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Nan Li
Researcher at University of Massachusetts Lowell
Publications - 6
Citations - 485
Nan Li is an academic researcher from University of Massachusetts Lowell. The author has contributed to research in topics: Wireless network & Visual sensor network. The author has an hindex of 6, co-authored 6 publications receiving 472 citations.
Papers
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Proceedings ArticleDOI
Analysis of a Location-Based Social Network
Nan Li,Guanling Chen +1 more
TL;DR: This study is the first large-scale quantitative analysis of a real-world commercial LSN service and presents results of data analysis over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations.
Journal ArticleDOI
Sharing location in online social networks
Nan Li,Guanling Chen +1 more
TL;DR: This article collected 21 months of data traces from a commercial LSN and analyzed its users' location-sharing updates and found that the characteristics of the users' privacy protection behavior is correlated with their age, gender, mobility, and geographic region.
Proceedings ArticleDOI
Malware propagation in online social networks: nature, dynamics, and defense implications
TL;DR: This comprehensive study uses extensive trace-driven simulation to study the impact of initial infection, user click probability, social structure, and activity patterns on malware propagation in online social networks.
Journal ArticleDOI
Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments
TL;DR: Empirical evaluation of the performance of streaming camera images over wireless networks in both residential and office environments and the feasibility of using wireless backbones for camera surveillance systems found that the automatic camera hand-off enabled by SICS was effective for continuous camera monitoring.
Proceedings ArticleDOI
Measurement study on wireless camera networks
Nan Li,Bo Yan,Guanling Chen +2 more
TL;DR: It is observed that the suitable choices of several network parameters are often dependent on the radio environment, and network-layer throughput is not sufficient to predict the performance of camera applications, so cross-layer joint network optimizations must be tailored to meet the specific requirements of the applications using distributed cameras.