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Shiming He
Changsha, China, 41011

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Journal article
Published: 21 May 2021 in IEEE Transactions on Industrial Informatics
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Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, Sparse Tensor Factorization (STF) is a useful tool for dimension reduction to analyze High-Order, High-Dimension, and Sparse Tensor (HOHDST) data which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real-time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this paper proposes a Multiple-strategies Differential Privacy framework on STF (MDPSTF) for HOHDST network traffic data analysis. MDPSTF comprises three Differential Privacy (DP) mechanisms. Furthermore, the theoretical proof of privacy bound is presented. Hence, MDPSTF can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets (Abilene and GEANT). The experimental results show that MDPSTF has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.

ACS Style

Jin Wang; Hui Han; Hao Li; Shiming He; Pradip Kumar Sharma; Lydia Chen. Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.

AMA Style

Jin Wang, Hui Han, Hao Li, Shiming He, Pradip Kumar Sharma, Lydia Chen. Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.

Chicago/Turabian Style

Jin Wang; Hui Han; Hao Li; Shiming He; Pradip Kumar Sharma; Lydia Chen. 2021. "Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.

Journal article
Published: 06 November 2020 in IEEE Transactions on Industrial Informatics
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Intelligent anomaly detection for Key Performance Indicators (KPIs) is important for keeping services reliable in industrial-based Cyber-Physical Systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types. We propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts, we propose a Normalized version of Unequal Cross-Correlation (NUCC). To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization based alignment algorithm. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.

ACS Style

Shiming He; Zhuozhou Li; Jin Wang; Neal N. Xiong. Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems. IEEE Transactions on Industrial Informatics 2020, 17, 5799 -5809.

AMA Style

Shiming He, Zhuozhou Li, Jin Wang, Neal N. Xiong. Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems. IEEE Transactions on Industrial Informatics. 2020; 17 (8):5799-5809.

Chicago/Turabian Style

Shiming He; Zhuozhou Li; Jin Wang; Neal N. Xiong. 2020. "Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems." IEEE Transactions on Industrial Informatics 17, no. 8: 5799-5809.

Journal article
Published: 26 April 2020 in Sensors
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Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. More and more works start to apply natural language processing (NLP) methods, and in particular word2vec, in the log feature extraction. Word2vec can extract the relevance between words and vectorize the words. However, the computing cost of training word2vec is high. Anomalies in logs are dependent on not only an individual log message but also on the log message sequence. Therefore, the vector of words from word2vec can not be used directly, which needs to be transformed into the vector of log events and further transformed into the vector of log sequences. To reduce computational cost and avoid multiple transformations, in this paper, we propose an offline feature extraction model, named LogEvent2vec, which takes the log event as input of word2vec to extract the relevance between log events and vectorize log events directly. LogEvent2vec can work with any coordinate transformation methods and anomaly detection models. After getting the log event vector, we transform log event vector to log sequence vector by bary or tf-idf and three kinds of supervised models (Random Forests, Naive Bayes, and Neural Networks) are trained to detect the anomalies. We have conducted extensive experiments on a real public log dataset from BlueGene/L (BGL). The experimental results demonstrate that LogEvent2vec can significantly reduce computational time by 30 times and improve accuracy, comparing with word2vec. LogEvent2vec with bary and Random Forest can achieve the best F1-score and LogEvent2vec with tf-idf and Naive Bayes needs the least computational time.

ACS Style

Jin Wang; Yangning Tang; Shiming He; Changqing Zhao; Pradip Kumar Sharma; Osama Alfarraj; Amr Tolba. LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things. Sensors 2020, 20, 2451 .

AMA Style

Jin Wang, Yangning Tang, Shiming He, Changqing Zhao, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba. LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things. Sensors. 2020; 20 (9):2451.

Chicago/Turabian Style

Jin Wang; Yangning Tang; Shiming He; Changqing Zhao; Pradip Kumar Sharma; Osama Alfarraj; Amr Tolba. 2020. "LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things." Sensors 20, no. 9: 2451.

Conference paper
Published: 04 December 2019 in Lecture Notes in Electrical Engineering
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Simultaneous wireless information and power transfer (SWIPT) can prolong the life of wireless nodes by harvesting energy and decoding information from a same RF signal, which changes the design of energy-constrained wireless network. In multi-hop wireless network, the interference generally exists. Route, interference and SWIPT are dependent. However, the exist works consider SWIPT link resource allocation with given route or only select path for only one flow without interference. Therefore, this paper analyze the influence of interference on SWIPT, and formulate the interference-aware SWIPT routing problem. The simulation results show that it can obtain more performance gains from interference and SWIPT with more flows.

