SAOA: Skill archimedes optimization algorithm based privacy enhancement for blockchain storage optimization in medical IoT environment

This paper develops an efficient technique for storage optimization in medical IoT based on privacy enhancement. Initially, in the medical IoT devices, the transactions are generated and sent them to the base station (BS), where the data sensing is performed and the IoT devices collect the data. After that, the data from BS is transferred to peers in the blockchain (BC). Before storing the data in the cloud, adaptive segmentation is performed using a fuzzy clustering-based time series approach. Subsequently, during the encryption process, the data blocks are encrypted with a privacy protection model employing the Advanced Encryption Standard (AES) algorithm. The Deep Kronecker Network (DKN) is utilized for key generation. Finally, the blocks are selected optimally for each peer by using the Skill Archimedes Optimization Algorithm (SAOA). Here, SAOA is the combination of the Skill Optimization Algorithm (SOA) and Archimedes Optimization Algorithm (AOA). The performance of the developed SAOA model is evaluated based on metrics, such as transmission time, query probability, storage cost, local space occupancy, sensitivity level, trust level, and transmission time and achieved maximum values of 0.392, 19.672, 51.7 MB, 0.925, 0.866 and 0.610 s, respectively.