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Modeling Induction and Routing to Monitor

Hospitalized Patients in Multi-hop

Mobility-aware Body Area Sensor Networks

N. Javaid1,2,, A. Tauqir3, S. Akram3, A. Ahmad2, U. Qasim4, Z. A. Khan5, N. Alrajeh

1CAST, 2EE Dept, COMSATS Institute of Information Technology, Islamabad, Pakista

3Institute of Space Technology, Islamabad, Pakistan

4University of Alberta, Alberta, Canada

5Internetworking Program, FE, Dalhousie University, Halifax, Canada

6B.M.T., C.A.M.S., King Saud University, Riyadh, Saudi Arabia

Corresponding authors: Website: www.njavaid.com,

[email protected], [email protected]

Abstract

This paper proposes two techniques. First is the non-invasive inductive link model to recharge the

battery of an implanted bio medical device (pacemaker) and second is the Distance Aware Relaying

Energy-efficient (DARE) routing protocol for on-body sensors in Multihop Mobility Body Area Sensor

Networks (MM-BASNs). The routing protocol as well as the non-invasive inductive link model are

tested with the consideration of eight patients in a hospital unit under different topologies, where, thevital signs of each patient are monitored through seven on-body sensors and an implanted pacemaker.

To reduce energy consumption of the network, sensors communicate with sink via an on-body relay

which is affixed on the chest of each patient. Moreover, the behaviour (static/mobile) and position of

sink are changed in each topology, and the impact of mobility due to postural changes of the patient(s)

arms, legs and head is also investigated. Simulation results show that the proposed techniques performs

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better than the selected ones in terms of energy efficiency.

Index Terms WBASN, Network Lifetime, Energy Consumption, Mobility, Inductive Link, Implant,

Link Efficiency, Voltage Gain

I. INTRODUCTION

Wireless Body Area Sensor Networks (WBASNs) as a subfield of Wireless Sensor Networks

(WSNs) [1] is gradually making advancements to monitor humans under different scenarios like

players in a play ground, astronauts in space, patients in the hospital or home and many others

as shown in fig. 1. In patient monitoring system, these networks enable the medical personnel

to provide urgent medical aid [2].These networks prevent patient(s) suffering from poor health

condition(s); body stiffness, muscular weaknesses, dependency on the nurses to get their postures

changed, etc.

The sensors are equipped with limited energy resources. As major portion of sensors energy

is utilized because these gather information about body organs and transmit the data to the end

station [3], [4], [5], [6]. During this process, excess of energy is dissipated in the form of heat.

This dissipation of energy in the form of heat severely damages the body tissues. Keeping this

issue in mind, research is still underway to reduce the energy consumptionof sensors and to

enhance the lifetime of network by proposing energy efficient routing protocols, and inductive

models to keep the batteries of the implanted devices charged before these completely deplete [7].

Patients under monitoring may show some degree of mobility such as postural changes of

on-bed patients. However, mobility leads to topological changes, degradation in link quality,

isolation of the nodes, etc. The network needs to be structured in such a way that they are able

enough to cater for these issues and provide satisfactory level of communication sessions.

We consider different scenarios in a hospital unit. The unit consists of eight properly aligned

patients such that each one is equipped with seven on-body sensors, one relay and an in-body

pacemaker. The paper presents DARE routing protocol to maximize the lifetime of on-body

sensors, whereas, an induction technique by using a simple Linear Transformer Circuit (LTC)

is designed to induce power from the primary side (outside the body) to the secondary side

(inside the body) thereby providing recharging mechanism for the pacemaker. Out of the seven

sensors, two monitor the patients vital signs when there reaches a certain critical level. Thus, the

routing protocol is capable of supporting and monitoring multiple data types. In each scenario,



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the equivalent circuit topologies and generalized voltage driven model. In addition, different

parameters are analyzed with respect to different parameters like resonating impedances. Results

are analyzed by using the parameters of voltage gain and link efficiency. In conclusion, the

series resonating impedances cause increase in voltage gain however these do not cause the link

efficiency to ulter.

In [11], an inductive power system is provided which combines power transfer with data

transmission for implantable bio microsystems. The implanted device receives power from an

external transmitter through an inductive link between an external power transmission coil and

the implanted receiving coil.

In [12], a transcutaneous link is designed for medical implants by using inductively coupled

coils. It also describes the design of an indigenously developed transcutaneous link from com-

mercial off-the-shelf components to demonstrate the design process. Moreover, this work an

outline for inductive coils and optimized parameters of class E amplifiers. Their experimental

results show almost 40% link efficiency at 2.5 MHz operating frequency and an output power

of 100 mW.

In [13], a medical relevance of the monitoring of deformation of implants is presented. It a

powerful tool to evaluate nursing and rehabilitation exercises for tracing dangerous overloads

and anticipating implant failure and also to observe the healing process. Furthermore, Sealstrain

and Linkstrain (implantable wireless designs) are charged via magnetic induction. The Sealstrain

deteriorates inductive links efficiency more as compared to the Linkstrain.

In [14], authors present a survey with respect to current development and future demands on

implantable and wearable WBASN systems. The paper focuses on state of the art technology to

provide people (especially patients and elder people) with quality care. In addition, the authors

discuss various design considerations as well; energy efficiency, scalability, unobtrusiveness, and

security. Besides the designs considerations and state of the art technology for quality care, this

paper discusses benefits and drawbacks of the wearable and implantable BASN systems.

