Wireless Sensor Network, 2011, 3, 135-146
doi:10.4236/wsn.2011.34016 Published Online April 2011 (http://www.SciRP.org/journal/wsn)
Ambient Intelligence: Awareness Context Application in
Industrial Storage
Ahmed Zouinkhi1, 2, Eddy Bajic1, Eric Rondeau1, Mohamed Ben Gayed2,
Mohamed Naceur Abdelkrim2
1Research Center for Automatic Co ntrol – CRAN – CNRS UMR, Henri Poincaré University, Nancy, France
2Research Unit MACS, National Engineering School of Gabes, Gabes, Tunisia
Received February 28, 2011; revised March 11, 2011; accepted Marc h 18, 2011
Abstract
WSNs are designed to efficiently collect data and monitor environments, among other applications. This ar-
ticle describes the concept and realization of an Active Security System for security management of ware-
housing of chemical substances using WSNs. We present an approach to modeling and simulating coopera-
tion between intelligent products that are equipped with a platform of sensor networks and ambient commu-
nication capabilities to increase their security, in a context of ambient intelligence of a deposit for chemical
substances. Behavior evolution of every intelligent product is modeled by hierarchical Petri Nets. The simu-
lation of the model is implemented in the Castalia-OMNET++ Tools language.
Keywords: Ambient Intelligence, Intelligent Product, Cooperation, Security, WSN, Petri Nets, Castalia
1. Introduction
Amongst the main constraints and objectives in industrial
processes is the security issue. Especially, in industrial
environment workers have to deal with unavoidable th-
reats from products, resources and machines that are parts
of work risks. Currently, many security systems depend
on safety measurements that are taken by interacting de-
vices eventually exposing people’s lives to unpredictable
situation as an example in storage and transport activities
of hazardous chemical substances.
Our research approach to study such fully distributed
and discrete industrial environment which is based on
communicating object’s concept which represents a
physical product equipped with perception, communica-
tion, actuation and decision making capabilities.
The communicating object’s approach has attracted
the interest of several research projects as COBIS project
(Collaborative Business Items) [1] that has developed a
new approach to business processes involving physical
entities such as goods and tools in enterprise. The inten-
tion is to embed business logic in the physical entities.
Also, the computing department at Lancaster University
[2] conceived cooperative products with perception,
analysis and communication capacities that operated by
information sharing principle. Also, [3] is considering
the problem of Object Safety: how objects endowed with
processing, communicating, and sensing capabilities can
determine their safety. He assigned an agent to each ob-
ject capable of looking out for its own self interests,
while concurrently collaborating with its neighbors and
learning/reinforcing its beliefs from them. Each product
is represented by “an object safety agent”, it deals with
information from environmental sensors, in a known si-
tuation. When the agent detects a threat, it seeks confir-
mation from its neighbors.
Ambient intelligence and communication technologies
bring new visions in creating reliable systems for securi-
ty management where dangerous products can be turned
into smart products to control, prevent and react to secu-
rity threats in the ambient process. Each product plays
the role of an active node of the overall security system
by means of an embedded reactive model for the security
assurance.
The concept of intelligent products supported by a
model that we propose, offers the possibility for objects
to interact between them in an autonomous, transparent
and intelligent way, without any human help. Indeed, the
model presented exploits the advantages offered by a net-
work of sensors to generate active interactions between
the products in order to guarantee active security, i.e., an
interaction bilateral protected, transparent, autonomous
and intelligent.

