Conceptual Design of Fuzzy Rule Based Context Aware Meeting Room System

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International Journal of Modern Trends in Engineering

and Research www.ijmter.com

@IJMTER-2014, All rights Reserved `673

e-ISSN: 2349-9745

p-ISSN: 2393-8161

Conceptual Design of Fuzzy Rule Based Context

Aware Meeting Room System

Dr.Thyagaraju.GS1, Shivakumar C

2, Nithin

3

1Associate Professor and HOD,

2, 3 Assistant Professor

1, 3 Department of CSE,

2 Department of CSE/ISE/Mech,

1,2,3SDMIT (VTU), Ujire, 574240

Abstract-- Due to the exponential growth of wireless network based miniaturized device and

sensors, the dream of context aware ubiquitous computing world is becoming realistic. In this

ubiquitous world of context aware applications the users can get the information and share the

information elsewhere instantaneously. Context aware meeting room is one of the interesting context

aware systems where in the meeting of a given set of users will be organized as per the situation of

users. In this paper we present the conceptual design and development of service recommendation

system for prototypical context aware meeting room using Fuzzy Rules .The proposed

recommendation system for context aware meeting room recommends the services by considering

the users contextual parameters like role, priority and environmental conditions. The fuzzy rule is

constructed using history database and knowledge base of the meeting.

General Terms-- Context aware meeting room, Fuzzy Rule, Bluetooth, Participants.

I. INTRODUCTION

In the current trend of computing technology the context-aware computing is getting more

significant attention from researchers as ubiquitous computing environments take into account the

overall situation of users, environment and applications. In context aware computing environment,

the computing applications will adapt to the situation of users and provide the service as per the

requirement of the situation. Context aware ubiquitous computing environment may be associated

with single or multiusers. In the context of multiuser environment usually there will be a visible or

invisible competition among users for availing one or more multiple services. In such multi-user

ubiquitous computing environments, conflicts among user’s contexts need to be detected and suitable

service has to be recommended. In the proposed system the design of context aware meeting room

(CAMR) system is done to facilitate the system to act autonomously in the way the meeting

members desires in single and multiuser environment utilizing the contextual parameters like

Personal (Age, Name), Temporal (Time, Date, Season), social (Dean, Principal, Staff, Students) and

schedule agendas in addition to the user ratings for the services. The proposed system makes use of

simple if then else based fuzzy rules algorithm. The fuzzy rules are framed by utilizing various

factors like user’s social, personal and environmental. The motivation behind the proposed work is:

i) To automate the meeting activity ii) To improve the meeting participants satisfaction level and iii)

To improve the social relationship between meeting participants and meeting room. The motto of

this project is to enable context aware applications to offer socialized and personalized services to

single and multiple users by recommending the services based on user’s overall context.

International Journal of Modern Trends in Engineering and Research (IJMTER)

Volume 02, Issue 01, [January - 2015] e-ISSN: 2349-9745, p-ISSN: 2393-8161


II. LITERATURE SURVEY

From the last decade, most of the research work is being done on consumer product like context

aware meeting room, smart car, smart homes, intelligent office, interactive tourist guide, intelligent

environment etc. Context aware applications will understands the situation and intentions of users in

the physical environment with the help of sensors and recommend appropriate services through the

actuators to the users automatically. This inherent characteristic of context aware applications is

motivating the researcher to design and develop context aware meeting room by embedding the

intelligent algorithms to the system.

There are several research contributions [1-15] that focused on context aware applications to

facilitate the process of meeting. Chen et al. in their research work [1] have designed the context

aware meeting room by making use of Bluetooth based architecture. Bluetooth MAC address has

been used as identification number of user and relevant context aware services are recommended to

the user. Bourcier et al in their research work [2] have designed the system which can provide

context aware services on home controlled applications based on the users location and identification

.In their research work they have designed the context aware meeting room which performs the tasks

like : Schedule Managing, Communicating to Users, Attendance Management, updating the

documents and Report Generation. Ahmed et al. in their research work [3] have presented the design

of a context aware meeting room that performs the following tasks:1. Fixes the Time Duration of

Meeting (Beginning and End) 2. Presents the contextual information of each and every user

3.Establishes a Group Communication 4.Preserves the Privacy and Confidentiality 4. Automatically

configures the situation to meeting mode. Dai and Xu in their research work have presented the

design of context aware meeting room that can do the following tasks: Multimodal analysis of

multiuser meeting, online analysis of meetings. Cutrell et al [5] have developed context aware

applications integrating with applications like desktop calendars, e-mail, file search engine, which

can be used for conducting context aware meetings. Similar type of work on context aware meeting

room has been done by Goker et al[6] by using Bluetooth technology and mobile phones.

