Tsinghua Science and Technology

SPECIAL SECTION ON INTERNET OF THINGS

  • Efficient Top/Bottom-k Fraction Estimation in Spatial Databases Using Bounded Main Memory

    Jinbao Wang;Zhuojun Duan;Xixian Han;Donghua Yang;

    Spatial databases store objects with their locations and certain types of attached items. A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects,such as searching points of interest, hot topics, or users' attitude in specified spatial regions. In many scenarios,the high and low-frequency items in a spatial region are worth noticing, considering they represent the majority's interest or eccentric users' opinion. However, existing works have yet to identify such items in an interactive manner, despite the significance of the endeavor in decision-making systems. This study recognizes a novel type of analytical query, called top/bottom-k fraction query, to discover such items in spatial databases. To achieve fast query response, we propose a multilayered data summary that is spread out across the main memory and external memory. A memory-based estimation method for top/bottom-k fraction queries is proposed. To maximize the use of the main memory space, we design a data summary tuning method to dynamically allocate memory space among different spatial partitions. The proposed approach is evaluated with real-life datasets and synthetic datasets in terms of estimation accuracy. Evaluation results demonstrate the effectiveness of the proposed data summary and corresponding estimation and tuning algorithms.

    2022年02期 v.27 223-234页 [查看摘要][在线阅读][下载 651K]
    [下载次数:28 ] |[网刊下载次数:0 ] |[引用频次:0 ] |[阅读次数:9 ]
  • Efficient Publication of Distributed and Overlapping Graph Data Under Differential Privacy

    Xu Zheng;Lizong Zhang;Kaiyang Li;Xi Zeng;

    Graph data publication has been considered as an important step for data analysis and mining. Graph data, which provide knowledge on interactions among entities, can be locally generated and held by distributed data owners. These data are usually sensitive and private, because they may be related to owners' personal activities and can be hijacked by adversaries to conduct inference attacks. Current solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed manners. Therefore, this work proposes a novel framework for distributed graph publication. In this framework, differential privacy is applied to justify the safety of the published contents. It includes four phases, i.e., graph combination, plan construction sharing,data perturbation, and graph reconstruction. The published graph selection is guided by one data coordinator, and each graph is perturbed carefully with the Laplace mechanism. The problem of graph selection is formulated and proven to be NP-complete. Then, a heuristic algorithm is proposed for selection. The correctness of the combined graph and the differential privacy on all edges are analyzed. This study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

    2022年02期 v.27 235-243页 [查看摘要][在线阅读][下载 326K]
    [下载次数:34 ] |[网刊下载次数:0 ] |[引用频次:2 ] |[阅读次数:6 ]
  • Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning

    Kainan Zhang;Zhi Tian;Zhipeng Cai;Daehee Seo;

    The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods, aiming at learning a continuous vector space for the graph, which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations. Graph embedding methods build an important bridge between social network analysis and data analytics, as social networks naturally generate an unprecedented volume of graph data continuously. Publishing social network data not only brings benefit for public health, disaster response, commercial promotion, and many other applications, but also gives birth to threats that jeopardize each individual's privacy and security. Unfortunately, most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks. To be specific, attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding. In this paper, we propose a novel link-privacy preserved graph embedding framework using adversarial learning, which can reduce adversary's prediction accuracy on sensitive links, while persevering sufficient non-sensitive information, such as graph topology and node attributes in graph embedding. Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

    2022年02期 v.27 244-256页 [查看摘要][在线阅读][下载 823K]
    [下载次数:29 ] |[网刊下载次数:0 ] |[引用频次:2 ] |[阅读次数:6 ]
  • Implementation of Abstract MAC Layer Under Jamming

    Yifei Zou;Minghui Xu;Dongxiao Yu;Liandong Chen;Shaoyong Guo;Xiaoshuang Xing;