A. ZOUINKHI ET AL. 136
The aim of this work is to propose a Petri nets hierar-
chical modeling framework with internal cooperation
model of intelligent products by using the High Level
Petri Nets (HLPN) formalism. Conceptual modeling was
validated by the software CPN-Tools from Aarhus Uni-
versity [4]. An internal model of an active product is im-
plemented and then was validated by the simulation soft-
ware Castalia based on the OMNET platform.
Our paper is organized as follows: After the introduc-
tion, the second part presents the ambient intelligent con-
cept and the intelligent product. The third part presents
the ambient security management system and the coop-
eration mechanisms between products based on messag-
es exchanges. Section 4 exposes the Petri Nets modeling
of a cooperation of intelligent products. Finally the last
part will expose the simulation results of the system.
Future research developments will be discussed in the
conclusion.
2. Ambient Intelligence (AmI)
Ambient Intelligence (AmI) [5-7] is growing fast as a
multidisciplinary approach which can allow many areas
of research to have a significant beneficial influence into
our society. AmI has a decisive relation with many areas
in computer science. The relevant areas are depicted in
Figure 1. Here we must add that whilst AmI nourishes
from all those areas, it should not be confused with any
of those in particular. Networks, sensors, interfaces, ubi-
quitous or pervasive computing and AI are all relevant
but none of them conceptually covers AmI. It is AmI
which puts together all these resources to provide flexi-
ble and intelligent services to users acting in their envi-
ronments.
As Raffler succinctly expressed [8], AmI can be de-
fined as: A digital environment that supports people in
their daily lives in a nonintrusive way.
Ubiquitous -
Pervasive
Computing
Human
Computer
Interfaces
Networks and
Mobility
Sensors
Ambient
Intelligence
Figure 1. Relation between AmI and other areas.
AmI is aligned with the concept of the disappearing
computer [9,10]: the most profound technologies are
those that disappear. They weave themselves into the fa-
bric of everyday life until they are indistinguishable from
it.
2.1. Intelligent Product
According to [11,12], an intelligent product is defined as
a physical and informational representation of an object
offering the following characteristics:
1) It possesses an unique identification;
2) It is capable to communicate effectively with its en-
vironment;
3) It can retain or store data about itself;
4) It deploys a language to display its features and its
needs over its lifecycle;
5) It is capable of participating in or making decisions
relevant to its own destiny;
6) It can survey and control its environment;
7) It can generate interaction by services offering: con-
textual, personal, reactive services.
It is important to note that in the definition of intelli-
gent product, it is possible to distinguish two levels of
complexity: the product that contains the information in
its environment and a product that supports decision-
making mechanisms [13]. The latter is more complex
because in this case it must give the product decision-
making mechanisms in implying that the product must
have a capacity for integrated analysis to assess and
make the best decision according to its condition and
context.
According to [14], the concept of intelligent product is
associated with the act of managing information of an in-
dividual product through its life cycle by integrating the
flow of information and equipment to provide services in
an internet network.
As concrete example of applying the concept of intel-
ligent product we quote the traceability [15] in its life
cycle product automatic identification of each individual
product to link a product with its physical representation
of information in a distributed information system. The
goal in this case is to record and update all information
associated with a dynamic product (such as his state-
ments, the operations he has endured ···). Indeed, the in-
troduction of an automatic identification system allows
the physical product to be recognized as providing the in-
formation to influence decisions and operations that a sys-
tem performs with him. This involves assigning a more
active role in a physical product. In this vein, [11] states
that a product is an intelligent article of manufacture, that
has the ability to monitor, analyze and reason about its
current or future, and if it is necessary to influence his
destiny.

