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AN ACCURATE PREDICTION MODEL FOR computational overheadS OF SECURITY MECHANISMS IN WIRELESS SENSOR NETWORKS

Journal of Electronics(China) 2009年第5期26卷 699-705页

作者： Liu Wei Luo Rong Yang Huazhong Department of Electronic Engineering Tsinghua University Beijing 100084 China

In Wireless Sensor Networks (WSNs), it is necessary to predict computational overheads of security mechanisms without final implementations to provide guidelines for system design. This paper presents an accurate and flexible model to predict overheads of these mechanisms. This model is based on overheads of basic operations frequently used in cryptography algorithms, which are essential elements of security mechanisms. Several popular cryptography algorithms and security mechanisms are evaluated using this model. According to simulation results, relative prediction errors are less than 7% for most cryptography algorithms and security mechanisms.

关键词： Wireless Sensor Network (WSN) Cryptography algorithm Security mechanism computational overhead

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Out of sight, out of mind: a distance-aware forgetting strategy for adaptive random testing

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Science China(Information Sciences) 2017年第9期60卷 78-98页

作者： Chengying MAO Tsong Yueh CHEN Fei-Ching KUO School of Software and Communication Engineering Jiangxi University of Finance and Economics Department of Computer Science and Software Engineering Swinburne University of Technology

Adaptive random testing(ART) achieves better failure-detection effectiveness than random testing by increasing the diversity of test cases. However, the intention of ensuring even spread of test cases inevitably causes an overhead problem. Although two basic forgetting strategies(i.e. random forgetting and consecutive retention) were proposed to reduce the computation cost of ART, they only considered the temporal distribution of test cases. In the paper, we presented a distance-aware forgetting strategy for the fixed size candidate set version of ART(DF-FSCS), in which the spatial distribution of test cases is taken into consideration. For a given candidate, the test cases out of its "sight" are ignored to reduce the distance computation cost. At the same time, the dynamic adjustment for partitioning and the second-round forgetting are adopted to ensure the linear complexity of DF-FSCS algorithm. Both simulation analysis and empirical study are employed to investigate the efficiency and effectiveness of DF-FSCS. The experimental results show that DF-FSCS significantly outperforms the classical ART algorithm FSCS-ART in efficiency, and has comparable failure-detection effectiveness. Compared with two basic forgetting methods, DF-FSCS is better in both efficiency and effectiveness. In contrast with a typical linear-time ART algorithm RBCVT-Fast, our algorithm requires less computational overhead and exhibits the similar failure-detection capability. In addition, DF-FSCS has more reliable performance than RBCVT-Fast in detecting failures for the programs with high-dimensional input domain.

关键词： adaptive random testing software testing test cases computational overhead diversity

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