Good News in 2022:

• Our paper "Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing" has been accepted by IEEE Transactions on Network Science and Engineering.
• Our paper "TAFS: A Truthful Auction for IoT Application Offloading in Fog Computing Networks" has been accepted by IEEE Internet of Things Journal.

Good News in 2021:

• Our paper "IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes" has been accepted by IEEE INFOCOM2022.

Good News in 2019:

• Ruozhou Yu joined North Carolina State University as a tenure-track Assistant Professor of Computer Science.
  Congratulations to Ruozhou!

Good News in 2018:

• Dr. Jian Tang got elevated to IEEE Fellow, for contributions to optimization in wireless networks and mobile crowdsourcing systems.
  Jian was the Outstanding PhD Graduate of CS@ASU in 2006 and a recipient of NSF Career Award in 2008.
  Congratulations to Jian!

• Our paper, ``Application Provisioning in Fog Computing-enabled Internet-of-Things: A Network Perspective", authored by
  Ruozhou Yu, Guoliang Xue, and Xiang Zhang, was one of the 9 finalists for Best Paper Award at IEEE INFOCOM'2018,
  and invited for fast-track review to IEEE/ACM Transactions on Networking.
  Considering that there were 309 accepted papers among 1606 submissions, making to the final 9 candidates is not easy.
  Congratulations to Ruozhou and Xiang!

• United States Patent No. US 9996803, "Systems and Methods for Authenticating a User through an Unobservable Re-authentication System";
  was issued to Lingjun Li, Xinxin Zhao, and Guoliang Xue on June 12, 2018.
  This patent was based on our NDSS'2013 paper, which uses machine learning on data from fingerprint on touch screens to authenticate users.
  This patent officially made me an inventor.
  Congratulations to Lingjun and Xinxin!

• I was invited to give a Keynote Lecture at IFIP WWIC'2018, titled "Payment Channel Networks for Blockchain-based Cryptocurrencies".
  The talk is based on Ruozhou Yu's recent work on blockchain.

  My students make me look better than I am. To all of my students: Thank You!