<p>Lossless image compression has a significant contribution in designing energy efficient WSNs with cheaper data acquisition techniques for task-oriented as well as IoT based information systems. In this direction, compressive sensing (CS) has emerged as a promising research area for sparse signal acquisition. According to CS framework, an image with appreciable sparsity can be reconstructed from fewer samples. However, due to inherent scarcity in energy and limited communication resources in WSNs, there is a high demand for image compression prior to transmission over the sensor network. Therefore, in this paper we applied CS based image compression for WSN surveillance application for data collection at a reduced sampling rate. Towards this goal, in this paper we proposed a sparse driven adaptive BCS algorithm for colour image compression. For image compression, sampling matrix and hence the compression rate is adaptively determined by sparsity strength of the image block. The proposed sparse driven adaptive BCS algorithm makes it suitable for WSN surveillance application for data acquisition at a reduced sampling rate. The proposed method achieved good objective and subjective rating as compared to the state-of-the-art methods. The proposed method achieved an average PSNR of 34.42db and average SSIM 0.9 for the standard dataset images. Whereas for CCTV captured images proposed method achieved an average PSNR of 33.96 db and SSIM of 0.89.</p>