WIRELESS BODY AREA NETWORKS

Information Dissemination Analysis

Yasir Faheem and Saadi Boudjit

Laboratoire L2TI, Institut Galil

ee, Universit

e Paris 13, Villetaneuse, France

Keywords:

WBANs, Wearable Sensors, Energy Dissipation, Health Care Computing.

Abstract:

Telemedicine integrated wearable health monitoring system is a novel technology with aiming to support early

detection of abnormal conditions and prevention of its serious consequences. Recent Advances in technology

has led to the development of small, intelligent, wearable sensors capable of remotely performing critical

health monitoring tasks and then transmitting patient’s data back to health care centers over wireless medium.

Patients beneﬁt from continuous ambulatory monitoring as a part of a diagnostic procedure, optimal mainte-

nance of a chronic condition or during supervised recovery from an acute event or surgical procedure. This

requires continuous functioning of the wearable sensor devices. But to the day, energy remains to be a big

constraint in enhancing Wireless Body Area Networks (WBAN) (IEEE 802.15 WPAN Task Group, 2003)

lifetime. Some recent literature on WBANs proposes multi-hop sensor-to-gateway data relay as more energy

efﬁcient than single hop communication. There are studies which argue contrarily. This study analyzes the

single vs multi-hop energy consumption effect for real short range sensor devices.

1 INTRODUCTION

Health cost represents a considerable ratio in the eco-

nomic budget of developed countries, and certain ten-

dency studies are not optimistic about an improve-

ment in the situation. Average age of the population

tends to increase. Number of people requiring more

or less care intensive medical monitoring is not small.

This increases overall cost of medical care. No doubt,

using socio-medical establishments to place people at

risk, under surveillance is impractical for cost rea-

sons, but also for reasons of quality of life. Many

of these people are fully autonomous, though weak-

ened. Their psychological conﬁnement due to pres-

ence nursing staff would be a breach of their free-

dom. Therefore, partially replacing the assistance of

nursing staff by small health surveillance & commu-

nication equipments like sensors, networks, monitor-

ing software could be cost effective and would also

increase life standard. Focusing on this topic, we

are developing a wireless health monitoring platform

which aims to continuously monitor mobile patients

needing permanent surveillance. The objective of this

project is to develop and implement innovative solu-

tions based on information technologies and wireless

communication for the beneﬁt of those needing medi-

cal permanence. In ﬁrst step, we propose to study the

issues related to the acquisition of medical informa-

tion concerning a patient via a set of wireless sensors

embedded in the patient himself. Secondly, we focus

on treatment and use of this information either by a

local contractor equipment (central device) with a ca-

pacity of calculation or offset after transfer in GPRS

and/or WiFi connection to a data server based at the

attending physician or hospital.

In this project, we will propose and evaluate solu-

tions to some of related problems that can be encoun-

tered in such an environment:

- Information dissemination between wireless sen-

sors and the central device (mobile phone for in-

stance)

- Sensors’ auto-conﬁguration

- Authentication and security

The proposed solutions will be evaluated on an exper-

imental platform. This will be achieved by the help of

our medical partner LIM&Bio laboratory (LIM&Bio,

2009). Although the platform will not be tested on

real patients, but it will be a prototype proof of con-

cept taking into account experimental constraints re-

lated to vital parameters of the human body. Thus,

the diversity of information that can be collected,

their frequency, their importance as, for example, set

thresholds for various measurements and above which

an alert is sent to the doctor, are ﬁxed by our medical

partner.

489

Faheem Y. and Boudjit S. (2010).

WIRELESS BODY AREA NETWORKS - Information Dissemination Analysis.

