An energy-efficient and QoS-effective resource allocation scheme in WBANs

Full text

An Energy-efficient and QoS-effective Resource
Allocation Scheme in WBANs
Zhiqiang Liu, Bin Liu, Chang Chenand Chang Wen Chen‡
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, P. R. China
University of Science and Technology of China, School of Information Science and Technology
Email: lzhq28@mail.ustc.edu.cn, flowice@ustc.edu.cn, chench@ustc.edu.cn
‡University at Buffalo, State University of New York, Dept. of Computer Science & Engineering, USA
Email: chencw@buffalo.edu
Abstract—Wireless Body Area Networks (WBANs) repre-
sent one of the most promising networks to provide health
applications for improving the quality of life, such as ubiq-
uitous e-Health services and real-time health monitoring. The
resource allocation of an energy-constrained, heterogeneous
WBAN is a critical issue that should consider both energy
efficiency and Quality of Service (QoS) requirements with
the dynamic link characteristics, especially when the limited
resource cannot satisfy the expected QoS requirements. In
this paper, we propose an Energy-efficient and QoS-effective
resource allocation that considers a mix-cost parameter char-
acterizing both energy cost and QoS cost between attainable
QoS support and QoS requirements. Based on the mix-cost
parameter, we first formulate the resource allocation prob-
lem as a mixed integer nonlinear programming (MINP) for
optimizing the transmission power, the transmission rate and
allocated time slots for each sensor to minimize total mix-cost
of the system. Then we propose a sub-optimal greedy resource
allocation algorithm, which has a much lower complexity
compared to exhaustive search. Simulation results demonstrate
the advantage of the mix-cost parameter to evaluate energy
efficiency and attainable QoS support, as well as verifying the
effectiveness of the proposed resource allocation algorithm.
Keywords: Wireless body area network (WBAN), qual-
ity of service (QoS), energy efficiency, resource allocation
I. INTRODUCTION
To improve the healthcare efficiency of the rapidly
increased aging population, wireless body area network
(WBAN) has emerged as a key technology to provide health
applications, such as e-Health services and real-time health
monitoring [1]. The WBAN usually contains several energy-
constrained wireless body sensors and one energy-rich hub.
For health applications, the energy-constrained body sensors
collect physiological data streams and transmit them to
the medical sever through the hub. IEEE 802.15.6 task
group (TG6) has published the IEEE 802.15.6 standard [2],
which tremendously facilitates the development of WBAN.
However, there are still several issues need to be solved in
WBAN.
Firstly, the resources of body sensors such as processing,
storage and battery energy supply are constrained due to the
small size requirements, while the resource requirements for
typical WBAN applications are various. Secondly, channel
fading of the on-body links is affected by many factors
such as clothing, body movement and so on [3]. When
the environment or postures change, the channel status of
some links will inevitably change. The last but not the least,
Quality of Service (QoS) in WBAN should be guaranteed
to prevent some fatal accidents caused by an much longer
delay or some loss of the vital data streams. Obviously, these
issues are interrelated and interact with each other, thus how
to overcome these issues at the same time is a key challenge
in WBAN.
The transmission power control (TPC), which is a clas-
sical method to improve system energy efficiency with
dynamic links, has been studied in the literature [3]–[5].
A dynamic postural position inference mechanism [3] was
proposed to assign the proper power level to a link based on
the linear relationship between transmission power (TP) and
received signal strength indicator (RSSI) [6]. However, the
linear relationship was obtained by assuming the wireless
sensors in fixed positions. In [4], the authors found that
the measured BAN channels had the partial periodicity
characteristic, which was used to adjust the transmission
power. However, rapid changes of the body postures se-
riously affect the accuracy of the channel prediction. The
authors in [5] explored the body link states through the
experiments and obtained that the body link was greatly
influenced by the body motion and posture. Then, both of a
short-term estimation method and a long-term estimation
method were involved to the transmission power control
approach. As only one parameter, the transmission power,
is adjusted in the TPC methods, it is certainly not sufficient
to satisfy various QoS requirements in WBAN applications.
