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 èpredicting the user’s next cell towers èbuffering 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


3.  Problem Formulation; Empirical Observations on 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


3.  Problem Formulation; Empirical Observations on 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


Agenda

1.  Introduction


3.  Problem Formulation; Empirical Observations on Cellular Mobility Trace; and MPaaS Prediction Algorithms Design


5.  Discussion


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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