Mathematical Methods for Data Fusion in IoT: A Survey

Hamda, Nour El Imane; Lagha, Mohand; Hadjali, Allel

doi:10.1007/978-3-030-90639-9_88

Cited by 3 publications

(1 citation statement)

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“…Multisensor data fusion technique can be performed at various levels, depending on the representation of data to be merged and according to the stage at which the combining operation takes place. One can distinguish low, intermediate, and high-level fusion [1]. Several mathematical methods are used for the data fusion process; they are mainly classified into three categories: probability-based, Sensors 2023, 23, 5141 2 of 24 artificial intelligence-based, and evidence-based techniques [2].…”

Section: Introductionmentioning

confidence: 99%

Multisensor Data Fusion in IoT Environments in Dempster–Shafer Theory Setting: An Improved Evidence Distance-Based Approach

Hamda

Hadjali

Lagha

2023

Sensors

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In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster–Shafer (D–S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D–S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. Fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.

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Section: Introductionmentioning

confidence: 99%