Award Abstract # 1318748
NeTS: Small: Collaborative Research: Distributed Robust Spectrum Sensing and Sharing in Cognitive Radio Networks

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY
Initial Amendment Date: August 19, 2013
Latest Amendment Date: August 19, 2013
Award Number: 1318748
Award Instrument: Standard Grant
Program Manager: wenjing lou
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2013
End Date: September 30, 2016 (Estimated)
Total Intended Award Amount: $304,840.00
Total Awarded Amount to Date: $304,840.00
Funds Obligated to Date: FY 2013 = $304,840.00
History of Investigator:
  • Yingying Chen (Principal Investigator)
    yingche@scarletmail.rutgers.edu
  • Yi Guo (Co-Principal Investigator)
Recipient Sponsored Research Office: Stevens Institute of Technology
ONE CASTLE POINT ON HUDSON
HOBOKEN
NJ  US  07030-5906
(201)216-8762
Sponsor Congressional District: 08
Primary Place of Performance: Stevens Institute of Technology
NJ  US  07030-5991
Primary Place of Performance
Congressional District:
08
Unique Entity Identifier (UEI): JJ6CN5Y5A2R5
Parent UEI:
NSF Program(s): Networking Technology and Syst
Primary Program Source: 01001314DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7923
Program Element Code(s): 736300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The future Cognitive Radio Networks (CRNs) will consist of heterogeneous devices such as smartphones, tablets and laptops moving dynamically. Accurate and robust spectrum sensing and identification of unauthorized spectrum usage are essential components of spectral efficiency in future radio systems. This project aims to utilize consensus-based cooperation featuring self-organizable and scalable network structure to capture the swarming behaviors of spectrum users and providing cooperative spectrum sensing in a fully distributed manner. By using a combination of control theory and machine learning techniques, the project designs secure weighted average consensus for cooperative spectrum sensing that can not only capture the swarming behaviors in CRNs with heterogeneous devices, but also is robust to practical channel conditions. Robust localization approaches are developed grounded on dynamic signal strength mapping, which have the capability to localize multiple malicious users. Additionally, the new techniques are validated using an actual testbed with on-campus deployment and system demonstration to industrial collaborators. The integration of control theory with dynamic spectrum access will enable a new revolution in the way for enhancing spectrum efficiency in CRNs. The project serves as a pioneer in exploiting multi-disciplinary knowledge (e.g., control systems and machine learning techniques) to achieve a more efficient spectrum usage in future radio systems, aiming to alleviate the increasing crowdness of the spectrum occupancy and support the co-existence of heterogeneous devices. This project also carries out a broad range of education and outreach activities to encourage students to pursue careers in the fields of science and engineering. Research results will be disseminated to academia and industry through presentations and publications in meetings, conferences and journals.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Guoru Ding, Jinlong Wang, Qihui Wu, Fei Song, Yingying Chen "Spectrum Sensing in Opportunity-Heterogeneous Cognitive Sensor Networks: How to Cooperate?" IEEE Sensors Journal , v.13 , 2013 , p.4247 1530-437X
Guoru Ding, Qihui Wu, Yu-Dong Yao, Jinlong Wang, Yingying Chen "Kernel-based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions" IEEE Signal Processing Magazine , v.30 , 2013 , p.126 1053-5888
Hongbo Liu, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, Fan Ye "Accurate WiFi Based Localization for Smartphones Using Peer Assistance" IEEE Transactions on Mobile Computing (IEEE TMC) , 2014 10.1109/TMC.2013.140
Tao Shu, Yingying Chen, and Jie Yang "Protecting Multi-Lateral Localization Privacy in Pervasive Environments" IEEE/ACM Transactions on Networking , v.23 , 2015 , p.1688
Wenlin Zhang, Yi Guo, Hongbo Liu, Yingying Chen, Zheng Wang, Joseph Mitola III "Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing" IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS) , 2014 http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2307951
Xiuyuan Zheng, Jie Yang, Yingying Chen and Hui Xiong "An Adaptive Framework Coping with Dynamic Target Speed for Device-Free Passive Localization" IEEE Transactions on Mobile Computing , v.14 , 2015 , p.1138

