Events

TBSI Team Won Awards in ACM SenSys 2016

December 05 2016

In  November 16th, the 14th ACM SenSys 2016 was held in  Stanford University, U.S. at which the conference committee announced that two  papers coauthored by Associate Professor Zhanglin’s team from TBSI along with the  team from Carnegie Mellon University were issued Best Demo Award and Best  Poster Runner-Up Award.

 

Campus of Stanford


The  authors of “Collaborative Localization and Navigation in Heterogeneous UAV  Swarms” that won the Best Demo Award are Carlos Ruiz (CMU), Xinlei Chen (CMU), Associate Professor Lin Zhang (Tsinghua University, TBSI), Associate Professor Pei  Zhang (CMU).

 


The  authors of “HAP-Fine-Grained Dynamic Air Pollution Map Reconstruction by Hybrid  Adaptive Particle Filter” that won the Best Poster Runner-Up Award are Xinlei  Chen (CMU), Xiangxiang Xu (Tsinghua University), Xinyu Liu (Tsinghua  University), Hae Young Noh (CMU), Associate Professor Lin Zhang (Tsinghua  University, TBSI), Pei Zhang (CMU).

 

These  two pieces of award-winning work were all instructed by Lin Zhang, Associate  Professor at Department of Electronic Engineering Tsinghua University, Vice  Co-Director of TBSI, meanwhile, they were co-conducted by Internet of Things  and Societal Cyber Physical System Laboratory, at TBSI, and Pervasive Electronic  Innovation Laboratory at Carnegie Mellon University, CMU.

 


SenSys is the most influential sensor network conference held by ACM, which is funded  by SIGCOMM, SIGMOBILE, SIGARCH, SIGOPS, SIGMETRICS, SIGBED, and other ACM  committees. It has been successfully held for fourteen consecutive times since the year of 2003, and so far, only a few papers from mainland China have been  admitted.

 

The  academic conference in the field of Computer Science is rated on a scale of A+, A, B, C, and L, in which ACM SenSys is A+ Level that belongs to top academic  meetings. ACM SenSys has provided a platform for scientific researchers from the fields of sensor and intelligent sensor system on a broad definition.