Lei Yang
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NSF-1559696: WiFiUS: Social Structure for Cooperative Mobile Networking

Project Description

To meet the rapidly growing demand of mobile data traffic, regulatory agencies around the world are actively working on policies and regulations for dynamic spectrum access that are mutually beneficial to the cognitive devices and the licensed spectrum users of the under-utilized spectrum. One of the primary contributors to the explosive mobile traffic growth is the rapid proliferation of mobile social applications. One key observation is that, since mobile networks are designed and deployed to meet the social needs of humans, connections and behaviors of people in the social domain shape the ways in which they access mobile services. With this insight, this project advocates a social-aware approach to enable shared spectrum access, cooperative spectrum sensing and intelligent device-to-device (D2D) communications, by leveraging the social structure among mobile users. Such social trust-based cooperation among mobile devices enables self-organizing networking, and has the potential to achieve substantial gains in spectral efficiency and lead to significant increases in network capacity. By combining theoretical studies with practical applications, this project aims to integrate social elements into the design of cooperative mobile networks, thereby accelerating the evolution of future mobile networks.

Under the common theme of exploiting the social structure for cooperative mobile networking, this project is organized into four well-coordinated thrusts: 1) Thrust I focuses on social recommendation-aided dynamic spectrum access by exploring the collective wisdom of secondary users for distributed spectrum sharing; 2) Thrust II investigates social-enhanced D2D communications; 3) Thrust III designs and analyzes collaboration protocols among secondary users; 4) Thrust IV studies social assisted information dissemination in mobile networks. The proposed research is expected to enable a paradigm shift from traditional approaches to social-aware approaches to enable shared spectrum access, cooperative spectrum sensing and intelligent device-to-device (D2D) communications, via exploiting the social structure among mobile users. The broader impacts also include educational elements, such as promoting diversity by providing research opportunities to woman and underrepresented students.

Personnel

  • Lei Yang, PI at University of Nevada Reno

  • Junshan Zhang, PI at Arizona State University

  • Amir Ghasemkhani, PhD Student at University of Nevada Reno

Publications

  • X. Chen, X. Gong, L. Yang, and J. Zhang, “Amazon in the white space: Social recommendation aided distributed spectrum access,” IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 536-549, 2017.

  • X. Chen, X. Gong, L. Yang, and J. Zhang, “Exploiting social tie structure for cooperative wireless networking: A social group utility maximization framework,” IEEE/ACM Transactions on Networking, vol. 24, no. 6, pp. 3593-3606, 2016.

  • M. Zhang, L. Yang, X. Gong, and J. Zhang, “Privacy-preserving crowdsensing: privacy valuation, network effect, and profit maximization,” in Proc. IEEE GLOBECOM 2016, Washington, DC, USA, 2016.

  • M. Zhang, L. Yang, X. Gong, and J. Zhang, “Impact of network effect and congestion effect on price competition among wireless service providers,” in Proc. IEEE CISS 2016, Princeton, NJ, USA, 2016.

  • D. Shin, S. He, and J. Zhang. “Joint sensing task and subband allocation for largescale spectrum profiling,” in Proc. IEEE INFOCOM 2015, Kowloon, Hong Kong, 2015.

  • L. Yang, M. Zhang, S. He, M. Li, and J. Zhang, “Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing,” in ACM Mobihoc 2018, Los Angeles, USA, 2018. (Acceptance rate 16.9%)

  • B. Hutchins, A. Reddy, W. Jin, M. Zhou, M. Li, and L. Yang, “Beat-PIN: A user authentication mechanism for wearable devices through secret beats,” in ACM ASIACCS 2018, Incheon, Korea, 2018. (Acceptance rate 20%)

  • W. Jin, M. Li, L. Guo, and L. Yang, “DPDA: A differentially private double auction scheme for mobile crowd sensing,” in Proc. IEEE CNS 2018, Beijing, China, 2018.

  • A. Ghasemkhani and L. Yang, “Reinforcement learning based pricing for demand response,” in Proc. IEEE ICC 2018 Workshop-ICT4SG, Kansas, USA, 2018.

  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, and H. Xu “Adaptive neural network based intelligent secondary control for microgrids,” in Proc. IEEE TPEC 2018, College Station, TX, 2018.

  • A. Ghasemkhani, V. Sarfi, L. Yang, and H. Livani, “Decentralized dynamic state estimation with missing and delayed PMU measurements,” in Proc. IEEE PES T&D 2018, Denver, USA, 2018.

  • M. Zhang, L. Yang, X. Gong, S. He, and J. Zhang, “Wireless Service Pricing Competition under Network Effect, Congestion Effect, and Bounded Rationality,” IEEE Transactions on Vehicular Technology, vol. PP, no. 99, pp. 1-1. doi: 10.1109/TVT.2018.2822843.

  • X. An, X. Zhou, X. Lv, F. Lin, and L. Yang, “Sample selected extreme learning machine based intrusion detection in fog computing and MEC,” Wireless Communications and Mobile Computing, vol. 2018, 10 pages, 2018