Compressive sensing techniques based on secure data aggregation in WSNs

Abstract

Abstract This research paper presents an efficient data collection scheme for Wireless Sensor Networks (WSNs) that simultaneously compresses and encrypts sensor data to extend network lifespan. To address WSN resource limitations, the scheme combines Compressive Sensing (CS) with Elliptic Curve Cryptography (ECC) and Elliptic Curve Diffie–Hellman (ECDH) key exchange. Sensor data is securely compressed and encrypted using ECC-based public key mechanisms, mitigating CS-related attacks during aggregation and transmission. The measurement matrix seed serves as a private key that is exchangeed between sensor nodes and the base station, enhancing both security and efficiency. A prime-number-based Tree Path Identifier (TPID) routing and Cluster Head (CH) selection strategy is employed to optimize communication. Seven CS algorithms—including Orthogonal Matching Pursuit (OMP), Binary Compressive Sensing (BCS), Subspace Pursuits (SP), Approximate Message Passing (AMP), Split Bregman Iterative (SBI), Basis Pursuit (BP) and Compressive Sampling Matching Pursuit (CoSaMP) algorithms—are evaluated across various data sparsity levels. Results show that SP, AMP, and SBI algorithms outperform others in preserving energy, extending network life, and delaying the First Dead Node (FDN) appearance. Performance metrics include residual energy, network lifetime, total energy dissipation, and throughput. Energy savings confirm the superiority of the proposed hybrid scheme over traditional CS algorithms.