scholarly journals An Efficient and Secure Malicious User Detection Scheme Based on Reputation Mechanism for Mobile Crowdsensing VANET

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhihua Wang ◽  
Jiahao Liu ◽  
Chaoqi Guo ◽  
Shuailiang Hu ◽  
Yongjian Wang ◽  
...  
Keyword(s):  
Data Quality ◽  
Traffic Safety ◽  
Quality Evaluation ◽  
Experimental Simulation ◽  
Detection Scheme ◽  
Mobile Crowdsensing ◽  
Sensing Data ◽  
Malicious User ◽  
Sensing System ◽  
Evaluation Phase

With the increasing development of wireless communication technology and Vehicular Ad hoc Network (VANET), as well as the continuous popularization of various sensors, Mobile Crowdsensing (MCS) paradigm has been widely concerned in the field of transportation. As a currently popular data sensing way, it mainly relies on wireless sensing devices to complete large-scale and complex sensing tasks. However, since vehicles are highly mobile in this scenario and the sensing system is open, that is, any vehicle equipped with sensing device can join the system, the credibility of all participating vehicles cannot be guaranteed. In addition, malicious users will upload false data in the sensing system, which makes the sensing data not meet the needs of the sensing tasks and will threaten traffic safety in some serious cases. There are many solutions to the above problems, such as cryptography, incentive mechanism, and reputation mechanisms. Unfortunately, although these schemes guaranteed the credibility of users, they did not give much thought to the reliability of data. In addition, some schemes brought a lot of overhead, some used a centralized server management architecture, and some were not suitable for the scenario of VANET. Therefore, this paper firstly proposes the MCS-VANET architecture-based blockchain, which consists of participating vehicles (PVs), road side units (RSUs), cloud server (CS), and the blockchain (BC), and then designs a malicious user detection scheme composed of three phases. In the data collecting phase, to reduce the data uploading overhead, data aggregation and machine learning technologies are combined by fully considering the historical reputation value of PVs, and the proportion of data uploading is determined based on the historical data quality evaluation result of PVs. In the data quality evaluation phase, a new reputation computational model is proposed to effectively evaluate the sensing data, which contains four indicators: the reputation history of PVs, the data unbiasedness, the leadership of PVs, and the spatial force of PVs. In the reputation updating phase, to achieve the effective change of reputation values, the logistic model function curve is introduced and the result of the reputation updating is stored in the blockchain for security publicity. Finally, on real datasets, the feasibility and effectiveness of our proposed scheme are demonstrated through the experimental simulation and security analysis. Compared with existing schemes, the proposed scheme not only reduces the cost of data uploading but also has better performance.

Methods for detecting and correcting contextual data quality problems

2021 ◽  
Vol 25 (4) ◽  
pp. 763-787
Author(s):  
Alladoumbaye Ngueilbaye ◽  
Hongzhi Wang ◽  
Daouda Ahmat Mahamat ◽  
Ibrahim A. Elgendy ◽  
Sahalu B. Junaidu
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Web Applications ◽  
Support Vector ◽  
Similarity Metrics ◽  
Distributed Data ◽  
Contextual Data ◽  
Formal Taxonomy ◽  
E Learning ◽  
High Scalability

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

scholarly journals Data Quality Aware Task Allocation With Budget Constraint in Mobile Crowdsensing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 48010-48020 ◽  
Author(s):  
Xiaohui Wei ◽  
Yongfang Wang ◽  
Jingweijia Tan ◽  
Shang Gao
Keyword(s):  
Data Quality ◽  
Task Allocation ◽  
Budget Constraint ◽  
Mobile Crowdsensing

scholarly journals Harmonizing National Topographic Data Using an Automated Quality Validation Process

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Nils Mesterton ◽  
Mari Isomäki ◽  
Antti Jakobsson ◽  
Joonas Jokela
Keyword(s):  
Data Quality ◽  
Spatial Data ◽  
Quality Evaluation ◽  
Validation Process ◽  
Topographic Data ◽  
Spatial Data Quality ◽  
General Rules ◽  
Land Survey ◽  
Quality Specifications ◽  
Quality Process

