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.