Information Integrity for Multi-sensors Data Fusion in Smart Mobility

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Abstract

The security of smart environments is a very important issue for data and application. The smart city is considered that the automatic management for city relies on internet-of-things (IoT) technology. Internet-of-things refers to the interconnected set of sensors via Internet that targets to improve management and analytics. Smart City includes smart mobility, smart tourism, smart agriculture, smart water, smart energy, smart health, etc. According to Statista [1], there is an evolution of investment of smart cities a world that achieves to 81 billion dollars in 2018, and 95.8 billion dollars in 2019. The predicted investment of Smart city technology statistics reaches to 158 billion dollars in 2022. The data integrity is one of essential dimensions of secure the data in Internet-of-things domains. Multi sensor fusion is an essential process for making decisions automatically, remotely and concurrently. Sensor fusion is an integrated method for variant data and signals from multi-sources for managing the IoT devices. The Safety Internet-of-things Environment affected on Information protection and Integrity on Sensors Fusion network. The data protection depends on data integrity that targets reaching the data accuracy and data consistency (validity) over the internet-of-things fusion. Data integrity is very sensitive data so protecting data integrity is the main focus of many projects security solutions. This paper shows the evolution of smart mobility for variant smart cities and presents the Integrity challenges of multi-data fusion. It can facility to identify and classify challenges on big data. This paper provides measuring the factors of quality the data integrity. It proposes a taxonomy data fusion model for challenges on the data fusion from multi-source in smart domains.

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El-Din, D. M., Hassanien, A. E., & Hassanien, E. E. (2020). Information Integrity for Multi-sensors Data Fusion in Smart Mobility. In Studies in Computational Intelligence (Vol. 846, pp. 99–121). Springer Verlag. https://doi.org/10.1007/978-3-030-24513-9_6

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