A NOVEL APPROACH FOR SIMULATING A

BIO-CONTAMINATION PROCESS

Gerard Chalhoub, Antonio Freitas and Michel Misson

LIMOS-CNRS, Network and Protocols Team, Blaise Pascal University

B.P. 86, 63172 AUBIERE CEDEX, France

Keywords: Simulation, CSMA/CA, bio-contamination, Ad-hoc, WLAN.

Abstract: The phenomenon of bio-contamination in a population of individuals being contaminated in a near by near

physical, viral or bacterial contact could be compared by analogy with a near by near exchange of "atomic"

data between mobile entities of an ad hoc network. Would the tools of wireless communication engineering

then make it possible to contribute in the modeling of a bio-contamination process? Does the use of

CSMA/CA in order to share the “contamination medium” make it possible to simulate this process of

contagion? To establish the limits of the analogy, we consider the most unfavorable case, the systematic

contamination of proximity. A susceptible mobile becomes contaminated if it passes near a contaminant

mobile at a distance lower than the contamination distance. Simulations under NS2 highlight the effect of

the overall radiation compared to the power used for emitting the atomic data representing the virus and

reveal an optimal frequency of atomic data diffusion in the case of a population with strong geographical

density moving in confined environment.

1 INTRODUCTION

The symbolic system and the vocabulary used in

part(a) of figure 1 give the impression that it is an

ad-hoc wireless communication system between

entities moving according to a certain trajectory and

a given speed. A mobile emits or receives

information by means of an antenna characterized

by the range of an electromagnetic radiation. This

exchange of data takes place only if the receiver is

within range from the transmitter, more precisely the

distance traveled by the signal, is lower than a given

threshold.

Let us suppose now that this range does not

concern the distance at which the mobiles can

exchange information but the distance of a

contamination by air following a cough, a sneeze, or

simply breathing, between individuals during an

ordinary day. Part (b) of figure 1 shows that the

entity A (the contaminant) contaminates the entities

B then C which become in their turn contaminators.

The question put here is the following one: can

the engineering of the wireless networking be usable

to include (represent) a process of bio-

contamination? More precisely, prototyping using

light wireless communicating equipments, or

simulating using simulators developed for the

wireless communications like OPNET and NS2, can

be of a certain utility to model a process of

contagion?

The most important criteria that affect a

contagion process identified by (Shane C. St. John,

1997) were:

- The probability of infection.

- The probability of recovery.

- The number of encounters between

individuals which depends on: the density of

the population and the dynamic degree of

that population.

- The initial number of infected individuals.

Each one of these parameters can be associated

with a step of the modulation process using

simulation tools like OPNET and NS2 (G.

Chalhoub, A. Freitas & M. Misson, 2007). Let us

consider the probability of infection: a susceptible

individual touched by a contaminant is considered

infected with a probability of infection P. In the

123

Chalhoub G., Freitas A. and Misson M. (2008).

A NOVEL APPROACH FOR SIMULATING A BIO-CONTAMINATION PROCESS.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 123-129

DOI: 10.5220/0001049901230129

 SciTePress

Figure 1: Wireless Communications / Bio-contamination process.

context of a network modulation process, the

transition from susceptible to infected will not take

place unless the susceptible individual receives a

contaminant message with a certain probability of

success (figure 2). In this article we are considering

a systematic contamination (P=1).

Figure 2: Susceptible becomes infected.

2 ASSUMPTIONS

If we consider the cough or the sneeze as means of

contamination by air between individuals, the

analogy with a wireless networking will result in the

diffusion on the radio medium of a frame containing

atomic data. Nevertheless, we make the assumption

in this article that the contamination is regarded as

succeeded, if this information is received with no

errors by one or more other entities.

For an individual the support of viral

transmission, the air, is always available. A cough or

a sneeze of an individual can be contaminant even if

other individuals have an activity which coincides in

time at the same place. In that sense the activities of

contamination are rather cumulative. Here we see a

limit with our analogy arising. In a WLAN several

simultaneous emissions cause a superposition of

signals, a collision, which makes impossible the

deciphering of information (with the exception of

the capture effect). In this case, the contention-based

access methods implement an arbitration which will

try to order the access to the medium. Data to be

transmitted will then undergo a random delay. The

access method CSMA/CA is today the most

“popular” method in the field of the WLAN

(ANSI/IEEE Std 802.11, 1999),

(IEEE Std

802.15.4™-2003)…, and will be detailed in part 3 of

this article.