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project builds a framework that performs distributed robust spectrum sensing and sharing to enhance the spectrum efficiency in cognitive radio networks (CRNs) by exploiting a combination of control theory and machine learning techniques. Our proposed framework takes the unique view point of utilizing the characteristics of radio propagation and the spatial pattern of received signal energy, which is hard to falsify, as the basis for three main research directions: (1) developing consensus-based cooperative spectrum sensing; (2) identifying unauthorized spectrum usage based on sensing results, and (3) localizing malicious users after unauthorized spectrum usage detection. Such a framework could benefit a broad array of applications to facilitate dynamic spectrum access ranging from primary communication detection, to interference avoidance, to detecting unauthorized users, and to building a spectrum map. The proposed framework has resulted in the following major accomplishments.

(1) Distributed Consensus-based Cooperative Spectrum Sensing. To make the distributed spectrum sensing in CRNs more robust to practical channel conditions and link failures, we develop cooperative spectrum sensing based on weighted average consensus. This approach is fully distributed since the combining process only involves local one-hop communication without a centralized fusion center. The weighted combining makes the final decision statistics adaptive with respect to the measurement channel conditions and is robust with respect to communication channel failures and corrupted readings from compromised devices.

(2) Detecting Unauthorized Spectrum Usage. Based on the results from cooperative spectrum sensing, we utilize the characteristics of radio propagation to detect the existence of unauthorized spectrum usage in CRNs. In particular, the spatial pattern of received signal energy is exploited by multiple spectrum sensors (i.e., secondary users) to collaboratively detect the anomaly. We use machine learning techniques to identify the presence of the unauthorized users under various practical conditions when the (authorized/unauthorized) users are either static or mobile.

(3) Localizing Malicious Spectrum Users. Detecting the presence of unauthorized spectrum usage provides first order information towards defending against malicious spectrum users. Learning the physical location of the malicious users allows us to further exploit a wide range of defense strategies. For instance, one can cope with a reckless secondary user by localizing it and then neutralizing it through human intervention. We develop robust localization mechanisms grounded on dynamic signal strength mapping that have the capability to localize multiple unauthorized or malicious users, and are resistant to corrupted signal measurements.

The integration of control theory with dynamic spectrum access will enable a new revolution in the way for enhancing spectrum efficiency in CRNs. The proposed framework will serve as a pioneer in exploiting multi-disciplinary knowledge (e.g., control-based theory and machine learning techniques) to achieve a more efficient spectrum usage in future radio systems, which aims to alleviate the increasing crowdness of the spectrum occupancy and support the co-existence of heterogeneous devices. Broader impacts of this project include the collaboration with industry, ECE department weekly seminar, an embedded systems course module, training for 2 PhD students with one completed dissertation so far, and outreach to the general public by featuring into various news outlets. In particular, the PI’s group has established collaboration with AT&T Labs, AT&T Chief Security Office, and IBM T. J. Watson Research Center. Our industrial collaborators are informed of the latest development of the new techniques, and they can utilize their expertise to help validating the practical needs of our work and explore the possibility of technology transfer of the project. Students in the course of CPE/EE556 "Computing Principles for Embedded Systems" have the opportunity to conduct course projects which are related to the research tasks in this project. Moreover, through the management of the regular ECE departmental seminars, we coordinate and help to invite researchers from both academia and industry to give talks about cutting-edge research topics once every week. Our speakers are prime candidates to exchange new research ideas, explore the collaboration, and validate my current work. Furthermore, we expect that these seminars will provide a vehicle to present project results to students, faculty and experts from local industry. 

 


Last Modified: 12/23/2016
Modified by: Yingying Chen

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