Technical Note of EU FP7 MONICA Project: Mobility Prediction as a Service of Telecom Cloud

Institut Mines-Télécom
H. Xiong1, D. Zhang1, D. Zhang1,
V. Gauthier1, K. Yang2, M. Becker1
1.  Institut Mines-Telecom, TELECOM SudParis
2.  Network Convergence Laboratory, University of Essex
MPaaS: Mobility Prediction as
a Service of Telecom Cloud

Agenda
1.  Introduction
2.  MPaaS System Overview
3.  Problem Formulation; Empirical Observations on
Mobility Trajectories and MPaaS Prediction
Algorithms Design
4.  Evaluation Result
5.  Discussion

Objectives and Motivation
■ Overall Research Objectives:
●  In order to improve the performance of mobile services,
●  Predicting the future locations of each mobile user by
leveraging the telecommunication system and the
cloud computing facility.
■  Our research is motivated by three observations:
1.  Mobility prediction service
2.  Telecommunication System
3.  Telecommunication cloud

Observation 1
■ Mobility Prediction Service
● Predicting users’ future locations can help to
improve the performance of many mobile
services
− E.g.,: an user watching the online video in a fast-
moving train èpredicting the user’s next cell towers
èbuffering the video content on the next cell towers
in advance, in order to provide seamless handoff.
● Mobility prediction should be a fundamental
service for many mobile services.

Observation 2
■ Telecom System
● The telecommunication system is promising to
continuously track users’ location trajectories,
which could be used to predict users’ future
locations:
− When a user appears in a cell tower, the telecom
system would leave a signaling log (user id, time-
stamp) in the cell tower.
− Given the stream of signaling logs from each cell
tower, it is possible to track the continuous location
trajectory (e.g., cell tower id sequence) of each
mobile user

Observation 3
■ Telecom Cloud
●  Telcos in nowadays establish their own cloud computing
platform to facilitate the mobile service deployment.
●  The telecom cloud combining telecommunication/cloud
computing facilities:
−  Continuously tracks each mobile user’s location in real-time.
−  Provides the computational power to enable large-scale real-
time data mining/machine learning for mobility prediction.

Motivating Examples
•  Predictive Telecom Resource Management
•  Using users’ future locations to dynamically manage the resource
(e.g., power, bandwidth and storage) of communication systems
•  Mobility-based Service Personalization
•  Using users’ future locations to personalize the telecom services (e.g.,
location-aware recommendation, and location-based advertising )

Research Assumptions & Objectives
Given the stream of signaling logs real-timely-generated by
each cell tower; Given a set of mobile users;
For each mobile user, Predicting the cell towers that the user
would pass-by in next few hours of each mobile user.

Research Issues
■  Given the stream of signaling logs in each cell tower,
querying the cell traces of each individual mobile
user
■  Given the each user’s cell traces, extracting the
mobility trajectory (the sequence of locations)
■  Given each user’s historical trajectory, predicting the
user’s future locations
●  The low accuracy of predicting each user’s future locations
using the individual user’s mobility trajectory
●  Using the social interplay between mobile users to enhance
the mobility prediction.

Contributions
■  Investigating the research issues of mobility
prediction leveraging the telecom cloud; Proposing
MPaaS system.
■  Formulating the mobility prediction problem of the
MPaaS system; Proposing the mobility prediction
algorithm using the individual/collective mobility
patterns.
■  Evaluating the mobility prediction algorithms using
the real-world data traces.

Agenda
1.  Introduction
Cellular Mobility Trace and MPaaS Prediction
Algorithms Design
5.  Discussion

MPaaS System Overview
Signaling Logs of
each cell tower
Cellular traces
of each user
Mobility
Trajectory of each
user
Future Mobility Traje-
ctory of each user

Cellular Trace Query from Telecom Sys.
Querying each user’s cellular traces from each cell tower
using a Publisher/Subscriber system

Example of Cellular Traces
•  Ping-Pong Noise in
cellular traces (overlapped
area)
•  Example of cellular traces

Mapping Cellular Traces (with Ping-Pong
Noise) to Mobility Trajectory
■ Two Steps
●  Splitting the overlapped area among multiple cell
towers into the unoverlapped subregions
●  Mapping the cell tower id sequence (with ping-pong
noise) into the sequence of unoverlapped subregions
(mobility trajectories).

Mapping Cellular Traces to Mobility
Trajectory
■  Splitting the overlapped regions between cell towers
into subareas
E.g., three cell towers
A, B and C
1.  A,B,C
2.  AB, BC, AC
3.  ABC

Mapping Cellular Traces to Mobility
Trajectory
■  Given the cellular traces (with ping-pong noise) of
each individual user; e.g.,:
■  Finding the subregion where the user is most
likely to receive the cell tower id sequence

Agenda
1.  Introduction
Cellular Mobility Trace; and MPaaS Prediction
Algorithms Design
5.  Discussion

MPaaS Problem Formulation

Data Analysis
■  Before introducing our algorithms, we first pay a glance
on our mobility dataset
■  We select 13 users’ 3-month cellular traces from the
dataset, and extract the mobility trajectories (sequence
of regions) of these 13 users from the cellular traces
(using the aforementioned algorithm).
■  Putting 13 users’ trajectories together, we observe the
collective behavior patterns.

Data Analysis: Observing Collective
Behaviors in Cellular Mobility Traces
Users*regions
Time slots
e.g.,

MPaaS Predictor Design
•  Markov-based Predictor using each individual’s historical trajectories.
•  CBP-based Predictor using other users’ current locations by leveraging
collective behavior pattern mining.

MPaaS Prediction Algorithms—Markov-
based Predictor
■ Markov-based Predictor
●  Given the region sequence of each user’s historical
trajectory
●  Given the current region, and finding the list of neighboring
region (including current subregion)
●  For each neighboring region, Calculating the probability of
the user moving to the region using Markov-Chain and
historical region sequence
The highest probability region is the result

MPaaS Prediction Algorithms—CBP-
based Predictor
■ CBP-based Predictor
● Learning Phase
1.  Given the historical trajectories of all mobile users
2.  For each user and each time-slot (in history), learning
the association pattern from all rest users’ regions
in the time-slot to the user’s region in the next
time-slot.
● Predicting Phase
1.  Given a user, and the current regions of all users;
Finding the list of the user’s neighboring regions;
2.  For each neighboring region, Calculating the
probability of the user appearing in the region in
next time-slot.(using the association patterns)
3.  Finding the highest probability region as the
prediction result.

MPaaS Prediction Algorithms—Predictor
Fusion
■ Predictor Fusion
● Using DS-Evidence theory to fuse two
predictors;
− Combining the probability distributions
(probability on each region) from two
predictors è the joint probability
distribution
− Finding the highest joint probability region
as the prediction result

Evaluation

Discussion
■  The realistic deployment on the telecom cloud is
required.
●  Will deploy a prototype system in SFR network…
■  Need large dataset to evaluate the mobility prediction
algorithms
●  The current evaluation dataset is acquired from a small
group of mobile users, where collective behavioral
patterns could be easily observed/extracted.

Many Thanks!

Technical Note of EU FP7 MONICA Project: Mobility Prediction as a Service of Telecom Cloud

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