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Practical Swarm Optimization based Fault-Tolerance Algorithm for the Internet of Things
( Shiliang Luo ),( Lianglun Cheng ),( Bin Ren ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.4
The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.
Practical Swarm Optimization based Fault-Tolerance Algorithm for the Internet of Things
( Shiliang Luo ),( Lianglun Cheng ),( Bin Ren ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.3
The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.
Zhang Weiwen,Cheng Lianglun,Huang Guoheng 한국유전학회 2021 Genes & Genomics Vol.43 No.10
Background Population stratifcation modeling is essential in Genome-Wide Association Studies. Objective In this paper, we aim to build a fne-scale population stratifcation model to efciently infer individual genetic ancestry. Methods Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratifcation model, together with parameter optimization. We explore diferent PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly fnd the optimal number of principal components, kernel function for PCA and parameters of the random forest. Results Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively. Conclusion With the optimal parameter confguration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants.
A Regular Expression Matching Algorithm Based on High-Efficient Finite Automaton
Wang, Jianhua,Cheng, Lianglun,Liu, Jun Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.2
Aiming to solve the problems of high memory access and big storage space and long matching time in the regular expression matching of extended finite automaton (XFA), a new regular expression matching algorithm based on high-efficient finite automaton is presented in this paper. The basic idea of the new algorithm is that some extra judging instruments are added at the starting state in order to reduce any unnecessary transition paths as well as to eliminate any unnecessary state transitions. Consequently, the problems of high memory access consumption and big storage space and long matching time during the regular expression matching process of XFA can be efficiently improved. The simulation results convey that our proposed scheme can lower approximately 40% memory access, save about 45% storage space consumption, and reduce about 12% matching time during the same regular expression matching process compared with XFA, but without degrading the matching quality.
Compressed Sensing-based Multiple-target Tracking Algorithm for Ad Hoc Camera Sensor Networks
( Xu Lu ),( Lianglun Cheng ),( Jun Liu ),( Rongjun Chen ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.3
Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.
A Regular Expression Matching Algorithm Based on High-Efficient Finite Automaton
Jianhua Wang,Lianglun Cheng,Jun Liu 한국정보과학회 2014 Journal of Computing Science and Engineering Vol.8 No.2
Aiming to solve the problems of high memory access and big storage space and long matching time in the regular expression matching of extended finite automaton (XFA), a new regular expression matching algorithm based on high-efficient finite automaton is presented in this paper. The basic idea of the new algorithm is that some extra judging instruments are added at the starting state in order to reduce any unnecessary transition paths as well as to eliminate any unnecessary state transitions. Consequently, the problems of high memory access consumption and big storage space and long matching time during the regular expression matching process of XFA can be efficiently improved. The simulation results convey that our proposed scheme can lower approximately 40% memory access, save about 45% storage space consumption, and reduce about 12% matching time during the same regular expression matching process compared with XFA, but without degrading the matching quality.