A Survey of Data Cleansing Techniques for Cyber-Physical Critical Infrastructure Systems

Woodard, Mark; Wisely, Michael; Sarvestani, Sahra Sedigh

doi:10.1016/bs.adcom.2016.05.002

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…The Status (A9.2) subcategory contains information about network statistics that are not part of the Connectivity subcategory. Road Side Unit (A9.3) holds context elements representing information collected from Road Side Units in the same network of the vehicle, such as traffic flow on a road segment [Woodard et al, 2016]. Peer information (A9.4) would hold context elements about the peers, the kind of device they are, and their interfaces to obtain more information.…”

Section: Activitymentioning

confidence: 99%

Context Elements Taxonomy for Intelligent Transportation Systems

Chagas

Ferraz

2023

JBCS

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Design and development of context-aware Intelligent Transportation Systems (ITS) are not trivial due to the large number of possible context elements that may be relevant to the application and the lack of structured information to guide system designers in this task. This paper proposes that context elements with common characteristics can be grouped into categories, and these categories can be organized in a taxonomy. This taxonomy could help system designers with the task of modeling and developing new context-aware ITS. We performed a literature review of 68 articles describing 70 ITS applications with context-aware features to identify context elements used in this type of application. Furthermore, we also analyzed three commercial ITS applications. We used data collected from the analysis of these 73 projects to define the categories and identify their relationships. We propose a taxonomy with 79 categories, with 57 leaf categories (a category without children subcategories). We also performed two experiments to validate whether the exposure to this taxonomy could improve the quality of an ITS application during its design, with favorable results showing a 2.7 times increase in the average amount of relevant context elements used in the application. Finally, we compiled a knowledge base of which context element categories are used in the 73 analyzed projects. It is another companion information that can be used to help system designers. The proposed taxonomy of context element categories organizes the information of the context-aware ITS domain in a way that can ease the task of designing such systems and improve the usage of context-aware features. The overall methodology used in this work to create the taxonomy for the ITS domain could be applied to other popular domains of context-aware applications.

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Section: Activitymentioning

confidence: 99%

Context Elements Taxonomy for Intelligent Transportation Systems

Chagas

Ferraz

2023

JBCS

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“…There are many services in the ITS that require technical support for traffic forecasting, such as travel route planning [11], urban traffic management [12], and public transport scheduling [13]. Many services have personalized needs for traffic forecasting, among which the rapid development of UTCS has put forward higher and higher requirements for real-time traffic forecasting [14,15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information

Guan

2022

Sustainability

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Before traffic forecasting, it is usually necessary to aggregate the information by a certain length of time. An aggregation size that is too short will make the data unstable and cause the forecast result to be too biased. On the other hand, if the aggregation size is too large, the data information will be lost, resulting in the forecast results tending towards an average or slow response. With the development of intelligent transportation systems, especially the development of urban traffic control systems, high requirements are placed on the real-time accuracy of traffic forecasting. Therefore, it is an essential topic of traffic forecasting research to determine aggregation sizes. In this paper, the mutual information between the forecast input information and the forecast result and the sequence complexity of the forecast result measured by approximate entropy, sample entropy, and fuzzy entropy are considered; then, the optimal data aggregation size is given. To verify the proposed method, the validated data obtained from the simulation is aggregated and calculated with different aggregation sizes, then used for forecasting. By comparing the prediction performance of different aggregate sizes, the optimal aggregate size was found to reduce MSE by 14–30%. The results show that the method proposed in this paper is helpful for selecting the optimal data aggregation size in forecasting and can improve the performance of prediction.

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