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Jammer Localization for Wireless Sensor Networks

Chinese Journal of Electronics 2023年第4期20卷 735-738页

作者： SUN Yanqiang WANG Xiaodong ZHOU Xingming National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circle-covering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.

关键词： Location awareness Wireless communication Geometry Wireless sensor networks Sensitivity Simulation Jamming

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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CHIME: A Cache-Efficient and High-Performance Hybrid Index on Disaggregated Memory 24

CHIME: A Cache-Efficient and High-Performance Hybrid Index o...

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30th ACM Symposium on Operating Systems Principles, SOSP 2024

作者： Luo, Xuchuan Shen, Jiacheng Zuo, Pengfei Wang, Xin Lyu, Michael R. Zhou, Yangfan National Key Laboratory of Parallel and Distributed Computing Changsha China School of Computer Science Fudan University Shanghai China Duke Kunshan University Kunshan China Huawei Cloud Shenzhen China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400712517

Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range indexes are widely adopted by storage systems on DM to efficiently locate and query remote data. However, existing range indexes on DM suffer from either high computing-side cache consumption or high memory-side read amplifications. In this paper, we propose CHIME, a hybrid index combining B+ trees with hopscotch hashing, to achieve low cache consumption and low read amplifications simultaneously. There are three challenges in constructing CHIME on DM, i.e., the complicated optimistic synchronization, the extra metadata access, and the read amplifications introduced by hopscotch hashing. CHIME leverages 1) a three-level optimistic synchronization scheme to synchronize read and write operations with various granularities, 2) an access-aggregated metadata management technique to eliminate extra metadata accesses by piggybacking and replicating metadata, and 3) an effective hotness-aware speculative read mechanism to mitigate the read amplifications of hopscotch hashing. Experimental results show that CHIME outperforms the state-of-the-art range indexes on DM by up to 5.1× with the same cache size and achieves similar performance with up to 8.7× lower cache consumption. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory architecture

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DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

DPSS: Dynamic Parameter Selection for Outlier Detection on D...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Ruyi Zhang Yijie Wang Haifang Zhou Bin Li Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Outlier detection on data streams identifies unusual states to sense and alarm potential risks and faults of the target systems in both the cyber and physical world. As different parameter settings of machine learning algorithms can result in dramatically different performance, automatic parameter selection is also of great importance in deploying outlier detection algorithms in data streams. However, current canonical parameter selection methods suffer from two key challenges: (i) Data streams generally evolve over time, but these existing methods use a fixed training set, which fails to handle this evolving environment and often results in suboptimal parameter recommendations; (ii) The stream is infinite, and thus any parameter selection method taking the entire stream as input is infeasible. In light of these limitations, this paper introduces a Dynamic Parameter Selection method for outlier detection on data Streams (DPSS for short). DPSS uses Gaussian process regression to model the relationship between parameters and detecting performance and uses Bayesian optimization to explore the optimal parameter setting. For each new subsequence, DPSS updates the recommended parameter setting to suit the evolving characteristics. Besides, DPSS only uses historical calculations to guide the parameter setting sampling and adjust the Gaussian process regression results. DPSS can be employed as an auxiliary plug-in tool to improve the detection performance of outlier detection methods. Extensive experiments show that our method can significantly improve the F-score of outlier detectors in data streams compared to its counterparts and obtains more superior parameter selection performance than other state-of the-art parameter selection approaches. DPSS also achieves better time and memory efficiency compared to competitors.

关键词： Training Fault diagnosis Machine learning algorithms Memory management Gaussian processes Detectors Bayes methods

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Evaluating matrix multiplication-based convolution algorithm on multi-core digital signal processors

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Guofang Keji Daxue Xuebao/Journal of national University of Defense Technology 2023年第1期45卷 86-94页

作者： Wang, Qinglin Pei, Xiangdong Liao, Linyu Wang, Haoxu Li, Rongchun Mei, Songzhu Li, Dongsheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The matrix multiplication-based convolutional algorithm, which can efficiently implement convolutions with different parameters, is the first choice of convolution performance optimization for a given chip. Based on the architecture of Phytium heterogeneous multi-core DSPs(digital signal processors) developed by national University of Defense Technology and the characteristic of the matrix multiplication-based convolutional algorithm, a parallel implementation of the matrix multiplication-based convolutional algorithm (called ftmEConv) for different convolutions on multi-core DSPs was proposed. The ftmEConv consists of four parallelized parts(input feature maps transformation, filter transformation, matrix multiplication, and output feature maps transformation), all of which were optimized for multi-core DSPs, and the performance of each part was improved by effectively exploiting the potential of all functional units in DSP cores. The experimental results demonstrate that ftmEConv achieves computational efficiency of up to 42.90%. Compared with other implementations of the matrix multiplication-based convolutional algorithm on heterogeneous chips, ftmEConv gets a speedup of up to 7.79 times. © 2023 national University of Defense Technology. All rights reserved.

关键词： Convolution

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A survey of script learning

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Frontiers of Information Technology & Electronic Engineering 2021年第3期22卷 341-373页

作者： Yi HAN Linbo QIAO Jianming ZHENG Hefeng WU Dongsheng LI Xiangke LIAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410000China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410000China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.

关键词： Script learning Natural language processing Commonsense knowledge modeling Event reasoning

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on Software Engineering and Service science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection

Smoothing Point Adjustment-Based Evaluation of Time Series A...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingyu Liu Yijie Wang Hongzuo Xu Xiaohui Zhou Bin Li Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation.

关键词： Protocols Smoothing methods Time series analysis Detectors Signal processing Distance measurement Acoustics

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Effective Anomaly Detection Based on Reinforcement Learning in Network Traffic Data 27

Effective Anomaly Detection Based on Reinforcement Learning ...

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27th IEEE International Conference on parallel and distributed Systems, ICPADS 2021

作者： Wang, Zhongyang Wang, Yijie Xu, Hongzuo Wang, Yongjun National University of Defense Technology Science and Technology on Parallel Distributed Processing Laboratory College of Computer China

ISBN: (纸本)9781665408783

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Anomaly detection

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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