Body Area Sensor Networks (BASN) with miniature sensors providing wireless communications capabilities have become a promising tool for monitoring and logging vital parameters of patients suffering from chronic diseases such as diabetes, asthma, and heart diseases. Particularly, as the technical development in BASN, a low-cost, high-quality, convenient Electrocardiographic (ECG) diagnosis system becomes a future major tool for healthcare systems. However, numerous important research issues remain to be addressed in BASN for ECG transmissions. Among these, communication energy efficiency and security are the most concerning issues. In this chapter, the authors introduce, survey, and analyze effective cross-layer strategies for wireless ECG transmissions in BASN. The key idea of these cross-layer communication techniques is to take advantage of both source data properties and communication strategies for the optimization of the system energy efficiency while providing secure wireless ECG transmissions. The goal of improving communication energy efficiency is achieved by matching the source coding of ECG signals with the channel coding strategy. In addition one can leverage biometric ECG properties to implement an energy-efficient cross-layer security strategy. As an example the authors showcase two security methods in this chapter-selective encryption and self-authentication. Thanks to the dependency property of the compressed ECG data, a selective encryption algorithm needs only to be applied on a very small portion of the transmitted data, and at the same time it provides a level of security equivalent to traditional full-scale encryption using block or stream ciphers without the burden of the associated energy and computational expense. In the example authentication scheme, sensors for the same body can authenticate each other by common traits such as inter-pulse intervals. The authors analyzed the proposed cross-layer techniques for ECG transmissions and validated the achieved energy efficiency improvements by both simulation and experimental results.
All Science Journal Classification (ASJC) codes
- Computer Science(all)