<p><strong>Abstract.</strong> The Finnish National Topographic Database (NTDB) is currently developed by the National Land Survey of Finland (NLS) together with municipalities and other governmental agencies. It will be a harmonized database for topographic data in Finland provided by municipalities, the NLS and other agencies. The NTDB has been divided into several themes, of which the buildings theme was the focus in the first stage of development. Data collection for the NTDB is performed by different municipalities and governmental organizations. Having many supplying organizations can lead to inconsistencies in spatial data. Without a robust quality process this could lead to a chaos. Fortunately data quality can be controlled with an automated data quality evaluation process. Reaching a better degree of harmonization across the database is one of the main goals of NTDB in the future, besides reducing the amount of overlapping work and making national topographic data more accessible to all potential users.</p><p>The aim of the NTDB spatial data management system architecture is to have a modular architecture. Therefore, the Data Quality Module named as QualityGuard can also be utilized in the National Geospatial Platform which will be a key component in the future Spatial Data Infrastructure of Finland. The National Geospatial Platform will include the NTDB data themes but also addresses, detailed plans and other land use information. FME was chosen as the implementation platform of the QualityGuard because it is robust and highly adaptable, allowing development of even the most complicated ETL workflows and spatial applications. This approach allows effortless communication with different applications via various types of interfaces, thus efficiently enabling the modularity requirement in all stages of development and integration.</p><p>The QualityGuard works in two modes: a) as a part of the import process to NTDB, and b) independently. Users can validate their data using the independent QualityGuard to find possible errors in their data and fix them. Once validated and the data is fixed, data producers can import their data using the import option. The users receive a data quality report containing statistics and a quality error dataset regarding their imported data, which can be inspected in any GIS software, e.g. overlaid on original features. Geographical locations of quality errors are displayed as points. Each error finding produces a row in the error dataset, containing information about the type and cause of the error as short descriptions.</p><p>Data quality evaluation is based on validating the conformance against data product specifications specified as quality rules. Three different ISO 19157 quality elements are utilized: format consistency, domain consistency and topological consistency. The quality rules have been defined in a co-operation with specialists in the field and the technical developing team. The definition work is based on the concept developed in the ESDIN project, quality specifications of INSPIRE, national topographic database quality specifications, national and international quality recommendations and standards, quality rules developed in European Location Framework (ELF) project and interviews of experts from National Land Survey of Finland and municipalities. In fact the NLS was one of the first agencies in the world who published a quality model for the digital topographic data in 1995.</p><p>Quality rules are currently documented in spreadsheet documents representing each theme. Each quality rule has been defined using RuleSpeak, a structured notation for expressing business rules. RuleSpeak provides a consistent structure for each definition. The rules are divided in general rules and feature-specific rules. General rules are relevant for all feature types of a specific theme, although exceptions can be defined.</p><p>A nation-wide, centralized automated spatial data quality process is one of the key elements in an effort towards achieving better harmonization of the NTDB. In principle, the greater aim is to achieve compliance with the auditing process described in ISO 19158. This process is meant to ensure that the supplying organizations are capable of delivering data of expected quality. However, implementing a nation-wide process is rather challenging because municipalities and other organizations might not have the capability or resources to repair the quality issues identified by the QualityGuard. Inconsistent data quality is not desirable, and data quality requirements will be less strict at first phases of implementation. Some of the issues will be automatically repaired by the software once the process has been established, but the organizations will still receive a notification about data quality issues in any conflicting features.</p><p>The Finnish NTDB is in a continuous state of development and currently effort is made towards reaching automation, improved data quality and less overlapping work in co-operation with municipalities and other data producers. The QualityGuard has enabled an automated spatial data quality validation process for incoming data and it is currently being evaluated in practice. The results have already been well received by the users. Automating data quality validation is no longer a work of fiction. As indicated earlier we believe this will be a common practice with all SDI datasets in Finland.</p></p>

scholarly journals User-Oriented Approach to Data Quality Evaluation

2020 ◽  
Vol 26 (1) ◽  
pp. 107-126
Author(s):  
Anastasija Nikiforova ◽  
Janis Bicevskis ◽  
Zane Bicevska ◽  
Ivo Oditis
Keyword(s):  
Data Quality ◽  
Quality Evaluation ◽  
Open Data ◽  
Evaluation Process ◽  
Specific Model ◽  
Third Party ◽  
Quality Model ◽  
Levels Of Abstraction ◽  
Quality Requirements ◽  
Data Object

The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

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