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- 저자 : ( Xiaolin Gui ) (검색결과 2 건)
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A Bi-Target Based Mobile Relay Selection Algorithm for MCNs
( Huijun Dai ),( Xiaolin Gui ),( Zhaosheng Dai ),( Dewang Ren ),( Yingjie Gu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.11
Multi-hop cellular networks (MCNs) reduce the transmit power and improve the system performance. Recently, several research studies have been conducted on MCNs. The mobile relay selection scheme is a rising issue in the design of MCNs that achieves these advantages. The conventional opportunistic relaying (OR) is performed on the single factor for maximum signal-to-interference-plus-noise ratio (SINR). In this paper, a comprehensive OR scheme based on Bi-Target is proposed to improve the system throughput and reduce the relay handover by constraining the amount of required bandwidth and SINR. Moreover, the proposed algorithm captures the variability and the mobility that makes it more suitable for dynamic real scenarios. Numerical and simulation results show the superiority of the proposed algorithm in both enhancing the overall performance and reducing the handover.
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A Framework for measuring query privacy in Location-based Service
( Xuejun Zhang ),( Xiaolin Gui ),( Feng Tian ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5
The widespread use of location-based services (LBSs), which allows untrusted service provider to collect large number of user request records, leads to serious privacy concerns. In response to these issues, a number of LBS privacy protection mechanisms (LPPMs) have been recently proposed. However, the evaluation of these LPPMs usually disregards the background knowledge that the adversary may possess about users` contextual information, which runs the risk of wrongly evaluating users` query privacy. In this paper, we address these issues by proposing a generic formal quantification framework,which comprehensively contemplate the various elements that influence the query privacy of users and explicitly states the knowledge that an adversary might have in the context of query privacy. Moreover, a way to model the adversary`s attack on query privacy is proposed, which allows us to show the insufficiency of the existing query privacy metrics, e.g., k-anonymity. Thus we propose two new metrics: entropy anonymity and mutual information anonymity. Lastly, we run a set of experiments on datasets generated by network based generator of moving objects proposed by Thomas Brinkhoff. The results show the effectiveness and efficient of our framework to measure the LPPM.
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