Social Sensing Lab at the University of Notre Dame
Dr. Dong Wang
Department of Computer Science and Engineering
Interdisciplinary Center for Network Science and Applications (iCeNSA)
University of Notre Dame
Email: dwang5 at nd dot edu
Chao Huang (Ph.D. student, started in Fall 2014)
Jermaine Marshall (Ph.D student, started in Fall 2015)
Daniel Zhang (Ph.D. student, started in Fall 2016)
Yang Zhang (Ph.D. student, started in Fall 2017)
Brian Mann (Undergraduate Student, ND)
Steven Mike (Undergraduate Student, ND)
Ben Cunning (Undergraduate Student, ND)
Herman Tong (Undergraduate Student, ND)
Rungang Han (Undergraduate Student, Zhejiang University)
Tianhong Sheng (Undergraduate Student, Sichuan University)
The advent of online social media (e.g., Twitter and Flickr), the ubiquity of wireless communication capabilities (e.g., 4G and WiFi), and the proliferation of a wide variety of sensors in the possession of common individuals (e.g., smartphones) allow humans to create a deluge of unfiltered, unstructured, and unvetted data about their physical environment. This opens up unprecedented challenges and opportunities in the field of social sensing, where the goal is to distill accurate and credible information from social sources (e.g., humans) and devices in their possession that accurately describes the state of the physical world. The problem requires multi-disciplinary solutions that combine data mining, statistics, network science and cyber physical computing. My research addresses the aforementioned needs by building theories, techniques and tools for accurately extracting high quality information from data generated with humans in the loop, and for reconstructing the correct "state of the world" both physical and social. I believe my research can lead to the next generation of information distillation services, where predictable, reliable, and timely answers are found from the huge amount of real-time and heterogeneous data feeds, empowering humans to better understand, utilize and make sound decisions from such data.
The primary research focus of the lab lies in the emerging area of Social Sensing and Cyber-Physical Systems in Social Spaces, where data are collected from human sources or devices on their behalf. Social sensing systems are one example of information distillation systems in current era of Big Data. I carreid out a set of projects to address several key challenges in social sensing and I beleived the theories, algorithms, frameworks and systems developed in these projects are useful in building future information distillabtion systems in general.
From Big Data to Small Data: Taming the two V's of Veracity and Variety
Data of questionable quality has led to significantly negative economic and social impacts on organizations, leading to overrun in costs, lost revenue, and decreased efficiencies. The issues on data reliability, credibility, and provenance has become even more daunting when dealing with the variety of data, especially data that are not directly collected by an organization, but from the third-party sources such as social media, data brokers, and crowdsourcing. To address such issues, this project aims to develop a Data Valuation Engine (DVE) that solves the critical problem of data reliability, credibility and provenance, and provides accountability and quality processes right from data acquisition. The DVE leverages and innovates techniques in estimation theory, data fusion and machine learning to fill a critical gap in data accountability and quality, thereby providing a transformative step in countering the ubiquitous data quality issues found in almost every application domain from business to environment to health to national security. The DVE will be integrated in the Hadoop ecosystem and will be agnostic to the data source, application or analytics, and provided as a hosted solution to the community. The results have been published in IEEE BigData 16 , ACM Recsys 16 , IEEE Transanction on Big Data.
Location-based Social Sensor Profiling
While many social sensing studies focus on sensing
and recovering the status of the physical world, this project investigates
the problem of profiling the social sensors (i.e., humans). In
particular, we study the problem of accurately inferring the localness and the home
locations of people from the noisy and sparse social sensing
data they contribute. In this study, we propose a new method to accurately infer the home locations of people by explicitly exploring the localness
of people and the dependency between people based on their
check-in behaviors under a rigorous analytical framework. We
perform extensive experiments to evaluate the performance of
our scheme and compared it to the state-of-the-art techniques
using three real world data traces collected from Foursquare.
The results showed the effectiveness of our scheme in accurately
profiling the home locations of people. The results have been published in IEEE ASONAM 16 , , IEEE INFOCOM 17 .
Social-aware Interesting Place Finding
This project studies an interesting place finding problem in social sensing,
in which the goal is to correctly identify the interesting places in a city (e.g., parks, museums, historic sites, scenic trails, etc.).
