Imputing the Missing Values in IoT using FRBIM
I. Priya Stella Mary

I. Priya Stella Mary, Assistant Professor Department of Computer Applications Bishop Heber College (Autonomous) Tiruchirappalli.
Manuscript received on 6 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 3375-3380 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5024098319/2019©BEIESP | DOI: 10.35940/ijrte.C5024.098319
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Abstract: The Internet of Things (IoT) is the new-fangled communication paradigm in which the internet is stretched out from the virtual world to intermingle with the objects in the physical world. It unleashes a new dimension of services but at the same time, colossal challenges have to be conquered to reap the full benefits of the IoT. One such challenge is missing data imputation in Internet of Things. The presence of missing values hampers the subsequent processes such as prediction, control, decision making etc. due to the dependency of these processes on complete information. In this paper, a novel FRBIM (Fuzzy Rule-Based Imputation Model) model is proposed to impute missing data based on the characteristics of IoT data to accomplish high accuracy rate. Experimental results have proved that the proposed method has outperformed the existing KNN and AKE imputation model in terms of accuracy.
Keywords: IoT , Imputation, Pre-Processing

Scope of the Article:
Image Processing and Pattern Recognition