3. MOTIVATION
“These are the days of innovation ,one has seen how
experts of a particular domain falter in their own fields.
Just wait and watch how a semi IT ,semi telecom engineer
takes the lead in the coming times.” - Franco Bernabè
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4. OVERVIEW
Telecom cloud and cloud computing are two different
concepts.
The future locations of each mobile user can be
predicted .
Leveraging the telecommunication system and the
cloud computing facility user location can be predicted.
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5. INTRODUCTION
Convergence among mobile devices, wireless
communication, and cloud computing.
This sparked the development of location-based mobile
social services.
Here the relationship between telecom call patterns and their
mobility behaviour is investigated.
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6. EXISTING SYSTEMS
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Human Community-Based Mobility
Inter-Call Mobility
Real-world mobile traces might not be available for public
access.
Different users exhibit different mobility patterns in a
loose manner.
Contd….
THEIR DRAW BACKS
7. PROPOSED SYSTEM
Consists of a cloud system of mobility prediction.
Contains processing mobile call logs and user traces,
call pattern recognition, and prediction modules.
It is capable of tackling symbolically represented
locations of cell towers.
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Contd….
8. 1. Mobility prediction service
2. Telecommunication System
3. Telecommunication cloud
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Contd….
Our research is motivated by three observations :
9. MOBILITY PREDICTION IN TELECOM
CLOUD USING MOBILE CALLS
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11. CELLULAR TRACE QUERY FROM TELECOM
SYSTEMS
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Querying each user’s cellular traces from each cell tower
Contd….
12. CONCEPTS OF INTER-CONTACT TIME AND INTER-
CALL-CONTACT TIME
The relationship between telecom calls and user mobility
patterns defines two kinds of events
1. Cellular call event
2. Co-cell event
For a pair of given users we introduce two concepts based
on these events
1. Inter-contact time
2. Inter-call contact time
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Contd….
14. NEXT ME
How many cell towers user will go?
Which cell tower user will reach?
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Next Me consists of two sub goals:
Data pre-processing
Call pattern recognizing
Periodicity-based module
Social-based interplay module
Self-adjust learner
It consists of several components namely
16. CELLULAR TRACES SHOWING UNNECESSARY
HANDOFFS AND OVERLAPPED AREA
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cells A, B, and C are mutually overlapped
Contd….
17. 1. Data preprocessing:
To handle user mobility regularity.
2. Periodicity module:
Foretells a user location .
3. Social interplay module:
Forecasts user location .
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Contd….
18. 4. A self-adjust learner:
It involves two steps ,top-L selection of cell towers and
prediction aggregation.
5. Call Pattern Recognition:
Identify when the social interplay-based predictor works.
Concepts of critical call patterns and critical calls have
been proposed.
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Contd….
19. Next Me will perform a call pattern monitoring algorithm for each
call pair, which aims to answer three questions
1) When will the call pair co-locate?
Once it gets a critical call
2) To where will the call pair co-locate?
Region of users preference
3) For how long will the pair co-locate?
Forthcoming one to six hours
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Contd….
21. OPPURTUNITES
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F
A
B
U
L
O
U
S
FLEXIBILITY
AGGREGATION
BILLING
ULTIMATE PERFORMANCE SPEEDS
LAUNCHES RICH SET OF SERVICES
OFFER SECURITY SRORAGE AND
NETWORK MANAGEMENT SERVICES
URGES ECONOMIES OF SCALE
SOPHISTICATION
22. CONCLUSION
Telecom call patterns are highly correlated with co-locate
patterns, and call patterns .
Next Me was proposed for predicting user location at the
cell tower level .
It is expected to deliver a public service running in the
telecom cloud.
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