Important challenges exist in solving this problem: (i) the interestingness of a
place is not only related to the number of users who visit it, but also depends
upon the travel experience of the visiting users; (ii) the user's social connections
could directly affect their visiting behavior and the interestingness judgment
of a given place. In this project, we develop a new Social-aware Interesting
Place Finding framework that addresses the above challenges by
explicitly incorporating both the user's travel experience and social relationship
into a rigorous analytical framework. Our framework can find interesting
places not typically identified by traditional travel websites (e.g., TripAdvisor,
Expedia). We valid the effectiveness of our framework through
real-world datasets collected from location-based social network services. The results have been published in IEEE Smart City 15 , IEEE DCoSS 16 , Elsevier KBS .
Truth Discovery in Social Sensing
This project solves a fundamental problem in information distillation in social sensing where data are collected from human sources or devices in their possession: how to ascertain the credibility of information and estimate reliability of sources, as the information sources are usually unvetted and potentially unreliable. We call this problem truth discovery. Current research in data mining and machine learning (e.g., fact-finding) solves similar problems with important limitations on analysis semantics and suboptimal solutions. In contrast, our research presented, for the first time, an optimal truth discovery framework and system that provides accurate and quantifiable conclusions on both information credibility and source reliability without prior knowledge on either. Our work provides a new generic foundation for distilling reliable and quantifiable information from unreliable sources (e.g., humans). The results have been published in Fusion 11 , ACM/IEEE IPSN 12 , ACM ToSN , IEEE SECON 15, ICWSM 16 , IEEE DCoSS 16.
Quality of Information (QoI) Assurance in Social Sensing
This project investigates another critical problem in social sensing: how to accurately assess the quality of the truth discovery results by quantifying estimation errors and providing confidence bounds. This guaranteed quality analysis is immensely important in any practical settings where errors have consequences. However, this is largely missing in current literature. We successfully derived the first performance bound that is able to accurately predict the estimation errors of the truth discovery results. Our work allows real world applications to assess the quality of data obtained from unreliable sources to a desired confidence level, in the absence of independent means to verify the data and in the absence of prior knowledge of reliability of sources. Our work was mentioned explicitly in the National Academies Press as a "good example of Army's cross-genre research" in 2013. The research results have been published in DMSN 11 , IEEE SECON 12 , IEEE JSAC .
Link Analysis across Multi-genre Networks
Social sensing data is generated through the complicated interactions of information, social and physical networks. The interdisciplinary network systems are so complex that link analysis across multi-genre networks is essential. However, link analysis taking into account the three networks altogether is rare in current research.
In this project, we generalized the truth discovery framework to jointly analyze links across multi-genre networks and developed a new information distillation system, called Apollo. Apollo has been continuously tested through real world case studies using large-scale datasets collected from open source media and smart road applications. The results showed good correspondence between observations deemed correct by Apollo and ground truth, demonstrating the power of using link analysis across multi-genre networks for efficient information distillation. The results have been published in IEEE RTSS 13 , Journal of Real-time Systems .
Using Humans as Sensors: The Uncertain Data Provenance Challenge
The explosive growth in social network content suggests that the largest "sensor network" yet might be human . Extending the social sensing model, this project explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this project lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into our tool Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth. The results have been published in ACM/IEEE IPSN 14 , ACM/IEEE IPSN 16.
Provenance-assisted Classification in Social Networks
Signal feature extraction and classification are two common tasks in the signal processing literature. This project investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals . We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. While the design of such classifiers is application-specific, social signals share in common one key property: they are augmented by the explicit identity of the source. This motivates investigating whether or not knowing the source of each signal allows the classification accuracy to be improved. We call it provenance-assisted classification. This project answers the above question affirmatively, demonstrating how source identities can improve classification accuracy, and derives confidence bounds to quantify the accuracy of results. Evaluation is performed in two real-world contexts: (i) fact-finding that classifies microblog entries into true and false, and (ii) language classification of tweets issued by a set of possibly multi-lingual speakers. The results show that provenance features significantly improve classification accuracy of social signals. This observation offers a general mechanism for enhancing classification results in social networks. The results of this work are going to appear in IEEE J-STSP 14 .
