Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

By F. Alam, Rashid Mehmood, Iyad A. Katib, Nasser N. Albogami, Aiiad Ahmad Albeshri. 2017

320 citations recorded by the source corpus.

The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.

Subfields: Artificial Intelligence, Environmental Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[16] presented a survey paper in which data fusion techniques for IoT environments are reviewed followed by several opportunities and challenges.”
A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “[16] focused on the data fusion in smart ubiquitous environments but ignored the security and privacy issues in IoT.”
A survey of data fusion in smart city applications (2019, methodology): “We use smart city single perspective data fusion review papers [19], [26] and non-smart city data fusion classiﬁcation papers [18], [39]– [41] as references.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “The mathematical methods of sensor fusion usually can be divided into three categories, including probability-based methods, artiﬁcial-intelligence-based methods and theory-of-evidence-based methods [301].”
Smart cities with big data: Reference models, challenges, and considerations (2018, background): “The study focused on the challenges of combining IoT and big data and their e ﬀ ect for achieving the goal of future smart cities.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “To obtain insights from these data, researchers [39-44] have discussed different methods of integrating big data analytical methods with IoT design.”
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, background): “Advancements in Information and Communication Technologies (ICT) such as cloud computing [2], [3], Internet of Things (IoT) [4], [5], wireless communications (WSN, WBAN) [6], [7], big data [8], [9], robotics [10], [11], and artificial intelligence [12]–[14], have played a major role in changing the ”
Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature (2020, background): “Decision networks [70]; [77]; [80]; [82]; [90]; [98]; [112]; [134]; [136]”