A. ZOUINKHI ET AL. 137
3. Active Security Management System
3.1. General Context
In order to present the general situation of subject, we
have defined the elements which constitute the global
framework of cooperation. A warehouse is an environ-
ment where we stoke dangerous chemicals products. In
order to ensure the safety of these products, we will check
only brightness, moisture and temperature. The follow-
up of these variables can help to ensure the wellness of
products.
For example, starting from a value of temperature ra-
ther high one can note that the product in subject under-
goes poor circumstances from where a critical condition
is announced. Each containing chemicals must be equip-
ed with a node of sensor containing: a temperature gauge,
a sensor of moisture and a sensor of light, have fine to
collect the variables of environment, the cycle of opera-
tion of each intelligent product is the following: to ac-
quire the values of the sensors, to evaluate these values
by consulting the clean knowledge base and decision
making after having to compare the variables of envi-
ronment with the critical variables. All intelligent prod-
ucts communicate with the manager who has the level
higher (as shown in Figure 2). On the other hand, the ob-
jective consists with stage the mechanisms of interaction
in which the intelligent product are able to communicate,
acquire information, to decide and react to the stimuli
and disturbances of its environment in order to make it
possible that the product to deal with its intrinsic safety
and total safety in its interactions with other products or
people finally touching a decentralized aspect. But it is
necessary to highlight which the decentralized aspect is
Figure 2. Intelligent product interactions in an AmI envi-
ronment.
not single, because, for example, the suitable knowledge
base for have are sent by an administrator who also has
like role of configured remotely these intelligent prod-
ucts. In made, our management system of active safety
includes/understands a whole of product initially intelli-
gent being the subject of the mutual interactions and
sharing between them a flow of information and in se-
cond place a manager who undertakes to initialize and to
gather the data coming from each intelligent product. Co-
operation between intelligent products takes place by the
exchange of messages.
3.2. Exchanged Messages
Communication between products works by using sever-
al types of messages which are sent by a broadcasting
mode and classified according to their.
Product’s announcement in the products’ community
is of great importance for the overall security manage-
ment. For this, we propose two types of messages:
CTR (Control Timestamp Request): message which
declares to the administrator the arrival of a new product.
Ack_CTR: the acknowledgement message from the ad-
ministrator.
After registration the product needs a setup configura-
tion to allow it to interact within the community. This
configuration concerns the type of product regarding its
hazardous classification (safety symbols) and its static, dy-
namic and community related rules as well. When not
configured, a product announces its status with three
types of messages: NCF0: Product has no hazardous clas-
sification and no security rules configuration, NCF1: Pro-
duct has only hazardous classification configuration and
NCF2: Product has only security rules configuration.
Then the system administrator answers by an appro-
priate product configuration command message respec-
tively: CMD1: Configuration of the product classifica-
tion and CMD3: Configuration of the security rules.
Once the product is correctly configured; it becomes
completely capable of surveying its neighborhood: it is
now an effective Intelligent Product (IP).
Any environment modification or event that break in-
dividual or mutual security rules must be detected by pro-
ducts diagnosed and has to generate external actions al-
lowing to recover the normal safety level by actions or
directed information of the ambient environment. These
interactions are made by means of the following mes-
sages: GRE: a greeting message carrying specific prod-
uct information (name, safety symbols) and has a further
role contributing to the calculation process of the dis-
tance separating two IP. RSI: a message sent after recep-
tion of a GRE message, indicates the APs Inter-distance
value calculated with the power loss of received signal.

