| 1. |
- Liu, Xiuming, et al.
(författare)
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Secure Information Fusion using Local Posterior for Distributed Cyber-Physical Systems
- 2020
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE. - 1536-1233 .- 1558-0660. ; 20:5, s. 2041-2054
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Tidskriftsartikel (refereegranskat)abstract
- In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated "detection + fusion" solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general chi(2) distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation.
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| 2. |
- Li, Yuanzi, et al.
(författare)
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Optimization of the l-tyrosine metabolic pathway in Saccharomyces cerevisiae by analyzing p-coumaric acid production
- 2020
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Ingår i: 3 Biotech. - : Springer Science and Business Media LLC. - 2190-572X .- 2190-5738. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we applied a series of genetic modifications to wild-type S. cerevisiae strain BY4741 to address the bottlenecks in the l-tyrosine pathway. A tyrosine ammonia-lyase (TAL) gene from Rhodobacter capsulatus, which can catalyze conversion of l-tyrosine into p-coumaric acid, was overexpressed to facilitate the analysis of l-tyrosine and test the strain's capability to synthesize heterologous derivatives. First, we enhanced the supply of precursors by overexpressing transaldolase gene TAL1, enolase II gene ENO2, and pentafunctional enzyme gene ARO1 resulting in a 1.55-fold increase in p-coumaric acid production. Second, feedback inhibition of 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase and chorismate mutase was relieved by overexpressing the mutated feedback-resistant ARO4(K229L) and ARO7(G141S), and a 3.61-fold improvement of p-coumaric acid production was obtained. Finally, formation of byproducts was decreased by deleting pyruvate decarboxylase gene PDC5 and phenylpyruvate decarboxylase gene ARO10, and p-coumaric acid production was increased 2.52-fold. The best producer-when TAL1, ENO2, ARO1, ARO4(K229L), ARO7(G141S), and TAL were overexpressed, and PDC5 and ARO10 were deleted-increased p-coumaric acid production by 14.08-fold (from 1.4 to 19.71 mg L-1). Our study provided a valuable insight into the optimization of l-tyrosine metabolic pathway.
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| 3. |
- Liu, Xiuming, et al.
(författare)
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Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood
- 2020
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Ingår i: 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Espoo, Finland, September 21-24, 2020. - : IEEE. - 9781728166629 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- Nonparametric regression using Gaussian Process (GP) models is a powerful but computationally demanding method. While various approximation methods have been developed to mitigate its computation complexity, few works have addressed the quality of the resulting approximations of the target posterior. In this paper we start from a general belief updating framework that can generate various approximations. We show that applying using composite likelihoods yields computationally scalable approximations for both GP learning and prediction. We then analyze the quality of the approximation in terms of averaged prediction errors as well as Kullback-Leibler (KL) divergences.
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| 4. |
- Liu, Xiuming, et al.
(författare)
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Cloud-Based Data Fusion in Green IoT for Smart Cities
- 2017
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Ingår i: Proc. 14th International Conference on Embedded Wireless Systems and Networks. - : ACM Digital Library. - 9780994988614 ; , s. 216-217
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Konferensbidrag (refereegranskat)
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| 5. |
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| 6. |
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| 7. |
- Liu, Xiuming, et al.
(författare)
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Gaussian Process Learning for Distributed Sensor Networks Under False Data Injection Attacks
- 2019
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Ingår i: 2019 IEEE Conference On Dependable And Secure Computing (DSC). - 9781728123196 ; , s. 190-195
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Konferensbidrag (refereegranskat)abstract
- Distributed sensor networks are the backbone of many modern intelligent systems. The collected sensor data are fused and processed to learn the underlying model (such as the Gaussian process) of interested physical process, which serves as critical knowledge for the further decision making process. On the other hand, the distributed and heterogeneous nature of connected devices makes them vulnerable to cybersecurity attacks. One common type of attacks is the false data injection attack, which is implemented by hijacking nodes and modifying sensor measurements. In this work, we study the problem of Gaussian process learning for distributed sensor networks under false data injection attacks. The proposed algorithm is based on formulating consensus for the unknown hyper-parameters of the Gaussian process over the network, where statistical measures of the reliability of the local maximum likelihood estimates are used as the weights in the consensus formulation. Furthermore, we investigate the impact of choosing different subset of nodes to deploy the FDIA, based on the topological properties of the subset of nodes. The simulation result shows that extra effort must be invested to protect the integrity of data from nodes with high centrality, due to their critical positions in the information flow over the network.
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| 8. |
- Liu, Xiuming, et al.
(författare)
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Path planning for aerial sensor networks with connectivity constraints
- 2017
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Ingår i: 2017 IEEE International Conference on Communications (ICC). - : IEEE. - 9781467389990
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Konferensbidrag (refereegranskat)abstract
- Wireless sensor networks (WSN) based on unmanned aerial vehicles (UAV) are ideal platforms for monitoring dynamics over larger service area. On the other hand, aerial sensor networks (ASNs) are often required to be connected with a command center for sending data and receiving control messages in real time. In this paper, we study the problem of path planning for ASNs with connectivity constraints. The primary goal of path planning is driving UAVs to locations where the most informative measurements can be collected. Meanwhile, the worst link's capacity is assured to be greater than a pre-defined requirement. We proposed a solution for the path planning problem and it consists of two modules: network coordinator (NC) and motion controller (MC). The NC manages the topology of relay-assisted wireless communication networks. For the design of MC, we compare two motion strategies: maximum entropy and maximum mutual information. The simulation results show that our proposed solution achieves accurate signal reconstruction while maintaining the connectivity. We conclude that it's important to enable UAV-to-UAV communications for future ASN-based applications.
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| 9. |
- Liu, Xiuming, et al.