In the case of a contamination caused by

coughing or sneezing, the recurrence of the events is

about a few seconds. This recurrence is not really

periodic but, nevertheless, we can consider it as a

"Burst" activity like it is illustrated in figure 3.

Although T

and T

are not periodic, the time t

separating 2 messages in a "Burst" transmission can

be considered as a pseudo periodic activity.

While transposing this activity in a wireless

networking domain with the CSMA/CA method, the

density of traffic representing the information of

contamination will be easily assured by the MAC

layer (G. Chalhoub, A. Freitas & M. Misson, 2007).

This approach differs from the stochastic model

based on the cellular automata used by (H.

Situngkir, 2004).

This leads us now to consider a contamination

only due to proximity. Any susceptible individual

who is near a contaminated person, at a distance

lower than the distance of contamination, becomes

contaminant himself. We will call this model of

contamination by proximity, the geometrical model.

In the context of wireless networking, the

contaminated entity must broadcast atomic

information of contamination. Our objective is thus

to answer the following question: compared to the

geometrical model, which is the optimal frequency

of broadcasting the contamination data, with

CSMA/CA as the access method, in a context of

Susceptible Infected

Contaminant

message and (1-P)

Contaminant

message and P

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

124

strong density of individuals and a contamination by

proximity?

Figure 3: Burst activity.

3 CSMA/CA AND THE SHARED

MEDIUM

The basic principle of CSMA consists in listening to

the medium before emitting when a station has a

pending (ready to be emitted) frame (Chen, 1994).

On the discharge of the medium the station applies a

method to manage the possible competition with

other stations. In the case of the CA (Collision

Avoidance) method the stations draw a back-off

period to desynchronize the potential candidates.

The detection of an activity on the network is

carried out by the measurement of the power of the

received electromagnetic radiation. If this

measurement is higher than the fixed threshold for

the noise, the medium is regarded as being busy.

In the case of WiFi, the contention resolution

mechanism is governed by the 802.11 standard

specifications

(ANSI/IEEE Std 802.11, 1999). For

the DCF (Distributed Coordination Function) mode

a station having a pending frame may begin to

access only when the radio channel is sensed Idle.

That is the case when the PHY layer performs a

“Clear Channel Assessment (CCA)” which returns

“IDLE” as the value of the CCA Indicator. This is

the case when the energy level received is lower

than a threshold very often estimated at - 95 dBm

(value given by the suppliers of WiFi interface). It is

this value which is passed in parameter (CSThresh:

Carrier Sense Threshold) for a simulation by NS2

(Wu Xiuchao, n.d.) and which creates a little

polemic for a simulation by OPNET

(S. Roy, H. Ma,

R. Vijayakumar & J. Zhu, 2006). Let us consider a

signal received with a power Pr higher than the

Carrier Sense Threshold, it allows to identify the

fact that the channel is indeed busy but it is not a

sufficient condition so that the information

transported by the signal can be suitably interpreted.

For that it is necessary that the receiver has a Margin

of Decoding (MD) which depends on the

modulation used for the transmission (Intersil Data

Sheet HFA3861B, 2001). For example, it is admitted

that in the case of a WiFi network the decoding of a

frame with 11 Mbps requires that the energy of the

signal received be higher than - 82 dBm. It is the

value which is passed via the RXThresh parameter

in a NS2 simulation. This obviously implies that the

area in which the signal is perceived as higher than -

95 dBm is much larger than the zone of reception. If

we illustrate that by a mechanism of contamination

by cough simulated using an access method of the

type CSMA/CA, a person who coughs prevents from

coughing people whom it does not reach!

This is illustrated in the parts (a) and (b) of

figure 4. The most external disc represents the

surface in which the CCA indicator has the value

BUSY, the disc delimited by -82 dBm corresponds

to the surface in which the reception is done with an

acceptable error rate.

Regardless of the nature of the medium, at a

short distance from the transmitter, it is standard to

consider that the law of dispersion of energy is in

1/D².