Dong Wang, Tarek Abdelzaher, and Lance Kaplan. "Social Sensing: Building Reliable Systems on Unreliable Data," 1st Edition, Elsevier, 2015.
- Dong Wang. "Big Data and Information Distillation in Social Sensing," Taylor & Francis LLC, CRC Press, 2015.
- Tarek Abdelzaher and Dong Wang. "Analytic Challenges in Social Sensing," The Art of Wireless
Sensor Networks, Springer, 2014.
Journal paper: [J]; Conference paper: [C]; Workshop paper: [W];
Author Notation: Ph.D. students I advised: *; Undergraduate students I advised: +; Students I supervised in my course: #.
Daniel Zhang*, Chao Zhang, Dong Wang, Doug Thain, Xin Mu#, Greg Madey and Chao Huang*. Towards Scalable and Dynamic Social Sensing Using A Distributed Computing Framework, The 37th IEEE International Conference on Distributed Computing (ICDCS 2017) , Full Paper, Atlanta, GA, USA on June. (Acceptance rate: 16.9%)
Chao Huang*, Dong Wang, Nitesh Chawla. Scalable Uncertainty-Aware Truth
Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems, IEEE
Transactions on Big Data (TBD) , in press, 2017.
- [J] Chao Huang*, Dong Wang, Brian Mann+. Towards Social-aware Interesting Place
Finding in Social Sensing Applications, Elsevier Knowledge Based Systems (KBS), in
Chao Huang*, Dong Wang, Shenglong Zhu. Where Are You From: Home Location Profiling of Crowd Sensors from Noisy and Sparse Crowdsourcing Data, IEEE International Conference on Computer Communications (IEEE INFOCOM 2017) , Full Paper, Atlanta, GA, May, 2017. (Acceptance rate: 21%)
Chao Huang*, Dong Wang, Tao Jun. An Unsupervised Approach to Inferring the Localness
of People Using Incomplete Geo-Temporal Online Check-in Data, ACM Transactions on
Intelligent Systems and Technology (TIST), in press, 2017
Chao Huang*, Dong Wang. Towards Unsupervised Home Location Inference from Online Social Media, 2016 IEEE International Conference on Big Data (IEEE BigData 2016), Indianapolis, IN, October, 2016. (Acceptance rate: 19%)
Daniel Zhang*, Rungang Han+, Dong Wang. On Robust Truth Discovery in Sparse Social Media Sensing, 2016 IEEE International Conference on Big Data (IEEE BigData 2016), Indianapolis, IN, October, 2016. (Acceptance rate: 19%)
Chao Huang*, Xian Wu, Dong Wang. Crowdsourcing-based Urban Anomaly Prediction System for Smart Cities, The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, October, 2016. (Acceptance rate: 14%)
Jermaine Marshall*, Dong Wang. "Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing," 10th ACM Conference on Recommender Systems (Recsys 2016), Full Paper, Boston, MA, USA, September 2016. (Acceptance rate: 18%)
Chao Huang*, Dong Wang. "Exploiting Spatial-Temporal-Social Constraints for Localness Inference Using Online Social Media", The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
(ASONAM 2016) , Full Paper, San Francisco, August, 2016. (Acceptance rate: 13%)
Jermaine Marshall*, Munira Syed, Dong Wang. "Hardness-aware Truth Discovery in Social Sensing Applications," 12th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 16), Full Paper, Washington D.C., May, 2016.
Chao Huang*, Dong Wang. "Unsupervised Interesting Places Discovery in Location-Based Social Sensing," 12th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 16), Full Paper, Washington D.C., May, 2016.
Chao Huang*, Jermaine Marshall*, Dong Wang, Mianxiong Dong. "Towards Reliable Social Sensing in Cyber-Physical-Social Systems", The 1st IEEE Workshop on Parallel and Distributed Processing for Computational Social Systems (ParSocial 2016), Full Paper, Chicago., May, 2016.
