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Feng J, Yang LT, Gati NJ, Xie X, Gavuna BS. Privacy-preserving computation in cyber-physical-social systems: A survey of the state-of-the-art and perspectives. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.07.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Wang X, Yang LT, Wang Y, Liu X, Zhang Q, Deen MJ. A Distributed Tensor-Train Decomposition Method for Cyber-Physical-Social Services. ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS 2019. [DOI: 10.1145/3323926] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
C
yber-
P
hysical-
S
ocial
S
ystems (CPSS) integrating the cyber, physical, and social worlds is a key technology to provide proactive and personalized services for humans. In this paper, we studied CPSS by taking
h
uman-
i
nteraction-aware
b
ig
d
ata (HIBD) as the starting point. However, the HIBD collected from all aspects of our daily lives are of high-order and large-scale, which bring ever-increasing challenges for their cleaning, integration, processing, and interpretation. Therefore, new strategies for representing and processing of HIBD become increasingly important in the provision of CPSS services. As an emerging technique, tensor is proving to be a suitable and promising representation and processing tool of HIBD. In particular, tensor networks, as a significant tensor decomposition technique, bring advantages of computing, storage, and applications of HIBD. Furthermore,
T
ensor-
T
rain (TT), a type of tensor network, is particularly well suited for representing and processing high-order data by decomposing a high-order tensor into a series of low-order tensors. However, at present, there is still need for an efficient Tensor-Train decomposition method for massive data. Therefore, for larger-scale HIBD, a highly-efficient computational method of Tensor-Train is required. In this paper, a
d
istributed
T
ensor-
T
rain (DTT) decomposition method is proposed to process the high-order and large-scale HIBD. The high performance of the proposed DTT such as the execution time is demonstrated with a case study on a typical form of CPSS data,
C
omputed
T
omography (CT) image data.
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Affiliation(s)
- Xiaokang Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, China and Department of Computer Science, St. Francis Xavier University, Nova Scotia, Canada
| | - Laurence T. Yang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, China and Department of Computer Science, St. Francis Xavier University, Nova Scotia, Canada
| | - Yihao Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xingang Liu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qingxia Zhang
- School of Computer Science, Fudan University, Shanghai, China
| | - M. Jamal Deen
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, China and Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada
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Liu Y, Liu X, Jing Y, Zhou S. Adaptive backstepping H ∞ tracking control with prescribed performance for internet congestion. ISA TRANSACTIONS 2018; 72:92-99. [PMID: 29079061 DOI: 10.1016/j.isatra.2017.10.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 09/06/2017] [Accepted: 10/06/2017] [Indexed: 06/07/2023]
Abstract
This paper extends the well-known control method, prescribed performance control (PPC), to network congestion control problems. An adaptive H∞ tracking problem for Transmission Control Protocol/Active Queue Management (TCP/AQM) system with external disturbance is studied. Firstly, a modified network model is given. And then, the model is changed to an equivalent error model by using error transformation. Next, to solve the network congestion problem, prescribed performance, backstepping technique, adaptive control and H∞ control are combined to design a congestion controller. Due to the use of prescribed performance, the controller can guarantee both the transient and steady state performance of the system. Meanwhile, the output of the system can track the desired queue, and unknown link capacity can be estimated. Finally, a simulation result is shown to clarify the feasibility and effectiveness of proposed approach.
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Affiliation(s)
- Yang Liu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, PR China; Department of Electrical Engineering, Lakehead University, Thunder Bay, Ontario, Canada P7B 5E1.
| | - Xiaoping Liu
- Shandong Jianzhu University, Jinan 250101, Shandong, PR China; Department of Electrical Engineering, Lakehead University, Thunder Bay, Ontario, Canada P7B 5E1.
| | - Yuanwei Jing
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, PR China.
| | - Shaowei Zhou
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, PR China.
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Xiong N, Zhang L, Zhang W, Vasilakos AV, Imran M. Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks. SENSORS (BASEL, SWITZERLAND) 2017; 17:s17102166. [PMID: 28934171 PMCID: PMC5677408 DOI: 10.3390/s17102166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 08/31/2017] [Accepted: 09/02/2017] [Indexed: 06/07/2023]
Abstract
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage.
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Affiliation(s)
- Naixue Xiong
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
- Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.
| | - Longzhen Zhang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Wei Zhang
- Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310037, China.
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden.
| | - Muhammad Imran
- College of Computer and Information Sciences, Almuzahmiyah, King Saud University, Riyadh 11451, Saudi Arabia.
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Li H, Liu J, Liu RW, Xiong N, Wu K, Kim TH. A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis. SENSORS 2017; 17:s17081792. [PMID: 28777353 PMCID: PMC5579835 DOI: 10.3390/s17081792] [Citation(s) in RCA: 91] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Revised: 07/27/2017] [Accepted: 08/01/2017] [Indexed: 12/02/2022]
Abstract
The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.
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Affiliation(s)
- Huanhuan Li
- Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
| | - Jingxian Liu
- Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
- National Engineering Research Center for Water Transport Safety, Wuhan 430063, China.
| | - Ryan Wen Liu
- Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
| | - Naixue Xiong
- School of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.
| | - Kefeng Wu
- School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Tai-Hoon Kim
- Department of Convergence Security, Sungshin Women's University, 249-1 Dongseon-dong 3-ga, Seoul 136-742, Korea.
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Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles. SENSORS 2016; 16:s16010088. [PMID: 26761013 PMCID: PMC4732121 DOI: 10.3390/s16010088] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 12/28/2015] [Accepted: 12/28/2015] [Indexed: 11/25/2022]
Abstract
The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.
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de Assis MV, Rodrigues JJ, Proença ML. A seven-dimensional flow analysis to help autonomous network management. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.03.102] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Adaptive active queue management controller for TCP communication networks using PSO-RBF models. Neural Comput Appl 2012. [DOI: 10.1007/s00521-011-0786-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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An information-theoretic characterization of weighted α-proportional fairness in network resource allocation. Inf Sci (N Y) 2011. [DOI: 10.1016/j.ins.2011.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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