Improved Audit-based malevolent Node Detection and Energy Efficiency for Healthcare Applications

Abstract

Recently Wireless Body Area Sensor Networks (WBANs) are going more democratic and have revealed great possible in real time supervising of the human body. WBANs have involved a wide range of supervising applications for example sports activity, healthcare, and psychotherapy systems. However, WBANs contains more challenging issues should be resolved such as Quality of Service (QoS), energy efficiency and security and privacy issues are the most significant concerns. Because these systems manage life-critical data, they must be secure. To overcome the above issues, Improved Audit-based Malevolent Node Detection for Healthcare Applications is proposed. Audit-based malevolent Detection (AMD) is proposed for discovering and separating malevolent nodes in WBANs. The AMD system incorporates reputation management, trustworthy route discovery, and recognition of malevolent nodes based on behavioral audits. It integrates three critical functions: reputation management, route discovery, and identification of malevolent nodes via behavioral audits. An AMD can build paths consisting of highly entrusted nodes, subject to a desired path length constraint. In addition, the node fitness function is utilized for improving the energy efficiency in WBAN. The simulation result shows that AMD_EE successfully avoids malevolent nodes, even when a large portion of the network drops to forward packets and enhance the lifetime.