    In the past decades, with the widespread implementation of wireless networks, such as the Internet of Things, an enormous demand for designing relative algorithms for various realistic scenarios has arisen. However,with the widening of scales and deepening of network layers, it has become increasingly challenging to design such algorithms when the issues of message dissemination at high levels and the contention management at the physical layer are considered. Accordingly, the abstract medium access control(absMAC) layer, which was proposed in2009, is designed to solve this problem. Specifically, the absMAC layer consists of two basic operations for network agents: the acknowledgement operation to broadcast messages to all neighbors and the progress operation to receive messages from neighbors. The absMAC layer divides the wireless algorithm design into two independent and manageable components, i.e., to implement the absMAC layer over a physical network and to solve higher-level problems based on the acknowledgement and progress operations provided by the absMAC layer, which makes the algorithm design easier and simpler. In this study, we consider the implementation of the absMAC layer under jamming. An efficient algorithm is proposed to implement the absMAC layer, attached with rigorous theoretical analyses and extensive simulation results. Based on the implemented absMAC layer, many high-level algorithms in non-jamming cases can be executed in a jamming network.

    2022年02期 v.27 257-269页 [查看摘要][在线阅读][下载 835K]
    [下载次数:19 ] |[网刊下载次数:0 ] |[引用频次:0 ] |[阅读次数:6 ]

SPECIAL SECTION ON AI POWERED SERVICE OPTIMIZATION FOR EDGE/FOG COMPUTING

  • Artificial Intelligence for Edge Service Optimization in Internet of Vehicles: A Survey

    Xiaolong Xu;Haoyuan Li;Weijie Xu;Zhongjian Liu;Liang Yao;Fei Dai;

    The Internet of Vehicles(IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resourceintensive cloud platform for service implementation. Edge Computing(EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled Io V, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence(AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

    2022年02期 v.27 270-287页 [查看摘要][在线阅读][下载 506K]
    [下载次数:150 ] |[网刊下载次数:0 ] |[引用频次:22 ] |[阅读次数:6 ]
  • Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR: A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks

    Mohammad R.Khosravi;Sadegh Samadi;

    This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing.Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources) in all weathers. First,an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed.Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms, Internet of Multimedia Things(IoMT), Video-SAR data processing issues, and real-world applications are investigated.

    2022年02期 v.27 288-302页 [查看摘要][在线阅读][下载 887K]
    [下载次数:92 ] |[网刊下载次数:0 ] |[引用频次:5 ] |[阅读次数:5 ]
  • Research Advances on AI-Powered Thermal Management for Data Centers

    Hui Liu;Abdus Salam Aljbri;Jie Song;Jinqing Jiang;Chun Hua;

    Given the complex nature of data centers' thermal management, which costs too many resources,processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence(AI) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines(VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy.This paper concludes with four issues in thermal management, which will be the scope of further research.

    2022年02期 v.27 303-314页 [查看摘要][在线阅读][下载 606K]
    [下载次数:66 ] |[网刊下载次数:0 ] |[引用频次:0 ] |[阅读次数:6 ]
  • A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing

    Chao Yan;Yankun Zhang;Weiyi Zhong;Can Zhang;Baogui Xin;

    In the mobile edge computing environments, Quality of Service(QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

    2022年02期 v.27 315-324页 [查看摘要][在线阅读][下载 705K]
    [下载次数:149 ] |[网刊下载次数:0 ] |[引用频次:20 ] |[阅读次数:6 ]
  • Heart-Rate Analysis of Healthy and Insomnia Groups with Detrended Fractal Dimension Feature in Edge

    Xuefei Wang;Yichao Zhou;Chunxia Zhao;

