Department of Electrical & Computer Engineering UC Davis

NSF SpecEES Project

PI: Z. Ding and L. Lai (UC Davis)

Project Overview

The overarching goal of this project is to develop a robust and secure wireless function computing framework to specifically address spectrum efficiency, energy efficiency, and security issues in wireless networks for IoT applications. The underlying principle of our framework is that in many IoT applications, instead of recovering the full data observed by various devices, the role of wireless networks is to assist certain decision making based on function computation of distributed data. Such IoT devices can send all their raw data to the decision maker, which can then compute the function of interest. However, this approach will incur a tremendous amount of unnecessary overheads since most of the raw data are discarded after the functions of interest are computed, and require more costy spectrum/energy. The main idea of this project is to develop secure and spectrum/energy efficient protocols that enable the decision maker to compute functions of interest without first recovering the full data from sensing devices. Thus, instead of being treated as mere data pipes, wireless links become an integral part of the smart decision process in IoT applications. This approach will undoubtedly improve spectrum and energy efficiency of wireless IoT systems and reduce decision delays.

Research Results
  1. Tu, Wenwen and Lai, Lifeng, ``On Function Computation With Privacy and Secrecy Constraints,'' IEEE Transactions on Information Theory, 65 (10), pp. 6716-6733, 2019

  2. Cho, Myung and Lai, Lifeng and Xu, Weiyu, ``Generalized Distributed Dual Coordinate Ascent in a Tree Network for Machine Learning,'' 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3512-3516, Brighton, UK, May 2019.

  3. Wu, Wenhao and Ding, Zhi, ``A Markovian Design of Bi-Directional Robust Header Compression for Efficient Packet Delivery in Wireless Networks'', IEEE Transactions on Wireless Communications, 18 (1), pp. 20-33, 2019.

  4. Bouchoucha, Taha and Chuah, Chen-Nee and Ding, Zhi, ``Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems'', IEEE/ACM Transactions on Networking, 27 (1), pp. 405-418, 2019