Detecting and Localizing Spectrum Offenders Using Crowdsourcing

NSF grant #1564287




Software defined radio (SDR) is emerging as a key technology to satisfy rapidly increasing data rate demands on the nation's mobile wireless networks while ensuring coexistence with other spectrum users. When SDRs are in the hands and pockets of average people, it will be easy for a selfish user to alter his device to transmit and receive data on unauthorized spectrum, or ignore priority rules, making the network less reliable for many other users. Further, malware could cause an SDR to exhibit illegal spectrum use without the user's awareness. The FCC has an enforcement bureau which detects interference via complaints and extensive manual investigation. The mechanisms used currently for locating spectrum offenders are time consuming, human-intensive, and expensive. A violator's illegal spectrum use can be too temporary or too mobile to be detected and located using existing processes. This project envisions a future where a crowdsourced and networked fleet of spectrum sensors deployed in homes, community and office buildings, on vehicles, and in cell phones will detect, identify, and locate illegal use of the spectrum across a wide areas and frequency bands. This project will investigate and test new privacy-preserving crowdsourcing methods to detect and locate spectrum offenders. New tools to quickly find offenders will discourage users from illegal SDR activity, and enable recovery from spectrum-offending malware. In short, these tools will ensure the efficient, reliable, and fair use of the spectrum for network operators, government and scientific purposes, and wireless users. New course materials and demonstrations for use in public outreach will be developed on the topics of wireless communications, dynamic spectrum access, data mining, network security, and crowdsourcing.


There are several challenges the project will address in the development of methods and tools to find spectrum offenders. First, the project will enable localization of offenders via crowdsourced spectrum measurements that do not decode the transmitted data and thus preserve users'; data and identity privacy. Second, the crowd-sourced sensing strategy will implicitly adapt to the density of traffic and explicitly adapt to focus on suspicious activity. Next, the sensing strategy will stay within an energy budget, and have incentive models to encourage participation, yet have sufficient spatial and temporal coverage to provide high statistical confidence in detecting illegal activity. Finally, the developed methods will be evaluated using both simulation and extensive experiments, to quantify performance and provide a rich public data set for other researchers.

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