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Providing Relevant Information in an Ambient Services Using Service Requester's Logical Area

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1
Providing Relevant Information in an Ambient Services Using Service Requester’s “Logical Area”
Anahid Bassiri
i
, Mohammad Reza. Malek
GIS Department, Faculty of Geodesy and Geomatic Eng., K.N.Toosi University of Technology, Mirdamad Cross, Valiasr St., Tehran, IRAN
Anahid_bassiri1984@yahoo.com mrmalek@kntu.ac.ir
Abstract.
Ambient service is a kind of context-aware services which is related to the surrounding environment of the users. In other words, geographic area around the users is considered as contextual data to provide services. This geographical area around the user is called logical area which would be equivalent for “here” from an ordinary user’s point of view. Most of the time users are looking for resources around themselves or “here”. When “here” area of a user is overlapped or contained by a service area; which called “service domain” in ambient services; then user can get the service. So it is very important to know where is “here”. “Here” is a fuzzy spatial concept which is being frequently used in daily speaking. So it may be stated in requests of users e.g. finding cheapest restaurant “here” or around of somebody. The main step in a spatial context-aware application is determination of relevant information. hence "here” concept, as a fundamental spatial concept, helps to provide relevant information to users so features located “here” or around of users, in which the best answer is located, should be found. As result, the area of “here” is very important to provide service in such cases. This issue becomes more complicated and important if the service requester is a mobile user, because the area of “here” changes over the time. In that case additional concepts and computations are needed to find “here” of that user. This paper is focused on modeling of “here” in an ambient service framework based on fuzzy set theory.
Keywords:
Fuzzy logic, ambient services, spatial uncertainty, inference engine
1 Introduction
Mobile computing is a revolutionary style of technology emerging from advances in developments of portable hardware and wireless communications. Within the last few years, we were facing advances in wireless communication, computer networks, location-based engines, and on-board positioning sensors. In this regard, mobile computing finds an important role in many fields e.g. telecommunications, computer science and information system. Integration of mobile agent, wireless network, and some GIS capability results in Mobile Geoinformation System (MGIS) which has fostered a great interest in the GIS field. This paper tries to explain one of the difficulties of providing relevant geoservices to mobile users. Ambient Services are specific kind of mobile services which put more emphasis on association of service with a geographical area around users. By “ambient services”, we have in view services that are related to the surrounding physical environment of the user and are locally useful i.e., they may not be relevant or useful beyond the boundaries of an area around the user (Loke 2006), this area is called “
logical area
”. So a logical area for a user is the area around the user in which ambient services is relevant and useful. Most of the time users call this “surrounding physical
environment” as their “here”. For example they want to find a restaurant or an ATM around themselves so they may express their request as follows: “I want a restaurant or an ATM which is located “here”. Many requesters are ordinary mobile users who do not know very much about process of parsing a request. Consequently, They would prefer use the phrases and words that are using frequently in their requests rather than mathematical concepts e.g. near and late instead of less than 50 meters, after 2 p.m.. Such fuzzy concepts, which suffer from lack of clarity and being difficult to test, make a big challenge in mobile geoservices (Bassiri 2012). They are frequently encountered because a customer or service requester asks a question about something which can be interpreted in many different ways. Computerized applications are not usually able to implement such vague concepts. In order to model these kinds of concepts, we need a framework which can handle vagueness of these linguistic phrases. Fuzzy logic provides one of the most powerful frameworks to model these vague concepts. One of the spatial concepts which are frequently used in daily life is “here”. Here” and “There” are two spatial concepts which are frequently used in daily life. As defined in Meriam-Webster dictionary, "here" means "in or at this place", "at or in this point", "Now", "in an arbitrary location", etc. Among them, the first two entries fit to the aim of this article. It is obvious that by moving with a certain velocity one can; within certain physical limit; choose the place by "here". There is no serious attempt to define and model it neither in GIS nor in database system. As mobile geoservices are provided for low-experienced users who are using fuzzy words like far, near, here, there and etc in their requests, it is very important to support such concepts by geoinformation systems and services. If a service requester uses “here” in his request e.g. “I want a restaurant which is located “here” ”, understanding the meaning and the valid area of “here” of him is the basis of providing appropriate and effective result. It is not only for defining the area but also for finding relevant information for a context-aware application. In a context-aware application, the main step is recognition of relevant information, hence "here” concept helps to pickup relevant information for users. Imagine a client who is looking for a supermarket around or “here” of him. Regarding his position, direction of movement, line of sight, etc his “here” can be different. Service provider should find and provide the address of a supermarket which is located in his “here”. So service provider is supposed to find “here” firstly, and then find a supermarket in this area. There are some criteria to find “here” of a user. Two different users can ask same request and the results can be different respect to their “here”. Determination of area of “here” becomes more complicated if the service requester moves. It is very important to find the valid area of a moving requester’s “here” in the mobile environment. Mobile users are one of the categories of service requesters whose locations and environments are changing over the time. In order to model their “here”, it is very important to include additional criteria like direction of their movements. To model “here” conceptually, mathematically and spatially, it is necessary to consider its fuzziness. As it explained before, some phrases have not predefined definition and “here” is one of them. ”Here” does not have a predefined and specific meaning and it can be uncertain and changeable respect to the context. Consequently
its valid area should not be modeled in a crisp framework. Regarding its vagueness and fuzziness, fuzzy logic as one of the most powerful tools to handle its fuzziness is implemented and a fuzzy model is proposed to model this unclear word; “here”. The paper is organized as follows. Second section is focused on principles of fuzzy logic and a fuzzy system. Section three explains fundamental criteria to find somebody’s “here” in a mobile information environment. Section 4 implements fuzzy membership functions and if-then rules to infer in which area “here” is valid. Last section summarizes this work and suggests future works.