The research work presented in this paper proposes a prototypical context aware meeting room

system using fuzzy rule base. The prototypical context aware room is designed on rectangular board

comprising Fan, Bulb and AC. The board meeting room was connected to the system which

controlled the context aware meeting room based on user context. As a subjective the proposed

system was tested in the meeting room of college campus. The structure of this paper is as follows:

Section 3 describes the modeling and architecture of the context aware meeting room system,

Section4 describes the Experimental implementation details of the system and Section 4 concludes

with future work.

III. MODELING AND ARCHTECTURE OF THE PROPOSED CONTEXT AWARE

MEETING ROOM SYSTEM

A regular activity at a work place like college or university is to organize, coordinate and attend

meetings. In addition the college administration department has to keep track of meeting schedules,

document the meeting contents, send the meeting notification and record the objectives and

outcomes of each meeting. Applications like Microsoft Outlook or Lotus Notes [13] can help with

some of the tasks, like keeping track of the schedule and administrating meeting invitations and

responses. When it is comes to recording the meeting minutes, objectives, outcomes, setting up the

entire meeting room for the meeting purpose, preparing the report for presentation, etc., the existing

tools come short. This can be changed by designing a dedicated context aware system for organizing,

coordinating and recording the meeting event for a particular purpose in a given meeting room. The

proposed context aware meeting room system presented in this paper has the following features:




1. Configures the meeting room component settings based on the user context, user role and

user priority of the meeting participants.

2. Sends a notification to the mobile and e-mail about the schedule and agenda of the Meeting

3. Records the meeting discussion

4. Records the objectives and outcomes of the meeting

5. Update the meeting files.

6. Generates the different user type based report.

7. Jams the mobile phone based on user priority when the meeting is going on.

System Data Modeling: In order to embed the user’s context and social awareness in the context

aware meeting room system, the system data was modeled into three domains of data:user’s data,

context data, and meeting content data. The research work proposed in paper combines the three

domains to provide personalize and socialize context aware meeting room applications making use

of the proposed algorithm.

1. Users domain has information about meeting members profiles, social roles and

relationships between members who participate in the meeting.

2. Context domain represents the situations that link users with meeting room and

3. Meeting Room content domain encapsulatesMeeting_Room_Configuration Manager,

Meeting_Room_Settings, Meeting_Calendar,Meeting_Scheduler, Meeting_Agenda,

Meeting_Files, Meeting_Messenger and Meeting_EventRecorder .

The three domains are conceptually orthogonal and as such act as independent sources of data for the

context aware meeting room service recommendation problem. From these three data sources the

system recommender aims to predict purposeful service selections, given the past meeting contexts.

1. User Domain: The users are categorized into different categories like Chairman, Manager and

Members. Each user is represented as a vector of attributes like user category, mobile number,

landline number, email, user role (Member, Manager or Chairman), and name. Whenever the

meeting has to be organized the CAMR system gives a message and preparedness for different user

with respect to meeting. Based on the priority of the member the meeting room services will be

provided. Table1 illustrates the services offered to different category of users.

Table1: Services offered to different category of users

2. Context domain: The system is designed to recommend the services based on the users context.

User’s context is derived based on the values of primitive contexts like location, time, weekday,

temperature, incoming call and physical fall.

Time Context: The system reads a time from the system clock. Each fuzzy time is represented using

a pair (L(t),TT). Whereas L(t) is the symbolic notation for linguistic terms (ex:

AM,PM,EG(Evening),NT(Night),MNT(MidNight),and EAM(Early Morning)) and TT is the Time

User Role Priority Example Services

Chairman High Meeting Time Fixing Authority, Meeting Agenda Fixing Authority , Meeting

Members Selection , Priority for Eatables and Soft Drinks ,Fixing outcomes and

Objective of the Meeting , Accessibility to all types of Meeting Files

Manager Normal Authorization to organize the meeting, Accessibility to Meeting Event Recording

File.

Members Normal Authorization to attend the meeting, Authority to receive the meeting notification ,

Accessibility to Personal Meeting Files




Types (Meeting Time or Free Time ). The time will be mapped into its corresponding Linguistic

terms using equation below [9]:

L(t) =

{

Temperature: The temperature was measured using the sensor embedded on the board. The

temperature measured in degree celsius was mapped into fuzzy linguistic terms Very Cold, Cold,

Warm, Hot and Very Hot using equation below:

L(Temp) =

{

3. Meeting Room Content domain: Meeting Room is modeled in terms of its components, services

and settings. The contents of meeting room service and settings can be classified into fixtures

settings (settings of Chair, Table, Door, Windows Curtain, Fan,AC, Tube light, Mobile Phone

JAMMER ,WIFI Connection ,LAN Connection,) Meeting Software Application Settings. For

experiment purpose we have considered services and settings related to the meeting room inthe

college campus.Table2 illustrates the services and settings provided based on the context of user.

