Lightweight Compressive Sensing for Joint Compression and Encryption of Sensor Data

Abstract

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.

Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing

“…Sudarsono et al [20] suggested a method that uses CP-ABE and Hashed MAC to securely transfer environmental data from sensor nodes to a gateway while maintaining integrity. To safely encrypt the sensor data, Chatamoni et al [21] adopted a portable compressive sensing technology that uses structurally random matrices and blocks compressive sensing.…”