If we know the transmission power, it will

possible then to deduce the received power at the

security (or contamination) distance which we

introduced. Thus for a transmission power of 20

dBm the power hoped at 2 meters is - 26 dBm. It is

what corresponds to the smallest disc of part (b) of

figure 4.

In the same way by adjusting the power of

transmission to -36 dBm, the threshold of reception

of -82 dBm corresponds to the distance of

contamination. This is represented by the part (c) of

figure 4.

At this stage we can discuss the effect of the

Clear Channel Assessment (CCA). A transmission

with 11 Mbps and a power of - 36 dBm has an

impact which goes well beyond a disc of 2 meters

because of the CCA which covers a surface with a

power higher than - 95 dBm. By reducing the power

of emission we also reduced considerably the

surface of carrier sensing. Using NS2 we will

evaluate the effects of reducing the power of

transmission, on the simulation of the process of

contagion.

Burst emission of

contaminant messa

A NOVEL APPROACH FOR SIMULATING A BIO-CONTAMINATION PROCESS

125

Figure 4: CSMA and thresholds.

4 SIMULATIONS

The object of this paper is to study if the choice of

the access method CSMA/CA exploited to broadcast

atomic information at a given frequency is an

acceptable way to model a process of contamination

by contact.

4.1 The Reference Taken for a

Contamination by Contact

In our approach of simulation we considered that the

ideal case was given by a geometrical approach of

the problem, i.e. at any moment (in fact every 10

ms) we calculate for each contaminated station the

distance which separates it from its neighbors. So if

for a neighbor of a contaminant, this distance is

lower than the threshold of contamination, this

neighbor becomes also contaminated.

The choice of the frequency of this calculation

depends on the velocity of the mobiles: the latter

being at a maximum of 2 m/s this gives us a

maximum error of 4 cm on the calculation of the

distance between mobile if calculation is made every

10 ms. By this geometric calculation we obtain a

number of mobiles contaminated throughout the

simulation time that we will indicate thereafter like

the result of the geometrical model. This result will

represent an asymptote for those which are obtained

by any other way.

4.2 The Simulation Assumptions

In this paper we chose to illustrate the results of a

contamination modeled by CSMA/CA and to

compare them with the results of the geometrical

model for two configurations of mobiles which

correspond to the same initial rate of contamination.

- The First simulation: 100 mobiles in a surface of

20 X 20m² with only one mobile contaminated at the

beginning of simulation

- The Second simulation: 400 mobiles in a surface of

40 X 20m² with 4 mobiles initially contaminated.

In these two cases two frequencies of

broadcasting (2 and 10 Hz) and two powers of

transmission (20 and - 36dBm) are studied.

All simulations were made for 20 scenarios of

nodes distribution and the curves displayed later on

represent the averages of the number of nodes

contaminated during these 20 simulations. Each

simulation represents an evolution of the contagion

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

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during 20 s, the number of contaminated mobiles is

calculated every 0,2 s. All nodes are randomly

moving within the simulated area with a maximum

speed of 2 m/s.

4.3 Analysis of the Effects of the

Transmission Power

In the case of the first simulation one can note that

the power of transmission does not have much

influence because the curves obtained for 20 dBm

and - 36 dBm are very close (Figure 5). The

frequency of broadcasting at 10 Hz makes it possible

to approach the geometrical model much more

clearly.

While multiplying by 2 the geographical density

of the nodes and by considering the same density of

contaminant (4 nodes initially contaminated), a

diffusion made with a limited power gives results

that approach more the geometrical model than

those done using the usual power of Wifi.

The simulation results:

With our assumptions everything takes place

during the first seconds of simulation. Figure 6

shows clearly how reducing the transmission power

improves the propagation by reducing the effect of

CSMA/CA on the delay of the diffusion in an area

with a dense population.

4.4 Examining the Effects of the

Frequency of Diffusion in Low

Power Transmission

Another factor which has also a big part in

determining the effect of CSMA/CA is the

frequency of diffusion. Our goal is to approach the

geometric model which gives us a representation of

an almost continuous contagion. Hence, one can

think of increasing the frequency of the broadcasting

of the contamination frames, that means increasing

the offered load on the network and reaching the

limits of the effectiveness of such an access method

(Chen, 1994). We can thus suppose that there exists,

for a given density of mobile stations, an optimum

frequency of transmission, and that's what we will

try to find by simulation.

