NeTS: Small: Collaborative Research:
Enhancing Crowdsourced Spectrum Sensing through Sybil-proof Incentives
Reference #: CNS 1717197/1717315
Sponsor: NSF CNS Core Programs
PIs: Guoliang Xue (1717197), Dejun Yang (1717315)
Duration: 10/01/2017 - 09/30/2020
Project Description:
Database-driven dynamic spectrum sharing has been advocated by the Federal
Communications Commission as one of the most promising methods to address
the spectrum shortage and improve the spectrum utilization. In such a system,
a spectrum service provider (SSP) accepts registrations from primary users
and determines spectrum availability based on their use of spectrum.
Secondary users interested in using the spectrum are required to contact the
SSP to inquire about spectrum availability in any band of interest. Leveraging
the power of crowd-sourcing, spectrum sensing becomes a key enabler for
effectively improving the spectrum-estimation accuracy. In crowd-sourced
spectrum sensing, an SSP outsources spectrum-sensing tasks to a large number
of recruited mobile users. However, many existing incentive mechanisms for
crowd-sourcing are vulnerable to Sybil attacks, where an attacker
illegitimately forges multiple identities to gain benefits but degrades the
performance. The goal of this project is to enhance crowd-sourced spectrum
sensing by designing Sybil-proof incentive mechanisms that overcome the
shortcomings of current incentive mechanisms. This project will raise
awareness about the possible Sybil attacks in crowd-sourced spectrum
sensing systems. The anticipated results will also break new grounds in
designing incentive mechanisms for crowdsourcing-based applications.
This project will engage minority students and under-represented groups.
The proposed research activities will complement and enrich the growing
curricula at Arizona State University and Colorado School of Mines through
course development and special topic seminars.
The proposed research consists of two inter-related research thrusts.
Thrust 1 concentrates on enhancing crowd-sourced spectrum sensing through
Sybil-proof incentive mechanism design under a direct crowd-sourcing
architecture, where mobile users communicate directly with the SSP.
Upon receiving the sensing task description, each mobile user proposes a
sensing plan together with a request for reward. The SSP selects the set
of winning users and decides the corresponding payment to each winning seller.
The winning users perform the sensing tasks and receive the corresponding
rewards.
Thrust 2 concentrates on enhancing crowd-sourcing through Sybil-proof
incentive mechanism design under a hierarchical crowd-sourcing architecture,
where mobile users contribute in both spectrum sensing and solicitation.
This model subsumes the approach that won the DARPA networking challenge,
but is vulnerable to Sybil attacks. The PIs will design Sybil-proof incentive
mechanisms. The proposed research will be evaluated and validated via
simulations and testbed experiments.
PERSONNEL:
PUBLICATIONS:
- Guoliang Xue, Yinxin Wan, Xuanli Lin, Kuai Xu, Feng Wang;
“An effective machine learning based algorithm for inferring user activities from IoT device events”;
JSAC:
IEEE Journal on Selected Areas in Communications,
Vol. 40 (2022), pp. 2733-2745.
- Yinxin Wan, Xuanli Lin, Kuai Xu, Feng Wang, Guoliang Xue;
"Inferring user activities from IoT device events in smart homes: challenges and opportunities";
ICCCN'2022:
International Conference on Computer Communications and Networks;
July 25-27, 2022, Virtual Conference.
- Yinxin Wan, Kuai Xu, Feng Wang, Guoliang Xue;
"IoTMosaic: Inferring user activities from IoT network traffic in smart homes";
INFOCOM'2022:
International Conference on Computer Communications;
May 2-5, 2022, Virtual Conference.
- Alena Chang, Guoliang Xue;
"Order matters: On the impact of swapping order on an entanglement path in a quantum network";
NetSciQCom'2022:
1st International Workshop on Network Science for Quantum Communication Networks;
May 2, 2022, co-located with INFOCOM’2022.
- Jia Xu, Yuanhang Zhou, Gongyu Chen, Yuqing Ding, Dejun Yang, Linfeng Liu;
"Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network";
TIOT:
Transactions on Internet Technology;
Vol. 22 (2022), pp. 1-23.
- Jia Xu, Yuanhang Zhou, Yuqing Ding, Dejun Yang, Lijie Xu;
"Bi-objective Robust Incentive Mechanism Design for Mobile Crowdsensing";
IoT-J:
IEEE Internet of Things Journal;
Vol. 8 (2021), pp. 14971-14984.
- Jia Xu, Gongyu Chen, Yuanhang Zhou, Zhengqiang Rao, Dejun Yang, Cuihua Xie;
"Incentive Mechanisms for Large-scale Crowdsourcing Task Diffusion based on Social Influence";
TVT:
IEEE Transactions on Vehicular Technology;
Vol. 70 (2021), pp. 3731-3745.