Prasanna Giridhar, Md Tanvir Amin, Tarek Abdelzaher, Dong Wang, Lance Kaplan, Jemin George, Raghu Ganti, "ClariSense+: An Enhanced Traffic Anomaly Explanation Service Using Social Network Feeds," Pervasive and Mobile Computing, Accepted in 2016
Dong Wang, Jermaine Marshall*, Chao Huang*. "Theme-Relevant Truth Discovery on Twitter: An Estimation Theoretical Approach," The 10th International AAAI Conference on Web and Social Media (ICWSM 16) , Full Paper, Cologne, Germany, May, 2016. (Acceptance rate: 17%)
Jermaine Marshall*, Dong Wang. "Towards Emotional-aware Truth Discovery in Social Sensing Applications," The 2nd IEEE International Conference on Smart Computing (SMARTCOMP 2016), Full Paper, St. Louis, Missouri, May, 2016.
Chao Huang*, Dong Wang. "Topic-Aware Social Sensing with Arbitrary Source Dependency Graphs," The 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 16), Full Paper, Viena, Austria, April, 2016. (Acceptance rate: 19%)
Jize Zhang#, Dong Wang. "Duplicate Report Detection in Urban Crowdsensing Applications for Smart City," 2015 IEEE International Conference on Smart City 2015 (SmartCity 2015), Full Paper, Chengdu, China, December, 2015. (Acceptance rate: 18%)
Chao Huang*, Dong Wang. "On Interesting Place Finding in Social Sensing: An Emerging Smart City Application Paradigm," 2015 IEEE International Conference on Smart City 2015 (SmartCity 2015), Full Paper, Chengdu, China, December, 2015. (Acceptance rate: 18%)
Dong Wang, Tarek Abdelzaher, Lance Kaplan, Raghu Ganti, Shaohan Hu and Hengchang Liu,
"Reliable Social Sensing with Physical Constraints: Analytic Bounds and Performance Evaluation,"
Journal of Real-Time Systems, Volume 51, Issue 6, 2015.
Chao Huang*, Dong Wang, Nitesh Chawla. "Towards Time-Sensitive Truth Discovery in Social Sensing Applications," The 12th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2015), Full Paper, Dallas, Texas, USA, October, 2015. (Acceptance rate: 26%)
Chao Huang*, Dong Wang. "Spatial-Temporal Aware Truth Finding in Big Data Social Sensing Applications," The 9th IEEE International Conference on Big Data Science and Engineering (BigDataSE 15), Full Paper, Helsinki, Finland, August, 2015.
Dong Wang, Chao Huang*. "Confidence-Aware Truth Estimation in Social Sensing Applications," The 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 15), Full Paper, Seattle, WA, June, 2015. (Acceptance rate: 28%)
Dong Wang. "Data Reliability Challenge of Future Cyber-Physical Systems for Smart Cities," 2015 NSF Early-Career Investigators Workshop on CPS and Smart City, Invited Paper, Seattle, WA, April, 2015.
Dong Wang, Tarek Abdelzaher, and Lance Kaplan. "Surrogate Mobile Sensing," IEEE Communications Magazine, Volume:52, Issue:8, 2014.
Dong Wang, Tanvir Amin, Tarek Abdelzaher, Dan Roth, Clare Voss, Lance Kaplan, Stephen Tratzy, Jamal Laoudiy, and Douglas Briesch. "Provenance-assisted Classification in Social Networks," IEEE Journal of Selected Topics in Signal Processing (J-STSP), Volume:8, Issue:4, 2014.
Shiguang Wang, Dong Wang, Lu Su, Lance Kaplan, and Tarek Abdelzaher, Towards Cyber-physical Systems in Social Spaces: The Data Reliability Challenge, IEEE Real-time Systems Symposium (RTSS), Rome, Italy, December 2014.
Md Tanvir Amin, Tarek Abdelzaher, Dong Wang , Boleslaw Szymanski. "Crowd-sensing with Polarized Sources," In Proc. 10th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS) , Marina Del Rey, CA, May 2014.
Siyu Gu, Chenji Pan, Hengchang Liu, Shen Li, Shaohan Hu, Lu Su, Shiguang Wang, Dong Wang, Tanvir Amin, Ramesh Govindan, Charu Aggarwal, Raghu Ganti, Mudhakar Srivatsa, Amotz Barnoy, Peter Terlecky, Tarek Abdelzaher "Data Extrapolation in Social Sensing for Disaster Response," In Proc. 10th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, CA, May 2014.
