{
    "article_id": 1064,
    "public_id": "486da154-419a-4703-b5a0-fa8fa9140ae4",
    "internal_identifier": null,
    "source_identifier": "42:69",
    "status": "published",
    "type": "research_article",
    "title": "Efficient Data Storage in Edge Cloud Computing For Real Time System",
    "title_secondary": null,
    "abstract": "At the last two decades the information world and associated information technologies have been progressed. This progress can be implemented from the concept of the internet to the internet of things. The Internet of things (IoT) refers to billions of electronic devices around the world that are lınk up to each other’s by using wireless communication networks techniques, such as Bluetooth, ZigBee, Wi-Fi, 2G, 3G, 4G mobile protocol and for example about (IoT) Smart infrastructure, Smart city, Smart Mobility, Smart technology, etc... By using like these smart systems will generate a huge of data sensing that captures from the physical world by sensors and will need to store and process in the centralized process (cloud computing) at less time in order to get faster feedback data response. Today the data analytics, which utilized on edge cloud computing has more and more important in order to get near real-time decisions. The Edge cloud computing is a new paradigm from a cloud computing environment which provides support the data distributed processing that store and process the data sensing in the node to stay close from data produced resources instead of sending it to centralized processing (cloud computing) to minimize the data latency and investigate a near real-time response. Because of limitation, storage on edge cloud computing, I am facing challenges to balance between the quality of data and the quantity of data stored on edge computing for taking near real-time decisions. In this thesis, I use three architectural layers for efficient data storage and management of edge cloud computing that include an adaptive algorithm that dynamically finds a trade-off between providing high prediction accuracy necessary to improve a real-time decision and decreasing the amount of data stored in limited space storage. The aim of this thesis focuses on time series data to get a near real-time decision.",
    "language": "en",
    "english_title": null,
    "english_abstract": null,
    "doi": null,
    "doi_url": null,
    "resource_url": "https://www.set-science.com/a/486da154-419a-4703-b5a0-fa8fa9140ae4",
    "pdf_url": "https://www.set-science.com/a/486da154-419a-4703-b5a0-fa8fa9140ae4/pdf",
    "og_image": "https://www.set-science.com/assets/img/og-card-v2.png",
    "authors": [
        {
            "public_id": "1216cfd7-50ac-45b0-be61-b30e4795213f",
            "given": "Alhakam ",
            "family": "Qassab  ",
            "full": "Alhakam  Qassab  ",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "hakamsameer1988@gmail.com",
            "corresponding": true,
            "sequence": "first",
            "affiliations": [
                {
                    "name": "Ankara Yildirim Beyazit University",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        },
        {
            "public_id": "85932330-34a0-4313-8b90-5e1ddd634ad0",
            "given": "Shafqat Ur ",
            "family": "Rehman  ",
            "full": "Shafqat Ur  Rehman  ",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "hakamsameer1988@gmail.com",
            "corresponding": false,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Ankara Yildirim Beyazit University",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        }
    ],
    "keywords": [
        "Internet of Thing, Cloud Computing, Edge Cloud Computing, Intelligent home system, Adaptive algorithm"
    ],
    "translations": [
        {
            "language": "en",
            "title": "Efficient Data Storage in Edge Cloud Computing For Real Time System",
            "abstract": "At the last two decades the information world and associated information technologies have been progressed. This progress can be implemented from the concept of the internet to the internet of things. The Internet of things (IoT) refers to billions of electronic devices around the world that are lınk up to each other’s by using wireless communication networks techniques, such as Bluetooth, ZigBee, Wi-Fi, 2G, 3G, 4G mobile protocol and for example about (IoT) Smart infrastructure, Smart city, Smart Mobility, Smart technology, etc... By using like these smart systems will generate a huge of data sensing that captures from the physical world by sensors and will need to store and process in the centralized process (cloud computing) at less time in order to get faster feedback data response. Today the data analytics, which utilized on edge cloud computing has more and more important in order to get near real-time decisions. The Edge cloud computing is a new paradigm from a cloud computing environment which provides support the data distributed processing that store and process the data sensing in the node to stay close from