SWIM (Social, WIreless, and Mobile Computing)



SWIM is shorten for the Social, Wireless, and Mobile Computing Group at Nanjing University. We develop novel technologiesa and algorithms for wireless networks, mobile cellphone systems, and social network applications. We are particularly interested in big data analysis for large-scale social networks, applying machine learning techniques for wireless network optimizations, and smart sensing techniques with cellphones to detect human behaviors and emotions.


Research Topics

  • Mobile Cloud Computing
  • Introduction:

    Nowadays, mobile devices such as smartphones and tablets are quickly becoming the prominent computing and communication platform, which allows billions of people to interact with each other and access to diverse resources and information through wireless networks efficiently and ubiquitously. The convergence of mobile computing and cloud computing forms the Mobile Cloud Computing (MCC) paradigm, which provides a uniform platform for resource-limited mobile devices to access a pool of virtualized resources, and enables a range of new computation-intensive and content-centric applications such as healthcare, online video gaming, and crowd sensing. This project focuses on the key mechanisms of cooperative resource offloading for mobile cloud computing in order to reduce energy consumption and system overhead. First, we propose efficient dynamic context measurement and application partition methods, which forms the context information model and data flow graph model for mobile applications. Then based on the derived models, we study the key techniques of resource offloading, which includes multi-level cooperation for resource sharing and multi-objective optimization for resource offloading decision. Finally, we combine the above techniques to form a comprehensive resource offloading system for mobile cloud and evaluate its performance using smartphones. The result of our research can be applied to a wide range of scenarios such as mobile device augmentation, distributed content distribution, massive storage, mobile big data processing and analysis.


    • Wenzhong Li, Yanchao Zhao, Sanglu Lu, Daoxu Chen, Mechanisms and Challenges on Mobility-augmented Service Provisioning for Mobile Cloud Computing, IEEE Communications Magazine, vol 53, issue 3, pp 89-97, Mar 2015. (Full Text)

    • Xu Chen, Lei Jiao, Wenzhong Li, Xiaoming Fu, Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing, IEEE/ACM Transactions on Networking (ToN), Vol 24, issue 5, Oct 26, 2016, Pages 2795 - 2808. (Full Text)

    • Wenzhong Li, Yuefei Hu, Xiaoming Fu, Sanglu Lu, Daoxu Chen, Cooperative Positioning and Tracking in Disruption Tolerant Networks, IEEE Transactions on Parallel and Distributed Systems, vol 26, issue 2, pp 382-391, Feb 2015. (Full Text)

    • Yaoyu Wang, Wenzhong Li, Sanglu Lu, The Capability of Error Correction for Burst-noise Channels Using Error Estimating Code, in IEEE International Conference on Sensing, Communication and Networking (SECON 2016), London, UK, 27-30 Jun, 2016.

    • Senyuan Tan, Xiaoliang Wang, Guido Maier, Wenzhong Li, Riding Quality Evaluation Through Mobile CrowdSensing, in IEEE International Conference on Pervasive Computing and Communications (PerCom 2016), Sydney, Australia, Mar 14-18, 2016.

    • Shuai Yu, Rami Langar, Wenzhong Li, Xu Chen, Coalition-based Energy Efficient Offloading Strategy for Immersive Collaborative Applications in Femto-Cloud, IEEE International Conference on Communications (ICC 2016), Kuala Lumpur, Malaysia, 23-27 May 2016.

  • Social Networks Analysis
  • Introduction:

    With the wide adoption of Internet technologies, electronic devices and web- based platforms, numerous data for individuals, public entities and enterprises has been created and some made available for research and even business development. Data-driven research � so-called big data � has attracted a lot of interests from both academia and industry. Examples of such data sources include public corporate data, wireless device mobility and vehicular data, online social network sites, e-Business, public health fora, or data from other Internet platforms.

    More and more Innovative applications creating and/or using these big data are being developed and deployed, changing our everyday life, such as work, business, transport, logistics, entertainment and health. They would provide new opportunities to discover and exploit the patterns of mobility and interactions of not only traditional networked devices but also the daily human beings which until now have been to us just “un-networked things. Social computing is a research area leveraging these opportunities where related researchers from computer science, complex systems, sociology, social psychology, communications science, business management, and other fields like healthcare and ecology, may work together to understand and possibly improve our digitalized societies.