Dong Wang, Tanvir Amin, Shen Li, Tarek Abdelzaher, Lance Kaplan, Siyu Gu, Chenji Pan, Hengchang Liu, Charu Aggrawal, Raghu Ganti, XinLei Wang, Prasant Mohapatra, Boleslaw Szymanski, Hieu Le, "Humans as Sensors: An Estimation Theoretic Perspective," The 13th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 14), Berlin, Germany, April, 2014. (Acceptance rate: 20%)
Dong Wang, Lance Kaplan, Tarek Abdelzaher, "Maximum Likelihood Analysis of Conflicting Observations in Social Sensing," ACM Transactions on Sensor Networks (ToSN),
Vol. 10, No. 2, Article 30, January, 2014.
Dong Wang, Tarek Abdelzaher, Lance Kaplan, Raghu Ganti, Shaohan Hu and Hengchang Liu,
"Exploitation of Physical Constraints for Reliable Social Sensing,"
IEEE 34th Real-Time Systems Symposium (RTSS 13)
Vancouver, Canada, December, 2013. (Acceptance rate: 22%)
Dong Wang, Tarek Abdelzaher, Lance Kaplan, and Charu Aggarwal, "Recursive Fact-finding:
A Streaming Approach to Truth Estimation in Crowdsourcing Applications," In Proc.
2013 IEEE 33rd International Conference on Distributed Computing Systems (ICDCS 13)
Philadelphia, PA, July 2013. (Acceptance rate: 13%)
Dong Wang, Lance Kaplan, Tarek Abdelzaher, and Charu Aggarwal, "On Credibility Estimation
Tradeoffs in Assured Social Sensing," IEEE Journal On Selected Areas in
Communication (JSAC), Vol 31. No. 6, June 2013.
Hongzhao Huang, Arkaitz Zubiaga, Heng Ji, Hongbo Deng, Dong Wang, Hieu Khac Le, Tarek Abdelzaher, Jiawei Han, Alice Leung, John Hancock and Clare Voss, "Tweet Ranking based on Heterogeneous Networks," in Proc. 24th International Conference on Computational Linguistics, Mumbai, India, December 2012.
Dong Wang, Lance Kaplan, Tarek Abdelzaher and Charu C. Aggarwal. "On Scalability and
Robustness Limitations of Real and Asymptotic Confidence Bounds in Social Sensing."
The 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications
and Networks (SECON 12) , Seoul, Korea, from June, 2012. (Acceptance rate: 19%)
Dong Wang, Lance Kaplan, Hieu Le, and Tarek Abdelzaher. "On Truth Discovery in Social Sensing:
A Maximum Likelihood Estimation Approach." The 11th ACM/IEEE Conference on Information Processing
in Sensor Networks (IPSN 12) Beijing, China April 2012. (Acceptance rate: 15%)
Dong Wang, Tarek Abdelzaher, Bodhi Priyantha, Jie Liu, Feng Zhao, "Energy-optimal Batching Periods
for Asynchronous Multistage Data Processing on Sensor Nodes: Foundations and an mPlatform Case Study,"
Journal of Real-Time Systems Volume 48, Issue 2 (2012), Page 135-165.
Dong Wang, Tarek Abdelzaher, Hossein Ahmadi, Jeff Pasternack, Dan Roth, Manish Gupta, Jiawei Han,
Omid Fatemieh, and Hieu Le, Charu Aggarwal, "On Bayesian Interpretation of Fact-finding in Information Networks",
14th International Conference on Information Fusion (Fussion 11) Chicago, IL, July, 2011.
Dong Wang, Hossein Ahmadi, Harsha Chenji, Tarek Abdelzaher, Radu Stoleru, C. C. Aggarwal
"Optimizing Quality-of-Information in Cost-sensitive Sensor Data Fusion",
7th IEEE International Conference on Distributed Computing in Sensor Systems, Barcelona (DCoSS 11)
Spain, June 2011. (Acceptance rate: 28%)
Dong Wang, Tarek Abdelzaher, Lance Kaplan, and Charu C. Aggarwal. "On Quantifying the Accuracy of
Maximum Likelihood Estimation of Participant Reliability in social sensing". 8th International Workshop
on Data Management for Sensor Networks (DMSN11) in Conjunction with VLDB , Seattle, WA, August 2011.
