Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

Akbar, Adnan; Kousiouris, George; Pervaiz, Haris; Sancho, J.; Ta-Shma, Paula; Carrez, François; Moessner, Klaus

doi:10.1109/access.2018.2804623

Cited by 66 publications

(36 citation statements)

References 18 publications

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“…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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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 high data flow, either device-based or text-based, is unable to process and leads to time delays, extra costs, and even inappropriate decisions due to missing or incomplete information [13,63].…”

Section: Data Fusionmentioning

confidence: 99%

“…The communication is realized over Bluetooth Low Energy (BLE) to create mobile network allowing local exchange of messages, which contain selected information about the state of the network nodes. This is an example of Smart Phone Ad hoc Network (SPAN) solution 13 . In the considered test study the Polar H7 Bluetooth Smart 4.0 heart rate sensor and Wiko Sunny phones are used -see Fig.…”

Section: Smartphone Ad Hoc Network (Span) For E-health Applicationmentioning

confidence: 99%

Medical Data Processing and Analysis for Remote Health and Activities Monitoring

Vitabile

Marks

Stojanović

et al. 2019

Lecture Notes in Computer Science

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Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human's health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for

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“…Akbar et al . proposed an architecture for fusing and analysing traffic, weather and social media streams, thereby predicting the probability of congestion in real time (Akbar et al , ). Adjusted by the feedback of traffic administrators, their case in Madrid reaches a premising predicting accuracy and similar results are also reported by Lu et al .…”

Section: Review Of Previous Work and Analysismentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

Cited by 66 publications

References 18 publications

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Medical Data Processing and Analysis for Remote Health and Activities Monitoring

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

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