In [15], E. Reusens et al. focus on increasing network lifetime by means of relaying and

cooperation. First, the relay nodes perform relaying of traffic only so that, high amount of

energy is available for communication purposes. Next, the relays cooperate in forwarding the

data from one node towards the central device. Moreover, the authors investigate path loss

with the consideration of different body parts. Based on this platform and simulations, the time



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domain channel characteristics (delay spread and mean excess delay) and parameters of path

loss are extracted. These parameters along with the proposed models are evaluated in terms

of energy efficiency for both single hop and multi hop topologies. Similarly, authors in [16]

propose a methodology to cater for end-to-end delay problem. As a solution, this methodology

uses topology control to account for access delays due to the underlying MAC layer. Results

obtained from simulations conclude that the proposed methodology is a compromising approach

between energy consumption and end-to-end data transfer latency.

In [17], A. Ehyaieet al. set an upper bound to determine the number of relay nodes, sensors

and their respective distances to the sink. A relay network is defined as a network of relay nodes

distributed along the body in combination with sensor nodes to serve as a transport network

for the WBASN sensor nodes. Hence, no sensor node needs to relay other sensors traffic any

more. Each sensor node does single-hop communication while relay nodes are responsible for

multi-hop communication to the sink.

In [18], J. Eliaset al. provide an optimal design for WBANs by studying the joint data routing

and relay positioning problem in order to increase the network lifetime. In this research work, the

authors present an inter based linear programming model which aims for: (i) optimization of the

number and positions of relays, (ii) efficient routing of data towards sink(s), (iii) minimization of

energy consumption of sensors and relays, and (iv) minimization of networks cost installation.

Based on numerical results, this framework has a very short computing time as compared to the

other frameworks.

In [19], authors study propagation models for improving the energy efficiency. Using these

models, calculations show that single-hop communication is inefficient, especially for the nodes

far away from sink, however, multi-hop proves to be more efficient. In order to avoid hot-spot

links, extra nodes in the network i.e., dedicated relay devices are introduced.

Authors in [20] focus on tele-health applications based on sensor networks. They begin with

an overview in sensor networks followed by concluding points in the form of summary. In

addition to energy efficiency of the WBASN, fall risk assessment and fall detection are also

dealt. Some limitations of the current research along with suggestions for future implementation

are also discussed.

In [21], B. Chen et al. introduce a new interference-aware WBAN that can continuously

monitor vital signs of multiple patients and efficiently prioritize data transmission based on



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patients condition. The authors proposed solution is based on an integrated hybrid schedular

which guarantee end to end delay with the capability to select the best possible route (best

link quality) and minimum generated interference. Thus, the end to end packet error rate (high

end to end packet reliability). On the other hand, in [22], sensing is considered as a service

while keeping energy efficiency in mind. In this regard, the authors present unique set of design

challenges, and propose different solutions which are very helpful for current as well as future

researchers.

To cater for an increase in an end to end network traffic, authors in [23] present any-cast

routing protocol for monitoring patients vital signs. To achieve minimum network latency the

protocol chooses a nearest data receiver related to the patient. The wireless network performs fall

detection, indoor positioning and ECG monitoring for the patients. Whenever, a fall is detected,

the hospital crew gets intimated of the exact position of the patient.

In [24], T. Watteyne et al. present a protocol named AnyBody which is a self-organization

protocol comprising of sensor nodes and group them into clusters. It focuses on relaying data via

cluster heads to improve the routing and energy efficiency. Initially, the protocol builds a cluster

based structure, and then efficiently transmits packets from source to destination. An interesting

feature of this technique is its constant number clusters as the number of nodes increases.

In [25], authors propose a global routing protocol with the capability to balance load of the

sensor nodes. This protocol is tested against real time experiments as well as computer based

simulation results. Similarly, authors in [26] introduce a Personal Wireless Hub (PWH) to collect

Personal Health Information (PHI) of its user through biomedical sensors. This information is

then routed towards the health care unit. The authors observe that only eligible data should be

routed to the PWH where security of information is essential due to privacy concerns.

In [27], C. A. Otto et al. describe a prototype system for continual health monitoring at

home. The system consists of an uninterrupted WBAN and a home health server. The WBASN

sensors monitor users heart rate and locomotive activity and periodically upload time-stamped

information to the home server. The home server may integrate this information into a local

database for users inspection or it may further forward the information to a medical server.

An idea of embedding medical devices into Hospital Information System (HIS) is presented

in [28]. Authors consider the ubiquitous Echograph which is a popular medical imaging device.

If this Echograph is embedded with the HIS information network, then it will be very easy for



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the doctors to immediately diagnose the patients. This information will be available to all the

doctors of the hospital at any time, thereby, saving time and lives.

In [29], C. Wanget al.present a distributed WBAN model for medical supervision. The system

consists of three tiers: sensor network tier, mobile computing network tier, and remote monitoring

network tier. This model provides collection, demonstration, and storage of vital information

like ECG, blood oxygen, body temperature, respiration rate, etc. The system demonstrates many

advantages such as low-power, easy configuration, convenient carrying, and real-time reliable

data.

