distributed slot assignment; TICOSS: time zone coordinated sleep scheduling; OBSFR: on-body store and flood routing; ETPA: energy efficient thermal and power aware routing; PSR: prediction based secure and reliable routing framework; DVRPLC: distance vector routing with postural link costs; PRPLC: probabilistic routing with postural link costs; ENSA-BAN: efficient next hop selection algorithm; TEEN: threshold sensitive energy efficient sensor network protocol; ARBA: adaptive routing and bandwidth allocation protocol; LAEEBA: link-aware and energy efficient scheme for body area networks; Co-LAEEBA: cooperative link-aware and energy efficient protocol for WBAN; MLEEBA: modified LAEEBA: link aware and energy efficient scheme for BAN; ZEQoS: Zahoor energy and QoS-aware routing protocol; QPRR: QoS aware peering routing protocol for reliability sensi-tive data; DARE: distance aware relaying energy efficient protocol; SIMPLE: stable increased-throughput multi-hop proto-col for link efficiency; QPRD: QoS-aware peering routing protocol for delay sensitive data; QRP: Q-learning based routing protocol; AMR: adaptive multihop tree-based routing; EPR: energy-aware peering routing protocol; EAWD: energy-aware topology design; EBRAR: energy-balanced rate assignment and routing protocol; EERS: energy-efficient routing scheme; MDGRA: modified Dijkstra’s global routing algorithm [49]; RACOON: random contention-based resource allocation proto-col; EAR: environment-adaptive routing algorithm; LOCALMOR: localized multi-objective routing protocol; RE-ATTEMPT: reliability enhanced-adaptive threshold based thermal unaware energy-efficient multi-hop protocol; TMQoS: thermal-aware multi constrained intra body QoS routing protocol; M-ATTEMPT: mobility-supporting adaptive threshold-based thermal-aware energy-efficient multi-hop protocol; THSR: thermal-aware shortest hop routing algorithm; HPR: hotspot preventing routing; LTRT: least total-route temperature routing protocol; LTR: least temperature routing protocol; ALTR: adaptive least temperature routing protocol; TARA: thermal-aware routing algorithm.
Authors’ contributionsVB conducted the survey work, analyzed the schemes and drafted the manuscript. CPS participated in literature review and helped to draft the manuscript. Both authors read and approved the final manuscript.
Authors’ informationV. Bhanumathi received the Bachelor Degree in Electronics and Communication Engineering from Madras University, Master Degree in Communication Systems from Anna University, Chennai and Ph.D. in Information and Communica-tion Engineering from Anna University, Chennai. She is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering, Anna University, Regional Campus, Coimbatore. She has published her works in various International Journals and conferences. Her areas of interest are Wireless Communication, VLSI Design, Network Security, and Digital Communication.
C. P. Sangeetha received her Bachelor Degree in Electronics and Communication and Master Degree from Cochin University of Science and Technology, Kerala. She is currently doing her Ph.D. in Information and Communication Engineering, Anna University, Chennai. She has worked as a Lecturer in Electronics and Communication Engineering, Toc H Institute of Science and Technology, Cochin for 8 years. She has published a number of papers in various International Journals and conferences. Her areas of interest include wireless sensor networks and mobile communications.
AcknowledgementsWe would like to thank the editors and the reviewers for their noble comments and constructive criticisms for improve-ment of the manuscript.
Competing interestsThe authors declare that they have no competing interests.
Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Received: 24 December 2016 Accepted: 25 June 2017
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