Table3 lists some of the allowable choice for some of the services and Settings.

Table2: Context aware Meeting Room services and Settings

Context System Values

Time Meeting Time , Free Time

Weekday Classifies the day as Meeting Day or holiday

Temperature Identifying the temperature as cold, very cold ,hot and very hot

Temperature specific settings and service as predefined by the chairman

For example if the temperature is very cold the system invokes the application

providing the services like : Setting AC to Normal, Coffee Services, etc.

Table3: Meeting Room Services and Settings with allowable choices

Layered Architecture of the Proposed System :The research work presented in this paper make

use of a generic layered architecture(proposed in our earlier work [9] for designing context aware

Meeting Room

Components

Allowable Choices/types

Fan ON or OFF With Speed equal Zero , Low, Medium ,Normal and High

ACSettings ON or OFF with temperature settings equal Cold , Warm, Norm, Hot

Meeting APP Settings ON ,OFF, START_MEETING , RECORD _MEETING, END_MEETING,

UPDATE_MEETING _DOC , CREATE_MEETING_AGENDA, SEND-

MEETNG_NOTIFICATION ,

Curtain OPEN or CLOSE

Door OPEN or CLOSE

Window OPEN /CLOSE/Partial OPEN

Tube Light ON / OFF

Mobile Phone jammer ON/ OFF

WIFI Connection ON/OFF

LAN CONNECTION ON/OFF




mobile) for designing context aware meeting room as illustrated in the Fig1. The proposed general

architecture consists of five layers described below:

1. User Meeting Space: It is a bounded space where in the user participates in meeting activity.

2. Sensor Layer: The sensor layer which is saturated with sensors like user identification

sensor, temperature sensors, time sensor, activity sensor, mood sensor, etc., provides a raw

sensor data to fuzzy layer for fuzzification. The sensor data are dynamic and represents the

changes in environment whenever some changes through events or actions changes occurs in

the meeting environment space.

3. Fuzzification Layer: The Conversion of Crisp input (discrete or continuous) into fuzzy values

represented by linguistic terms using membership functions called fuzzification. In the

fuzzification layer, sensors value will be fuzzified into linguistic terms using respective

membership functions like triangular, trapezoidal, singleton and Gaussian. Some of the

linguistic terms used in fuzzification are low, very low, normal, cold, very cold, hot, very

hot, warm, near, far, exactly, early, near, far etc. The layer provides a fuzzified sensor value

to the context layer to generate context.

4. Context Layer: In this layer aggregation of fuzzified linguistic terms takes place using logical

AND operation. The aggregated context provides complete meaningful information about the

situation of a given entity. For example the context of Raju is said to be in Meeting if,

“(Time==Meeting_Time)&&(User_Location==Meeting_Room)&&(Meeting_Room_Light

_Level==Bright)&&(MeetingRoom_Curtain_Status==CLOSED)&&(User_Activity==Talki

ng)&&(User_Physical_Posture == Sitting ) &&M”.

5. Recommendation Layer: In this layer context based services will be recommended. Conflict

resolution will be resolved, if any, in this layer before recommending the services. It includes

the repository of actions in the form of functions and APIs.

6. Output Layer: This layer is saturated with devices and includes the environment in total,

where in the context aware systems are situated. The actions will be executed using

appropriate devices.




Fig1: Layered Architecture of generic Context Aware System

Fuzzy Rules: The algorithm makes use of predefined fuzzy rules to recommend the services based on

the context of meeting room.Some of the sample fuzzy rules (for college meeting room) used for the

context aware meeting room system in the research work is illustrated in table4.

The fuzzy rule base is a knowledge base containing rules(R), conditions(C) and actions (A). Each

rule is made up ofconditions and actions. If conditions are satisfied then the actions get activated.

The rules are build using fuzzy condition attributes and fuzzy action attributes.