Compared with TPC, resource allocation methods have been
proved to be more effective to gain the better WBAN per-
formance [7]–[10]. More parameters, such as transmission
power, allocated slots, packet sizes and transmission rates,
could be adjusted to achieve better performance. In these
methods, specific QoS requirements were usually regarded
as the constraints of an optimization problem, and then
the optimization problem was formulated and solved to
allocate resources for each sensors. A typical optimization
problem is to minimize overall energy consumption under
the constraint of QoS requirements. However, when link
quality is very poor due to the dynamic links, some specific
QoS requirements may not be satisfied. In this case, the
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Fig. 1 A classical WBAN architecture.
resource allocation scheme still tries its best to satisfy all
the QoS requirements, which results in unacceptably high
energy consumption for some energy constrained WBAN
applications.
In this paper, we take both QoS requirements and en-
ergy efficiency requirement into consideration and design
an energy-efficient and QoS-effective resource allocation
method for WBAN. The key contributions of this paper are
two-fold:
Firstly, a mix-cost parameter is designed to jointly mea-
sure both QoS cost and energy cost. The QoS cost is defined
to assess the gap between the attainable QoS performance
and specific QoS requirements, and the energy cost is
designed to evaluate the energy efficiency of WBAN.
Secondly, we design an energy-efficient and QoS-
effective resource allocation scheme, in which we formulate
the resource allocation problem as a mixed integer nonlinear
programming (MINP) for optimizing transmission power,
transmission rate and allocated time slots for each sensor to
minimize the total mix-cost of the system. Then we propose
a sub-optimal greedy resource allocation algorithm, which
has a much lower time complexity.
The rest of the paper is organized as follows. In Sec-
tion II, the system model is presented. We describe the for-
mulation of the resource allocation problem in Section III.
In Section IV, a sub-optimal greedy resource allocation
algorithm is described and solved. The simulation results
are given in Section V, and in Section VI, the conclusions
are described.
II. SYSTEM MODEL
As shown in Fig. 1, we consider a typical WBAN model,
which contains one hub and Nbody sensors deployed on
the body. The index set of body sensors is denoted as
Cn={1,2,··· ,N}. One hop star topology and sched-
uled access mechanism in beacon mode with superframe
boundaries are adopted as recommended by IEEE 802.15.6
standards [2]. As presented in Fig. 2, a superframe is divided
into one beacon and Mslots, whose index set is presented as
Cs={1,2,··· ,M}. The hub broadcasts beacons to allocate
resources such as slots, transmission power, transmission
rate for each sensor. Then the sensor only turns active in
its dedicated slots to transmit its data streams. Here we
assume that optional transmission rates and transmission
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power of each sensor in narrowband physical layer are both
discrete, i.e., Rdev ={Rate1,Rate
2,··· ,Rate
NR}and
Pdev ={Power
1,Power
2,··· ,Power
NP}[2].
In WBANs, communication energy consumption is the
most part of total energy consumption of an energy-
constraint sensor, while energy consumption of processing
and listening can be negligible [11]. Due to the small size
of ACK packets, the energy consumption of receiving the
ACK packets can be ignored while the energy consumption
of transmitting data packets is mainly considered. There-
fore, the total transceiver energy consumption Econ mainly
consists of two parts: circuitry energy consumption Ect and
transmit amplifier energy consumption Etx [7]. Here, the
formulation of energy model is shown as follows [12],
Econ =(1+α)Etx +Ect (1)
where αis power amplifier inefficiency factor, Etx =Ptx t
and Ect =Pctt.Pct is the transmission circuitry power,
which is a constant depending on the specific transmitter
[13], and Ptx ∈Pdev is the transmission power. tis the
time duration of transmitting data packets.
