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Big Data Meet Cyber-Physical Systems: A Panoramic Survey

arXiv: 1810.12399
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, mobile-health 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.
- Texas A&M University at Qatar Qatar
- Virginia Tech United States
- University of Chile Chile
- InterDigital, Inc. United States
- Texas A&M University at Qatar Qatar
FOS: Computer and information sciences, Computer Science - Machine Learning, cybersecurity, Machine Learning (stat.ML), Machine Learning (cs.LG), Cyber-physical systems (CPS), green, big data, Statistics - Machine Learning, Machine learning, data analytics, cloud computing, context-awareness, social computing, data mining, space-time analytics, Internet of Things (IoT), TK1-9971, Sustainability, Electrical engineering. Electronics. Nuclear engineering, real-time analytics, clustering, energy
FOS: Computer and information sciences, Computer Science - Machine Learning, cybersecurity, Machine Learning (stat.ML), Machine Learning (cs.LG), Cyber-physical systems (CPS), green, big data, Statistics - Machine Learning, Machine learning, data analytics, cloud computing, context-awareness, social computing, data mining, space-time analytics, Internet of Things (IoT), TK1-9971, Sustainability, Electrical engineering. Electronics. Nuclear engineering, real-time analytics, clustering, energy
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