- Lingyun Jiang, Xiaofu Niu, Jia Xu, Dejun Yang, Lijie Xu;
"Incentive Mechanism Design for Truth Discovery in Crowdsourcing with Copiers";
TSC:
Transactions on Services Computing;
Early Access
- Zhuangye Luo, Jia Xu, Pengcheng Zhao, Dejun Yang, Lijie Xu, Jian Luo;
"Towards high quality mobile crowdsensing: Incentive mechanism design based on fine-grained ability reputation";
Comput. Commun.:
Computer Communications;
Vol. 180 (2021), pp. 197-209.
- Vishnu Kilari, Ruozhou Yu, Satyajayant Misra, Guoliang Xue;
"Robust Revocable Anonymous Authentication for Vehicle to Grid Communications";
TITS:
IEEE Transactions on Intelligent Transportation Systems;
accepted for publication.
- Haiqin Wu, Liangmin Wang, Guoliang Xue, Jian Tang, Dejun Yang;
"Privacy-Preserving and Trustworthy Mobile Sensing with Fair Incentives";
ICC:
IEEE Internation Conference on Communications;
Shanghai, China, May 20-24, 2019.
- Haiqin Wu, Liangmin Wang, Guoliang Xue;
"Privacy-aware Task Allocation and Data Aggregation in Fog-assisted Spatial
Crowdsourcing";
TNSE:
IEEE Transactions on Network Science and Engineering;
to be published.
- Jia Xu, Zhengqiang Rao, Lijie Xu, Dejun Yang Tao Li;
"Incentive Mechanism for Multiple Cooperative Tasks with Compatible Users in Mobile Crowd Sensing via Online Communities";
TMC:
IEEE Transactions on Mobile Computing;
to be published.
- Jian Lin, Dejun Yang, Kun Wu, Jian Tang, Guoliang Xue;
"A Sybil-Resistant Truth Discovery Framework for Mobile Crowdsensing";
ICDCS:
IEEE International Conference on Distributed Computing;
Dallas, TX, July 7-9, 2019.
- Lingyun Jiang, Xiaofu Niu, Jia Xu, Dejun Yang, Lijie Xu;
"Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers";
ICDCS:
IEEE International Conference on Distributed Computing;
Dallas, TX, July 7-9, 2019.
- Zhibo Wang, Jiahui Hu, Ruizhao Lv, Jian Wei, Qian Wang, Dejun Yang, Hairong Qi;
"Personalized Privacy-preserving Task Allocation for Mobile Crowdsensing";
TMC:
IEEE Transactions on Mobile Computing;
Vol. 18 (2019), pp. 1330-1341.
- Z. Wang, J. Hu, J. Zhao, D. Yang, H. Chen, Q. Wang;
"Pay On-demand: Dynamic Incentive and Task Selection for Location-dependent Mobile Crowdsensing Systems";
ICDCS'2018:
IEEE International Conference on Distributed Computing Systems;
July 2-5, 2018, Vienna, Austria.
- J. Lin, M. Li, D. Yang, G. Xue;
"Sybil-Proof Online Incentive Mechanisms for Crowdsensing";
INFOCOM'2018:
IEEE International Conference on Computer Communications;
April 15-19, 2018, Honolulu, USA.
- J. Lin, D. Yang, M. Li, J. Xu, G. Xue;
"Frameworks for Privacy-Preserving Mobile Crowdsensing Incentive Mechanisms";
TMC:
IEEE Transactions on Mobile Computing;
Vol. 17(2018), pp. 1851-1864.
- J. Xu, C. Guan, H. Wu, D. Yang, L. Xu, and T. Li;
"Online Incentive Mechanism for Mobile Crowdsourcing based on Two-tiered Social Crowdsourcing Architecture";
SECON'2018:
IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks;
June 11-13, 2018, Hong Kong, China.
- X. Zhang, D. Yang, G. Xue, R. Yu, J. Tang;
"Transmitting and Sharing: a Truthful Double Auction for Cognitive Radio Networks";
ICC2018:
IEEE International Conference on Communications;
May 20-24, 2018, Kansas City, USA.
- X. Zhang, G. Xue, R. Yu, D. Yang, J. Tang;
"Countermeasures Against False-Name Attacks on Truthful Incentive Mechanisms for Crowdsourcing";
JSAC:
IEEE Journal on Selected Areas in Communications;
Vol 35(2017), pp. 478-485.
- X. Zhang, G. Xue, R. Yu, D. Yang, J. Tang;
"Robust Incentive Tree Design for Mobile Crowdsensing";
ICDCS'2017:
IEEE International Conference on Distributed Computing Systems;
June 5-8, 2017, Atlanta, USA.