In this paper, we adopt the on-body propagation model
and the path loss model, including both Light-Of-Sight
(LOS) and None-Light-Of-Sight (NLOS) scenarios, which
can be expressed as follows,
PL(d)=PL
d0+10nlog10 d
d0+Xσ(2)
where PL
d0is the path loss at a reference distance d0, and
nis the path-loss exponent. Xσis the shadowing, which fol-
lows a normal distribution Nμs,σ
2
s. The mean value μs
and the standard derivation σsare various correspondingly
with the environment and the posture [14] [15].
III. FORMULATION OF RESOURCE ALLOCATION
PROBLEM
In this section, we will first introduce the mix-cost
parameter, which can characterize both energy cost and QoS
cost. Then we formulate the resource allocation problem as a
mixed integer nonlinear programming (MINP) for optimiz-
ing transmission powers, transmission rates and allocated
time slots for each sensor to minimize total mix-cost of the
system.
A. Mix-cost Parameter
1) QoS Cost: The average packet loss rate PLR
ave of
the body link can be expressed as follows,
PLR
ave =+∞
0
PLR(γ)P(γ|μγdB ,σ
γdB )dγ (3)
342

where γ=10
Ptx−PL(d)−PN
10 B
Ris the bit signal to noise
ratio (SNR), and PNis the power of noise. Bis the sys-
tem bandwidth. P(γ|μγdB ,σ
γdB )indicates the probability
density function of bit SNR γ, and follows a log-normal
distribution as the shadowing in mW .μγdB is the mean of
γin dB, and σγdB denotes the standard deviation of γin
dB.PLR(γ)is the packet loss rate with current γ, and
it is the decreasing function of γ. The details of PLR(γ)
depend on the modulation and coding mode [16].
A WBAN contains a limited number of heterogeneous
sensors that collect different signals, and the QoS require-
ments of each sensor may be different. The sensor collects
data streams, and then the data streams are packetized into
packets, which are stored in a data queue. The sensor
transmits the packets to the hub in a First In First Out (FIFO)
order. As for transmitting packets, the queuing system of
each sensor needs to satisfy the throughput requirement in
order to be stable [17]. Considering the packet loss rate, the
equivalent time required in one superframe for sensor ican
be calculated as follows,
tth,i =Si·Tframe
Ri·(1 −PLR
ave,i)(4)
where Siis the average source rate of sensor i, i ∈C
n,
and Tframe is the length of one superframe. Here tth,i
means the minimum required time in one superframe need
to be allocated to the sensor ifor satisfying the throughput
condition.
The slot assignment variables ρi,j,i ∈C
n,j ∈C
sare
defined as follows,
ρi,j =1,if slot jis assigned to sensor i
0,otherwise (5)
where i∈Cnρi,j ≤1, i.e, one slot of the superframe can
only be allocated to one sensor. Finally, we can assess the
QoS cost as follows,
CQoS,i =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
1,if 
j∈Cs
ρi,j ·tslot <t
th,i
PLR
ave,i−PLR
th,i
PLR
ave,i ,if ( 
j∈Cs
ρi,j ≥tth,i
tslot )∧(PLR
ave,i >PLR
th,i)
0,if ( 
j∈Cs
ρi,j ·tslot ≥tth,i)∧(PLR
ave,i ≤PLR
th,i)
(6)
where PLR
th is the predefined PLR threshold. tslot is the
length of one time slot in a superframe. j∈Csρi,j tslot is
the total allocated time of sensor iin one superframe, and
if it is smaller than the required time tth,i, the throughput
condition cannot be satisfied, and the QoS cost is set to 1.
Once the throughput condition is satisfied, we evaluate the
Qos cost based on the PLR performance. If the attainable
average PLR is smaller than the PLR threshold, it means
the PLR requirement can be satisfied and then the QoS
cost is set to 0. Otherwise, we evaluate the QoS cost with
the distance between the attainable average PLR and the
PLR threshold.
2) Energy Cost: In general, the energy of wireless
sensors is constraint while the energy of the hub is sufficient.