In [30], authors use wearable sensors to monitor daily activities of humans. Humans perform

different movements during different activities. The correct monitoring of these complex actions

is challenging. For this purpose, authors use a notation of Motion Node for the purpose of activity

recognitions which is a wearable sensing device. It provides accurate measurement of inertia,

integrating a three axis accelerometer, a three axis gyrometer and a three axis magnetometer.

III. MOTIVATION

In spite of many advantages, WBASN also faces many issues. One of the major issues is the

limited energy [31] of the on-body sensors as well as the in-vivo pacemaker. The battery of the

sensor/pacemaker exhausts quickly because the transmission process consumes more energy as

compared to the sensing and processing processes. Many routing protocols have been proposed

to prolong the battery lifetime of on-body sensors like the short propagation delay in [32] is

beneficial in cases where delay cannot be tolerated. However, the nodes exhaust much earlier

in terms of rounds due to surplus energy consumption. Likewise, once the in-vivo pacemaker

completely exhausts in terms of energy supply then it is either impossible or not feasible to

replace its battery. Subjected to this problem, wireless recharging is the only possible or at least

one of the feasible solution(s). Thus, the motivation to enhance lifetime of on-body sensors,

recharge the implanted battery of pacemaker and to improve the quality of living leads us to

propose DARE routing protocol as well as a non-invasive inductive link model. The routing part

offers energy efficient routing of biomedical information collected via on-body sensors through

the network and the inductive link model provides a mechanism to recharge the battery of in-body

pacemaker.



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IV. MATHEMATICAL MODEL FOR INDUCTION

In this section, a mathematical model is proposed to charge the battery of a pacemaker which

is implanted inside the body to monitor arrhythmic patients. Arrhythmia refers to an abnormal

heart rhythm due to changes in the conduction of electrical impulses through the heart whichmay be life-threatening. The heart may not be able to pump enough blood (least amount of

blood needed for proper functioning of different body parts) to the body causing damage to the

brain, heart itself, and other organs.

The following subsections discuss about the working of pacemaker, induction link along with

its parameters and equivalent circuits for induction.

A. Pacemaker and its Working

The afore mentioned disease of arrythmia can be well controlled by using pacemaker that

creates forced rhythms according to natural human heart beats which are required for the normal

functioning of heart. The circuitry of a pacemaker consists of a small battery, a generator and

wires attached to the sensor. It senses a heartbeat and sends the signals to the generator through

wires. If the heartbeat is not normal, it generates small electrical signals to regulate the heartbeat.

Working mechanism of pacemaker is shown in fig. 2. Fig. 3 shows the parts of the heart which

are attached with the probes of the pacemaker.

B. Induction Link and its Parameters

An inductive link consists of two coils, forming a transformer. The primary circuit is powered

by a voltage source which generates magnetic flux in order to induce power in the secondary

side (inside the human body) as shown in fig. 4. The skin acts like an interface or a barrier

between the two circuits. Coupling coefficient (k) shows the degree of coupling between the

two circuits. To avoid damage to the body tissues, value of k should be less than 0.45. The

parameters considered for the validation of link efficiency are as follows:

Voltage gain: It is the ratio of the output voltage to the input voltage i.e., Vout / Vin.

Link efficiency (): The ability of transferring power from primary side to secondary side

is known as link efficiency.

Quality Factor: It is a widespread measure used to characterize resonators and is denoted

by QF. Higher the QF, the higher is the resonating effect.



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Fig. 2. Working mechanism of pacemaker.

Fig. 3. Placement of pacemakers probes inside a human heart.

The first two parameters depend upon k. However,QF

depends upon the frequency as well

as on the load resistor at secondary side.

C. Equivalent Circuits

The equivalent circuits are discussed below in the following two subsections.



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Fig. 4. Schematic view of an inductive link.

1) Series Tuned Primary Circuit (STPC): In STPC, a capacitor is connected in series at

primary side. Only small amount of voltage is induced due to low coupling factor i.e., 0.45. So,

a series tuned circuit is used to induce sufficient voltage in the secondary coil. The circuit is

shown in fig. 5.

Fig. 5. STPC.

Vs is the source voltage and Rs is the series resistance. C1s is the series resonating capacitor

at primary side and L1 and L2 are the inductors at primary and secondary side with their

series resistancesRL1 and RL2, respectively. M is the mutual inductance, and Rload is the load

resistance across which the output voltage Vload is measured which then drives the implanted

sensors or a device. The values of the parameters are shown in table 1 (adopted from [11]).