In Proceedings of the Third International Conference on Health Informatics, pages 489-494

DOI: 10.5220/0002798104890494

 SciTePress

2 ARCHITECTURE AND

POSITIONNING OF THE

PROPOSED PLATFORM

A general multi-tier system architecture is shown in

Figure 1; the lowest level encompasses a set of intel-

ligent sensors, the second level is the personal server

(central device) which could be an Internet enabled

PDA, a cell-phone, or a home computer. The third

level encompasses a remote health care server with

a set of its possible related users (Physician, Clinic,

Emergency). Each level represents a fairly complex

subsystem with a local hierarchy employed to ensure

efﬁciency, portability, security, and reduced cost. The

personal server, running on a PDA or a 3G cell phone,

provides the human-computer interface and commu-

nicates with the remote server.

Figure 1: Wireless Health Monitoring Platform.

There are several projects implementing platforms

for medical supervision at a distance. BASUMA (BA-

SUMA, 2006) is an example of such a platform of

which Philips is the consortium leader. Another simi-

lar platform called CodeBlue (CodeBlue, 2008) is be-

ing developed at Harvard University. However, these

platforms are typically installed in homes of patients,

and therefore limit patients’ mobility because they

must constantly be close to their Internet connection

for transmitting real time data. Secondly, security is-

sue has not been addressed in these platforms.

Our purpose in this work is to propose an archi-

tecture that combines several wireless technologies

(WiFi, ZigBee, GPRS) allowing patients to transmit

data in a secure manner to the remote health care

server regardless of their location.The proposed plat-

form will address the following issues:

• A Wireless Body Area Network (WBAN) com-

posed of various wireless sensors ultimately con-

nected to a retransmission device and commu-

nicating using wireless technologies like Blue-

tooth (Bluetooth, 2009) and ZigBee (Zigbee Al-

liance, 2009).

• A retransmitter device (central device), which

could be a cell-Phone or a PDA. This compo-

nent will implement the functions of active patient

monitoring, especially in the case of connection

loss with the remote server. It will serve as the

bridge between the WBAN and the global Inter-

net network and will also ensure the security and

conﬁdentiality of that route.

• A remote server that collects all data from various

sources and stores it in database. It will gener-

ate statistics, information for doctors and alarms,

if any, that may be transmitted to a personal care

unit.

• The doctor or, generally speaking, the medical

staff will be the privileged consumer of platform

provided information. Due to this, user can have

multiple interactive interfaces:

– Standard web Interface: interactions with the

platform are done using a conventional web

browser.

– Mobile Interface: this interface will be de-

signed for personal digital assistants (PDAs) or

smartphones.

However, the establishment of such an architec-

ture requires solving certain scientiﬁc problems at all

the platform levels. Under this project, we concen-

trate our analyses on the patient side and we focus

on three key issues related to wireless body area net-

works: routing between sensors and the central de-

vice, sensors auto-conﬁguration, and secure transmis-

sions.

3 OUR CONTRIBUTION

Several studies have shown that multi-hop routing

in WBAN result in non-negligible lifetime increase

of sensors as compared to direct communication be-

tween sensors and the central device. In this case, in-

formation dissemination between sensors and the cen-

tral device requires an ad hoc routing protocol.

Some other studies, however, argue contrarily.

They show that direct communication between sen-

sors and the central device considerably increase the

lifetime of a wireless body area network. They show

that executing a multi-hop adhoc routing protocol on

embedded sensors consumes more energy.

In this paper we are exploring both information

dissemination techniques in a WBAN scenario. Our

objectif here is to ﬁnd a tradeoff between the number

of hops in the network and the energy consumption.

Energy consumption for various scenarios is evalu-

ated through simulations.

HEALTHINF 2010 - International Conference on Health Informatics

490

4 PROBLEM ANALYSES

Power consumption for transceivers is different

in different communication states i.e. transmis-

sion/reception/idle/sleep. Transmission energy de-

pends upon the power with which signal is propa-

gated to attain longer ranges while consumption in

other states is less variable. Unlike other technolo-

gies, reception/idle state consumption for low power,

limited range WSN is not negligible as compared to

transmission state power consumption. Thus global

energy minimization requires optimum selection of

transmission range. Latr

e et. al. in (Latre et. al.,

2004) showed that utilizing multi-hop communica-

tion by reducing transmission power in WBAN re-

duces overall energy consumption. This is true but

authors in (Latre et. al., 2004) ignore increased en-

ergy consumption due to multiple receptions. Wang

et. al. in (Wang et. al., 2006) propose a realistic

power consumption model for WSN. (Wang et. al.,

2006) shows that multi-hop communication is more

energy efﬁcient when destination is out of reach. That

is when destination cannot be reached in single hop.

