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366
The Gaia project [6] developed at the University of Illi-
nois is a distributed middleware infrastructure that provides
support for ubiquitous computing.
The EasyLiving project [7] from Microsoft focuses on
development of an architecture and technologies for intel-
lige nt en vir onm en t s.
Work at Illinois has developed the Universal Interoper-
able Core (UIC) which is a reflective middleware platform
designed for handheld devices [8].
Recent research work has focused on middleware exten-
sions for pervasive computing by standardizing on web
services and service discover-oriented technology [9].
To sum up, it is important that middleware for con-
text-aware application should be able to fulfill the follow-
ing functionalities and objectives.
1) The middleware architecture should be modular and
extensible.
2) The middleware should be based on a service-oriented
architecture, in which ea ch application and device is repre-
sented as a service entity.
Based on the above-mentioned study, we developed a
middleware for pervasive computing, which adopted fuzzy
logic as context processing method.
3. Fuzzy Logic Based Context Processing
3.1. A Formal Context Representation Model
We use a context space theory model shown in [10] for
model fundamental nature of context and enable context
and situation awareness for context processing. Our con-
text model gives a common representation for context
that all entities in the environment use of pervasive
computing. Instead, it provides a common base on which
various reasoning mechanisms can be specified to handle
context.
Definition 1:
We define a attribute value as any type of data that is
associated with Contextual information, include physical
contexts, environmental contexts, informational contexts,
personal contexts, social contexts, application contexts
and system contexts [11].
i
a
Definition 2:
For a context state, defined over a
collection of N attribute-values, where each value
corresponds to an attribute’s value at time t.
),,( 21 t
N
ttt
iaaaC
i
a
t
i
a
Definition 3:
Let be a context space in an environment of per-
vasive computing, describes the application’s current
state in relation to chosen context. In our model, context
spaces are represented as first-order calculus. The basic
model has the form of predicate(subject,Catt), in which
space
C
-: set of the expression entity of context
information, e.g. visitor, locatio n, etc.
*
Ssubject
-: set of predicate name, e.g. is located
in, has status, etc.
*
Vpredicate
-: set of all values of context state in , e.g.
warm, cold, open, close, empty, etc.
*
att
CC*
S
For example, Location (Marry, laboratory) means
Marry is located in the laboratory.
The basic context model can be extended to form a set
of contexts by combining the predicate and Boolean al-
gebra (union, intersection and comple ment).
For example, y,90)Pluse(Marry,38)ature(MarrBodyTemper repre-
sents physical signs about Marry.
3.2. Fuzzy Logic-Based Context Processing
Fuzzy logic was proposed by Lotfi A. Zaheh in 1965 [12]
to emulate the way that the human brain processes un-
certainty, uncertainty, imprecision, and vagueness. Fuzzy
logic is suitable for context management because it is
capable of processing imprecise and unreliable informa-
tion coming from pervasive computing, and it can de-
scribe a problem in a common sense format in which
expert knowledge, instead of differential equations, can
be applied [13].
For , be an input is applied to a FLC
system, the inference engine computes the output set
corresponding to each rule. On analyzing the context
processing of various potential services, we use singleton
fuzzification and “IF-THEN” rules of form [14].
space
C, ii aa
Rl: IF is and is and …and is ,
THEN y is .
1
a
G
l
F12
al
F2N
al
N
F
l
Assuming singleton fuzzification, when an input
is applied, the degree of firing corre-
sponding to lth rule is computed as
},,{A''
1
'N
aa
)(T)(**)(*)( '
1
''
2
'
121 i
F
N
iN
FFF aaaa l
i
l
N
ll
Where
* and T both indicate the chosen t-norm. In this paper,
we focus on the height defuzzifier and used trapezoidal
membership ship functions to represent low, high, very
strong, very weak to represent moderate, medium, strong,
and weak.
In this paper, we are primarily interested in developing
middleware based on the structure of FLC mentioned
above. We design a FLC system for travels services,
which is one of component in our pervasive computing
prototype Tourist Reminder.
The FLC system in this paper receives context infor-
mation from sensor equipments as the inputs of the FLC
and the fuzzification module converts inputs into fuzzy
linguisti c vari able inputs.
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