ACS Style

Shiming He; Kun Xie; Jin Wang; Dafang Zhang. Interference-Aware Routing for Multi-hop Energy-Constrained Wireless Network with SWIPT. Lecture Notes in Electrical Engineering 2019, 592 -598.

AMA Style

Shiming He, Kun Xie, Jin Wang, Dafang Zhang. Interference-Aware Routing for Multi-hop Energy-Constrained Wireless Network with SWIPT. Lecture Notes in Electrical Engineering. 2019; ():592-598.

Chicago/Turabian Style

Shiming He; Kun Xie; Jin Wang; Dafang Zhang. 2019. "Interference-Aware Routing for Multi-hop Energy-Constrained Wireless Network with SWIPT." Lecture Notes in Electrical Engineering , no. : 592-598.

Conference paper
Published: 04 December 2019 in Lecture Notes in Electrical Engineering
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Deep learning is the most useful tool for may applications, such as image recognize, nature language processing. But huge computation power and millions of parameters are needed in large models which may can’t be supported and stored. For this problem, some works tried to compress the dense weight matrices with sparse representations technologies, such as matrix decomposition and tensor decomposition. But it is still unknown which is the largest compress ratio. Therefore, in this paper, we analyse the relationship between the shape of tensor and the number of parameters, formulate the problem of minimizing the number of parameters, and solve it to find the best compress ratio. We compare the compressed ration on three data sets.

ACS Style

Shiming He; Zhuozhou Li; Jin Wang; Kun Xie; Dafang Zhang. Compressing Deep Neural Network. Lecture Notes in Electrical Engineering 2019, 625 -631.

AMA Style

Shiming He, Zhuozhou Li, Jin Wang, Kun Xie, Dafang Zhang. Compressing Deep Neural Network. Lecture Notes in Electrical Engineering. 2019; ():625-631.

Chicago/Turabian Style

Shiming He; Zhuozhou Li; Jin Wang; Kun Xie; Dafang Zhang. 2019. "Compressing Deep Neural Network." Lecture Notes in Electrical Engineering , no. : 625-631.

Conference paper
Published: 04 December 2019 in Lecture Notes in Electrical Engineering
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As the development of smart grid and energy internet, the amount of transmitted data in real time significantly increase. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation and periodicity of electricity data which is generated by human’s actions and feelings, we treat the electricity data as a tensor where the three dimensional are user, weeks, days. We divide the electricity data tensor into the sum of multiple rank-1 tensors and use the known data to approximate the electricity data tensor and recover the lost electrical data. Based on the real electricity data, we analyze the sparseness of the electricity data tensor and perform the CP decomposition-based method on the real data. The experimental results verify the recovery efficiency of the proposed scheme.

ACS Style

Shiming He; Zhuozhou Li; Jin Wang; Kun Xie; Dafang Zhang. Tensor Decomposition Based Electrical Data Recovery. Lecture Notes in Electrical Engineering 2019, 618 -624.

AMA Style

Shiming He, Zhuozhou Li, Jin Wang, Kun Xie, Dafang Zhang. Tensor Decomposition Based Electrical Data Recovery. Lecture Notes in Electrical Engineering. 2019; ():618-624.

Chicago/Turabian Style

Shiming He; Zhuozhou Li; Jin Wang; Kun Xie; Dafang Zhang. 2019. "Tensor Decomposition Based Electrical Data Recovery." Lecture Notes in Electrical Engineering , no. : 618-624.

Journal article
Published: 01 October 2019 in Electronics
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Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cloud and EC are generally communicated through groups and have special Quality of Service (QoS) and reliable requirements. Therefore, we are devoted to the problem of reliable Virtual Network Function (VNF) layout with various deployment costs in multi-source multicast. To guarantee QoS, we take into account the bandwidth, latency, and reliability constraints. Additionally, a heuristic algorithm, named Multi-Source Reliable Multicast Tree Construction (RMTC), is proposed. The algorithm aims to find a common link to place the Service Function Chain (SFC) in the multilevel overlay directed (MOD) network of the original network, so that the deployed SFC can be shared by all users, thereby improving the resource utilization. We then constructed a Steiner tree to find the reliable multicast tree. Two real topologies are used to evaluate the performance of the proposed algorithm. Simulation results indicate that, compared to other heuristic algorithms, our scheme effectively reduces the cost of reliable services and satisfies the QoS requirements.

