Secure and Efficient Mobile Edge Computing in Wireless Heterogeneous Networks

This project is sponsored by NSF under grant CNS-2007995, "CNS Core: Small: Secure and Efficient Mobile Edge Computing in Wireless Heterogeneous Networks".

Overview

This project is sponsored by NSF under grant CNS-2007995, "CNS Core: Small: Secure and Efficient Mobile Edge Computing in Wireless Heterogeneous Networks".

Project Abstract

Future wireless networks will support massive energy-limited computation-constrained user equipment that are often required to execute latency sensitive yet computation-intensive tasks. Although technologies that can elevate local device computation capability and battery capacity have been substantially pushed forward, there still exists a huge gap between the high computation/processing demands and the low computation/battery capacities in user equipment. Mobile edge computing (MEC) allows user equipment to offload partial or complete computation-intensive tasks to the edge computing servers to save power and reduce latency. Inspired by recent advances in wireless technologies and challenges, the proposed research aims to explore a novel framework that can jointly consider communications and computations in a mobile edge computing-based wireless heterogeneous network to realize secure and efficient offloading and achieve desirable trade-offs among computation throughput, computation efficiency, latency, and user fairness. The proposed research activities have significant potentials to revolutionize the next generation wireless network design by jointly considering edge computations and communications in delivering secure, latency critical, computation-intensive applications such as augmented reality/virtual reality, connected and autonomous vehicle, and remote medical diagnosis. It can significantly facilitate the understanding in the field of emerging mobile edge networks, which will play a key role in the modem society to realize smart environments with computation intensive mobile applications.

The proposed research framework develops secure multiple access schemes during offloading, computation coordination and scheduling schemes for selecting user equipment and computation tasks to offload. The research will identify unique technical challenges and explore many new aspects in the mobile edge computing enabled wireless heterogenous networks, including non-orthogonal multiple access, computing offloading mode selection, success interference cancellation decoding order design, hybrid multiple access analysis, and heterogeneous MEC coordination, driven by joint consideration on security, efficiency, and user fairness. The theoretical framework formulation and analysis, engineering design guidelines for practical implementation and deployment will be obtained, and prototyping/simulation tools will be shared with the scientific research and engineering communities. The success of this project can greatly advance the understanding of the critical issues in the mobile edge computing-based wireless network design and contribute a new resource allocation framework that can remarkably improve the performance of future wireless network computing. The project will also both undergraduate and graduate students research opportunities with developing and deploying new wireless network technologies, thus serving the growing need for educating and training students, especially female students and students from underrepresented groups.

Personnel

Dr. Rose Qingyang Hu (PI and Lab Director)

Students:

  • Qun Wang (PhD student)
  • Xiang Ma (PhD student)

Collaborators on this project

  • Prof. Yi Qian, University of Nebraska-Lincoln
  • Prof. Haijian Sun, University of Wisconsin-Whitewater

Publications

Journal Papers

  1. S. Gyawali, Y. Qian and R. Q. Hu, "A Privacy-Preserving Misbehavior Detection System in Vehicular Communication Networks," in IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6147-6158, June 2021, doi: 10.1109/TVT.2021.3079385.
  2. S. Gyawali, Y. Qian and R. Q. Hu, "Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks," in IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6136-6146, June 2021, doi: 10.1109/TVT.2021.3079379.
  3. Q. Wang, L. T. Tan, R. Q. Hu and Y. Qian, "Hierarchical Energy-Efficient Mobile-Edge Computing in IoT Networks," in IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11626-11639, Dec. 2020, doi: 10.1109/JIOT.2020.3000193.
  4. Q. Wang, F. Zhou, R. Q. Hu and Y. Qian, "Energy Efficient Robust Beamforming and Cooperative Jamming Design for IRS-Assisted MISO Networks," in IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2592-2607, April 2021, doi: 10.1109/TWC.2020.3043325.

Conference Papers

  1. Y. Jiang, K. Zhang, Y. Qian, and R. Q. Hu. 2021. "Efficient and Privacy-preserving Distributed Learning in Cloud-Edge Computing Systems", In Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning (WiseML '21). Association for Computing Machinery, New York, NY, USA, 25–30. DOI:https://doi.org/10.1145/3468218.3469044
  2. X. Ma, H. Sun and R. Q. Hu, "Scheduling Policy and Power Allocation for Federated Learning in NOMA based MEC," GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1-7, doi: 10.1109/GLOBECOM42002.2020.9322270.
  3. H. Sun, Q. Wang, X. Ma, Y. Xu and R. Q. Hu, "Towards Green Mobile Edge Computing Offloading Systems with Security Enhancement," 2020 Intermountain Engineering, Technology and Computing (IETC), 2020, pp. 1-6, doi: 10.1109/IETC47856.2020.9249092.
  4. X. Ma, H. Sun, Q. Wang, and R. Q. Hu, "User Scheduling for Federated Learning Through Over-the-Air Computation." arXiv preprint arXiv:2108.02891 (2021).

Education

ECE5600 Introduction to Computer Networks

Course Objectives

This course covers all the basic and fundamental algorithms and protocols for computer networks. The scope mainly focus on TCP/IP protocols stack layers and its applications. The PI gave undergraduate students research updates on new communcations technologies such as D2D and architecures such as Cloud/Fog and Mobile Edge Computing that can enhance IoT network reliability and scalabiity.


ECE6600 Wireless and Mobile Networks

Course Objectives

This course provide students an understanding of the structure, system aspects and protocols of wireless networks and basic performance evaluation capabilities. The focus is on the generations of cellular networks, satellite networks, WirelessMANs, WirelessLANs, and Wireless PANs. The PI gave entry-level graduate students research updates on new communcations technologies such as D2D, NOMA, mmWave, massive MIMO, and architecures such as Cloud/Fog and Mobile Edge Computing that can enhance IoT network reliability and scalabiity.

Prerequisites
ECE 5600 Introduction to Computer Networks

ECE7600 Advanced Wireless Networks

Course Objectives

In this course, we will discuss issues that better define and characterize wireless links and their implications for higher-layer protocol design and optimization. Specifically, we will study the following issues: (T1) control knobs for improving wireless network capacity, including interference management, power control, physical carrier sense tuning, radio resource management, temporal/spatial diversity, and scheduling; (T2) mobility management; (T3) energy efficiency, spectral efficiency, scalability, and reliability in wireless networks; and (T4) case studies: wireless heterogeneous networks and IoT networks.

Research papers related to this project were presented as lectures in the class.

Prerequisites
ECE 5600 Introduction to Computer Networks
ECE6600 Wireless and Mobile Networks


New lecture materials are available upon request

Acknowledgments

NSF