research projects in the mobile computing and networking (mcn) lab guohong cao department of...

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Research Projects in the Mobile Computing and Networking (MCN) Lab Guohong Cao Department of Computer Science and Engineering The Pennsylvania State University http://www.cse.psu.edu/~gcao

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Research Projects in the Mobile Computing and Networking (MCN) Lab

Guohong Cao

Department of Computer Science and Engineering

The Pennsylvania State University

http://www.cse.psu.edu/~gcao

Mobile Computing and Networking (MCN) Lab

• MCN lab conducts research in many areas of wireless networks and mobile computing, emphasis on designing and evaluating mobile systems, protocols, and applications.– Current Projects: smartphones, wireless network security, data

dissemination/access in wireless P2P networks, vehicular networks, wireless sensor networks, resource management in wireless networks.

– Support: NSF (CAREER, ITR, NeTS, NOSS, CT, CNS), Army Research Office, NIH, DoD/Muri, DoD/DTRA, PDG/TTC and member companies Cisco, Narus, Telcordia, IBM and 3ETI.

• Current students:– 10 PhD students– 1 PostDoc– 3 visiting scholars

Alumni

• 15 PhDs Hao Zhu (8/2004), Qualcomm. Liangzhong Yin (12/2004), Microsoft. Wensheng Zhang (8/2005), Associate Professor, Iowa State University Hui Song (8/2007), Assistant Professor, Frostburg State University Jing Zhao (8/2008), Cisco Systems. Min Shao (12/2008), Microsoft Changlei Liu (5/2010), UMUC Yang Zhang (2/2011), Palo Alto Networks. Baojun Qiu (Co-chaired with J. Yen) 8/2011, eBay. Bo Zhao (10/2011), AT&T. Zhichao Zhu (2/2012), Nokia. Qiang Zheng (5/2012), Google Wei Gao (5/2012), Assistant Professor, University of Tennessee. Qinghua Li (5/2013), Assistant Professor, University of Arkansas. Yi Wang (5/2013), Google.

• 12 MS students went to various companies• 5 visiting scholars

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Outline

• Efficient Energy-Aware Web Access in Wireless Networks

• Social-Aware Data Dissemination in Delay Tolerant Networks

• Resilient and Efficient Data Access in Cognitive Radio Networks

• Privacy-Aware Mobile Sensing

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Web Browsing in 3G/4G Networks

• Smartphones in 3G/4G networks:– Increasingly used to access the Internet– Consume more power

• Cellular interface consumes lots of energy– 30%-50% of total energy

• Current status:– 3G/4G radio interface always on, timer control– Radio resource is not released, reduce network

capacity

Characteristics of 3G Radio interface

T2 = 15 sec

T1 = 4 sec

Traffic Load of Opening Webpages

Radio interface is always on during data transmission

8

Reorder the Computation Sequence

• Reorganize the computation sequence of the web browser, so that it first runs the computations that will generate new data transmissions and retrieve these data from the web server. – Then, the web browser can put the 3G radio interface into low power

state, and then run the remaining computations.

Reducing the Energy of FACH State

• After a webpage is downloaded, predict the user reading time on the webpage– This time > a threshold (delay vs. power): switch into low power state– Prediction is based on Gradient Boosted Regression Trees (GBRT).

• Selected 10 features such as Data transmission time, webpage data size, figure size, no. of downloaded objects, etc.

• Also consider user interest.

10

Evaluations

• The prototype:– Android Phones– T-Mobile 3G/UMTS network

• Implement the prototype and collect real traces• Experimental results:

– Reduce power consumption: 30%– Reduce loading time: 17%– Increase network capacity: 19%

Motivation

Power

t

Power

t

Power

t

Power

t

Power

t

How to reduce tail energy and promotion delay?

Promotion

Data transmission

Tail

Tail

Basic idea

• Aggregation traffics on one node (proxy)– How? An optimization problem.

• Forward via P2P (Bluetooth or WiFi direct)

Power

t

Power

t

Power

t

Power

t

Power

t

P2P interface

Proxy

Testbed Results

• Total energy saving rate: 30.4%• Average delay reducing rate: 31%

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Outline

• Efficient Energy-Aware Web Access in Wireless Networks

• Social-Aware Data Dissemination in Delay Tolerant Networks

• Resilient and Efficient Data Access in Cognitive Radio Networks

• Privacy-Aware Mobile Sensing

Data Dissemination in DTNs

• Lack of infrastructure support in disaster recovery, battlefield, environmental monitoring, etc.