Qing Cao, Dong Wang, Tarek Abdelzaher, Bodhi Priyantha, Jie Liu, Feng Zhao, "Energy-optimal Batching
Periods for Asynchronous Multistage Data Processing on Sensor Nodes: Foundations and an mPlatform Case Study",
16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2010)
Stockholm, Sweden, April, 2010 (Best Paper Award) .
Jin Heo, Praveen Jayachandran, Insik Shiny, Dong Wang, Tarek Abdelzaher, and Xue Liu,
"OptiTuner: An Automatic Distributed Performance Optimization Service and its Application to a Server Farm
Energy Minimization Case Study", IEEE Transactions on Parallel and Distributed Systems, 2010.
Qing Cao, Dong Wang, Tarek Abdelzaher, "End-User Diagnosis of Communication Paths in Sensor Network
Systems". The 38th International Conference on Parallel Processing (ICPP-2009) (Acceptance rate: 32%)
Jin Heo, Praveen Jayachandran, Insik Shiny, Dong Wang, and Tarek Abdelzaher "OptiTuner: An Automatic
Distributed Performance Optimization Service and a Server Farm Application" , FeBID: Fourth International
Workshop on Feedback Control Implementation and Design in Computing Systems and Networks 2009.
Jianwei Wang, Dong Wang, TIMO Korhonen, Yuping Zhao, "A Novel Anti-Collision Protocol in Multiple
Readers RFID Sensor Networks", Chinese Journal of Sensors and Actuators, Vol.21 No.8, 2008
Dong Wang, Zimin Liu, Yuping Zhao, Yan Luo, "An Efficient Multiplexing Scheme to Improve the
Capacity of VoWLAN", Journal of Peking University, Vol. 1 No. 3, Sept. 30, 2006
Dong Wang, Zimin Liu, Yuping Zhao, Yan Luo, "A Novel Unicast Multiplexing Scheme to Guarantee the
QoS of VoWLAN" , IEEE GLOBECOM Conference, Nov.27-Dec.1, 2006 , San Francisco, USA
Dong Wang, Jianwei Wang Yuping Zhao, "A Novel Solution to the Reader Collision Problem in RFID System",
IEEE 2nd International Conference on Wireless Communications, Networking and Mobile Computing, Sept.22-24, 2006,
Jianwei Wang, Dong Wang, Yuping Zhao, "A Novel Anti-Collision Algorithm with Dynamic Tag Number Estimation",
IEEE International Conference on Communication Technology, Nov. 27-30, 2006, Gulin, China
Jianwei Wang, Dong Wang, Yuping Zhao, "Multi-path Combining Scheme for ISI Suppression in DS UWB System",
IEEE 2nd International Conference on Wireless Communications, Networking and Mobile Computing,
Sept. 22-24, 2006, WuHan China
Dong Wang, Kun Qiu, Licun Wang, "A New DBA Algorithm in EPON Upstream Channel in support of SLA",
Journal on Communications of China, Vol. 26 No. 6, June, 2005
Dong Wang, Kun Qiu, Kicun Wang, "A QoS supportive MAC Design in EPON System",
Journal of University of Electronic Scince and Techonlogy of China, Vol.33 No.6, Dec, 2004
Licun Wang, Kun Qiu, Dong Wang, "Design of MAC of Fixed Frame Size of EPON",
Journal of University of Electronic Scince and Techonlogy of China , Vol.33 No.6, Dec, 2004
Note: In Computer Science, conference publications are as competitive as journals.
Tool and Demo
Our research work also generated a reliable information distillation tool called Apollo that is used to summarize the flow of important events that are fundamentally changing our world and lives (e.g., Egyptian uprising, Japanese nuclear disaster, Hurricane Sandy, Boston Marathon Explosion etc.). Apollo was demoed to very high-level army personnel (e.g., Mr. Gary Martin, the Executive Deputy to the Commanding General, Dr. Thomas Russell, the Director of Army Research Lab), as well as the United States Army Intelligence and Security Command (INSCOM). Apollo was selected as one of very few top showcases of the Network Science Collaborative Technology Alliance founded by the U.S. Army Research Laboratory (ARL) in 2011, 2012, and 2013. Now Apollo is now used by different branches at ARL.