ACS Style

Shiming He He; Kun Xie; Xuhui Zhou; Thabo Semong; Jin Wang. Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing. Electronics 2019, 8, 1106 .

AMA Style

Shiming He He, Kun Xie, Xuhui Zhou, Thabo Semong, Jin Wang. Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing. Electronics. 2019; 8 (10):1106.

Chicago/Turabian Style

Shiming He He; Kun Xie; Xuhui Zhou; Thabo Semong; Jin Wang. 2019. "Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing." Electronics 8, no. 10: 1106.

Journal article
Published: 14 September 2019 in Sensors
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The main challenges of sensing in harsh industrial and biological environments are the limited energy of sensor nodes and the difficulty of charging sensor nodes. Simultaneous wireless information and power transfer (SWIPT) is a non-invasive option to replenish energy. SWIPT harvests energy and decodes information from the same RF signal, which is influencing the design of a wireless sensor network. In multi-hop multi-flow wireless sensor networks, interference generally exists, and the interference has a different influence on SWIPT. Route, interference and SWIPT are dependent. However, existing works consider SWIPT link resource allocation with a given route or only select path for one flow without interference. Therefore, this paper firstly analyzes the influence of interference on SWIPT, and select the SWIPT routing with interference. We design an interference-based information and energy allocation model to maximize the link capacity with SWIPT. Then, we design an interference-aware route metric, formulate SWIPT routing problem, and design an interference-aware SWIPT routing algorithm. The simulation results show that as the number of flows increases, there is more likely to obtain performance gains from interference and SWIPT.

ACS Style

Shiming He; Yangning Tang; Zhuozhou Li; Feng Li; Kun Xie; Hye-Jin Kim; Gwang-Jun Kim. Interference-Aware Routing for Difficult Wireless Sensor Network Environment with SWIPT. Sensors 2019, 19, 3978 .

AMA Style

Shiming He, Yangning Tang, Zhuozhou Li, Feng Li, Kun Xie, Hye-Jin Kim, Gwang-Jun Kim. Interference-Aware Routing for Difficult Wireless Sensor Network Environment with SWIPT. Sensors. 2019; 19 (18):3978.

Chicago/Turabian Style

Shiming He; Yangning Tang; Zhuozhou Li; Feng Li; Kun Xie; Hye-Jin Kim; Gwang-Jun Kim. 2019. "Interference-Aware Routing for Difficult Wireless Sensor Network Environment with SWIPT." Sensors 19, no. 18: 3978.

Journal article
Published: 07 June 2019 in IEEE Access
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Recently Network Function Virtualization has been proposed to execute Virtual Network Functions (VNF) on software middle-boxes hosted on commodity servers (substrate nodes). Normally, a request needs to invoke several VNFs in a particular order. As we know, activating and running a substrate node comes at a certain energy cost, and deploying a service chain requires several open substrate nodes which lead to high energy consumption. Furthermore, the deployment of service chains also need to consume bandwidth resource on a substrate network. After embedment of some service chains, the substrate network may consist of many links with fragmented remaining resources that are too little to be utilized by any other requests. The nodes resources then can not be fully utilized and are expended quickly, which causes the low resources utilization. Since the resources of the physical network are limited, due to their faster consumption, the acceptance ratio remains very low. In this paper, we aim to investigate the problem of high energy consumption, low resources utilization and low acceptance ratio in embedment concurrently. To tackle this problem, we consider deploying service chains on splitted paths to utilize the fragmented resources and minimize the number of open nodes to save the energy consumption. To minimize the number of open nodes, we first formulate the embedment of a service chain as a Mixed Integer Linear Program with binary constraints on nodes and continuous constraints on flows. For the purpose of supporting an operation of max-cut that support splitted paths, we consider constructing a network flow model by setting the cost weight based on state of the nodes. Then, we devise a service chains constrained Min-Cost Flow(SC-MCF) Algorithm to find an optimal splitted path with minimal cost weight. Finally, we conduct extensive experiments based on two real topologies EasyNet and GrNet. The experiments show that our proposed SC-MCF Algorithm decreases the number of open nodes and increases the acceptance ratio while saving the resources in the long run.

ACS Style

Dan Chen; Wei Li; Kun Xie; Thabo Semong; Dafang Zhang; Shiming He; Baolin Sun. Path Splitted and Energy Efficient Virtual Network Function Chains Embedding. IEEE Access 2019, 7, 176681 -176692.

