A precise and accurate monitoring of different parameters such as temperature, relative humidity or gas level, in cold chain logistic, is important for preserving the quality of the transported goods. Constant parameters monitoring requires a large number of sensors and a large storage capacities, and can cause overloading during the communication. Therefore, in this paper we have observed an under-sampled signal describing the level of CO2 in the cold chain, with an aim to recover the missing information by applying the Compressive Sensing approach. The reduced number of measurements will lead to decreased number of required sensors, reduced storage demands and will speed up the communication.