A. ZOUINKHI ET AL. 138
INA: this message carries the ambient sensors values em-
bedded in the product. CFG: a message emitted by IP
after an administrator request, contains the specific con-
figuration in the IP. SER: a broadcast message contain-
ing the IP security rules values. ALE: an alert message to
report to the administrator about a threat or a defective
security state.
The administrator participates in the communication
part by specific command messages: CMD2: Adminis-
trator requires the configuration of the IP through this
message, CMD4: Administrator asks for Security rules
Configurations and CMD5: Administrator asks for spe-
cific ambient information of IPs.
3.3. Interaction Mechanism
3.3.1. Centr ali zed Tasks
Then in order to get through chemical community, any
foreign product has to introduce itself to the community
manager, this product has to be announced to the man-
ager by sending a CTR message which is an empty mes-
sage that affirms to the manager the product being into
the network, this message is sent continuously in broad-
cast mode until the manager answers by a Ack_CTR me-
ssage which represents the acknowledgment of the man-
ager after the reception of the CTR message. After hav-
ing to finish the phase d’ inscription with the network,
the product must ask for to the manager his rules and its
symbols of safety by the sending of messages NCF. The
manager in his turn already identified the product (by
message CTR), can provide him these needs by consult-
ing his database by sending CMD1 containing to him the
symbols for safety or CMD3 containing the safety regu-
lations. It is noticed that the two spots announcement and
configuration obey centralized approaches because each
time the IP must refer to the manager for s’ to identify or
to update its knowledge base. The second spot is the sur-
veillance and communication where an IP must com-
municate with the products of vicinity. The communica-
tion between IPs is done by the greeting message GRE. It
represents a message of greeting carrying information
clean of the product (name, symbols of safety, ···), its cu-
rrent security level; and has as a role to contribute later
on to the computing process of the distance separating
two IPs. As soon as an IP receives a message GRE it will
transmit a Message RSSI (Received Signal Strength Indi-
cator): The information of this type of message contains
mainly the difference in power of the signal. This me-
thod of measurement is used to estimate compatibility
with minimal distance between IPs.
3.3.2. Ubiqui t ous T ask s
Equipped knowledge base (rules and symbols of safety)
and of a capacity of collecting and decision, the IP can
carry out two spot essence to be well as shown in Figure
3. The first spot is the internal monitoring where it be-
comes able to supervise its vicinity, whereas any mod-
ification of its environment, violating the individual or
mutual safety regulations must be detected, analyzed and
finally, following a difference between the variables of
environment and those basic of knowledge, with the re-
actions are associated such as the sending of Rapp_D to
the manager announcing a state of danger. The second
spot is the monitoring and communication where an IP
must communicate with the products of vicinity.
3.4. Security Rules
To insure a good security surveillance of the product,
three safety levels were established: (G) good level, (A)
average level, (D) dangerous level. Determining security
levels results after applying some security rules which
are divided into three categories: Static rules, Dynamic
rules and Community rules.
3.4.1. St a ti c Rules
Each intelligent product (IP) has environment values
(light, moisture and temperature), in order to avoid bad
reactions, the static rule requires that these variables
didn’t have to exceed breaking values (min or max), fol-
lowing the environment constraints of product (chemical
characteristics). For that, the sensor’s values must be
ones memorized, be compared with a static rules appro-
priate to the IP. The rules which we defined are founded
on a whole of limits for each size to measure. The tem-
perature has a high limit (HiLim) and low limit (LoLim)
thus defining the safety intervals.


, Sensor value
ssi
VV i
and


Temperature, Humidity, Lighti
Each sensor value Vsi is characterized by two critical
Neighbor
product
Sensor unit Recieving module
+ RSSI
Reading sensor’s data
Evaluating sensor’s values
Decision making
Sending module
Data
Base
E
nvironment
Figure 3. Autonomous behavior of an intelligent product.

A. ZOUINKHI ET AL. 139
values Vsi min and Vsi max associated also with a safety
margin ΔVsi. For each sensor i, we have to evaluate his
security level Si which is between 3 states: SGi if the val-
ue of the sensor i defines a good state, SAi if this value
announces an average or bad state and SDi if the value of
sensor indicates a dangerous state. From where



 
min max
min minmaxmax
min max
if ,
if ,,
if ,,
Gisisisi sisi
iAisisisi sisisisi
Di sisisi
SVV VVV
SSVVVV VVV
SV V V
 
 






(1)
Ssr is the state to be concluded starting from the static
rules, this state is between good (G), average (A) or dan-
gerous (D).
sr
Sif
if
if
,
,
,
iDi
iDi
iAi
iDi
iAi
DiSS
iS S
AiS S
SS
Gi
SS













(2)
3.4.2. Dynamic Rules
The purpose of these rules is to develop the temporal,
non-existent variable at the static rules. Because, if a bad
condition persists for one considerable period, this state
must be announced as dangerous state. For example, if
the temperature persists in average state for considerable
period, a dangerous state must be announced, also if we
notice a swing between good state and bad condition,
counter must be present to announce a state of danger if
the number of swings exceeds a critical value.
In the same way the dynamic rules Sdr can conclude a
dangerous (D) state described by:


111
,, ()
if
Occur(( ))
cr sr
dr
sr c
ttttTStA
SDou
St n
 




(3)
With Tcr is a critical period fixed according to the pro-
duct at not overcome when an average state is reached,
and nc is the number of swing of Ssr authorized between
states G and A and Occur is a function initialized with
zero that is incremented when Ssr swings from state G to
state A.
3.4.3. Community Rules
According to their chemical characteristics, certain pro-
ducts can have constraints of compatibility with other pro-
ducts according to the compatibility matrix between
them. So the need of a procedure which seeks to deter-
minate the level of compatibility between products stored
in the same warehouse.
In the same way the community rules Scr can conclude
one of the 3 states describes previously by:




min
Symbol ofproduct
Symbol ofproduct
if And
, Incompatible
product, product
yi
yj
cr
Compyj yj
Si
Sj
SD
FSS
Di jD
















