Conceptual Design of Fuzzy Rule Based Context Aware Meeting Room System

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International Journal of Modern Trends in Engineering
and Research
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e-ISSN: 2349-9745
p-ISSN: 2393-8161
Conceptual Design of Fuzzy Rule Based Context
Aware Meeting Room System
Dr.Thyagaraju.GS1
, Shivakumar C2
, Nithin3
1
Associate Professor and HOD, 2, 3
Assistant Professor
1, 3
Department of CSE, 2
Department of CSE/ISE/Mech,
1,2,3
SDMIT (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 AnA (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 .
REFERENCES
[1]. H. Chen, F. Perich, D. Chakraborty, T. Finin, and A. Joshi, “Intelligent Agents Meet Semantic Web in a Smart
Meeting Room,” Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent
Systems - Volume 2, New York, New York: IEEE Computer Society, 2004, pp. 854- 861.
[2]. J. Bourcier, C. Escoffier, and P. Lalanda, “Implementing home- control applications on service platform,” 4th IEEE
Consumer Communications and Networking Conference (CCNC’07), Las Vegas, 2007.
[3]. S. Ahmed, M. Sharmin, and S.I. Ahamed, “A Smart Meeting Room with Pervasive Computing Technologies,”
Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and
Parallel/Distributed Computing and First ACIS International Workshop on Self- Assembling Wireless Networks,
IEEE Computer Society, 2005, pp. 366-371.
[4]. P. Dai and G. Xu, “Context-aware computing for assistive meeting system,” Proceedings of the 1st international
conference on Pervasive Technologies Related to Assistive Environments, Athens, Greece: ACM,2008, pp.1-7.
[5]. E. Cutrell, D.C. Robbins, S.T. Dumais, and R. Sarin, “Fast, flexible filtering with Phlat-Personal search and
organization made easy,” CHI-CONFERENCE-, 2006, p. 261.
[6]. A. Göker, S. Watt, H.I. Myrhaug, N. Whitehead, M. Yakici, R. Bierig, S.K. Nuti, and H. Cumming, “An ambient,
personalised, and context-sensitive information system for mobile users,” Proceedings of the 2nd European Union
symposium on Ambient intelligence, Eindhoven, Netherlands: ACM, 2004, pp. 19-24.
[7]. Chao Li,Lijun Sun, Xiangpei Hu, “ A context aware lighting control system for smart meeting rooms” , Published in
2nd
International Conference on Complexity Science and Information Engineering , 2211-3819 , Elsevier Ltd,
Desheng Dash Wu, doi: 10.1016/j.sepro.2011.11.081 ,Systems Engineering Proceedia 4(2012) 314-323
[8]. Tor – Morten Gronli,JarleHansen,GheorgitaGhinea, “A Context Aware Meeting Room-Mobile Interaction and
Colaboration using Android , Java ME and Windows Mobile”, Proceedings of 2010 34th
Annual IEEE Computer
Software and Applications Conference Workshops. 978-0-7695-4105-1/10 ,DOI : 10.1109/COMPSACW.2010.61
[9]. Thyagaraju.GS,et al , “ Design and Implementation of User Context Aware Recommendation Engine for Mobile
using Bayesian Network ,Fuzzy Logic and Rule Base”,In International Journal of Pervasive Computing and
Communications ,ISSN 1742-7371,Vol8 Issue2,pp133-157,DOI :10.1108/17427371211245364.Emerald Group
Publishing Limited House ,United Kingdom ,EMERALD INSIGHT International Journal .
[10]. url: http://code.google.com/p/protobuf/.
[11]. url: http://code.google.com/ p/protobuf/wiki/ThirdPartyAddOns.
[12]. url: http://code.google.com/apis/ protocolbuffers/docs/overview.html.
[13]. url: http://code.google.com/p/ protobuf/wiki/ThirdPartyAddOns.
[14]. url:http://bluecove.org/.
[15]. url: http://code.google.com/p/google- guice/.

Conceptual Design of Fuzzy Rule Based Context Aware Meeting Room System

Conceptual Design of Fuzzy Rule Based Context Aware Meeting Room System

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