Mobile Phone Enabled Social Community

Extraction for Controlling of Disease

Propagation in Healthcare

Yingying (Jennifer) Chen

Director of Data Analysis and Information Security Lab

Graduate Director of Networked Information Systems (NIS)

Department of Electrical and Computer Engineering

Stevens Institute of Technology

Hoboken, NJ, USA

Email: yingying.chen@stevens.edu

Web: personal.stevens.edu/~ychen6

March 2014

DAISY Data Analysis and Information SecuritY Lab

Research Interests

Cyber security and privacy

Secure localization systems; privacy in Location Based Services (LBS)

Security in mobile cloud computing, smartphone privacy

Wireless security: identity-based attacks, anti-jamming, intrusion detection

Security in smart grid and cognitive radio networks

Mobile healthcare and social networks Wellbeing monitoring; smart vaccine delivery

User authentication

Activity recognition

Wireless localization and context-aware sensing Wireless localization algorithms & testbeds (using mobile phone, sensor, RFID)

Personnel and asset monitoring and tracking

Smartphone enabled mobile social networks

Mobile Computing and vehicular applications Detect driver phone use to reduce driver distraction caused by cell phone usage

Exploit cell phone signal strength to perform vehicular speed estimation

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Data Analysis and Information Security Lab

(DAISY): www.stevens.edu/daisy

Funding

National Science Foundation, Google Research, Department of Defense (DoD), Army Research Office, and Air Force Research Labs

Collaborators

Industry: AT&T, IBM T. J. Watson Research Center, InfoBeyond.

Academia: Columbia University, Rutgers University, Robert Wood Medical School, Lehigh University, University of South Carolina.

Students

Postdoc researchers, Ph.D., master and undergraduate students

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Publications and Awards

Book: Yingying Chen, Wenyuan Xu, Wade Trappe, Yanyong Zhang, Securing Emerging Wireless

Systems, ISBN:978-0-387-88490-5,Springer, 2009.

Book chapters: Jie Yang, Yingying Chen, Richard P. Martin, Wade Trappe and Marco Gruteser, "On the

Performance of Wireless Indoor Localization Using Received Signal Strength", Position Location - Theory, Practice and Advances: A Handbook for Engineers and Academics, Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE, chapter 12, 2011.

Yingying Chen, Jie Yang, Wade Trappe and Richard P. Martin, "Impact of Anchor Placement and Anchor Selection on Localization Accuracy", Position Location - Theory, Practice and Advances: A Handbook for Engineers and Academics, Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE, chapter 13, 2011.

Yingying Chen, Jie Yang, Wade Trappe and Richard P. Martin, "Defending against Identity-Based Attacks in Wireless Networks", Securing Cyber-Physical Infrastructures: Foundations and Challenges, Sajal Das, Krishna Kant and Nan Zhang (eds.), chapter 4, 2011.

Published over 80 journal articles and referred conference papers.

National Science Foundation CAREER Award 2010.

Google Faculty Research Award 2010.

IEEE Outstanding Contribution Award from IEEE New Jersey Coast Section, 2005, 2006, 2007, 2008 and 2009.

Best Paper Award of the ACM International Conference on Mobile Computing and Networking (ACM MobiCom) 2011.

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5

Talk Outline

Introduction and Motivation

Framework Overview

Disease Propagation Model

Community and Kernel Structure Extraction

Vaccine and Alert Messages Distribution

Simulation Evaluation

Conclusion

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Introduction and Motivation

Rapid deployment of sensing technology in mobile

phones.

The collected sensing data can be used for supporting a

broad range of applications:

monitoring/tracking, first responder and military applications.

Few studies used mobile phone-based sensor data to

address healthcare related problem.

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Framework Overview

Main components

Disease propagation model.

Community and kernel structure extraction.

Vaccine and alert messages distribution.

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Disease Propagation Model

Four states are used grounded on the standard

epidemic SIR model:

Susceptible without alert;

Susceptible with alert;

Infective;

Immunized;

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Community and Kernel Structure Extraction

Main components:

Flow overview Dividing encounter events into multiple trace to construct contact graph

and extract communities and kernel structures.

Merging the extracted communities and kernel structures with existing

community and kernel structures.

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Mobile Device Trace Segmentation

Each entry of traces includes the starting and

ending time of the contact as well as IDs of the

mobile devices.

Assume that we have D time windows:

Divide the contact trace into multiple trace pieces

according to these time windows.

0 1 1 2 1[ , ],[ , ],...,[ , ]D DT T T T T T-

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Construct Contact Graphs

Contact graph G=(V,E) which consists of a

vertex set V and an edge set E.

Each vertex denotes a person and each edge

denotes two persons encounter for at least W

times.

We choose for community and

for kernel structure with .

1W w=2W w=

2 1w w>

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Community Extraction and Merging

Communities are extracted from contact

graph by hierarchical clustering and modularity

Q in time window i:

Merging the communities using the community

merge operation for two communities if they

satisfy:

j

iA

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Vaccine and Alert Message Distribution

Two types of messages received by users:

Vaccination messages: a user should go to obtain a

vaccine shot;

Alert Messages: a user should take precautions as

directed.

The kernel structures are considered first:

The top K users are selected for vaccination or alert

messages;

The weight of each person reflects the priority;

kV

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Simulation Evaluation

Traces collected from smart phones:

MIT traces (97 participants, 20 days)

Implemented our framework in a home-grown simulator;

Compare with existing techniques:

Random Distribution method: server randomly chooses the

users to receive the vaccination and alert messages.

Encounter-based method: vaccination and alert messages

are sent if sick person encounters with a susceptible

person.

Simulation setup:

Extensive experiments on MIT trace by varying different

parameters in our epidemiology infection model.

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Simulation Results: Ratio of Infected Persons (1)

Ratio of infected person Versus Different recovery cycle

Our proposed method achieves a lower infection ratio than Random

Distribution and Encounter-based methods for each recovery

cycle.

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Simulation Results: Ratio of Infected Persons (2)

Our proposed method achieves a lower final infection ratio than

other methods.

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Conclusion

Explored the design of a mobile phone enabled

community based disease propagation rate reduction

framework for the healthcare domain.

Developed an online algorithm for identifying dynamic

communities of different relationship levels .

Designed a community-based framework to make

decisions on the alert messages and vaccine shot

distribution.

Simulation studies show our community based disease

control scheme results in lower final infection ratio than

existing techniques

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Thanks and Questions ?