By applying Kirchoffs Voltage Law, source voltage is given as:

Vs= I1A jMI2. (1)

Here, A= Rs+RL1+jL1+ 1

jC1s. The output voltage Vload is given as:

Vload= I2(jL2+RL2) +jMI1. (2)



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TABLE I

INDUCTIVE LINK PARAMETERS

Parameters Values

Operating frequency f=13.56 MHZ

Primary coil L1 = 5.48 H

Secondary coil L2 = 1 H

Parasitic resistance of the transmitter coil RL1 2.12

Parasitic resistance of the receiver coil RL2 1.63

Load resistance Rload = 320

The current in secondary side I2 is:

I2= Vload

Rload

. (3)

By proper substitutions:Vload

Vs=

jMRloadAB+2M2

, (4)

where, B = Rload+jL2+RL2. To find the link efficiency, we use the following formula,

=P2P1

=VloadI2

VsI1=

Vload

Vs

I2I1

. (5)

By substituting the values of VloadVs

,I1 from equation (1) andI2 from equation (3), link efficiency

becomes:

=

jMRloadAB+M22

VloadRload

jMRload

VloadB

. (6)

Taking real of equation (6), becomes:

= 2M2Rload

Re[B](Re[A]Re[B] +2M2), (7)

where, Re[A] =Rs+RL1, and Re[B] =Rload+RL2. QFfor the secondary side is:

QF=2f L1

Rload(8)

2) Series Tuned Primary and Parallel Tuned Secondary Circuit (STPPTSC): The primary

side of the circuit in fig. 6 is the same as the primary side of series tuned circuit in fig. 5.

However, a capacitor C2p is connected in parallel at secondary side such that the sensors operate

at low frequencies. This parallel capacitor lets the circuit to act as a low pass filter (allowing

low frequencies to pass through and preventing high frequencies), thereby, preventing damage(s)



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Fig. 6. STPPTSC.

to body tissues. Moreover, this topology achieves high voltage gain and better link efficiency as

well.Equations for primary side of the circuit are the same as given for the previous circuit.

However, the equations for secondary side are discussed below. The combined load Zload, is

given as:

Zload=Rload jC2pR

2

load

1 +2C22pR

2

load

. (9)

where, R2 is the real component and X2 is the reactance ofZload given as:

R2= Rload

1 +2R2load

C22p

X2= R2loadC2p

1 +2R2loadC22p

.

Now, solving for voltage at secondary side,

Vload= I2(jL2+RL2) +jMI1. (10)

Also,

Vload= IloadRload= I2Zload. (11)

Now, by proper substitutions, Vload/Vs becomes:

VloadVs

= jMZload

A(Zload+jwL2+RL2) +2M2 (12)

For link efficiency,

= j22M2ZloadC(AC+2M2)

(13)



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where, C=Zload+jL2+RL2. For actual consumption at the output, we take real part of

equation (13) and avoid imaginary parts. The final equation for becomes:

= 2M2Re[Zload]

Re[C](Re[A]Re[C] +2M2) (14)

where, Re[C] =Re[Zload] +RL2, and Re[Zload] =R2

QFat the secondary side of the circuit is given by:

QF = 2f L2

RL2 +Rload(15)

The second induction model is further modified by placing a flyback diode in parallel to the

primary coil as shown in fig. 7.

Fig. 7. STPPTSC with flyback diode.

The flyback diode placed in reverse biased mode prevents the voltage surges or spikes into

the secondary side.

V. THE PROPOSED ROUTING PROTOCOL

DARE protocol aims to monitor patients in a heterogeneous network while reducing energy

consumption of the monitoring sensors by deploying relay node . The relay node reduces

communication distance between sender and receiver. The monitored data travels through the

hierarchy of sensors towards the destination node to avoid the sensors to die out at an earlier stage.

The following subsections separately discuss network topology, types of data and communication

flow in detail.



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A. Network Topology

The DARE protocol is based on inspecting a hospital ward by assuming the dimensions of

40f t 20f t, under five different scenarios in which the patients are monitored to detect any

ambiguity in the normal functioning.Seven on-body sensors along with an in-body pacemaker are equipped with the body of each

patient at different positions. The topology is kept same throughout the entire ward. This makes

a total of fifty six body sensors, eight body relays and a pacemaker. body sensors, body relays

and the pacemaker have limited energy resources. A Main Sensor (MS) is also attached to

bed in one of the scenarios to reduce the energy consumption. The MS is assumed to have

either unlimited or at least very high power source as compared to the other sensors. The sink

is considered to have unlimited energy resources which transmits the final information to the

external network. From this external network, the doctor is then able to completely know about

the patients conditions. The combination of body sensors and body relays exchange the data in

a multi-hop manner. The scenario is shown in fig. 8.

B. Types of Data Reporting

The sensors measure ailment either by continuously monitoring the data on the basis of time-

driven events or on an event-driven basis. The glucose and temperature level monitoring sensors

transmit the data, whenever, the levels of both readings either fall below the lower limit or exceed

the upper limit indicating an alarming condition. The low and high critical levels of temperature

are set at 35 C0 and 40 C0, respectively. Similarly, the values for glucose level are 110 mg/dL

and 125 mg/dL, respectively. Rest of the sensors continuously monitor the parameters labelled

in fig. 8. Monitoring of variable data types makes this protocol feasible for a wide range of

applications; remote monitoring of patients in hospital ward(s) or home(s) suffering from heart

diseases, diabetes, drug addiction, etc.

C. Communication Flow

During the protocol operation, eight patients are independently monitored. For each patient,

each of the body sensors conveys information to the corresponding in range body relay, equipped

with higher energy resources. The received data is finally transmitted to the MS. The protocols

flow chart is shown in fig. 9.



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Fig. 8. Patient Scenario.

The algorithm for patient monitoring is as follows:

First, the body sensor is checked whether it is alive or not. If yes, then the algorithm checks

for the body relay to be alive. If body sensor is found alive, then it checks whether it is a

threshold measuring sensor or a continuously monitoring sensor.

