No abstract

:In Internet of Things, occurrence of missing data is inevitable due to its intrinsic characteristics. This missing data phenomenon occurs due to a variety of reasons such as uneven network communication, synchronization difficulties, untrustworthy sensor devices, environmental aspects and other device malfunctions which often resulted in data incompleteness. A robust approach to missing data is an indispensible component of analysis to promote the perfect explanation of research findings.As the data generated by the IoT devices is usually correlated in space and time, in this paper it is demonstrated experimentally that substituting missing sensor values with spatially and temporally correlated sensor readings using thenovel extended spatial and temporal correlated proximate missing data imputation model (ESTCP)has considerably improved the accuracy than that of the previously proposed STCP model and the existing single imputation and multiple imputation techniques.

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.