(4)



 
min min
Symbol ofproduct
Symbol ofproduct
if And
, Incompatible
producti, product,
yi
yj
cr
Compyj yj
Si
Sj
SA
FSS
DjDDD
















(5)
cr
SG

if




 

min min
Symbolofproduct
Symbol ofproduct
And
, Incompatible
product, product,
Or
, Compatible
yi
yj
Compyj yj
Compyj yj
Si
Sj
FSS
DijDDD
FSS























(6)
Where Fcomp(Syi, Syj) is a function which studies the
compatibility between the safety symbols of two prod-
ucts i and j, D (product i, product j) is the distance which
separates these two products, Dmin is a critical distance to
respect when the incompatibility between products and
∆D is a margin of distance fixed by the nature of prod-
uct.
In end after having gathered the states of each rule
(static, dynamic and community) it is necessary of for-
malized a global state SG which describes the absolute
circumstances of the product.


,,
G srdrcr
SfSSS
whether


,,
rrr
SdC  
G
S

if ,
if ,
if
DSD
SD
ASA
SD
GSA





















(7)

A. ZOUINKHI ET AL. 140
4. Modeling by Petri Nets
Petri Nets are used for a long time as modeling tools of
discrete events systems. Several works opted for the Petri
Nets modeling in fields like communication systems,
flow shop and logistic chain. [16] proposed a model of
TCP/IP communication behavior; [17] presented a model
of a network controlled system.
The major advantages that promote the use of Petri
Nets are, first the possibility to verify the system beha-
vior have good properties and to give specifications in
formal way and to provide graphic of system, and then,
the possibility to model and to simulate the system [18].
The objective of our work is to represent the behavior
of the active product and the stream of messages through
a wireless network in order to achieve interaction be-
tween products; we opted for colored Petri Nets models
designed, validated with CPN-Tools software. CPN-Tools
allow creating hierarchical models in order to simplify
complex ones and divide it into other submodels. This
means that in the Hierarchical Petri Net model certain
transitions represent another Petri Net submodel.
4.1. Global Model
The model of cooperation is equipped with six elements
(P1, P2, P3, manager (Administrator), Operator, Cart)
which communicate between them, in order to form a com-
munity of wireless cooperation. Each element is repre-
sented by a transition (hierarchical) which presents the
services and suitable task quoted in details in what fol-
lows. As the Figure 4 shows, each node presents two
places: Net Input and Net Output which respectively pre-
sents the output buffers of each element and input one.
These aims of this buffers is to memorize the messages
temporarily received from network before being treated
(in the processing unit) and those emitted by the ele-
ments in the network.
4.2. Network Level
In this part, we will present the network’s model where
the sensor’s nodes interact; firstly, we are going to model
the hierarchical transition network which is represented
by the Figure 4 as a perfect network (without any dis-
turbance) to evaluate the impact of progressive increas-
ing of node’s number existing in this network, and the-
reafter, we will create a disturbance in this network to
check the robustness of allover the system.
The Figure 4 indicates the lower level of the Network:
the higher places Net input indicate the output’s buffers
of the node where the messages are stored before being
emitted in the network; these messages pass by a classi-
fication’s stage which classify them according to their
transmitting nodes before being stored in the place “me-
ssage transmitted in network”. The network being perfect
(without any disturbance), then all the messages will pass
directly through the transition network (which is not sim-
ple transition) towards the buffers from exit of the net-
work messages received thus, all the messages will be to
reclassify again according to their destination before be-
ing emitted towards the entry’s buffers of the nodes.
The network presented to the Figure 5 defines a dis-
turbed network where there is risk of loss of message.
Each token (message), which is presented in the place
Figure 4. Global cooperation model.