• Mobile devices can form mobile opportunistic networks or Disruption Tolerant Networks (DTN).

• General methodology: Carry-and-forward• The key issue is to select which node (relay) to

forward the data.

Japan tsunami 2010

Social-Aware Data Dissemination

• Exploiting social relations among mobile nodes for relay selections– Stable long-term characteristics compared to node

mobility– Centrality (Degree or betweenness), which shows the

importance of some nodes to help communications among other nodes. • High centrality nodes can be used as relay nodes.

– Community, i.e., nodes have common acquaintances have higher probabilities to know each other. • data can reach the destination easier if it reaches someone in

the same social community as the destination.

Our Results

• Social interest: User-Centric Data Dissemination in Disruption Tolerant Networks (infocom’11)

• Social Contact Patterns: On Exploiting Social Contact Patterns for Data Forwarding in Delay Tolerant Networks (icnp’10, TMC’13)

• Social selfishness: Routing in socially selfish disruption tolerant networks (infocom’10, Adhoc’12)

• Social-aware caching: Supporting Cooperative Caching in Disruption Tolerant Networks (icdcs’11, icdcs’12, TMC’13)

• Social relationship: Social-Aware Data Diffusion in Delay Tolerant MANETs (book chapter’12)

• Social-aware multicast: Social-aware Multicast in Disruption Tolerant Networks (Mobihoc’09, ToN’12)

Social Interest

• System development: recording users’ interests– Data access via Samsung Nexus S smartphones– Categorized web news from CNN

• Application scenarios– Public commute systems: bus, subway– Public event sites: stadium, shopping mall– Disaster recovery

Android

webpage XML format phone display

Social Interest

• User interests: dynamically updated by users’ activities• System execution

– 30 users at Penn State, 5-month period– 11 categories, 306,914 transceived, 40, 872 read by users

Contact

A

B

C

Social Contact

• System development– Testbed: TelosB sensors– Deployment: 1000+ sensors distributed to high school students

• Heterogeneity of centrality, community, high cluster coefficient• Flu immunization

A B

C

802.15.4/ZigBee compliant10kB RAM, 250kbps data rateTinyOS 2.0

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Outline

• Efficient Energy-Aware Web Access in Wireless Networks

• Social-Aware Data Dissemination in Delay Tolerant Networks

• Resilient and Efficient Data Access in Cognitive Radio Networks

• Privacy-Aware Mobile Sensing

Emergence of Cognitive Radio• Unlicensed use of licensed spectrum is approved by

government agencies– Cognitive radio – dynamically configure the operating

spectrum

Cognitive Radio Networks Dynamic spectrum access

Must avoid interference with primary users (licensed users) With infrastructure / without infrastructure (ad-hoc)

Our Work

Data Caching

• No caching

• Caching(delay is statistically bounded)

27

Outline

• Efficient Energy-Aware Web Access in Wireless Networks

• Social-Aware Data Dissemination in Delay Tolerant Networks

• Resilient and Efficient Data Access in Cognitive Radio Networks

• Privacy-Aware Mobile Sensing

28

Proliferation of Mobile Devices

• Mobile devices– Smartphone, tablet, vehicle,

medical device, pollution sensor

• Sensing capabilities– Camera, microphone,

accelerometer, GPS

• Communication capabilities– 3G/4G, WiFi, BluetoothA huge opportunity for

mobile sensing

Obstacles in Collecting Sensing Data

• Privacy concern– Location, activity, health

• <location, noise>• <amount of exercise>

• Cost of participation– Power, bandwidth,

human attention

• Lack of network connectivity– Devices without comms infrastructure (e.g., 3G)– Circumstances of unavailable or cost-inefficient

infrastructure

Research Summary

Obstacles

Privacy concern

Cost of participation

Lack of network

connectivity

Privacy-aware incentives

Solutions

Privacy-aware

incentive

Privacy-aware

aggregation

Secure opportunistic mobile networking

[PerCom’13] [ICNP’12,PETS’13]

[Infocom’10]: selfishness

[TDSC’13]: flood attack[TIFS’12]: drop attack

More data collected from more users

More data collected from more devices

Summary

• Efficient energy-aware web access in wireless networks– reducing the power consumption of smartphones by dealing with

the special characteristic of the 3G/4G radio interface

• Social-aware data dissemination in delay tolerant networks– Exploiting the knowledge of social contact patterns, social

interests, and social relationships. – Two testbeds for data collection.

• Resilient and efficient data access in cognitive radio networks– mitigating the effects of primary user appearance

• Privacy-aware mobile sensing