Figure 5: Low density contamination.

100

120

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4

time

number of nodes

2m0.1 wifi0.1 2m0.5 wifi0.5 geo

A NOVEL APPROACH FOR SIMULATING A BIO-CONTAMINATION PROCESS

127

Figure 6: High density contamination.

We tested several frequencies using the previous

model by using a low power transmission (- 36

dBm). For frequencies of broadcasting going from

0.5 to 200 Hz, we will consider the average of 20

simulations, to calculate the number of contaminated

nodes. Each simulation lasts 20 seconds and gives a

value every 0,2 second, we will thus have 100

values per frequency. These 100 values will be

compared with those corresponding to the same

moment, given by the geometrical model to identify

a difference: "an error" between the theoretical

contamination given by the geometrical model and

that approached by CSMA/CA.

Let M be the average of these 100 differences

between the two corresponding values for each

frequency of transmission, we thus obtain the

following curve (Figure 7):

We can notice that with a frequency of diffusion

between 10 and 20 Hz the average number of

contaminated nodes is 0.5 node less when compared

to the ideal number given by the geometric model.

We can clearly identify 3 different zones illustrated

by that curve. In the first zone we are under-loading

the network using frequencies between 0.5 and 4 Hz

for emitting contaminant messages and not being

able to produce the same result given by the

geometric model, which is easily explained by the

fact that we are missing some encounters due to the

mobility of the individuals that cross into each other

without being able to exchange contaminant

messages. By increasing the frequency beyond 20

Hz we are overloading the network and the effect of

CSMA/CA causes a delay on the transmitted

messages which prohibit some individuals to emit

and therefore miss some encounters. We determined

effectively an optimal frequency of diffusing

contaminated messages in a continuous way which,

for our assumptions of simulation, varies between 10

and 20 Hz.

5 CONCLUSION AND

PERSPECTIVES

CSMA/CA and simulators for the wireless networks

are original assets to simulate processes like a

contamination by contact. We have just shown that

by adjusting the transmission power and the

frequency of the access to the medium, it is possible

to simulate with a simulator like NS2 the way in

which a virus is propagated in a mobile human being

community.

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

128

Figure 7: Frequency effect.

To approach more the real nature of a

contamination by proximity, the action of the

contaminant radiation of a node must be limited to

the distance of contamination. It would be

interesting to study next the effect of the bandwidth

which finishes the surfaces dilemma between the

zone of carrier sensing and that of a good reception.

In the case of a 1 Mbps transmission these two

surfaces are overlapping which brings us even closer

to the geometrical model (part (d) of figure 4).

Following a meeting, that took place June 2007,

with a group of doctors who were interested in our

work, we will improve our simulation model by

defining individual profiles which define the

personal biological characteristic of each individual

(degree of vulnerability, being immunized or not,

and time needed to be recovered…), and by

applying the "burst" activity to emit the contaminant

messages instead of the continuous emission.

Having a virus profile with all its characteristics will

also help us build a specific transmission behavior

that will reproduce the virus' nature.

REFERENCES

ANSI/IEEE Std 802.11, 1999 Edition (R2003)

G. Chalhoub, A. Freitas & M. Misson, 2007. Using

Wireless Networking Simulation Tools and Technics to

model a bio-contamination process –First Approach-

Unpublished internal report of the N&P Team

LIMOS.

Hokky Situngkir, 2004. Epidemiology Through Cellular

Automata .WPF2004 Bandung Fu Institute.

IEEE Std 802.15.4™-2003.

Intersil Data Sheet HFA3861B 2001. Direct Sequence

Spread Spectrum Baseband Processor.

Kwang-Cheng Chen, 1994. Medium Access Control of

Wireless LANs for Mobile Computing.

S. Roy, H. Ma & R. Vijayakumar, J. Zhu, 2006.

Optimizing 802.11 Wireless Mesh Network

Performance Using Physical Carrier Sensing.

University of Washington Electrical Engineering

Technical Report.

Shane C. St. John, 1997. Population Dynamics in spatially

explicit lattice epidemic models. AAT 1385006.

Wu Xiuchao, n.d.. Simulate 802.11b Channel within NS2.

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