A computer-executable method for adaptively determining a sampling scheme applied at a first sensor (121,...., 128) from among a plurality of sensors for sampling sensor data values corresponding to a signal, the method comprising: predicting a sparsifying transform for a subsequent sampling time window of the first sensor, wherein the sparsifying transform is determined based on a predictive model of a sparsity of the signal; determining a subsampling parameter for the subsequent sampling time window, the subsampling parameter corresponding to a number of sensor data values to acquire within a sampling time window, wherein said subsampling parameter is determined based on the predicted sparsifying transform; updating a compressive sampling scheme for the subsequent sampling time window of the first sensor, wherein the compressive sampling scheme is updated based on the predicted sparsifying transform as determined based on the predicted sparsity of the signal; and characterised by determining a critical signal condition associated with the first sensor, wherein said determining comprises: approximating a frequency decomposition of the signal sampled by the first sensor based on the received sensor data associated with the first sensor, wherein said approximating is further based on a sparsity and a sparsifying transform associated with the the first compressive sampling scheme, comparing the approximated frequency decomposition with a predefined frequency decomposition, and determining that the critical signal condition is fulfilled if the approximated frequency decomposition matches the predefined frequency decomposition. predicting a sparsifying transform for a subsequent sampling time window of the first sensor, wherein the sparsifying transform is determined based on a predictive model of a sparsity of the signal; determining a subsampling parameter for the subsequent sampling time window, the subsampling parameter corresponding to a number of sensor data values to acquire within a sampling time window, wherein said subsampling parameter is determined based on the predicted sparsifying transform; updating a compressive sampling scheme for the subsequent sampling time window of the first sensor, wherein the compressive sampling scheme is updated based on the predicted sparsifying transform as determined based on the predicted sparsity of the signal; and characterised by determining a critical signal condition associated with the first sensor, wherein said determining comprises: approximating a frequency decomposition of the signal sampled by the first sensor based on the received sensor data associated with the first sensor, wherein said approximating is further based on a sparsity and a sparsifying transform associated with the the first compressive sampling scheme, comparing the approximated frequency decomposition with a predefined frequency decomposition, and determining that the critical signal condition is fulfilled if the approximated frequency decomposition matches the predefined frequency decomposition. approximating a frequency decomposition of the signal sampled by the first sensor based on the received sensor data associated with the first sensor, wherein said approximating is further based on a sparsity and a sparsifying transform associated with the the first compressive sampling scheme, comparing the approximated frequency decomposition with a predefined frequency decomposition, and determining that the critical signal condition is fulfilled if the approximated frequency decomposition matches the predefined frequency decomposition. <EOS>