If the body sensor is a threshold measuring sensor, it checks if the low or high threshold

level is reached. If yes, then it measures the distance between that body sensor and body

relay.

Afterwards, it calculates the transmitted energy of body sensor and the received energy of

body relay.

Then it estimates delay in propagating the data from body sensor to body relay.

If threshold is not reached, then the algorithm proceeds to check the other body sensors

in the same manner and calculates the distance, remaining energy, and delay. Finally, the

estimated parameters are separately stored in different variables.



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Remaining energy= Initial energy Transmission energy

After checking all the sensors of the first patient the protocol operation advances towards

checking the next patient.

D. Different Scenarios

The protocol considers five different scenarios by having the same topology for body sensors

and body relays. However, the sink is either mobile or static. The scenarios are shown below in

figures 10-14. Sinks different positions in scenarios 2 and 5 are shown in TABLE 2.

TABLE II

LOCATIONS OF MULTIPLE SINKS IN THE HOSPITAL WARD

Sink Position

x (ft) y (ft)

Sink1 0 10

Sink2 20 20

Sink3 40 10

Sink4 20 0

E. Radio Model

The basic radio model proposed for WBASN is given below [19]. We choose the radio model

of [19] due to two reasons; (i) this model meets all the design considerations in our work, and

(ii) it is mostly used in literature. To transmit w bit data over a distance d, the transmitter

dissipates:

Etx(w, d) = ETXelec w+ampn w dn. (16)

Equation for the reception energy is given as:

Erx(w) =ERXelec w (17)

where,ETXelec andERXelec represent the per bit energy dissipation by radio to run the circuitry

for transmitter and receiver respectively. amp represents the amplification factor, n is the Path

Loss (PL) coefficient. The value of n for Line Of Sight (LOS) is 3.38 and for Non-LOS its

value is 5.9. DARE protocol assumes LOS communication value between sensors. The values

for the aforementioned parameters are tabulated in table 3.



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F. Mobility in DARE

The DARE protocol also considers mobility of the patients legs, arms, and head such that

on bed patients can have different postural changes. We have considered four different positions

for each arm, three different positions for each leg and head for all the patients.

Fig. 10. Scenario-1: Sink is placed at the center of the ward. Body sensors on different patients carry information and transmit

to their respective body relay which then aggregates and relays the received data to sink. The communication flow is from body

sensors body relays Sink.

TABLE III

PARAMETERS OF ENERGY MODEL

Parameter Value

ETXelec 16.7nJ/bit

ERXelec 36.1nJ/bit

Eamp 1.97nJ/bit

Eamp 7.99nJ/bit

nforLOS 3.38

nforNon LOS 5.9

w 4000bits

G. Comparison between DARE and M-ATTEMPT

The proposed protocol is compared with M-ATTEMPT. The similarities and differences be-

tween the two protocols in terms of topology, technical parameters, communication type, etc.



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Fig. 13. Scenario-4: The sink is made mobile along the center of the ward. The sink first starts moving from center towards

the right wall. Afterwards, it comes back towards the center and moves towards the left wall. Then again move towards the

center. This happens throughout the network lifetime. The sink mobility helps the body relay to consume less energy in some

cases while more in some other cases. It also has a significant impact over the propagation delay. The communication flow is

the same as for scenario-1.

V I. EXPERIMENTAL RESULTS AND DISCUSSIONS

This section separately discusses the simulation results for inductive link model and DAREprotocol.

A. The Mathematical Model

For an inductive link, three parameters namely, voltage gain, link efficiency, and quality factor

are used to carry out simulations in the following subsections.

1) STPC: The results are shown and discussed below.

Voltage gain: The equation for voltage gain, Vload/Vs for STPC is directly proportional to

Rload. In fig. 15, when the value of k increases from 0.2 to 0.8, Vload/Vs also increases.

For every value of k; Rload increases from 0 to 100, so, Vload/Vs shows almost a linear

response.

For k = 0.4 andRload= 320,Vload/Vs possesses a value of 2.2. The aforementioned values

of k and Rload [10], are considered to be harmless for human tissues.



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Fig. 14. Scenario-5: It assumes the same topology as in scenario-2, however, now all the four sinks are made mobile around

the walls of the ward. All the sinks start moving from center of the wall, then move in one direction, come back towards the

center and then move in the opposite direction and finally come back again towards the center. The sinks move altogether. This

combined movement facilitates the body relays of all the patients to consume less energy consumption as compared to all the

other scenarios. This is due to decrease in the communication range between the body relay and sinks. Mobility of multiple

sinks helps in increasing the network lifetime and reducing the delay in transmitting the data. In this scenario, each of the body

relays communicates with the minimum distant sink. The communication flow is: body sensors to body relay to the nearest

moving Sink (Sink1 or Sink2 or Sink3 or Sink4).

Link efficiency: It is clear from fig. 16 that the value of link efficiency is highly dependent

on k. For k= 0.4 and Rload, the value of is 0.75.

2) STPC and STPPTC: Results for STPC and STPPTC are shown and discussed below.