A. ZOUINKHI ET AL. 141
Figure 5. Network model.
(“message sent through network”) must cross the transi-
tion where it will be to assign to another place, in this
moment this token will be lost or gone, after this passage
(transition “gone”), this token enters a buffer of entry and
afterwards enters a buffer of exit to be finally in the place
“message received”.
4.3. Intelligent Product Level
As indicated in the Figure 6, each product presents some
internal and external tasks of which some conform the
centralized approach however the others follow the ap-
proach of omnipresence, each transition in this network
has a hierarchical structure described explicitly later.
4.3.1. Product’s De pen de nce Tasks
Two tasks represented of hierarchical transition: an-
nouncement P1 and P1 configuration, illustrate the cen-
tralized approach where each IP must refer to the man-
ager initially to announce themselves (to enter in the net-
work and to have an ID) and also to configure them-
selves (to ask the manager for the safety rules).
Announcement is used to introduce a foreign product
into the community of the other intelligent products. The
need to launch out in this community requires announce-
ment towards the manager so that this last detects it and
adds it in its database which contains the products
Figure 6. Intelligent product mode l.

A. ZOUINKHI ET AL. 142
already existing. This model manages the registration of
products that announce them self in the community by
sending continuously a CTR message to the manager.
After switching on the IP, a token (P1, Man, CTR) will
be put in the net input msg P1 place indicating this way
the fact of sending a CTR message from P1 to the man-
ager, the transition Ack will be valid if a token (Man,
Ackctr, P1) is sent back. The absence of acknowledge-
ment token will lead to the validation of the Ack bar
transition and the same process will be repeated over
again till haven an answer from manager.
Two tasks represented of hierarchical transition: an-
nouncement and configuration, illustrate the centralized
approach where each product must refer to the manager
initially to announce themselves (to enter in the network
and to have an ID) and also to configure themselves (to
ask the manager for the safety rules).
The configuration’s role is to provide to the IP the ne-
cessary configurations enabling him to cooperate in the
interaction community, each IP must check that it has its
safety rules (its ambient critical variable) as symbol of
safety (which are the IPs that presents a threat to him).
And ones announced the IP has to be configured by
checking if it has a safety rules and safety symbols, so in
each case the IP has to react in order to get messing fea-
ture from manager by sending a request for that.
4.3.2. Product’s Au t on omic Tasks
Two other hierarchical transitions: surveillance and com-
munication and internal surveillance, follow the distri-
buted approach where each IP is equipped with a deci-
sion capacity (autonomy) which illustrates the concept of
reactivity.
For the internal surveillance, the IP each time collects
information from the sensor (temperature, light and mois-
ture) and evaluates (for each variable) the safety level, so
that each time, if a dangerous level is reached, the IP
sends a rapp_D message to the manager to inform him
that one of its sensor’s variables reached a critical level
[19].
The transition surveillance and communication also il-
lustrates the distributed intelligence by the sociability
concept. In this place the accepted messages are CMD2
and CMD4 and CMD5: received from manager (proac-
tive concept) and RSSI messages: received from other
IPs neighborhood (sociability concept). RSSI Messages
illustrates the collaboration between IPs: each time an IP
receives a GRE message and due to a module RSSI that
IP will estimate the distance that separates it from the
sender IP. This distance is compared to two values: L_inf
and L_sup (received during the configuration).
The surveillance and communication model repre-
sented in Figure 7, also illustrates the distributed intelli-
gence by the concept of sociability. In this model the
accepted messages are CMD2, CMD4 and CMD5: re-
ceived from manager (proactive concept) and RSSI me-
ssages: received from other products neighborhood (so-
ciability concept). RSSI Messages illustrate the collabo-
ration between products: each time a product receives a
GRE message and due to a module RSSI that product
will estimate the distance that separates it from the send-
er product. After the reception of these messages, a
knowledge base serves for treating the different messag-
es.
For the internal surveillance model represented in
Figure 8, each time, the product collects information
from the sensors (temperature, light and moisture) and e-
vluates (for each variables) the safety level, so that, if a
dangerous level is reached, the product sends a rapp_D
message (dangerous report) to the manager to inform
him that one of its sensor’s variables reached a critical
level [19].
Figure 7. Surveillance and communication model.