Fitness Degree: The fitness degree FD (Rj) for the Rule Rj is given by the equation [9],

FD(Rj) =

Rj will be recommended only if FD(Rj) ≧τ, where τ is threshold function and its value is user defined. For example, if the user wants at least 2 attributes like participant1 and participant2 has to

be matched for the rule base given in table4, then the threshold value can be adjusted to 2/7.If

suppose no full matching rule is found then the rule whose FD is maximum and greater than the

threshold is selected. If no attributes gets matched then the new rule will be created and default value

gets recommended. The Fuzzy rule based algorithm described above is given as follows :

Algorithm:

S1.Read the Current Context Attributes

S2.for (Ri=1 to Rn )

Determine FD(Ri)

S3.Recommend the Actions Corresponding to the Rule with Maximum FD

S4.Execute the Actions

Users Meeting Space (Meeting Room)

S1 S2 S3 ----

-

---- ---- Sn

S1_Data S2_Data S3_Data Sn_Data

Fuzzification

Meeting Context –Generation

Input / Sensor Layer

Fuzzif-ication

Layer

Context

Layer

Meeting Room Actions Recommendation

Meeting Room Actions Repository

A1 A2 A3 A4 An A (n-1)

Actions /Services (Initializing the Meeting Room APP, Sending Meeting Notifications, Recording

Meeting_Event, FAN/AC Settings, etc )

Recom-mendati-on

Layer

Output

Layer




Table4: Example Fuzzy Rule Base

IV. IMPLEMENTATION OF THE CONTEXT AWARE MEETING ROOM

PROTOTYPICAL SYSTEM

The proposed prototypical system was implemented and tested for engineering college meeting

room, wherein regularly at least once in a day the meeting used to take place. The category of

participants was President, Principal, Secretary, Office Manager, Various Deans, HODs, Director,

Teaching and Non-Teaching Staff Members, etc. The test bed used for experimenting the context

aware meeting room system is as illustrated in the Fig2below:

CONTEXT RULE1 RULE2 RULE3 RULE4 - RULEn

Meeting Title Students

Conduct

Sports Syllabus Placement Academics

Meeting Day Monday Tuesday Wednesday Thursday Saturday

Start Time 10.00AM 10.00AM 2.30 PM 3.00 PM 2.00 PM

End Time 12.30PM 12.30PM 5.00 PM 4.00 PM 5.00PM

Agenda File.doc File.doc File3.doc FileP.doc FileS.doc

Participant1 Chairman Sports

Director

BOE-

Chairman

PO Principal

Participant2 Manager1 Manager2 Manager3 APO Dean

Participant3 HOD1 Staff1 Member1 Member1 Member1





Temperature Normal Hot Cold Normal Normal

Light Intensity Dim Dim Normal Bright Bright

ACTIONS

AC Temperature Normal Normal Warm OFF OFF

Tube Lights ON ON OFF OFF OFF

Meeting

Notifying App

Activate Activate Activate Activate Activate

Meeting

Scheduling App


Meeting

Initializing App


Meeting Room

Components

Settings

Manager


Meetings Event

Recorder


Objectives Generate after

meeting time

Generate after

meeting time

Generate

after

meeting

time

Generate

after

meeting

time

Generate after

meeting time

Outcomes Generate after

meeting time Generate after

meeting time Generate

after

meeting

time

Generate

after

meeting

time

Generate after

meeting time

Final Meeting

Report

Generate after

meeting time

Generate after

meeting time

Generate

after

meeting

time

Generate

after

meeting

time

Generate after

meeting time

Meeting Report

Files Dispatching

App





Fig2: Experimental Set up of Prototypical Context Aware Meeting Room System

Bluetooth Enabled Mobile Phones are used as user identification tags. Florescent Lamp with wide

range of light intensity is used to simulate the behaviour of AC. The sensors like temperature and

Light sensors are used for measuring the environmental context.Fig3 illustrates the snapshot of

graphical user interface designed for the purpose of configuring and activating the context aware

meeting room system.

Fig3: Graphical user interface for configuring Context Aware Meeting Room

Bluetooth Dongle

CAMRS UI

USER ID TAGS

AC Status Lamp State

Light & Temperature

Sensors

Fan




Fig4:Configuring user profile settings

Fig4 is the snapshot illustrating how to add the user profiles to the CAMR system. Initially the

administrator has to register the users for CAMR system by adding the details like Bluetooth MAC

Address, User preference for light, fan and AC during different environmental conditions. The

Administrator of the System will have authorization to add any number of users, to update any user

and to delete user. Fig5 is a snapshot illustrating the situation as recorded by the context aware

meeting Room system during meeting participated by management staff and professor.

Fig5: Snapshot of situation of Meeting Room as recorded by the CAMR System.




V. CONCLUSION

The research work presented in this paper proposed a design and implementation of conceptual

architecture of context aware meeting room. The proposed prototypical system adapts the meeting

room and its components based on the situation of participants and environmental conditions. The

system makes use of simple if else fuzzy rules stored in database. As a future work we like to design

and implement context aware meeting room using AI techniques like Bayesian Network, Rough Set

Theory, Neural Network and Genetic Algorithms. In addition we like to add the feature like

automatic configuration of user profiles by mining the data available in the cloud .

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