Thus we only take into consideration the energy efficiency
of sensors. The energy cost CEis defined as the equivalent
energy consumed per transmitting a bit considering the
packet loss. The value of energy cost is in a wide range,
so we need to normalize the energy cost into the range
[0,1] for further better calculating the mix-cost. Finally, the
energy cost CEcan be expressed as follows,
CE=1,if (PLR
ave ≥PLR
th)
(1+α)Ptx+Pct
R(1−PLR
ave)·Rmin (1−PLR
th)
(1+α)Ptx,max+Pct ,otherwise
(7)
where Ptx ∈P
dev is the transmission power of each sensor,
and Ptx,max is the maximum transmission power in Pdev.
R∈R
dev is the transmission rate of each sensor, and Rmin
is the minimum transmission rate in Rdev. If the attainable
average PLR is larger than the PLR threshold, the energy
cost of the retransmissions for successfully transmitting
packets will be increasing sharply, therefore the energy
cost is set to 1. Otherwise, we use the equivalent energy
consumption per bit to evaluate the energy cost.
Finally, after properly defining the energy cost and the
QoS cost that influence the performance of the WBAN, we
can combine both of them to define the mix-cost as the
weighted average of energy cost CEand QoS cost CQoS.
The mix-cost is mathematically expressed as,
CMix,i =δ·CE,i +(1−δ)·CQoS,i,i ∈C
n,δ ∈[0,1]
(8)
where δis a weight value used to adjust the tradeoff between
the energy efficiency and the QoS satisfactory. The value of
mix-cost CMix is in range of [0,1], and a smaller value
indicates that the sensor with current allocated resources
achieves a higher performance, such as energy efficiency
and QoS effectiveness.
B. Problem Formulation
Here we use the weighted sum of all sensors’ mix-cost as
the performance metric and formulate the optimal resource
allocation scheme for WBAN. The weight of the sensor is
set according to the sensor’s priority. The larger the weight,
the higher the priority. Then the optimization problem for
resource allocation can be formulated as,
min
Ri,Ptx,i,ρi,j 
i∈Cn
ωi·CMix,i (9)
s.t. Ptx,i ∈Pdev,(9a)
Ri∈Rdev,(9b)

i∈Cn
ρi,j ≤1,(9c)
ρi,j ∈{0,1},∀i∈C
n,∀j∈C
s,(9d)
where ωidenotes the weight of sensor i. The objective
function (9) is to minimize the weighted sum mix-cost. And
the first constraint (9a) ensures the allocated transmission
power Ptx,i for sensor i, i ∈C
nis in the transmission power
343

set Pdev. Similarly the second constraint (9b) implies that
we can only choose the transmission rate Rifor sensor
i, i ∈C
nfrom the transmission rate set Rdev. The third
constraint (9c) and forth constraint (9d) guarantee that each
slot in a superframe can only be allocated to no more than
one sensor.
IV. SUB-OPTIMAL RESOURCE ALLOCATION
The resource allocation formulated in (9) is a mix integer
nonlinear programming (MINLP) problem, which is NP-
hard. A possible way to find the optimal solution is by
exhaustive search. However, the computational complexity
of the exhaustive search method is too high to run on the
hub. In this section, we propose a greedy resource allocation
algorithm, which has a much lower complexity than the
exhaustive search method. In the following, we will explain
how the greedy resource allocation algorithm works.
A. Greedy Resource Allocation Algorithm
The algorithm includes two steps. It first calculates
optimal transmission power and transmission rate assuming
the assignment of the time slots has already been fixed for
each sensor, and then finds the required number of time slots
corresponding to the minimum mix-cost for each sensor,
respectively. Then it allocates the resource based on whether
the total time slots in one superframe could satisfy the
required time slots with the minimum mix-cost for each
sensor.