    Insomnia, whether situational or chronic, affects over a third of the general population in today's society.However, given the lack of non-contact and non-inductive quantitative evaluation approaches, most insomniacs are often unrecognized and untreated. Although Polysomnographic(PSG) is considered as one of the assessment methods, it is poorly tolerated and expensive. In this paper, with the recent development of Internet-of-Things devices and edge computing techniques, we propose a detrended fractal dimension(DFD) feature for the analysis of heart-rate signals, which can be easily acquired by many wearables, of good sleepers and insomniacs. This feature was derived by calculating the fractal dimension(FD) of detrended signals. For the trend component removal, we improved the null space pursuit algorithm and proposed an adaptive trend extraction algorithm. The experimental results demonstrated the efficacy of the proposed DFD index through numerical statistics and significance testing for healthy and insomnia groups, which renders it a potential biomarker for insomnia assessment and management.

    2022年02期 v.27 325-332页 [查看摘要][在线阅读][下载 365K]
    [下载次数:40 ] |[网刊下载次数:0 ] |[引用频次:0 ] |[阅读次数:6 ]

REGULAR ARTICLES

  • Secure Scheme for Locating Disease-Causing Genes Based on Multi-Key Homomorphic Encryption

    Tanping Zhou;Wenchao Liu;Ningbo Li;Xiaoyuan Yang;Yiliang Han;Shangwen Zheng;

    Genes have great significance for the prevention and treatment of some diseases. A vital consideration is the need to find a way to locate pathogenic genes by analyzing the genetic data obtained from different medical institutions while protecting the privacy of patients' genetic data. In this paper, we present a secure scheme for locating disease-causing genes based on Multi-Key Homomorphic Encryption(MKHE), which reduces the risk of leaking genetic data. First, we combine MKHE with a frequency-based pathogenic gene location function. The medical institutions use MKHE to encrypt their genetic data. The cloud then homomorphically evaluates specific gene-locating circuits on the encrypted genetic data. Second, whereas most location circuits are designed only for locating monogenic diseases, we propose two location circuits(TH-intersection and Top-q) that can locate the disease-causing genes of polygenic diseases. Third, we construct a directed decryption protocol in which the users involved in the homomorphic evaluation can appoint a target user who can obtain the final decryption result. Our experimental results show that compared to the JWB+17 scheme published in the journal Science, our scheme can be used to diagnose polygenic diseases, and the participants only need to upload their encrypted genetic data once,which reduces the communication traffic by a few hundred-fold.

    2022年02期 v.27 333-343页 [查看摘要][在线阅读][下载 947K]
    [下载次数:41 ] |[网刊下载次数:0 ] |[引用频次:1 ] |[阅读次数:10 ]
  • Improved Heuristic Job Scheduling Method to Enhance Throughput for Big Data Analytics

    Zhiyao Hu;Dongsheng Li;

    Data-parallel computing platforms, such as Hadoop and Spark, are deployed in computing clusters for big data analytics. There is a general tendency that multiple users share the same computing cluster. The schedule of multiple jobs becomes a serious challenge. Over a long period in the past, the Shortest-Job-First(SJF) method has been considered as the optimal solution to minimize the average job completion time. However, the SJF method leads to a low system throughput in the case where a small number of short jobs consume a large amount of resources. This factor prolongs the average job completion time. We propose an improved heuristic job scheduling method, called the Densest-Job-Set-First(DJSF) method. The DJSF method schedules jobs by maximizing the number of completed jobs per unit time, aiming to decrease the average Job Completion Time(JCT) and improve the system throughput. We perform extensive simulations based on Google cluster data. Compared with the SJF method, the DJSF method decreases the average JCT by 23.19% and enhances the system throughput by 42.19%.Compared with Tetris, the job packing method improves the job completion efficiency by 55.4%, so that the computing platforms complete more jobs in a short time span.

