<p>The cognitive Internet of Things (CIoT), an emerging subfield of Internet of Things (IoT) research, aims to integrate cognitive capabilities into the design and operation of IoT systems. With many CIoT applications generating vast amounts of data, there is an urgent need for systems capable of efficiently extracting valuable information from streams of largely irrelevant or noisy data. This needs the importance of developing advanced methods for data analysis and knowledge discovery within CIoT frameworks. Efficient knowledge extraction from such large and diverse datasets demands intelligent, economical, and resource-efficient computational strategies. In response, this study proposes a novel approach for recovering meaningful information from this vast data landscape. In the first stage of the proposed method, a total variation regularizer is employed to mitigate the effects of noisy entries. In the subsequent phase, each sensory data point is transformed into its corresponding probabilistic value, which is then treated as a mathematical relation for generating asymmetrical pairs. The most significant pair is selected by identifying the one with the highest plausibility value, calculated across all possible combinations. Finally, the mean of the most plausible pair is computed and designated as the most significant data point. Experimental assessment employing 21.25&#xa0;years of meteorological data, supplemented by cross-validation against numerous performance metrics and computing time assessments, demonstrates the effectiveness of the suggested method (accuracy is greater than 99%) over competing approaches.</p>