To find the optimal resource allocation of each sensor
without the limitation of the number of time slots, we can
take the number of allocated slots as a preset constant, and
then allocate the optimal transmission power and transmis-
sion rate for sensor i, i ∈C
nby solving the following
optimization problem,
min
Ri,Ptx,i
δ·CE,i +(1−δ)·CQoS,i (10)
s.t. Ptx,i ∈Pdev,(10a)
Ri∈Rdev,(10b)

j∈Cs
ρi,j =m, (10c)
where mis a preset number of time slots in one superframe
for sensor i. Here we assume the path loss does not change
in one superframe. Thus we can get the optimal transmission
power P∗
tx,i(m)and transmission rate R∗
i(m)with every
preset number m∈[1,M]of allocated time slots for sensor
i. Then we can compare these resource allocation results
and achieve the optimal number m∗
iof allocated time slots
which has the minimum mix-cost for each single sensor
with corresponding optimal transmission power P∗
tx,i(m∗
i)
and transmission rate R∗
i(m∗
i).
If total optimal allocated slot number i∈Cnm∗
ifor
all sensors is no larger than the total slot number M
of the superframe, it means the optimal result for each
single sensor is the final optimal resource allocation result
of the system. However, when link quality is poor, the
Algorithm 1 Greedy Resource Allocation Algorithm
Initialization:
1: Calculate P∗
tx,i(m),R
∗
i(m),m ∈[1,M]for sensor
i, i ∈C
nby solving problem (10).
2: Find m∗
i=arg min

j∈Cs
ρi,j =mCMix,iP∗
tx,i(m),R
∗
i(m)
and set sub-optimal time slot number m†
i=m∗
i.
3: Get the difference of total required optimal slot number
and the superframe length Mby Δ= 
i∈Cn
m∗
i−M.
Iteration:
4: while Δ>0do
5: Set candidate time slot number mc
i=m†
i−
1,if m
†
i>1for each sensors.
6: Get the weighted mix-cost increment
caused by the reduction of a slot
τi=ωi·CMix,iP∗
tx,i(mc
i),R
∗
i(mc
i)−ωi·
CMix,iP∗
tx,i(m†
i),R
∗
i(m†
i).
7: Find the sensor with the minimum mix-cost incre-
ment ind =min
iτi, and update m†
ind =mc
ind.
8: Update the difference Δ=Δ−1.
9: end while
number of slots in one superframe cannot satisfy each sensor
for its own optimal resource allocation. In this case, we
choose a sensor to reduce a slot allocation step by step
until total allocated slot number is not larger than the total
slot number Mof the superframe. In each iteration, the
weighted mix-cost increments of each sensor caused by
the reduction of a slot are compared, and the sensor with
the minimum weighted mix-cost increment is chosen to
reduce one allocated slot. The pseudo codes of the proposed
algorithm is illustrated in Algorithm 1.
B. Complexity Analysis
The upper bound for the time complexity of the ex-
haustive search method for the original problem (9) can be
derived as Tsearch =O(M
n=N
n!
(n−N+1)!·(N−1)! ·NRNP),
where we treat the time complexity of calculating one mix-
cost parameter as O(1) and ignore the constant coefficients
and lower order terms. Compared to the exhaustive search
method, the upper bound for time complexity of the pro-
posed sub-optimal method is TsubOpt =O(NNRNP(M−
1) + (N−1)MN). We can find that the proposed sub-
optimal method decreases the complexity of the resource
allocation scheme significantly. Meanwhile the proposed
sub-optimal method has a larger space complexity, which
is given by the O(NNRNP(M−1)). Because the number
of sensors in a WBAN is usually small, the time and space
complexity of our proposed method is acceptable.