    2022年02期 v.27 344-357页 [查看摘要][在线阅读][下载 2089K]
    [下载次数:56 ] |[网刊下载次数:0 ] |[引用频次:1 ] |[阅读次数:7 ]
  • Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features

    Tianlei Wang;Jiuwen Cao;Ru Xu;Jianzhong Wang;

    Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization. Thus, designing a round-the-clock intelligent surveillance system has become crucial and urgent. In this study, we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array, an Analog-to-Digital Converter(ADC)module(ADS1274), and an industrial processor Advanced RISC Machine(ARM) cortex-A8 for signal collection and algorithm implementation. Then, a novel Statistical Time-Frequency acoustic Feature(STFF) is proposed, and a fast Extreme Learning Machine(ELM) is adopted as the classifier. Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features. In addition, the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.

    2022年02期 v.27 358-371页 [查看摘要][在线阅读][下载 1623K]
    [下载次数:31 ] |[网刊下载次数:0 ] |[引用频次:1 ] |[阅读次数:5 ]
  • An Axiom System of Probabilistic Mu-Calculus

    Wanwei Liu;Junnan Xu;David N.Jansen;Andrea Turrini;Lijun Zhang;

    Mu-calculus(a.k.a.μTL) is built up from modal/dynamic logic via adding the least fixpoint operator μ.This type of logic has attracted increasing attention since Kozen's seminal work.PμTL is a succinct probabilistic extension of the standard μTL obtained by making the modal operators probabilistic.Properties of this logic,such as expressiveness and satisfiability decision,have been studied elsewhere.We consider another important problem:the axiomatization of that logic.By extending the approaches of Kozen and Walukiewicz,we present an axiom system for PμTL.In addition,we show that the axiom system is complete for aconjunctive formulas.

    2022年02期 v.27 372-385页 [查看摘要][在线阅读][下载 381K]
    [下载次数:33 ] |[网刊下载次数:0 ] |[引用频次:0 ] |[阅读次数:7 ]
  • Nonnegative Matrix Tri-Factorization Based Clustering in a Heterogeneous Information Network with Star Network Schema

    Juncheng Hu;Yongheng Xing;Mo Han;Feng Wang;Kuo Zhao;Xilong Che;

    Heterogeneous Information Networks(HINs) contain multiple types of nodes and edges; therefore, they can preserve the semantic information and structure information. Cluster analysis using an HIN has obvious advantages over a transformation into a homogenous information network, which can promote the clustering results of different types of nodes. In our study, we applied a Nonnegative Matrix Tri-Factorization(NMTF) in a cluster analysis of multiple metapaths in HIN. Unlike the parameter estimation method of the probability distribution in previous studies,NMTF can obtain several dependent latent variables simultaneously, and each latent variable in NMTF is associated with the cluster of the corresponding node in the HIN. The method is suited to co-clustering leveraging multiple metapaths in HIN, because NMTF is employed for multiple nonnegative matrix factorizations simultaneously in our study. Experimental results on the real dataset show that the validity and correctness of our method, and the clustering result are better than that of the existing similar clustering algorithm.

    2022年02期 v.27 386-395页 [查看摘要][在线阅读][下载 361K]
    [下载次数:42 ] |[网刊下载次数:0 ] |[引用频次:1 ] |[阅读次数:5 ]
  • Asymmetric Deep Hashing for Person Re-Identifications

    Yali Zhao;Yali Li;Shengjin Wang;

    The person re-identification(re-ID) community has witnessed an explosion in the scale of data that it has to handle. On one hand, it is important for large-scale re-ID to provide constant or sublinear search time and dramatically reduce the storage cost for data points from the viewpoint of efficiency. On the other hand, the semantic affinity existing in the original space should be preserved because it greatly boosts the accuracy of re-ID. To this end, we use the deep hashing method, which utilizes the pairwise similarity and classification label to learn deep hash mapping functions, in order to provide discriminative representations. More importantly, considering the great advantage of asymmetric hashing over the existing symmetric one, we finally propose an asymmetric deep hashing(ADH) method for large-scale re-ID. Specifically, a two-stream asymmetric convolutional neural network is constructed to learn the similarity between image pairs. Another asymmetric pairwise loss is formulated to capture the similarity between the binary hashing codes and real-value representations derived from the deep hash mapping functions,so as to constrain the binary hash codes in the Hamming space to preserve the semantic structure existing in the original space. Then, the image labels are further explored to have a direct impact on the hash function learning through a classification loss. Furthermore, an efficient alternating algorithm is elaborately designed to jointly optimize the asymmetric deep hash functions and high-quality binary codes, by optimizing one parameter with the other parameters fixed. Experiments on the four benchmarks, i.e., DukeMTMC-reID, Market-1501, Market-1501+500 k,and CUHK03 substantiate the competitive accuracy and superior efficiency of the proposed ADH over the compared state-of-the-art methods for large-scale re-ID.

