Game-Based Adaptive Risk Management in Wireless Body Area Networks

With the expansion of the Internet of Things (IoT) era and the tremendous progress of wireless communications, Wireless Body Area Networks (WBANs) have been introduced as a promising technology for remote health monitoring. However, the heterogeneity of IoT technologies, the open nature of wireless networks, and the existence of resource-constrained devices are the main challenges to design burdensome security protocols for WBANs. In this context, scalability, reliability, interoperability, and security requirements need risk-aware and adaptive security solutions that take into account the dynamic context changes. Therefore, a trade-off between risk mitigation actions and network performance can be perceived as a major performance objective. The main purpose of this work is to propose an adaptive risk management framework that considers the WBAN context changes to dynamically select the most appropriate security countermeasure and assess its implementation cost in terms of performance degradation. Furthermore, a case study is discussed to show the impact of adaptive risk assessment on network performance.