2 Principles and Review
The concept of Fuzzy Logic (FL) was conceived by Zadeh (1987) as a way of processing data by allowing partial set membership rather than crisp set membership. Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control. If applications can accept noisy, imprecise input, they would be much more effective and perhaps easier to be applied. Fuzzy logic starts with the concept of a fuzzy set. A fuzzy set is a set without a crisp, clearly defined boundary. It can contain elements with only a partial degree of membership. Fuzzy logic offers several unique features that make it a particularly good choice to handle uncertainty issues (Smith 1993). In general, fuzzy set concepts preserve details (Peterson 1993) and it can overcome the gap by providing mechanisms for ontologically and cognitively plausible (Sugeno 1985) and error-sensitive (Devi 1985) representation of the reality. Considering aforementioned advantages and fuzzy nature of “here” and “there” which is the main issue, in the next parts some important definitions and concepts related to fuzzy logic are explained.
2.1 Fuzzy Set and Membership Function
Fuzzy logic starts with the concept of a fuzzy set. A fuzzy set can contain elements with only a partial degree of membership. Fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. A fuzzy set is a pair (A, m) where A is a set and m: A→ [0, 1] (Zadeh 1973).The membership function of a fuzzy set is a generalization of the indicator function in classical sets (Zadeh 1985). The membership function which represents a fuzzy set A’ is usually denoted by
A
. For an element x of X, the value
A
(x) is called the membership degree of x in the fuzzy set A’ the membership degree
A
(x) quantifies the grade of membership of the element x to the fuzzy set A’ (Zadeh 1985). The value 0 means that x is not a member of the fuzzy set; the value 1 means that x is fully a member of the fuzzy set. The values between 0 and 1 characterize fuzzy members, which belong to the fuzzy set only partially.
2.2 Fuzzy Inference
Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. Mathematical concepts within fuzzy reasoning are very simple and it is easy to modify a Fuzzy Inference System (FIS) just by adding or deleting rules so there is no need to create a new FIS from scratch (Kosko1992), (Lee 1990). In general, a fuzzy inference system consists of four modules (
Mamdani 1977)
as it shown in figure 1.
Fig. 1
. Four
modules of a fuzzy inference system Fuzzification module transforms the system inputs, which are crisp numbers, into fuzzy sets. This is done by applying a fuzzification function. Knowledge base stores IF-THEN rules provided by experts. Inference engine simulates the human reasoning process by making fuzzy inference on the inputs and IF-THEN rules and defuzzification module transforms the fuzzy set obtained by the inference engine into a crisp value.
2.2.1. Fuzzy IF_THEN Rules
In its simplest form, a fuzzy “if-then” rule follows the pattern: “If x is A then y is B” A and B are linguistic values defined by fuzzy sets in the universes of discourse X and Y. x is the input variable and y is the output variable. The meaning of “is” is different in the antecedent and in the consequent of the rule. This is because the antecedent is an interpretation that returns a value between 0 and 1, and the consequent assigns a fuzzy set B to the variable y. The output to the rule is a fuzzy set assigned to the output variable y of the consequent. The rule is executed applying a fuzzy implication operator, whose arguments are the antecedent's value and the consequent's fuzzy set values. The implication results in a fuzzy set that will be the output of the rule (Lee 1990) and (Miller 1993).
2.2.2. Classification of fuzzy inference methods
Fuzzy inference methods are classified in direct methods and indirect methods. Indirect methods are more complex ones. As it shown in figure 2, Direct methods, such as Mamdani's and Sugeno's, are the most commonly used (these two methods only differ in how they obtain the outputs) (Devi1985) so we explain them in below.
Fig. 2.
Fuzzy inference classificationvm methods
Mamdani's fuzzy inference method (Mamdani 1975), is the most commonly seen fuzzy methodology. Mamdani's method proposed in 1975 by Ebrahim Mamdani, was among the first control systems built using fuzzy set theory. Mamdani-type inference expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzification. It is possible, and in many cases much more efficient, to use a single spike as the output memberships function rather than a distributed fuzzy set. In this regards, Mamdani’s method was implemented to deduce our output or “here”. In general, Sugeno-type systems can be used to model any inference system in which the output membership functions are either linear or constant. Mamdani’s method and Sugeno’s are similar with respect to many aspects (Sugeno 1977) and (Sugeno 1985). The first two parts of the fuzzy inference process, fuzzifying the inputs and applying the fuzzy operator, are exactly the same. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant. A typical rule in a Sugeno fuzzy model has the following form:
If Input 1 = x and Input 2 = y, then Output is z = ax + by + c In order to create our Fuzzy system to find out somebody’s “here, a set of criteria which has an outstanding role in finding “here” is defined. Next section is focused on explaining these criteria or input variable and their membership functions. Section 4 is deducing output applying these input variables in a fuzzy inference system.
3 Impact Criteria of “Here”
In this section the important criteria which make the meaning of “here” clear, are

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