V. S IMULATIONS
In this section, we evaluate the performance of the
proposed algorithm by simulations. In this section, the
performance A classical WBAN as shown in Fig. 1 consists
344

TABLE I Parameters of the Shadowing
σs(dB)
Sensor Index Still Walk Run
1 6.054 5.4153 6.1118
2 4.8497 7.4276 7.8011
3 5.113 4.9736 4.5625
4 2.6356 4.4678 4.0395
TABLE II Sensor Parameters
Index Location d(cm) LOS/NLOS n S (Kbps)
1 Head 69 LOS 3.11 25
2 Chest 36 LOS 3.23 40
3 R wrist 48 NLOS 3.35 30
4 Thigh 34 NLOS 3.45 35
of four sensors and one hub. The deployed positions, the
sample rates and the corresponding body link parameters of
all sensors are given in Table II. All sensors have the same
battery capacity 100J, and we assume the WBAN system
does not work when one sensor run out of its battery. In this
paper, the standard derivation σsof the shadowing will be
changed with the postures as shown in Table I, which are
set based on the measurement results in [15] [14]. For con-
venience, we assume the mean value μsof the shadowing
for all sensors are same, and the variation of μsis used to
simulate the variation of the environment [14]. The higher
value of μsmeans the higher path loss and the worse link
quality. The probability of different posture change can be
determined from real human posture trace and the Markov
chain can be used to model posture change sequences while
maintaining randomness of the posture selection [18]. Here
we only consider three types of body postures, i.e., still,
walk and run, and their steady-state probabilities are set to
0.5, 0.3 and 0.2, respectively. The extension to the case with
more body postures is straightforward. Other simulation
parameters are summarized in Table III.
we compare results among three methods: 1) the
proposed Optimal Resource Allocation Method (ORA)
using exhaustive search; 2) the proposed Sub-optimal
Greedy Resource Allocation Method (SGRA); 3) Link-
State-Estimation-Based Power Control Method (LSEPC)
TABLE III Simulation Parameters
Parameters Value
Bandwidth 1MHz
Noise Power PN-94dBm
Rate Set Rdev [121,243,486,971]Kbps
Power Set Pdev -30dBm to 0dBm with step 2dB
Weight of each sensors ω[0.25,0.25,0.25,0.25]
One slot length tslot 0.5 ms
Number of slots in a superframe M200
Transmission circuitry power Pct 0.5uW
Threshold of Packet Loss Rate 1%
Threshold of Packet Delay 200ms
Number of Sensors N4
Factor α1.4
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[5]. In Fig. 3, we illustrate the relationship between the
WBAN system lifetime and the mean value μsof the
shadowing. As shown in Fig. 3, both optimal and greedy re-
source allocation algorithm can achieve much longer system
lifetime compared with LSEPC, and the system lifetimes of
three approaches decrease with the increase of the mean
value μsof the shadowing. To improve energy efficiency,
the QoS performances of ORA and SGRA are worse than
LSEPC but close to the QoS requirements, as shown in
Fig. 4. This is because the proposed resource allocation
scheme take both QoS requirements and energy efficiency
requirement into consideration, while the LSEPC only tries
its best to satisfy all the QoS requirements. The performance
of the greedy resource allocation method is close to the
optimal resource allocation method, as shown in Fig. 3 and
Fig. 4, which demonstrate the effectiveness of the proposed
greedy method.
In this paper, the weight δin the mix-cost is designed to
be tunned for desired trade-off between energy consumption
and QoS performance. As shown in Fig. 5, when the
weight δis raised, it means energy performance should
be taken more seriously, therefore the system life increases
meanwhile the QoS performance becomes worse.
VI. CONCLUSION
In this paper, we design a resource allocation scheme for
WBAN based on a mix-cost parameter which measures both
345

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$YHUDJH3DFNHW'HOD\PV
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9DOXHRI 9DOXHRI
*UHHG\
2SWLPDO
*UHHG\
2SWLPDO
Fig. 5 System lifetime and QoS performance versus the weight δin mix-cost.
energy efficiency and QoS satisfactory. The resource allo-
cation problem is formulated as a mixed integer nonlinear
programming (MINP) for optimizing transmission power,
transmission rate and allocated time slots for each sensor to
minimize the overall mix-cost of the system. To reduce the
computational complexity, a sub-optimal greedy resource
allocation is proposed. The simulation results demonstrate
that the mix-cost based optimal resource allocation method
is adaptive to the dynamic link and the sub-optimal resource
allocation method provides the performance close to the
optimal method with much lower computational complexity.
ACKNOWLEDGMENT
This work is supported by the National Natural Science
Foundation of China (Grant No. 61202406).
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