Abstract
Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.
Highlights
Body area sensor networks (BANs) are a promising technology for convenience, safety, and health applications [1]
We present a biometric authentication protocol that intrinsically reflects the statistical properties of the uncertainties, to systematically balance the risk of false rejected authentications and false accepted attempts
The general idea of the biometric authentication protocol we introduce in Section 7 is to measure the IPIs for a certain time on different nodes
Summary
Body area sensor networks (BANs) are a promising technology for convenience, safety, and health applications [1]. Examples for BANs include fitness trackers, smart glasses [2], vital tracking of emergency response teams [3], and medical implantable devices such as heart pacemakers and insulin pumps. Such medical and safety related body area network (BAN) applications call for a high level of access control and data protection [4,5,6,7]. While security protocols and implementations exist to protect data on severely constrained devices [8,9], the question remains of how devices that belong to the same body area identify and trust each other. Solutions like pre-deployed keys [10] or manual setups are cumbersome and error-prone—in particular in environments with several interfering BANs
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