An Enhanced Data Security and Task Flow Scheduling in Cloud-enabled Wireless Body Area Network

Abstract

Wireless body area networks (WBAN) consist of various sensors to collect essential data signals to monitor blood pressure, heartbeat, blood sugar level, pulse, and body temperature. However, such dense sensor networks face serious issues due to interference which degrades the system throughput. Similarly, data security is another important factor to be considered in WBAN. Interference can be avoided by dividing the sensors into a group based on their operational characteristics and data security can be enhanced using suitable encryption modules. Recently, cloud-based WBAN has gained more attention, as concepts of cloud offer various advantages for efficient data management and security factors. Considering these issues in WBAN and the advantages of a cloud environment, this research work proposes an enhanced data security through Advanced Encryption Standard (AES) and efficient task flow scheduling using Genetic Algorithm (GA). The proposed model outperforms better in task scheduling which greatly reduces the interference and increases the system throughput. The proposed model is experimentally validated in terms of throughput, execution time, response time, and encryption time. Conventional models such as DES and 3DES are used to compare the outcomes of the proposed model to validate the superior performance, and the proposed model performs better than conventional models.