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Channel allocation and routing strategy in multi-radio multi-channel wireless sensor networks

Ruan Jian Xue Bao/Journal of Software 2014年 25卷 103-112页

作者： Ni, Lin-Yu Li, Jin-Bao School of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

To address the problems of transmission delay, transmission conflicts and low throughput in Wireless sensor networks, this paper proposes a channel allocation and routing strategy in Multi-Radio Multi-Channel Networks. The strategy is to dynamically establish a k-ary n-cube topology. It not only uses the optimal static channel allocation algorithm to improve the throughput of nodes but also employs the dimension order routing algorithm to reduce transmission conflicts. This method is suitable for the network of density nodes with many communication conflicts. It can also be used in both single hop and multi hop network environment. The experiment results show that compared with traditional methods, the channel allocation and routing strategy based on the k-ary n-cube topology can effectively reduce end to end delay, network conflicts and energy consumption, prolong the network lifetime and increase the throughput of network. ©2014 ISCAS.

关键词： Throughput

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A Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift

A Bidirectional Representation Recommendation Based On Relev...

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IEEE International Conference on Web Services (ICWS)

作者： Nan Wang Kun Li Xuefeng Jiang Jinbao Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China Shandong Artificial Intelligence Institute School of Mathematics and Statistics Qilu University of Technology Jinan China

The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users’ real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user’s historical interaction and the user’s new interest very weak. Meanwhile, the information contained in user’s sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user’s new interest from neighbors’ sessions. Then, we integrate the relevance information into user’s history sessions, which can effectively enhance the correlation between user’s history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users’ sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://***/LucasZen/paper_code.

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Cross-layer design for multi-power multi-radio wireless sensor networks

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2011年第SUPPL. 2期31卷 114-118页

作者： Guo, Xiao-Hang Li, Jin-Bao School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

In this paper, we investigate on the optimal routing jointly scheduling, channel and power assignment in multi-power multi-radio WSNs. We first formulate the optimal routing as a linear programming problem, and then design a polynomial time heuristic algorithm. Experiments show that the proposed cross-layer routing significantly reduce the energy consumption and the end-to-end transmission delay.

关键词： Wireless sensor networks

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Cross-level Relational Graph Contrastive Learning for Bundle Recommendation

Cross-level Relational Graph Contrastive Learning for Bundle...

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IEEE International Conference on Web Services (ICWS)

作者： Nan Wang Jiabao Sun Jinbao Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang University Harbin China Shandong Artificial Intelligence Institute School of Mathematics and Statistics Qilu University of Technology Jinan China

Different from the traditional recommendation system which regards a single item as an atomic unit, the purpose of bundle recommendation is to recommend a set of items to users. In recent years, some work often uses the model based on GNN to solve the bundle recommendation problem. However, compared with the traditional recommendation models, the GNN-based bundle recommendation has a more serious data sparse problem, which significantly reduces the performance of the model. Meanwhile, due to the lack of modeling ability of complex interactive information and dependent information, it is still far from enough to learn the representation of users and bundles. In view of the advantages of contrast learning in mining its own supervisory signals, we propose a model named Cross-level relational graph Contrastive Learning for Bundle Recommendation (CL 2 BRec). Different from previous contrastive learning methods, our method learns representations of users and bundles from multiple relational graphs of different levels through a crossview contrastive learning paradigm. Specifically, we regard the user-item/bundle graph as a local interaction relation view, the bundle-item graph as a local dependency relation view, and the user-bundle-item tripartite graph as a global structure view to build a cross-level bundle recommendation model. Extensive experiments conducted on two benchmark datasets demonstrate that CL 2 BRec outperforms the state-of-the-art methods by over 12.56% and 8.6%, respectively.

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Anti-theft technology based on wireless sensor network

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Ruan Jian Xue Bao/Journal of Software 2011年第SUPPL. 1期22卷 111-121页

作者： Duan, Jin-Sheng Guo, Long-Jiang Liu, Yong Zhu, Jing-Hua Department of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150001 China

There are shortcomings in traditional anti-theft technology which can not satisfy a user's demand. The common ways of anti-theft are limited in wireless sensor networks. On the topic of the shadowing effect in wireless communication, this paper introduces a anti-theft technology based on wireless sensor networks. System can check intrusion by analyzing received signal strengths. This paper establishes a framework of anti-theft system and studies the key technologies. By defining the graph model in anti-theft system, the agent distribution problem is abstracted into edges partitioned by vertices problem. B-EPV algorithm is proposed for obtaining the optimal solution. Blade algorithm is designed for solving k-mode set problem to get steady status of link. This paper constructs experimental environment with real motes and verifies the performance of the system. The result shows low false positives and false negatives in the system. © Copyright 2011, Editorial Department of Journal of Software. All rights reserved.