AMA Style

Dan Chen, Wei Li, Kun Xie, Thabo Semong, Dafang Zhang, Shiming He, Baolin Sun. Path Splitted and Energy Efficient Virtual Network Function Chains Embedding. IEEE Access. 2019; 7 (99):176681-176692.

Chicago/Turabian Style

Dan Chen; Wei Li; Kun Xie; Thabo Semong; Dafang Zhang; Shiming He; Baolin Sun. 2019. "Path Splitted and Energy Efficient Virtual Network Function Chains Embedding." IEEE Access 7, no. 99: 176681-176692.

Journal article
Published: 01 May 2019 in IEEE Systems Journal
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A node can provide a file to other nodes after downloading the file or data from the Internet. When more than one node have obtained the same file, this is considered a multisource transmission, in which all these nodes can act as candidate providers (sources) and transmit the file to a new request node (destination) together. In cases where there is negligible or no interference, multisource transmission can improve the download throughput because of parallel transmissions through multiple paths. However, this improvement is not guaranteed due to wireless interference among different paths. Wireless interference can be alleviated by the multiradio and multichannel technique. Because the source and multipath routing selections interact with channel assignment, the multisource transmission problem with multiradio and multichannel presents a significant challenge. In this paper, we propose a distributed joint source, routing, and channel selection scheme. The source selection issue can be concurrently solved via multipath finding. There are three sub-algorithms in our scheme, namely, interference-aware routing algorithm, channel assignment algorithm, and local routing adjustment algorithm. The interference-aware routing algorithm is used to find paths sequentially and is jointly executed with the channel assignment algorithm. After finding a new path, the local routing adjustment algorithm may be executed to locally adjust the selected paths so as to further reduce wireless interference. Extensive simulations have been conducted to demonstrate that our algorithms can effectively improve the network aggregate throughput, as well as reduce delay and packet loss probability.

ACS Style

Shiming He; Kun Xie; Kexin Xie; Chuan Xu; Jin Wang. Interference-Aware Multisource Transmission in Multiradio and Multichannel Wireless Network. IEEE Systems Journal 2019, 13, 2507 -2518.

AMA Style

Shiming He, Kun Xie, Kexin Xie, Chuan Xu, Jin Wang. Interference-Aware Multisource Transmission in Multiradio and Multichannel Wireless Network. IEEE Systems Journal. 2019; 13 (3):2507-2518.

Chicago/Turabian Style

Shiming He; Kun Xie; Kexin Xie; Chuan Xu; Jin Wang. 2019. "Interference-Aware Multisource Transmission in Multiradio and Multichannel Wireless Network." IEEE Systems Journal 13, no. 3: 2507-2518.

Journal article
Published: 27 March 2018 in IEEE Access
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Simultaneous wireless information and power transfer (SWIPT) transmits information and powers wireless nodes with the same radio frequency signal. It can prolong the life time of the energy-constrained wireless nodes. Current works of SWIPT focus on one-hop and two-hop wireless network. In order to verify the performance of SWIPT in multi-hop energy-constrained wireless network (MECWN) where the energy harvested by the receiver node can be as an energy compensation for data forwarding, this paper concurrently considers SWIPT and routing selection in MECWN. To reduce the energy consumption, we first formulate the information and energy allocation problem of link in a forwarding path, which is dependent on the next-hop node, and solve it by an iterative allocation algorithm. A novel routing metric evaluates the energy consumption of link transmitted with or without SWIPT. The energy-aware SWIPT routing algorithm allocates the information and energy of link with allocation algorithm during path finding process. To the best of our knowledge, this is the first solution that takes account of SWIPT and routing in MECWN. Our performance studies demonstrate that our proposed algorithms can effectively exploit those node resources whose energy are not enough and significantly decrease the energy consumption.

ACS Style

Shiming He; Kun Xie; Weiwei Chen; Dafang Zhang; Jigang Wen. Energy-Aware Routing for SWIPT in Multi-Hop Energy-Constrained Wireless Network. IEEE Access 2018, 6, 17996 -18008.

AMA Style

Shiming He, Kun Xie, Weiwei Chen, Dafang Zhang, Jigang Wen. Energy-Aware Routing for SWIPT in Multi-Hop Energy-Constrained Wireless Network. IEEE Access. 2018; 6 ():17996-18008.

Chicago/Turabian Style

Shiming He; Kun Xie; Weiwei Chen; Dafang Zhang; Jigang Wen. 2018. "Energy-Aware Routing for SWIPT in Multi-Hop Energy-Constrained Wireless Network." IEEE Access 6, no. : 17996-18008.