Data quality analysis and cleaning strategy for wireless sensor networks

Cheng, Hongju; Feng, D.; Shi, Xiaohu; Chen, Chongcheng

doi:10.1186/s13638-018-1069-6

Cited by 39 publications

(26 citation statements)

References 20 publications

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“…Biomedical signal monitoring often requires operating on a 24/7 basis; however, it is still uncomfortable and obtrusive for people to attach a number of sensors on some parts of the body round the clock and in the long term. On the other hand, sensors often with limited computing and storage capability, whilst they are generating massive amount of the data [38], not only significantly increase the memory and computational requirement, but also dramatically increase the complexity of the data analysis process. Hence, a more intelligent sensor datafiltering mechanism must be developed, in order to precisely retrieve the most valuable information.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Wearable IoT enabled real-time health monitoring system

Wan

Al-awlaqi

Li³

et al. 2018

J Wireless Com Network

200

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As the age profile of many societies continues to increase, in addition to the increasing population of people affected by chronic diseases, including diabetes, cardiovascular disease, obesity, and so on, supporting health, both mentally and physically, is of increasing importance if independent living is to be maintained. Sensing, remote health monitoring, and, ultimately, recognising activities of daily living have been an promising solution. From a technical perspective, the Internet of Things (IoT) is gaining a rapidly growing attention in many disciplines, especially in personalised healthcare. Meanwhile, body area sensor network (BASN) under the IoT framework has been widely applied for ubiquitous health monitoring, for example. ECG monitoring has been commonly adopted as vital approach for diagnosing heart disease. The main contribution of this paper include the following: firstly, this paper presents a novel system, the WISE (Wearable IoT-cloud-baSed hEalth monitoring system), for real-time personal health monitoring. WISE adopts the BASN (body area sensor network) framework in the support of real-time health monitoring. Several wearable sensors have been embedded, including the heartbeat, body temperature, and the blood pressure sensors. Secondly, the majority of existing wearable health monitoring systems requisite a smart phone as data processing, visualisation, and transmission gateway, which will indeed impact the normal daily use of the smart phone. Whilst in WISE, data gathered from the BASN are directly transmitted to the cloud, and a lightweight wearable LCD can be embedded as an alternative solution for quick view of the real-time data.

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Section: Discussion and Future Workmentioning

confidence: 99%

Wearable IoT enabled real-time health monitoring system

Wan

Al-awlaqi

Li³

et al. 2018

J Wireless Com Network

200

View full text Add to dashboard Cite

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“…This principle describes the recommended behavior guiding decision-making. Each principle describes the measures to be taken, but does not explain how and what to do [9]. In terms of the model, ISO38500 believes that the leaders of an organization should focus on three core tasks: first, to assess the current and future IT utilization; second, to guide the policies and plans for the implementation of governance readiness; and third, to establish a cycle model of "evaluation, guidance and supervision" [10].…”

Section: Related Workmentioning

confidence: 99%

Design of Overall Framework of Self-Service Big Data Governance for Power Grid

Qiao

Zhou

Song

et al. 2019

Machine Learning and Intelligent Communications

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At present, power grid companies have not formed a complete data quality control system and a comprehensive and effective data quality assurance mechanism, which restricts the deep mining of data value. In this paper, based on the full-service unified data center of power Grid Company, a general framework of self-service power grid big data governance is presented. Firstly, the related work of big data governance is reviewed; secondly, the architecture and characteristics of the grid company's full-service unified data center are analyzed; thirdly, the related requirements of self-service grid big data governance are analyzed; fourthly, the overall framework of self-service grid big data governance is proposed. Compared with other general big data governance framework, the proposed framework considers the specification of power grid as well as the features of self-service, making the framework more feasible.

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“…After collection, storage [1], processing [2] and analysis, they generate value. For example, in intelligent communication systems, users use wireless sensors [3][4][5][6][7] and mobile devices to collect data, and then analyze the data to provide decision-making basis for programs. The wireless sensor is a type of wireless data communication collector which integrates data acquisition, data management, data communication and, other functions.…”

Section: Introductionmentioning

confidence: 99%

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

Sun

Liang

et al. 2020

Electronics

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This paper aims to answer “why-not” questions in skyline queries based on the orthogonal query range (i.e., ORSQ). These queries retrieve skyline points within a rectangular query range, which improves query efficiency. Answering why-not questions in ORSQ can help users analyze query results and make decisions. We discuss the causes of why-not questions in ORSQ. Then, we outline how to modify the why-not point and the orthogonal query range so that the why-not point is included in the result of the skyline query based on the orthogonal range. When the why-not point is in the orthogonal range, we show how to modify the why-not point and narrow the orthogonal range. We also present how to expand the orthogonal range when the why-not point is not in the orthogonal range. We effectively combine query refinement and data modification techniques to produce meaningful answers. The experimental results demonstrate that the proposed algorithms have high-quality explanations for why-not questions in ORSQ in the real and synthetic datasets.

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Data quality analysis and cleaning strategy for wireless sensor networks

Cited by 39 publications

References 20 publications

Wearable IoT enabled real-time health monitoring system

Wearable IoT enabled real-time health monitoring system

Design of Overall Framework of Self-Service Big Data Governance for Power Grid

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

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