    • Xiao Chen, Charles Shang, Britney Wong, Wenzhong Li, Suho Oh, Efficient Multicast Algorithms in Opportunistic Mobile Social Networks using Community and Social Features, Computer Networks, Available online 26 July 2016, ISSN 1389-1286, http://dx.doi.org/10.1016/j.comnet.2016.07.007. (Full Text)

    • Bo-Lei Zhang, Zhu-Zhong Qian, Wen-Zhong Li, Bin Tang, Sang-Lu Lu, Xiaoming Fu, Budget Allocation for Maximizing Viral Advertising in Social Networks, Journal of Computer Science and Technology, July 2016, Volume 31, Issue 4, pp 759-775. (Full Text)

    • Ming Chen, Wenzhong Li, Xu Chen, Zhuo Li, Sanglu Lu, and Daoxu Chen, LPPS: A Distributed Cache Pushing Based K-Anonymity Location Privacy Preserving Scheme, Mobile Information Systems, vol. 2016, Article ID 7164126, 16 pages, 2016. doi:10.1155/2016/7164126. (Full Text)

    • Qisen Wang, Wenzhong Li, Xiao Zhang, Sanglu Lu, Academic Paper Recommendation Based on Community Detection in Citation-Collaboration Networks, The 18th Asia Pacific Web Conference (APWeb 2016), Sep 23-23, Suzhou, China, 2016.

    • Bolei Zhang, Zhuzhong Qian, Wenzhong Li, Sanglu Lu,Pricing Strategies for Maximizing Viral Advertising in Social Networks, The 20th International Conference on Database Systems for Advanced Applications (DASFAA'15), Hanoi, Vietnam, April 20-23, 2015.

  • Learning-based Wireless Network Optimization
  • Introduction:

    Nowadays wireless network infrastructure such as radio towers, femtocells, WiFi routers, and wireless Access Points (APs), are densely deployed to support simultaneous communication for the booming number of mobile devices. Interference is one of the most important factors to influence the quality of wireless communication links. Wireless network optimization aims to improve network performance by optimizing wireless spectrum resources allocation to reduce overall interference via techniques such as link scheduling, medium access control, and channel allocation. Such optimization heavily relies on the proper representation and assessment of network interference situation, which is still a challenging task for dynamic large-scale wireless networks.

    The conflict graph provided a graphical representation of the interference condition between wireless links, which had been widely adopted in the works of wireless network optimization. we propose a conflict graph embedding approach to assess network interference situations by representing the wireless nodes with low-dimensional vectors while preserving their conflict relationships at the same time. Our approach introduces a sliding-window based partial measurement method to sample the interference measurements in the network; adopts a learning algorithm to obtain the vector representation of the nodes; and infers the interference situations by exploring the feature vectors. The proposed approach has been proved to be low measurement overhead, low computational cost, and self-adaptive, which is suitable for large-scale dynamic wireless networks.


    • Wenzhong Li, Jinggong Zhang, Yanchao Zhao, Conflict Graph Embedding for Wireless Network Optimization, in IEEE Conference on Computer Communications (INFOCOM 2017), Atlanta, GA, USA, 1-4 May 2017.

    • Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu Lu, Quantized Conflict Graphs for Wireless Network Optimization, in IEEE Conference on Computer Communications (INFOCOM 2015), Hong Kong, Apr 26-30, 2015.

  • Emotion Detection with Smartphone/Social Sensing
  • Introduction:

    Emotion detection in online social networks benefits many applications such as recommendation systems, personalized advertisement services, etc. Traditional sentiment or emotion analysis mainly address polarity prediction or single label classification, while ignore the co-existence of emotion labels in one instance. We address the multiple emotion detection problem in online social networks, and formulate it as a multi-label learning problem. By making observations to an annotated Twitter dataset, we discover that multiple emotion labels are correlated and influenced by social network relationships. Based on the observations, we propose a model to incorporate emotion labels and social correlations into a unified framework, and solve the emotion detection problem by a multi-label learning algorithm.


    • Xiao Zhang, Wenzhong Li, Hong Huang, Cam-Tu Nguyen, Xu Chen, Xiaoliang Wang, Sanglu Lu, Predicing Happiness State Based on Emotion Representative Mining in Online Social Networks, in The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Jeju, South Korea, May 23-26, 2017.

    • Xiao Zhang, Wenzhong Li, Sanglu Lu, Emotion detection in online social network based on multi-label learning, in the 22nd International Conference on Database Systems for Advanced Applications (DASFAA 2017), Suzhou, China, March 27-30, 2017.

  • Dynamic Barcode for Visual Light Communication
  • Optimization Techniques for Multipath Data Transmission

  • Guideline for SWIMers

    Last updated:  Nov. 17th, 2008.