They show that multi-hop communication by control-

ling transmission power does not neccessarily result

in energy gain. This is due to non-negligible energy

consumptions in reception and Idle modes.

In sensor motes energy consumption in reception

and idle mode is relatively high. It can be equal to or

greater than transmission energy consumption for low

power transmissions. For low transmission ranges as

in WBAN, utilizing single hop data delivery to gate-

way node while other sensors are put in idle/sleep

state might be more energy efﬁcient. Real experi-

ments conducted in (Anastasi et. al., 2004) show

that reception and idle listening consume a consider-

able amount of energy. Especially for sensor nodes,

very low power transmissions for ranges as short as

in WBAN consume lesser power than reception. For

micaZ motes (Chipcon AS, 2007), reception energy is

higher than transmission energy even with maximum

power transmission. Above stated variations in liter-

ature, motivated us to better understand this energy

consumption scenario and choose a better communi-

cation strategy for our future test bed implementation

for WBAN.

Figure 2: Simulation scenarios.

Table 1: Simulated scenarios.

Scenario

Hops

Distance

Nodes

1 1 60 n

→d

2 2 30 n

→n

→d

3 3 20 n

→n

→d

4 6 10 n

→n

→d

Table 2: Mica2 Mote Power Consumption and Range Val-

ues.

power

(dBm)

Power

consumed

(mW)

power/Idle

power

(mW)

range

(meter)

05 76.2 30 60

-07 32.4 30 30

-14 27.9 30 20

-20 25.8 30 10

4.1 Channel Model and Energy

Consumption

Let P

and P

be the transmission and reception sig-

nal power respectively, where P

is equal to receive

sensitivity of mica2 node (-98 dBm). Let d be the

communicating nodes’ inter-node distance and, L the

system loss, then our WSN model can be represented

by the well known TwoRayGround radio model (NS2,

n.d.):

= ReceiveSensitivity =

(1)

Where G

=1.2 are antenna transmission and

reception gains respectively. h

=16cm are trans-

mission and receptor’s antenna heights. Transmission

power P

is varied according to desired range. Equa-

tion (1) can be rearranged to determine d for given

values of P

as follows:

d = [

]

(2)

We need minimum P

that ensures suc-

cessful reception of packet at destination with

>ReceiveSensitivity. For given values of P

approximate range values are obtained from radio

model given in (2). The actual power consumed by

mica2 while transmitting with permissible power P

is obtained from CC1000 data sheet (CC1000, 2007).

The range results for given P

(Table 2) conform to

WIRELESS BODY AREA NETWORKS - Information Dissemination Analysis

491

the MICA2 data sheet and experimentally obtained

range values.

Energy consumption of sensor nodes in various

states can be obtained by the following equations:

= P

× T

= P

rec

× T

idle

= P

idle

× T

idle

where P

, P

rec

, P

idle

are the powers consumed

by the Mica2 mote’s CC1000 transceiver in transmis-

sion, reception and idle mode respectively, and T

, T

idle

are times spent in each mode. Time for

transmitting a packet of size b bits is equal to [

]

where R is the data rate. Total energy consumed by

the network is given by;

total

= E

+ E

Rec

+ E

idle

4.2 Simulations

Network Simulator 2 (NS2) is used for performing

simulations. We performed simulations utilizing the

actual power consumption values of sensor motes in

various states. In order to have an insight view of

the energy consumption in various working modes,

the power consumption values of Mica2 (Crossbow,

n.d.) sensor motes have been used. Mica2 has an on-

board CC1000 transceiver for communication. Thus

power consumption values for communication have

been taken from CC1000 data sheet (CC1000, 2007).