(författare)
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Robust Prediction When Features are Missing
- 2020
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Ingår i: IEEE Signal Processing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1070-9908 .- 1558-2361. ; 27, s. 720-724
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Tidskriftsartikel (refereegranskat)abstract
- Predictors are learned using past training data which may contain features that are unavailable at the time of prediction. We develop an approach that is robust against outlying missing features, based on the optimality properties of an oracle predictor which observes them. The robustness properties of the approach are demonstrated on both real and synthetic data.
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| 10. |
- Liu, Xiuming, et al.
(författare)
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Scalable Belief Updating for Urban Air Quality Modeling and Prediction
- 2021
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Ingår i: ACM/IMS Transactions on Data Science. - : Association for Computing Machinery (ACM). - 2577-3224 .- 2691-1922. ; 2:1, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Air pollution is one of the major concerns in global urbanization. Data science can help to understand the dynamics of air pollution and build reliable statistical models to forecast air pollution levels. To achieve these goals, one needs to learn the statistical models which can capture the dynamics from the historical data and predict air pollution in the future. Furthermore, the large size and heterogeneity of today’s big urban data pose significant challenges on the scalability and flexibility of the statistical models. In this work, we present a scalable belief updating framework that is able to produce reliable predictions, using over millions of historical hourly air pollutant and meteorology records. We also present a non-parametric approach to learn the statistical model which reveals interesting periodical dynamics and correlations of the dataset. Based on the scalable belief update framework and the non-parametric model learning approach, we propose an iterative update algorithm to accelerate Gaussian process, which is notorious for its prohibitive computation with large input data. Finally, we demonstrate how to integrate information from heterogeneous data by regarding the beliefs produced by other models as the informative prior. Numerical examples and experimental results are presented to validate the proposed method.
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| 11. |
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| 12. |
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| 13. |
- Liu, Xiuming
(författare)
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Statistical Data Analysis for Internet-of-Things : Scalability, Reliability, and Robustness
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Internet-of-Things is a set of sensing, communication, and computation technologies to connect physical objects, such as wearable devices, vehicles, and buildings. From those connected “Things”, a large amount of data is generated. Data analysis plays a central role in the automated and intelligent decision-making process to manage and optimize IoT systems. In this thesis, we focus on tackling the challenges of analyzing large, incomplete, and corrupt IoT data. This thesis consists of three topics. In the first topic, we study scalable GP regression for big IoT data. We propose a novel scalable GP model for urban air quality modeling and prediction. Comparing to the existing scalable GP models, the proposed scalable GP model enables tractable analysis of approximation errors. The second topic is to handle the missing data problem. In the case of missing labels in training data, we investigate different missing data mechanisms. We propose a reliable semi-supervised learning approach, which provides accurate predictive error probability. In the case of missing features in testing data, we design a robust predictor. The predictor significantly reduces the prediction error caused by rare values of missing features, while incurring only a small loss on the overall performance. The third topic is information fusion for IoT systems under false data injection attacks. We propose a robust and distributed information fusion method. This proposed information fusion method only requires exchanging the latest local posterior distributions, instead of synchronizing the full historical measurements. Furthermore, we design a false data detector based on the clustering of local posterior distributions. The distributed information fusion method and false data detector enable secure state estimation for mobile IoT networks with probabilistic communication links. Altogether, this thesis is a step to scalable, reliable, and robust IoT data analysis.
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| 14. |
- van Zoest, Vera, et al.
(författare)
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Data Quality Evaluation, Outlier Detection and Missing Data Imputation Methods for IoT in Smart Cities
- 2021
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Ingår i: Machine Intelligence and Data Analytics for Sustainable Future Smart Cities. - Cham : Springer. ; , s. 1-18
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Bokkapitel (refereegranskat)abstract
- Low-cost IoT devices allow data collection in smart cities at a high spatio-temporal resolution. Data quality evaluation is needed to investigate the pre-processing steps required to use these data. Besides data pre-processing, outlier detection techniques are required to detect anomalies in the spatio-temporal IoT dataset. We distinguish between erroneous outliers and events based on spatio-temporal autocorrelation patterns, as well as correlations with other dynamic processes in the environment. We consider missing data imputation to fill gaps caused by sensor failures, maintenance, pre-processing and outlier detection. In this study, we use the temporal covariance structure within the data to impute missing data. We apply the methods for outlier detection and missing data imputation to an IoT testbed for air quality monitoring in the city of Eindhoven, the Netherlands. The methods can be applied in a more general sense to other continuous environmental variables which show a similarly strong spatio-temporal autocorrelation structure.
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| 15. |
- Xi, Teng, et al.
(författare)
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Spatio-Temporal Aware Collaborative Mobile Sensing with Online Multi-Hop Calibration
- 2018
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Ingår i: Proceedings of the 2018 the Nineteenth International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC '18). - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450357708 ; , s. 310-311
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Konferensbidrag (refereegranskat)abstract
- Real-time accurate air quality data is very important for pollution exposure monitoring and urban planning. However, there are limited high-quality air quality monitoring stations (AQMS) in cities due to their high equipment costs. To provide real-time and accurate data covering large area, this paper proposes a novel scheme that jointly considers online multi-hop calibration and spatio-temporal coverage in route selection for mobile sensors. A novel sensor carrier selection problem (SCSP) is formulated, which aims to maximize the spatio-temporal coverage ratio and guarantee the accuracy of measurements through sensor calibration. An online Bayesian based collaborative calibration (OBCC) scheme is proposed to relax the multi-hop calibration constraint in the SCSP. Based on the OBCC, a multi-hop calibration judgment algorithm (MCJA) is proposed to decide whether the data accuracy of a given set of routes can be guaranteed through collaborative calibration. Furthermore, a heuristic sensor route selection algorithm (SRSA) is then developed to solve the SCSP.
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