Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

  • Anil Kumar Chatamoni Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India
  • Rajendra Naik Bhukya Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India
Keywords: sensor data, block compressive sensing, stream cipher, structurally random matrix, pseudo error vector


The security and energy efficiency of resource-constrained distributed sensors are the major concerns in the Internet of Things (IoT) network. A novel lightweight compressive sensing (CS) method is proposed in this study for simultaneous compression and encryption of sensor data in IoT scenarios. The proposed method reduces the storage space and transmission cost and increases the IoT security, with joint compression and encryption of data by image sensors. In this proposed method, the cryptographic advantage of CS with a structurally random matrix (SRM) is considered. Block compressive sensing (BCS) with an SRM-based measurement matrix is performed to generate the compressed and primary encrypted data. To enhance security, a stream cipher-based pseudo-error vector is added to corrupt the compressed data, preventing the leakage of statistical information. The experimental results and comparative analyses show that the proposed scheme outperforms the conventional and state-of-art schemes in terms of reconstruction performance and encryption efficiency.


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How to Cite
A. K. Chatamoni and R. N. Bhukya, “Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data ”, Int. j. eng. technol. innov., vol. 12, no. 2, pp. 167-181, Feb. 2022.