A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

Jabbar, Sohail; Malik, Kaleem Razzaq; Ahmad, Mudassar; Aldabbas, Omar; Asif, Muhammad; Khalid, Shehzad; Han, Kijun; Ahmed, Syed Hassan

doi:10.1109/access.2018.2820176

Cited by 34 publications

(13 citation statements)

References 26 publications

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“…Each neuron receives the input information of other n neurons from the prior layer and then the result of weighted summation is transformed by the activation function. The work process of a neuron can be shown using formulas (8) and (9)…”

Section: Data Fusion Processingmentioning

confidence: 99%

An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Wang

Gao

Liu

et al. 2019

International Journal of Distributed Sensor Networks

164

105

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Numerous tiny sensors are restricted with energy for the wireless sensor networks since most of them are deployed in harsh environments, and thus it is impossible for battery re-change. Therefore, energy efficiency becomes a significant requirement for routing protocol design. Recent research introduces data fusion to conserve energy; however, many of them do not present a concrete scheme for the fusion process. Emerging machine learning technology provides a novel direction for data fusion and makes it more available and intelligent. In this article, we present an intelligent data gathering schema with data fusion called IDGS-DF. In IDGS-DF, we adopt a neural network to conduct data fusion to improve network performance. First, we partition the whole sensor fields into several subdomains by virtual grids. Then cluster heads are selected according to the score of nodes and data fusion is conducted in CHs using a pretrained neural network. Finally, a mobile agent is adopted to gather information along a predefined path. Plenty of experiments are conducted to demonstrate that our schema can efficiently conserve energy and enhance the lifetime of the network.

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Section: Data Fusion Processingmentioning

confidence: 99%

An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Wang

Gao

Liu

et al. 2019

International Journal of Distributed Sensor Networks

164

105

View full text Add to dashboard Cite

show abstract

“…Real-time data fusions are important for heterogeneous IoT/CPS data streams and unreliable networks with increasing data size. To support locally available computational values to help real-time analytics, the work [88] proposed the fusion of three different data models with relational, semantical, and big data based data and metadata. The paper [89] proposed twolayer architecture for IoT data analysis, where the first layer is with the service oriented gateway based generic interface to obtain data from multiple interfaces and IoT systems and store them scalably and make relevant real-time analysis to extract high-level activities, while the second layer works for the probabilistic fusion of these high-level activities.…”

Section: B Big Data Communications For Cpsmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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“…In [31], the author has shown the models of weather forecasting to study frost events in the US. They have used the advanced research model for soil data and specific topography map resolution [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

et al. 2020

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The forecast of frost occurrence requires complex decision analysis that uses conditional probabilities. Due to frost events, the production of crops and flowers gets reduced, and we must predict this event to minimize the damages. If the frost prediction results are accurate, then the damage caused by frost can be reduced. In this paper, an ensemble learning approach is used to detect frost events with Convolutional Neural Network (CNN). We have used this to get more efficient and accurate results. Frost events need to be predicted earlier so that the farmer can take on-time precautionary measures. So, for measurement and analysis of Google Play, we have scrapped a dataset of the Agricultural category from different genres and collected the top 550 application of each category of Agricultural applications with 70 attributes for each category. The prediction of frost events prior few days of an actual frost event with an accuracy of 98.86%.

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A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

Cited by 34 publications

References 26 publications

An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Integration of Google Play Content and Frost Prediction Using CNN: Scalable IoT Framework for Big Data

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