The Internet of Things (IOT) is a promising area which will boost the world economy. Theconstituent components of the IOT are smart objects which generate actuation signals or receivesensory signals which are usually noisy, have trend or has small signal-to-noise ratio. Processing thesesignals for filtering, detrending and enhancing the signal-to-noise ratio is crucial for embeddingintelligence in these smart objects. This research discovers the potential of CEEMD in preparingsignals for further intelligent applications such as event detection or pattern recognition in smartobjects. Algorithms are presented for signal filtering, detrending and event detection based on acombination of both CEEMD, the autocorrelation function and the learning vector quantizationclassifier.The performance of the proposed algorithms is compared for both CEEMD and the leastsquares fit approach. The CEEMD has shown promising results.