Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing

Details Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing

2011-11-22

arxiv.org/abs/1111.5080v2

Computer Science

Networking and Internet Architecture

Scientific paper

Collaborative spectrum sensing has been recognized as a promising approach to improve the sensing performance via exploiting the spatial diversity of the CR users. Such kind of cooperation, however, might be easily disrupted by the selfish users, especially for the distributed collaborative sensing. In this study, we identify a new kind of selfish behavior in collaborative sensing. Specifically, the selfish user could pretend to be an honest one by claiming a duplicated or slightly modified sensing report from others as a new one. This selfish behavior may significantly reduce the spatial diversity of the sensing reports and thus degrade the performance of collaborative spectrum sensing. We denote such a selfishness issue as Entropy Selfishness. Since it represents a challenge to decide whether a sensing report is a fresh one, entropy selfishness makes the existing incentive schemes designed for conventional wireless networks (e.g., mobile ad hoc networks) unsuitable for Cognitive Radio Networks (CRNs). To thwart entropy selfishness in distributed collaborative sensing, we propose a novel Puzzle based Message Masking scheme (PMM). In this scheme, instead of directly sharing the sensing results, each secondary user constructs and declares a sensing puzzle, masking its real sensing results. For any other user who intends to obtain the sensing results, he needs to firstly solve this puzzle by using his own sensing results as the puzzle keys. With this approach, an entropy selfish user who fails to sense the spectrum will be prevented from obtaining other secondary users' sensing results. Furthermore, we also propose an advanced PMM to defense against partial entropy selfishness, in which the user may guess the real sensing reports by only scanning a part of the spectrum. Lastly, we demonstrate the efficiency and effectiveness of the proposed schemes via extensive USRP-based simulation.

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