Deployed topology consists of a set of seven

equidistant nodes {n

, n

} deployed

linearly with adjacent inter-node distance of 10

meters. This accounts to maximal source-destination

→n

) distance of 60 meters. Source n

generates

packets at regular intervals and transmits them

towards sink n

. Simulations are performed with

four different relaying scenarios. Details are given in

ﬁgure 2 and in table 1. n

→n

transmission is varied

from single hop to a maximum of six hops. In all

the scenarios source node n

sends 50 bytes packets

towards desination node n

. MAC layer issues like

collisions, retransmissions and scheduling are not

taken into account. This is realistic assumption as the

goal here is to analyze only the energy consumption

effect with varying hop distances.

Figure 3 shows total network energy consumption

when radio is always active i.e. either in Tx/Rx state

or in idle state. In this case energy consumption re-

duces a bit with increasing number of hops. Note that

1.2

1.4

1.6

1.8

2.2

2.4

2.6

1 2 3 4 5 6 7

Energy (J)

Hops

Total Energy Consumption

"Consumption"

Figure 3: Total Energy Consumption when Radio always

ON/IDLE.

for this case P

rec

idle

=30mW. This is because cur-

rent draw through mica2 is same when it is either re-

ceiving a packet or performing idle listening. Power

transmission values P

for each case are obtained

from table 2. Energy consumption for direct n

→n

communication is 2.29J. For two hops n

→n

and three hops n

→n

scenarios total energy

consumption slightly reduces to 2.12J and 2.07J re-

spectively. Results are not very appealing. With least

transmission power of -20dBm, that draws 25.8mW

from mica2, minimum total consumption of 1.99J is

acheived.This accounts to maximum energy saving

of 13% as compared to direct n

→n

communica-

tion. At -20dBm, power consumed P

rec

(25.8

<30mW). As compared to this, multiple WBAN sen-

sors are very close to each other (tens of centimeters)

and to the gateway node e.g. PDA. In such scenario

further reduction in transmission power will increase

multiple receptions/Listening dominancy over trans-

mission energy. Thus multi-hop option is not practical

in small WBAN. Heinzelman et. al. in (Heinzelman

et. al., 2000) propose a WSN based energy model.

They also conclude that multi-hopping is energy efﬁ-

cient when destination cannot be reached in a single

hop. A contrary argument to this point could be that

transmission range reduction is neccessary to avoid

collisions and large number of overhearings. This is

true, but in small scale WBANs, source to sink syn-

chronized single hop communications while keeping

other nodes’ transceivers off would be more optimal.

Figure 4 shows network energy consumption

when nodes switch on their radio only when they need

to transmit their information. Energy consumption for

direct n

→n

communication is 0.93J. For two hops

→n

and three hops n

→n

scenar-

ios total energy consumption increases to 0.946J and

1.083J respectively. With least transmission power of

-20dBm, that draws 25.8mW from mica2, minimum

HEALTHINF 2010 - International Conference on Health Informatics

492

0.8

0.9

1.1

1.2

1.3

1.4

1.5

1.6

1 2 3 4 5 6 7

Energy (J)

Hops

Total Energy Consumption

" "

Figure 4: Total Energy Consumption when sensor node Ra-

dio ”ON” only for transmission.

total consumption of 1.55J is acheived.This accounts

to 40% increase in network’s total energy consump-

tion as compared to direct n

→n

. Clearly, reduc-

ing transmission range to perform data relaying does

not seem to be good option in WBAN. Energy efﬁ-

ciency gain in WSN is maximum when sensor nodes

periodically go to deep sleep mode. We are not con-

sidering this case here as turning off health sensing

equipment may prove fatal to patient’s life. Patient’s

health needs to be regularly monitered and transmit-

ted to the concerned data center. Although the period-

icity of monitering depends upon the nature of obser-

vation and patients condition. It may not be necessary

to transfer health update to data center. This could be

utilized only under emergency condition to trigger the

call for medical assistance. From this discussion, it is

concluded that, unless really required, unneccessary

relaying should be avoided. Some real experiments

show high path atenuation values in WBAN with α

as high as 5.8. If such condition arrives the deployed

routing mechanism should be able to adapt itself for

multi-hop communication. For this reason, we aim

to adopt a simple routing protocol that uses a smart

neighbour discovery mechanism as in (Jacquet et. al.,

2003). This would allow nodes to communicate with

sinks over two hops, if required.