    2022年02期 v.27 396-411页 [查看摘要][在线阅读][下载 499K]
    [下载次数:42 ] |[网刊下载次数:0 ] |[引用频次:4 ] |[阅读次数:6 ]
  • PrivBV: Distance-Aware Encoding for Distributed Data with Local Differential Privacy

    Lin Sun;Guolou Ping;Xiaojun Ye;

    Recently, local differential privacy(LDP) has been used as the de facto standard for data sharing and analyzing with high-level privacy guarantees. Existing LDP-based mechanisms mainly focus on learning statistical information about the entire population from sensitive data. For the first time in the literature, we use LDP for distance estimation between distributed data to support more complicated data analysis. Specifically, we propose PrivBV—a locally differentially private bit vector mechanism with a distance-aware property in the anonymized space. We also present an optimization strategy for reducing privacy leakage in the high-dimensional space. The distance-aware property of PrivBV brings new insights into complicated data analysis in distributed environments.As study cases, we show the feasibility of applying PrivBV to privacy-preserving record linkage and non-interactive clustering. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed scheme.

    2022年02期 v.27 412-421页 [查看摘要][在线阅读][下载 880K]
    [下载次数:48 ] |[网刊下载次数:0 ] |[引用频次:4 ] |[阅读次数:5 ]
  • Automatic Modulation Recognition Based on CNN and GRU

    Fugang Liu;Ziwei Zhang;Ruolin Zhou;

    Based on a comparative analysis of the Long Short-Term Memory(LSTM) and Gated Recurrent Unit(GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio(SNR),instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network(CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.

    2022年02期 v.27 422-431页 [查看摘要][在线阅读][下载 783K]
    [下载次数:187 ] |[网刊下载次数:0 ] |[引用频次:30 ] |[阅读次数:7 ]
  • Effective Algorithms to Detect Stepping-Stone Intrusion by Removing Outliers of Packet RTTs

    Lixin Wang;Jianhua Yang;Michael Workman;Pengjun Wan;

    An effective method to detect stepping-stone intrusion(SSI) is to estimate the length of a connection chain.This type of detection method is referred to as a network-based detection approach. Existing network-based SSI detection methods are either ineffective in the context of the Internet because of the presence of outliers in the packet round-trip times(RTTs) or inefficient, as many packets must be captured and processed. Because of the high fluctuation caused by the intermediate routers on the Internet, it is unavoidable that the RTTs of the captured packets contain outlier values. In this paper, we first propose an efficient algorithm to eliminate most of the possible RTT outliers of the packets captured in the Internet environment. We then develop an efficient SSI detection algorithm by mining network traffic using an improved version of k-Means clustering. Our proposed detection algorithm for SSI is accurate, effective, and efficient in the context of the Internet. Well-designed network experiments are conducted in the Internet environment to verify the effectiveness, correctness, and efficiency of our proposed algorithms. Our experiments show that the effective rate of our proposed SSI detection algorithm is higher than 85.7% in the context of the Internet.

    2022年02期 v.27 432-442页 [查看摘要][在线阅读][下载 331K]
    [下载次数:32 ] |[网刊下载次数:0 ] |[引用频次:1 ] |[阅读次数:6 ]
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