Voltage gain: In fig. 17, changes in the voltage gain Vload/Vs, by varying Rload for STPC

and STPPTC are shown. It is clear from the figure that, any change in Rload produces

no significant change in Vload/Vs. The only noticeable increase in Vload/Vs is produced by

increasing k. However, due to the fact that this link is used on human body to induce voltage

to the implanted device, value of k changes only from 0 to 0.45. From Rload = 0 to 100,

the behaviour ofVload/Vs is nearly constant. After the value of 100, it increases nearly

linearly. For k = 0.4 and Rload = 320, the voltage gain is about 3.7. Hence, the value of

Vload/Vs is significantly higher, when, the secondary circuit is tuned in parallel as compared



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TABLE IV

SIMILARITIES AND DIFFERENCES BETWEENDARE A ND M-ATTEMPT

Parameter DARE M-ATTEMPT

Types of devices Body Sensors

Body Relay

Main Sensor (MS)

Sink

sensors

Sink

Deployment body sensors, body relays and MS are fixed

Sink can either be static or mobile

sensors and Sink both are fixed

Topology per patient 7 body sensors

1 body relay on chest

7 sensors

1 Sink on chest

Communication flow Scenario-1: body sensors to body relays to

Sink

Scenario-2: body sensors to body relay to

nearest SinkScenario-3: body sensors to body relay to

MS to Sink


moving Sink


nearest moving Sink

sensors to Sink

or

sensors to other sensors to Sink

Energy parameters E0BSs = 0.3J

E0BR = 1J

EMS = infinite

ESink = infinite

E0sensors = 0.3J

ESink = infinite

Network type Heterogeneous in terms of energy of body

sensors and body relays

Homogeneous in terms of energy of sen-

sors

Communication type Multi-hop Single-hop

Multi-hop

Types of data reporting Event-driven

Time-driven

Event-driven

Time-driven

to the tuning of primary circuit. Moreover, the output voltage is nearly 4 times higher than

the driving input voltage.

Link efficiency: The link efficiency graph for STPPTSC is shown in fig. 18. This figure

depicts steepness for link efficiency till Rload = 100for every value of k. After this value,

the link efficiency of the circuit becomes constant. For k = 0.4 and Rload = 320, the link



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0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Rload (Ohms)

VoltageGain(Vload/Vs)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

Fig. 15. Voltage gain of STPC.

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Rload (Ohms)

LinkEfficiency(P2/P1)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

Fig. 16. Link efficiency of STPC.

efficiency is about 0.9, i.e. 90%. So, in comparison to the series tuned primary circuit, the

link efficiency of the parallel tuned secondary circuit increases by 15%. Furthermore, this

shows that, 90% of the input power is efficiently transferred to the secondary side.

QF: Fig. 19 shows the comparison plot of QF for both the equivalent circuits. It is clear

from the figure that QFincreases with increase in frequency. For the safety of body tissues,

the frequency for WBASN is set to be 13.56MHz.



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0 50 100 150 200 250 300 350 4000

1

2

3

4

5

6

Rload (Ohms)

VoltageGain(Vload/Vs)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

Fig. 17. Voltage gain of STPPTSC.

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Rload (Ohms)

LinkEfficiency(P2/P1)

k = 0.2

k = 0.4

k = 0.6

k = 0.8

Fig. 18. Link efficiency of STPPTSC.

In case of STPPTSC, QF is higher as compared to the other circuit. Thus, the second

equivalent circuit achieves good tuning under resonant conditions.

B. The DARE Protocol

DARE and M-ATTEMPT protocols are compared by taking mobility of the patients into

consideration. We investigate different parameters including stability period, network lifetime,

number of packets sent, number of received packets at sink, number of dropped packets, and



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0.5 1 1.5 2 2.5 3 3.5 4

x 107

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Frequency (Hz)

Quality

Factor

QF Series Circuit

QF Parallel Circuit

Fig. 19. QF.

propagation delay. The simulation parameters are listed in table III. During simulation setup,

nodes cease transmitting data packets when their energy is completely depleted.

1) Stability period and Network lifetime: The time from the establishment of network till the

death of first node is known as the stability period, whereas, the time from the establishment of

the network till the death of last node is known as network lifetime.

From fig. 20, it is clear that the stability period in M-ATTEMPT is about 239 rounds. Whereas,

in DARE; it is 859, 1027, 1150, 1437, and 1496 for scenario 1, 2, 3, 4, and 5, respectively. In

DARE, as soon as the first node dies, the other nodes also die very quickly, thereby, preserving

uniformity in the protocol. This means that is uniform for all the nodes.

In scenario-5, when multiple moving sinks are deployed, the nodes (both body sensors and

body relays) survive for increased number of rounds i.e., for about 3345 rounds. This is because

of the multiple sinks at different locations which facilitate the body relays to transmit data

at shorter distance. This topology significantly helps the body relays to stay alive for some

additional rounds. Scenario-5 shows the greatest stability period and network lifetime. The MS

in scenario-3, attached with the bed, enables body relays to die with decreased rate as compared

to the other scenarios. In scenario-1, as the sink is static, all the nodes remain alive till 858

rounds. This scenario has least stability period as compared to the other scenarios. Due to

appropriate deployment of body relays that help the body sensors to continue sending their data.