5 CONCLUSIONS AND FUTURE

WORK

An energy consumption comparison for various com-

munication scenarios has been made. It has been

shown that at very low ranges, transceivers consume

almost equal or more power on reception than trans-

mission. Thus deliberate reduction of transmission

range to induce multi-hop scenario is not efﬁcient.

Though this is device dependent but general charac-

teristics of very low power transceivers seem to show

the same results.

Our future work is to propose an energy efﬁcient, re-

liable routing architecture keeping in view the results

obtained through this study. Auto-conﬁguration for

multi-hop relaying should be added in proposed rout-

ing architecture, in case when direct sensor to gate-

way access is not attainable. Security issues to ensure

patient’s unique identity will be dealt. At the end, we

plan to implement a real working WBAN prototype

on specialized wearable Shimmer sensors (Shimmer

Platform, n.d.).

REFERENCES

IEEE 802.15 WPAN Task Group 6 (TG6) Body Area Net-

works. (2003). 802.15.4 Standard. Retrieved Aug.

2009 from www.ieee802.org/15/pub/TG6.html

LIM&Bio: Laboratoire d’Informatique Medicale & Bio-

Informatique. Retrieved Jul. 2009 from www.limbio-

paris13.org

BASUMA. (2006). Body Area System for Ubiquitous Mul-

timedia Applications Project. Retrieved Sept. 2009

from www.basuma.de

CodeBlue: Wireless Sensors for Medical Care.

(2008). Retrieved 13, November 2009 from

http://ﬁji.eecs.harvard.edu/CodeBlue

Bluetooth Technology. (2009). Retrieved Oct. 2009 from

www.bluetooth.com

ZigBee Alliance. (2009). Retrieved Oct. 2009 from

www.zigbee.org

B. Latre, I. Moerman, B. Dhoedt, P. Demeester. (2004). Net-

working in Wireless Body Area Networks 5th FTW

PHD Symposium

Q. Wang, M. Hempstead, W. Yang. (2006). A Realistic

Power Consumption Model for Wireless Sensor Net-

work Devices IEEE Comm. Society Conference on

Sensor, Mesh and Ad hoc Communications and Net-

works

G. Anastasi et. al. (2004). Performance Measurements of

Motes Sensor Networks International Workshop on

Modeling Analysis and Simulation of Wireless and

Mobile Systems

Chipcon AS. (2007). CC2420 2.4 GHz IEEE

802.15.4/ZigBee-ready RF Transceiver Retrieved

Oct. 2009 from http://inst.eecs.berkeley.edu/ cs150/

Chipcon AS. (2007). CC1000 Single Chip Very Low Power

RF Transceiver. Retrieved from Oct. 2009 from

http://focus.ti.com/lit/ds/symlink/cc1000.pdf

Crossbow Technology MICA2 Motes Retrieved June, 2009

from www.xbow.com

W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan.

(2000). Energy-efﬁcient communication protocol for

wireless microsensor networks Proceedings of the

WIRELESS BODY AREA NETWORKS - Information Dissemination Analysis

493

33rd Hawaii International Conference on System Sci-

ences

P. Jacquet, P. Muhlethaler, P. Minet, A. Qayyum, A. Laouiti,

T. Clausen, L. Viennot, C. Adjih. (2003). Optimized

Link State Routing Protocol Internet Engineering Task

Force Group, RFC 3626

Shimmer Platform. (n.d.) Oct. 2009 Retrieved from

http://shimmer-research.com

Network Simulator 2 (n.d.). www.isi.edu/nsnam/ns/

HEALTHINF 2010 - International Conference on Health Informatics

494