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  • 1.
    Batalla, Jordi Mongay
    et al.
    National Institute of Telecommunications, Warsaw University of Technology.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Gajewski, Mariusz
    National Institute of Telecommunications, Poland..
    Secure Smart Homes: Opportunities and Challenges2017In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 50, no 5, p. p75:1-75:32Article in journal (Refereed)
    Abstract [en]

    The Smart Home concept integrates smart applications in the daily human life. In recent years, Smart Homes have increased security and management challenges due to the low capacity of small sensors, multiple connectivity to the Internet for efficient applications (use of big data and cloud computing) and heterogeneity of home systems, which require inexpert users to configure devices and micro-systems. This article presents current security and management approaches in Smart Homes and shows the good practices imposed on the market for developing secure systems in houses. At last, we propose future solutions for efficiently and securely managing the Smart Homes

  • 2.
    Dai, Hong-Ning
    et al.
    Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau.
    Wong, Raymond Chi-Wing
    Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Kowloon, Hong Kong.
    Wang, Hao
    Norwegian University of Science and Technology, Norway.
    Zheng, Zibin
    School of Data and Computer Science, Sun Yat-sen University, Xiaoguwei Island, Guangzhou, China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Big Data Analytics for Large-scale Wireless Networks: Challenges and Opportunities2019In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 52, no 5, article id 99Article in journal (Refereed)
    Abstract [en]

    The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

  • 3.
    Perera, Charith
    et al.
    The Open University, UK.
    Qin, Yongrui
    University of Huddersfield, UK.
    Estrella, Julio C.
    University of Sao Paulo.
    Reiff-Marganiec, Stephan
    University of Leicester, UK.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Fog Computing for Sustainable Smart Cities: A Survey2017In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 50, no 3, article id 32Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

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