data produced resources instead of sending it to centralized processing (cloud computing) to minimize the data latency and investigate a near real-time response. Because of limitation, storage on edge cloud computing, I am facing challenges to balance between the quality of data and the quantity of data stored on edge computing for taking near real-time decisions. In this thesis, I use three architectural layers for efficient data storage and management of edge cloud computing that include an adaptive algorithm that dynamically finds a trade-off between providing high prediction accuracy necessary to improve a real-time decision and decreasing the amount of data stored in limited space storage. The aim of this thesis focuses on time series data to get a near real-time decision.",
            "keywords": [
                "Internet of Thing, Cloud Computing, Edge Cloud Computing, Intelligent home system, Adaptive algorithm"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2019-06-01",
    "published_year": "2019",
    "received_date": null,
    "accepted_date": null,
    "volume": "4",
    "issue": null,
    "page_first": "367",
    "page_last": "373",
    "article_number": null,
    "conference": {
        "short_name": "ISAS2019-ENS",
        "full_title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "name": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "acronym": "ISAS2019-ENS",
        "location": "Ankara, Türkiye",
        "start_date": "2019-04-19",
        "end_date": null,
        "public_id": "ed3b36e9-38cf-4aeb-a135-833c969cc89f"
    },
    "proceedings": {
        "title": "3rd International Symposium on Innovative Approaches in Scientific Studies (Engineering and Natural Sciences)",
        "series_title": "Setsci conference indexing system",
        "publisher": "SET Technology",
        "isbn": null,
        "eisbn": null,
        "issn": "2618-6446",
        "eissn": null,
        "doi": null,
        "doi_prefix": null,
        "slug": "setsci-volume-4-2019-legacy-42",
        "public_id": "24d215ef-a545-4351-9725-6e9bdb167b3c",
        "url": "https://www.set-science.com/p/setsci-volume-4-2019-legacy-42"
    },
    "license": {
        "name": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "code": "CC-BY-4.0",
        "open_access": true
    },
    "copyright": {
        "holder": "SET Technology",
        "year": "2019"
    },
    "funding_statement": null,
    "funders": [],
    "references": [
        {
            "order": 1,
            "raw": "https://www.dataversity.net/brief-history-internet-things/#J.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 2,
            "raw": "Ashton, K. (22 June 2009). \"That 'Internet of Things' Thing\". Retrieved 9 May 2017.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 3,
            "raw": "Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of things and big data analytics for smart and connected communities,” IEEE Access, vol. 4, pp. 766–773, 2016 (2002) The IEEE website. [Online]. Available: http://www.ieee.org/",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 4,
            "raw": "(Mathai, 2012) Madhavi, M. (2012). An Approach For Intrusion Detection System In Cloud Computing, 3 (5), 5219–5222.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 5,
            "raw": "-Archive/macros/latex/contrib/supported/IEEEtran/",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 6,
            "raw": "“Umass trace repository,”http://traces.cs.umass.edu/index.php/Main/Traces/, 2017, [Online;accessed 11-January-2017].",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 7,
            "raw": "S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht, “Smart*: An open data set and tools for enabling research in sustainable\r\nhomes,” SustKDD, August, vol. 111, p. 112, 2012.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 8,
            "raw": "A. Karnik, “Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, Indian Institute of Science, Bangalore, India, Jan. 1999.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 9,
            "raw": "J. Lin, S. Williamson, K. Borne, and D. De Barr, Pattern Recognition in Time Series, Advances in Machine Learning and Data Mining for Astronomy. Chapman and Hall, 2012.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        },
        {
            "order": 10,
            "raw": "J. L. Wong, R. Jafari, and M. Potkonjak, “Gateway placement for latency and energy efficient data aggregation [wireless SEnsornet works],”in29th Annual IEEE International Conference on Local Computer Networks, 2004, pp. 490–497.",
            "doi": null,
            "title": null,
            "authors": null,
            "source": null,
            "year": null,
            "volume": null,
            "pages": null
        }
    ],
    "statements": {
        "acknowledgements": null,
        "conflict_of_interest": null,
        "data_availability": null,
        "ai_disclosure": null
    },
    "credit": [],
    "history": {
        "published": "2019-06-01"
    }
}