A. ZOUINKHI ET AL. 143
Figure 8. Internal surveillance model.
After determining the security level, a state of the pro-
duct is evaluated. If the state is average (bad), a GRE
message is sent in broadcast in which the security level is
indicated. If the state is dangerous, a rapp_M message is
sent to the manager.
4.4. Manager Level
The manager’s model can be subdivided in two parts
according to the concept characterizing the node: reactif
or pro-actif.
The manager’s reactivity [2]: when a token containing
a message arrives to the entry’s buffer of manager, this
message passes by a stage of classification according to
the nature of message (INA, CFG, NCF0, NCF1, Ack_
CMD1, NCF2, Ack_CMD3, CTR, RAPP_D, RAPP_M).
According to each message received the manager must
react either by updating his database or by sending mes-
sages to provide information to the other IPs (safety rules,
acknowledgment of the received reports ···).
Pro-activity of manager [20]: The manager anticipates
sometimes by asking randomly for the variables’s infor-
mation of IP’s environment by sending (CMD5, CMD4
and CMD2) to a hazardous chosen IPs.
With Petri Nets, we have verified the consistency and
non-blocking states of our model. In addition, we have
simulated the cooperation between active products with
Petri Nets in terms of packets exchange and setup confi-
guration time [19]. But the simulation becomes more dif-
ficult when the number of products becomes increasingly
important. On the other hand, we cannot estimate energy
consumption which is important in the Wireless Sensor
Network context. For these reasons, we have to use a
tool that can meet our needs as Castalia.
5. Castalia Simulation
For evaluation purposes we have implemented the IPs
model into Castalia 2.0 a state of the art WSN simulator
based on the OMNet++ platform.
Castalia is a Wireless Sensor Network (WSN) simula-
tor based on the OMNet++ platform that can be used by
researchers and developers who wants to test their dis-
tributed algorithms and protocols within a realistic wire-

A. ZOUINKHI ET AL. 144
less channel and radio model which takes account of the
physical characteristics of the radio [21].
Several works opted for the Castalia simulation in
fields like communication systems. [22] used Casta-
lia/OMNET++ to demonstrate that model-based tech-
niques (like the model checker of UPPAAL) can be used
as an alternative approach to the design and analysis of
WSNs to complement traditional simulation-based. They
compared simulation results by UPPAAL for two medi-
cal scenarios with traditional simulation techniques. The
comparison shows that their analysis results coincide
close-ly with simulation results by OMNeT++.
[23] used Castalia/OMNET++ to evaluate their solu-
tion for distributed node monitoring called DiMo (Dis-
tributed Node Monitoring in Wireless Sensor Networks),
which consists of two functions: Network topology main-
tenance, and Node health status monitoring.
In order to measure the responsiveness of the system,
we have created a scenario. This scenario represents an
IP of the community, which has critical value for a tem-
perature of 200˚C. We have programmed the value of
this parameter so that it exceeds the limit at t = 200 s.
Subsequently we are going to measure the detection
time of danger by the manager after the transmission of
the critical condition of the IP. Then we are going to
measure the relative error in % (Er) is defined by:

