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A wireless network composed of wearable sensing along with computing systems connected via a wireless communication channel is termed Wireless Body Sensor Network (WBSN). It enables continuous monitoring through sensors for medical and nonmedical applications. WBSN faces several security problems such as loss of information, access control, and authentication. As WBSN collects vital information and operates in an unfriendly environment, severe security mechanisms are needed in order to prevent the network from anonymous interactions. The different security threats are evaluated with the support of the data transmitted via the sensor networks amongst smart wearable devices. The whole network lifetime together with the Data Transmission (DT) quality is mitigated whilst performing DT utilizing sensor networks, which consume more energy. Hence, in this paper, an energy-efficient secure data transmission mechanism is proposed in WBSN using a novel authentication id-based group signature model and SECC technique. At first, the Group Manager (GM) is selected from the sensors in the remote body sensor system using Normalized Opposition Based Learning BAT Optimization Algorithm (NOBL-BOA). Afterward, clustering with Information Entropy induced K-Means Algorithm (IEKMA) takes place to improve energy efficiency. Next, to provide security to the WBSN, message authentication is carried out based on novel authentication ID-based group signature protocol. Finally, Secret key induced Elliptic Curve Cryptography (SECC) is used to encrypt the message for secure transmission. The simulation results reveal that in comparison with existing works, the proposed work achieves improved security and energy efficiency. |
