Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.