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Stability periodNetwork lifetime

MATTEMPT

Scenario1

Scenario2

Scenario3

Scenario4

Scenario5

0

1000

2000

3000

4000

No.ofrounds(r)

Fig. 20. Stability period and network lifetime.

Accordingly, the network lifetime significantly increases. Alternate view of fig. 20 shows that

the nodes in M-ATTEMPT rapidly consume energy as compared to the nodes in DARE. There

is a sharp fall-off in the energy consumption of the M-ATTEMPT nodes, thereby, dying at an

earlier stage. On the other hand, the nodes in DARE protocol consume minimum energy because

these are facilitated by body relays.

2) Throughput: Fig. 21 shows that of all the scenarios and M-ATTEMPT, scenario-5 sends

maximum number of packets to the sink, because the sensors remain alive for more number of

rounds. Due to mobility and the introduction of body sensors and MS, communication distance

and transmission energy are significantly reduced. This leads to increased lifetime of the network

which ultimately increases the number of transmitted packets. As from fig. 21, more packets

are transmitted in scenario-5, the probability of packet drop also increases. Fig. 21 shows that

the dropped packets are highest in scenario-5, whereas, lowest in M-ATTEMPT because more

number of sensors die at an earlier stage, so, less number of packets are dropped in the network.

The probability of sensors, while receiving packets, is 0.7. As in scenario-5, network lifetime

is prolonged due to sink mobility which also allows to reduce large number of data packets.

Therefore, DARE proves to have more throughput than that of M-ATTEMPT as evident from

fig. 21.



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DroppedReceived

Sent

MATTEMPT

Scenario1

Scenario2

Scenario3Scenario4

Scenario5

0

0.5

1

1.5

2

2.5

3

x 104

No.ofpackets

Fig. 21. Number of packets: sent, dropped and received.

3) Propagation delay: Propagation delay is given as:

=d

c (18)

where, d is the distance between the sending and receiving nodes and c is the speed of light.

Propagation delay is directly proportional to the distance. From fig. 22, in scenario-3, a packet

propagates through a long route from body sensors to sink. The facility of multiple static sinks

provides minimum delay as is the case of scenario-2. In case of the proposed protocol DARE,

the delay continues to increase somewhat linearly for some initial rounds, after which it becomes

constant. The lowest propagation delay makes M-ATTEMPT protocol, favorable in case of the

network where huge delay cannot be tolerated. The reason for minimum delay is the attachment

of the destination node i.e., the sink, on the chest of the patient which, let the sensors to transmit

data at minimum distance. Hence, the proposed DARE protocol achieves energy efficiency at

the cost of propagation delay.

4) Comparison results: This subsection presents the comparison chart between the two ana-

lyzed circuits for induction link. It also includes the overall achievements and drawbacks of all

the scenarios of DARE over M-ATTEMPT.

Fig. 23 shows the merits of STPPTSC over STPC with respect to three parameters; voltage

gain, link efficiency and quality factor, whereas, table 5 shows the comparison between DARE



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MATTEMPT

Scenario1

Scenario2

Scenario3

Scenario4

Scenario5

0

0.5

1

1.5

2

2.5

x 103

Time(sec)

Fig. 22. Propagation delay.

and M-ATTEMPT with respect to the given network parameters.

Fig. 23. Comparison results between STPC and STPPTSC.

VII. CONCLUSION AND FUTURE WOR K

This paper focuses on two aspects. One is the modeling of an inductive link and other is the

proposition of an energy efficient routing protocol. The induction mechanism (modeling part) is



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TABLE V

COMPARISON RESULTS BETWEENDARE A ND M-ATTEMPT

Network Parameter DARE M-ATTEMPT

Stability period high low

Network lifetime high low

Energy consumption minimum maximum

Throughput high low

Propagation delay high low

used to recharge the battery of the in-body pacemaker. Simulation results show that the STPPTSC

achieves good results for voltage gain, link efficiency and quality factor as compared to STPC.

The circuit achieves about four times higher output voltage than the driving input voltage and

link efficiency. The routing part deals with the design and comparison of the proposed protocol

with an existing protocol; M-ATTEMPT. Being able to monitor data continuously or when a

certain threshold is reached, the newly proposed protocol DARE proved to be more versatile

than M-ATTEMPT. By introducing MS and mobile sink in the network, the protocol achieved

better performance in terms of energy consumption and propagation delay. Moreover, DARE

also considered mobility caused by different postural changes in the patients body and achieved

better results than M-ATTEPMT. However, M-ATTEMPT shows less propagation delay which

makes it feasible for delay sensitive applications.

Authors in [33] derived analytical channel modeling and propagation characteristics of arm

motion as a spherical model. Future work includes the proposition of a mathematical induction

model to recharge the sensors through an electric field which is produced due to the arm

movement of human body. We are also very much interested in the real time experimental test

bed implementation of our proposed techniques. Moreover, intelligent routing of data messages

via swarm optimization technique like [34] and energy aware quality of service routing like [35]

are also under consideration.

VIII. ACKNOWLEDGEMENTS

The authors extend their appreciation to the Research Centre, College of Applied Medical

Sciences, and the Deanship of Scientific Research at King Saud University for funding this

research.



