A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion

By Wenxiu Ding, Xuyang Jing, Zheng Yan, Laurence Tianruo Yang. 2019

203 citations recorded by the source corpus.

Abstract Internet of Things (IoT) aims to create a world that enables the interconnection and integration of things in physical world and cyber space. With the involvement of a great number of wireless sensor devices, IoT generates a diversity of datasets that are massive, multi-sourcing, heterogeneous, and sparse. By taking advantage of these data to further improve IoT services and offer intelligent services, data fusion is always employed first to reduce the size and dimension of data, optimize the amount of data traffic and extract useful information from raw data. Although there exist some surveys on IoT data fusion, the literature still lacks comprehensive insight and discussion on it with regard to different IoT application domains by paying special attention to security and privacy. In this paper, we investigate the properties of IoT data, propose a number of IoT data fusion requirements including the ones about security and privacy, classify the IoT applications into several domains and then provide a thorough review on the state-of-the-art of data fusion in main IoT application domains. In particular, we employ the requirements of IoT data fusion as a measure to evaluate and compare the performance of existing data fusion methods. Based on the thorough survey, we summarize open research issues, highlight promising future research directions and specify research challenges.

Subfields: Artificial Intelligence, Computer Networks and Communications, Privacy & Security

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “A thorough review has been conducted in [242], which works on the IoT requirements to address privacy issues.”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, unknown): “2019 [15] data fusion in IoT applications through ML N/A In-depth coverage of ML and DL in different aspects of IoT”
An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques (2020, background): “Various applications of data fusion in di ﬀ erent domains are brieﬂy explained as follows [52,53]: The multi-sensor data fusion approach can be applied in ships, aircraft, factories, etc., from a microscopic scale to a distance to hundreds of feet.”
Educational data mining and learning analytics: An updated survey (2020, background): “All these data could be gathered from multiple available sources and they could be fusioned (Ding et al. 2019b) in order to can be used for improving and personalizing the learning process to each single student in each specific moment of their live to a new level of precision.”