Database-driven Dynamic Spectrum Sharing (DSS) is the de-facto technical paradigm adopted by Federal Communications Commission (FCC) for increasing spectrum efficiency. In such a system, a geo-location database administrator (DBA) maintains spectrum availability information over its service region whereby to determines whether a secondary user can access a licensed spectrum band at his desired location and time. To maintain spectrum availability in its service region, it is desirable for the DBA to periodically collect spectrum measurements whereby to construct and maintain a Radio Environment Map (REM), where the received signal strength at every location of interest is either directly measured or estimated via proper statistical spatial interpolation techniques. Crowdsourcing-based spectrum sensing is a promising approach for periodically collecting spectrum measurements over a large geographic area, which is, unfortunately, vulnerable to false spectrum measurements. How to construct an accurate REM in the presence of false measurements remains an open challenge. This paper introduces SecREM, a novel scheme for securely constructing a REM in the presence of false spectrum measurements. SecREM relies on a small number of trusted spectrum measurements whereby to evaluate the trustworthiness of the measurements from mobile
users and gradually incorporate the most trustworthy ones to construct an accurate REM. Extensive simulation studies based on a real spectrum measurement dataset confirm the efficacy and efficiency of SecREM.
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A Spatiotemporal Approach for Secure Crowdsourced Radio Environment Map Construction
Database-driven Dynamic Spectrum Sharing (DSS) is the de-facto technical paradigm adopted by Federal Communications Commission for increasing spectrum efficiency, which allows licensed spectrum to be opportunistically used by secondary users. In database-driven DSS, a geo-location database administrator (DBA) maintains spectrum availability information over its service region in the form of a Radio Environment Map (REM), where the received signal strength from the primary user at every location is either directly measured via spectrum sensing or estimated via statistical spatial interpolation. Crowdsourcing-based spectrum sensing is a promising approach for periodically collecting spectrum measurements over a large geographic area but is unfortunately vulnerable to false spectrum measurements. Despite a large body of prior work on secure cooperative spectrum sensing, how to construct an accurate REM in the presence of false measurements remains an open challenge. In this paper, we introduce ST-REM, a novel spatiotemporal approach for securely constructing an REM in the presence of false spectrum measurements. Inspired by the self-label techniques developed for semi-supervised learning, ST-REM iteratively constructs an REM from a small number of spectrum measurements from trusted anchor sensors and many more measurements from mobile users. During each iteration, the DBA evaluates the trustworthiness of each measurement by jointly considering its spatial fitness with other trusted measurements and the mobile user's long-term behavior. By gradually incorporating the most trustworthy spectrum measurements, the DBA is able to construct a REM with high accuracy. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of ST-REM.
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- NSF-PAR ID:
- 10172910
- Date Published:
- Journal Name:
- IEEE/ACM Transactions on Networking
- ISSN:
- 1063-6692
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism.more » « less
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Database-driven Dynamic Spectrum Sharing (DSS) is a promising technical paradigm for enhancing spectrum efficiency by allowing secondary user to opportunistically access licenced spectrum channels without interfering with primary users' transmissions. In database-driven DSS, a geo-location database administrator (DBA) maintains the spectrum availability in its service region in the form of a radio environment map (REM) and grant or deny secondary users' spectrum access requests based on primary users' activities. Crowdsourcing-based spectrum sensing has great potential in improving the accuracy of the REM at the DBA but requires strong incentives and privacy protection to simulate mobile users' participation. To tackle this challenge, this paper introduces a novel differentially-private reverse auction mechanism for crowdsourcing-based spectrum sensing. The proposed mechanism allows the DBA to select spectrum sensing participants under a budget constraint while offering differential bid privacy, approximate truthfulness, and approximate accuracy maximization. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of the proposed mechanism.more » « less
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Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.more » « less
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It takes great effort to manually or semi-automatically convert free-text phenotype narratives (e.g., morphological descriptions in taxonomic works) to a computable format before they can be used in large-scale analyses. We argue that neither a manual curation approach nor an information extraction approach based on machine learning is a sustainable solution to produce computable phenotypic data that are FAIR (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016). This is because these approaches do not scale to all biodiversity, and they do not stop the publication of free-text phenotypes that would need post-publication curation. 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Fig. 1 shows the system diagram of the platform. The presentation will consist of: a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). 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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TNET.2020.2992939