关键词： Wireless sensor networks

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Hybrid Recommendation Algorithm Based on Social Trust Clustering

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 21-31页

作者： Zhu, Jing-Hua Wang, Chao Ma, Sheng-Chao School of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang Province Key Laboratory for Database and Parallel Computing Harbin150080 China

Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Collaborative filtering

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GPU acceleration of finding LPRs in DNA sequence based on SUA index

GPU acceleration of finding LPRs in DNA sequence based on SU...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Shufang Du Longjiang Guo Chunyu Ai Meirui Ren Hao Qu Jinbao Li School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang China Heilongjiang University Harbin Heilongjiang CN Division of Math & Computer Science University of South Carolina Upstate Spartanburg SC USA Key Laboratory of Database and Parallel Computing. Harbin Heilongiian China

ISBN: (纸本)9781479975761

The repetitions in biological sequence analysis are of great biological significance. Finding the repetitions has been a hot topic in gene projects naturally. In recent years, graphics processing unit (GPU) has been far exceeded the CPU in terms of computing capability and memory bandwidth, especially CUDA dramatically increases in computing performance by harnessing the power of the GPUs. This paper proposes efficient parallel algorithms on CUDA to accelerate finding PTRs which is redefined as LPRs based on the SUA Index. The proposed parallel algorithms have been utilized with the parallel primitives offered by Thrust library and the effective parallel bit compression technology based on division to achieve better acceleration. Optimization techniques include CUDA streams technology are also realized to reduce transmission latency. Experimental results show that the proposed parallel algorithms are faster than the benchmark with 1.6~5.4 speedup.

关键词： Graphics processing units parallel algorithms Arrays Acceleration Sorting Algorithm design and analysis DNA

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Tracking Nodes Selection Algorithm Based on Voronoi Structure in Sensor Networks

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Ruan Jian Xue Bao/Journal of Software 2017年 28卷 30-38页

作者： Ren, Qian-Qian Li, Jin-Bao Sun, Bei-Bei Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China Key Laboratory of Electronic Engineering Colleges of Heilongjiang Province Heilongjiang University Harbin150080 China School of Computer Science and Technology Heilongjiang University Harbin150080 China

Energy saving and the tracking performance are two important issues in moving target tracking. This paper presents a Voronoi structure-based nodes selection algorithm, which constructs a network model based on the property of Voronoi, and selects sensor nodes which are more close to the target to participate in tracking. This paper also presents a nodes scheduling mechanism, which minimizes the number of awaking nodes with tracking quality guarantee. Finally, a set of simulation experiments are made to analyze the effects of various parameters on the network performance. Experimental results show the excellent performance of the proposed algorithm in terms of energy saving and tracking quality. © Copyright 2017, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Target tracking

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GPU acceleration of finding frequent patterns over large biological sequence

GPU acceleration of finding frequent patterns over large bio...

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International Conference on parallel and Distributed Systems (ICPADS)

作者： Shufang Du Longjiang Guo Chunyu Ai Jinbao Li Meirui Ren Yahong Guo School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang China Key Laboratory of Database and Parallel Computing Harbin Heilongjiang China Division of Math & Computer Science University of South Carolina Upstate Spartanburg SC USA School of Information Science and Technology Heilongjiang University Harbin Heilongjiang China

Biological frequent patterns usually correspond to the important function (or structure) in biological sequences. Along with the rapid growth of biological sequences, it is significant to find frequent patterns over a large bio-sequence efficiently. However, most of existing algorithms need to produce lots of short patterns or projected databases, which influence the efficiency badly and also increase the cost of space. Graphics processing units (GPUs) embracing many core computing devices, have been extensively applied to accelerate computation performance in many areas. In order to meet the demand of biologists, we redefine the frequent pattern problem with length constraints for finding frequent patterns. We present pruning optimization method for the serial algorithm (POSA), and based on this technique, we propose a parallel algorithm (POPA) which not only reduces the time complexity with a low space cost but also obtains better performance on CUDA. To validate the presented algorithms, we implemented the algorithms on multiple-core CPU and various GPU devices. Also, CUDA optimization techniques are applied to speed up calculation in the paper. Finally, experimental results show that compared with the serial algorithm on CPU with six cores, POSA achieves 1.2~4.5 speedup, and POPA gains 3~20 speedup.

关键词： Arrays Graphics processing units Biology Data mining Indexes parallel algorithms

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keywords generation algorithm based on bipartite graph

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Tongxin Xuebao/Journal on Communications 2011年第9 A期32卷 146-152页

作者： Yang, Yan Zhu, Jing-Hua Jin, Hu Li, Wei Department of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin 150080 China

To improve the efficiency of keywords generation, a bipartite graph based keywords generation(BGKG) algorithm was proposed. It generated keywords based on search engine logs and built a bipartite graph between query terms and the clicked URLs. It took into account the rank of the URLs in result pages and the order of users' clicking. Experiments were done with real query logs. The results show that keywords generated by BGKG can satisfy the needs of enterprise clients and BGKG is more efficient than other keyword generation algorithms.

关键词： Search engines

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