% 100
mr
rr
tt
Et

 (8)
tm is the time of alert detection by the manager and the
very occurrence moment of the alert that is equal to 200s.
Some applications of wireless sensor networks and
primarily in the area of surveillance requires that the data
collected must reach the base station for a time limit so
that the data is useful and acceptable [24]. On the other
hand, a sensor node consumes, generally, the majority of
its energy during the exchange of data [25]. For this we
dedicate our study mainly on system responsiveness and
power consumption. To properly adjust the values of the
period readings of background values (Tcap), we made a
series of simulation.
5.1. Regulating of Reading Period Sensors
At first we changed the reading period of sensors and we
were measured the responsiveness for each value system.
To measure the responsiveness of the system, we have
created a scenario. This scenario represents a community
of IP (IP 3) which has the maximum value of ambient
setting is 200 (for temperature 200℃) and was pro-
grammed so that the value of this parameter exceeds the
limit at the t = 200 s. The following figure shows the
effect of the reading period sensors on the relative error
of system responsiveness. As it is shown in the Figure 9,
the error on the reactivity of the system is minimal for a
period of reading sensors equal to 0.5 seconds. On the
other hand, we have studied the loss of packets based on
number of intelligent products in a warehouse 25 m × 25 m
surface and for each value of the reading period sensors
during a simulation period equal 1000s.
In the following we will fix the sensor reading period
and the period of sending messages to 0.5 s.
In our case, we fixed three aims for the simulation step
that are: reactivity: The validation of all models proposed
of supervision and communication, scalability: studying
the model behaviour in a large-scale network and energy
consumption.
5.2. Studying Reactivity
When the IP is configured, and it has the security rules, it
will start to send the salutation message (GRE).
Value is the value captured by the sensor and VMax is
threshold value for the sensor. (see Figure 10)
In the scenario of triggering alert, we simulate the
sensed value as a value initialized by 7 and it would be
after increased by 2 each sensing period. So, at 41.637 175 s
this value reaches the threshold value (14), and in this
case, it sent an ALE message on broadcast.
Figure 9. Influence of the reading period sensors on the
system reactivity.
IP: 3 Value = 7.038 390 V
Max
= 14.000 000
IP: 3 Value = 7.840 240 V
Max
= 14.000 000
IP: 3 Value = 8.806 861 V
Max
= 14.000 000
IP: 3 Value = 10.787 591 V
Max
= 14.000 000
IP: 3 Value = 11.902 659 V
Max
= 14.000 000
IP: 3 Value = 12.927 368 V
Max
= 14.000 000
IP: 3 Value = 15.015 800 V
Max
= 14.000 000
IP: 3 -> sent ALE from Value on BROADCAST at 41.637 175
Figure 10. Scenario of triggering alert.

A. ZOUINKHI ET AL. 145
5.3. Studying Scalability
In order to verify the influence of the adding of the IPs
model in the node application under Castalia, we run mul-
tiple simulations and in each simulation, we modify the
IPs number. After that, we extract from each simulation
the probability of lost packets.
The histogram in Figure 11 shows that the probability
of lost packets exceeds 0.5 when the number of IPs in the
warehouse surpasses the 278. In addition, it exceeds 0.2
when tthe number of IPs surpasses the 38.
5.4. Energy Consumption
Resource management is of overriding importance for
Wireless Sensor Networks because the corresponding re-
source budgets need to be guaranteed in order to achieve
certain requirements. This is particularly true for energy
resources that are naturally limited. Our model should
respect this particularity. The Table 1 shows the value of
the spent energy for each IP in the network. When we
calculate the rate of the spent energy, we find that each
IP consumes 0.85% of its initial energy (18720 joules) in
a simulation time fixed to 1000s.
Figure 11. Influence of the number of products on the lost
packets probability.
Table 1. Spent energy for each state.
Request Spent energy (J)
Initialisation(CTR/ACKCTR) 0.011 764
Configuration(NCF0/CMD1/CMD3) 0.013 863
Reading security rules (CMD4/SER) 0.051 75
Reading parameters(CMD2/CFG) 0.051 75
Reading ambiant information(CMD5/INA) 0.051 75
6. Conclusions
In this work, we define a concept of an active security
distributed management system, with modelling of IP’s
behaviour dedicated to security management of hazard-
ous products. We proposed an IP’s behavior model re-
presented by hierarchical colored Petri nets. This hie-
rarchy includes sub-models where each one allows dis-
playing the evolution of every state of the IP (registration,
configuration, surveillance and communication and in-
ternal surveillance). With Petri Nets, we have verified the
consistency and non-blocking states of our model. Coope-
ration between IPs is provided by exchange of messages
in order to manage and control dynamically in real-time
the global active security level. To implement our ap-
proach, we are using self developed simulation test bed,
designed using Castalia and OMNET++ simulators. We
are currently implementing our approach in various real
time scenarios to check its adaptiveness but the success
and robustness of our model. Certainly, we only broke
the surface of the problems associated with more realistic
simulation and correspondence of real deployment data
with simulation. As perspective of this work, one will
develop an experimental platform in order to compare
these simulation results of with the experimental results.
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