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[16] P. K. Pothuri, D. L. Ranganathan, V. Sarangan, and S. Radhakrishnan, Energy-efficient routing in Wireless Sensor Networks

for delay sensitive applications, International Journal of Ad Hoc and Ubiquitous Computing, vol. 5, no. 2/2010, pp. 103

116, 2010. doi:10.1504/IJAHUC.2010.031225.

[17] A. Ehyaie, M. Hashemi, P. Khadivi, Using relay network to increase life time in wireless body area sensor networks, in:

World of Wireless, Mobile and Multimedia Networks & Workshops, WoWMoM, IEEE International Symposium, pp. 16,

2009.

[18] J. Elias, A. Mehaoua, Energy-aware topology design for wireless body area networks, in: Communications (ICC), 2012

IEEE International Conference, pp. 34093410, 2012.

[19] B. Braem, B. Latre, I. Moerman, C. Blondia, E. Reusens, W. Joseph, L. Martens, P. Demeester, The need for cooperation

and relaying in short-range high path loss sensor networks, in: Sensor Technologies and Applications, SensorComm,

International Conference, IEEE, pp. 566571, 2007.

[20] X. Sheng, J. Tang, X. Xiao, and G. Xue, Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls,

Sensors Journal, IEEE, vol. 13, no. 10, pp. 658670, 2013.

[21] B. Chen, J. P. Varkey, D. Pompili, J.-J. Li, I. Marsic, Patient vital signs monitoring using wireless body area networks, in:

Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast, pp. 12, 2010.[22] X. Sheng, J. Tang, X. Xiao, and G. Xue, Sensing as a Service: Challenges, Solutions and Future Directions, Sensors

Journal, IEEE, vol. 13, no. 10, pp. 37333741, 2013.

[23] S.-K. Chen, T. Kao, C.-T. Chan, C.-N. Huang, C.-Y. Chiang, C.-Y. Lai, T.-H. Tung, P.-C. Wang, A reliable transmission

protocol for zigbee-based wireless patient monitoring, Information Technology in Biomedicine, IEEE Transactions, vol.

16, no. 1, pp. 616, 2012.

[24] T. Watteyne, I. Auge-Blum, M. Dohler, D. Barthel, Anybody: a self-organization protocol for body area networks, in:

Proceedings of the ICST 2nd international conference on Body area networks, pages 6, 2007., 2007.

[25] G. R. Tsouri, A. Prieto, N. Argade, On increasing network lifetime in body area networks using global routing with energy

consumption balancing, Sensors, vol. 12, no. 10, pp. 1308813108, 2012.

[26] D. He,. Chen, S. Chan, J. Bu, and P. Zhang, Secure and Lightweight Network Admission and Transmission Protocol for

Body Sensor Networks, IEEE Journal of Biomedical and Health Informatics (J-BHI), vol. 17 , iss. 3, pp. 664674, 2011.

[27] C. A. Otto, E. Jovanov, A. Milenkovic, A wban-based system for health monitoring at home, in: Medical Devices and

Biosensors, 2006. 3rd IEEE/EMBS International Summer School, pp. 2023, 2006.

[28] T. Kuroda, H. Sasaki, T. Suenaga, Y. Masuda, Y. Yoshihiro, K. Hori, N. Ohboshi, T. Takemura, K. Chihara, and H.

Yoshihara, Embedded Ubiquitous Services on Hospital Information Systems, IEEE Journal of Biomedical and Health

Informatics (J-BHI), vol. 16, Iss. 6, pp. 1216-1223, 2012.

[29] C. Wang, Q. Wang, S. Shi, A distributed wireless body area network for medical supervision, in: Instrumentation and

Measurement Technology Conference (I2MTC), 2012 IEEE International, pp. 26122616, 2012.

[30] M. Zhang, and A. Sawchuk, Human Daily Activity Recognition with Sparse Representation Using Wearable Sensors, IEEE

Journal of Biomedical and Health Informatics (J-BHI), vol. 17, iss. 3, pp. 553-560, 2013.

[31] A. Djouama, L. Mokdad, M. Abdennebi, Performance evaluation of lifetime-driven admission control for infrastructure-less

clustegreen wireless networks, Concurrency and Computation: Practice and Experience, vol. 25, no. 5, pp. 718727, 2013.

[32] N. Javaid, Z. Abbas, M. Farid, Z. Khan, N. Alrajeh, M-attempt: A new energy-efficient routing protocol in wireless body

area sensor networks, 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013), 2013.

[33] Q. Ain, A. Ikram, N. Javaid, U. Qasim, Z. Khan, Modeling propagation characteristics for arm-motion in wireless body



32/32

32

area sensor networks, in: Broadband, Wireless Computing, Communication and Applications (BWCCA), 2012 Seventh

International Conference, IEEE, pp. 186191, 2012.

[34] S. Hoceini, A. Mellouk, A. Chibani, Y. Touati, B. Augustin, Swarm intelligence routing approach in networked robots,

annals of telecommunications-annales des telecommunications, vol. 67, no. 78, pp. 377386, 2012.

[35] Z. A. Khan, S. Sivakumar, W. Phillips, and B. Robertson, ZEQoS: A New Energy and QoS-Aware Routing Protocol for

Communication of Sensor Devices in Healthcare System, International Journal of Distributed Sensor Networks, vol. 2014,

article ID 627689, 18 pages. doi:org/10.1155/2014/627689.

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